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10.1371/journal.pcbi.1005859 | Invariant recognition drives neural representations of action sequences | Recognizing the actions of others from visual stimuli is a crucial aspect of human perception that allows individuals to respond to social cues. Humans are able to discriminate between similar actions despite transformations, like changes in viewpoint or actor, that substantially alter the visual appearance of a scene. This ability to generalize across complex transformations is a hallmark of human visual intelligence. Advances in understanding action recognition at the neural level have not always translated into precise accounts of the computational principles underlying what representations of action sequences are constructed by human visual cortex. Here we test the hypothesis that invariant action discrimination might fill this gap. Recently, the study of artificial systems for static object perception has produced models, Convolutional Neural Networks (CNNs), that achieve human level performance in complex discriminative tasks. Within this class, architectures that better support invariant object recognition also produce image representations that better match those implied by human and primate neural data. However, whether these models produce representations of action sequences that support recognition across complex transformations and closely follow neural representations of actions remains unknown. Here we show that spatiotemporal CNNs accurately categorize video stimuli into action classes, and that deliberate model modifications that improve performance on an invariant action recognition task lead to data representations that better match human neural recordings. Our results support our hypothesis that performance on invariant discrimination dictates the neural representations of actions computed in the brain. These results broaden the scope of the invariant recognition framework for understanding visual intelligence from perception of inanimate objects and faces in static images to the study of human perception of action sequences.
| Recognizing the actions of others from video sequences across changes in viewpoint, gait or illumination is a hallmark of human visual intelligence. A large number of studies have highlighted which areas in the human brain are involved in the processing of biological motion, while others have described how single neurons behave in response to videos of human actions. However, little is known about the computational necessities that shaped these neural mechanisms either through evolution or experience. In this paper, we test the hypothesis that this computational goal is the discrimination of action categories from complex video stimuli and across identity-preserving transformations. We show that, within the class of Spatiotemporal Convolutional Neural Networks (ST-CNN), deliberate model modifications leading to representations of videos that better support robust action discrimination, also produce representations that better match human neural data. Importantly, increasing model performance on invariant action recognition leads to a better match with human neural data, despite the model never being exposed to such data. These results suggest that, similarly to what is known for object recognition, supporting invariant discrimination within the constraints of hierarchical ST-CNN architectures drives the neural mechanisms underlying our ability to perceive the actions of others.
| Humans’ ability to recognize the actions of others is a crucial aspect of visual perception. Remarkably, the accuracy with which we can finely discern what others are doing is largely unaffected by transformations that substantially change the visual appearance of a given scene, but do not change the semantics of what we observe (e.g. a change in viewpoint). Recognizing actions, the middle ground between action primitives and activities [1], across these transformations is a hallmark of human visual intelligence, which has proven difficult to replicate in artificial systems. Because of this, invariance to transformations that are orthogonal to a learning task has been the subject of extensive theoretical and empirical investigation in both artificial and biological perception [2,3].
Over the past few decades, artificial systems for action processing have received considerable attention. These methods can be divided into global and local approaches. Some space-time global approaches rely on fitting the present scene to a joint-based model of human bodies, actions are then described as sequences of joint configurations over time [4]. Other global methods use descriptors that are computed using the entire input video at once [5–7]. Local approaches, on the other hand, extract information from video sequences in a bottom-up fashion, by detecting, in their input video, the presence of features that are local in space and time. These local descriptors are then combined, following a hierarchical architecture, to construct more complex representations [8–10]. A specific class of bottom up, local architectures, spatial-temporal Convolutional Neural Networks (ST-CNNs), as well as their recursive extensions [11], are currently the best performing models on action recognition tasks.
Alongside these computational advances, recent studies have furthered our understanding of the neural basis of action perception. Broadly, the neural computations underlying action recognition in visual cortex are organized as a hierarchical succession of spatiotemporal feature detectors of increasing size and complexity [10,12]. In addition, other studies have highlighted of which specific brain areas are involved in the processing of biological motion and actions. In humans and other primates, the Superior Temporal Sulcus, and particularly its posterior portion, is believed to participate in the processing of biological motion and actions [13–20]. In addition to studying which brain regions engage during action processing, a number of studies have characterized the responses of individual neurons. The preferred stimuli of neurons in visual areas V1 and MT are well approximated by moving edge-detection filters and energy-based pooling mechanisms [21,22]. Neurons in the STS region of macaque monkeys respond selectively to actions, are invariant to changes in actors and viewpoint [23] and their tuning curves are well modeled by simple snippet-matching models [24]. Finally, mirror neurons, cells that exhibit strong responses when subjects are both observing and performing goal directed actions, have been carefully described in recent years [25].
Despite the characterization of the regional and single-unit responses that are involved in constructing neural representations of action sequences, little information is available on what computational tasks might be relevant to explaining and recapitulating how these representations are organized, and in particular which robustness properties are present. The idea of visual representations, internal encodings of incoming stimuli that are useful to the viewer, has a long history in the study of human perception and, since its inception, has provided a powerful tool to link neurophysiology and brain imaging data to more abstract computational concepts like recognition or detection [26–28]. Fueled by advances in computer vision methods for object and scene categorization, recent studies have made progress towards linking neural recordings to computational concepts through quantitatively accurate models of single neurons and entire brain regions. Interestingly, these studies have highlighted a correlation between performance optimization on discriminative object recognition tasks and the accuracy of neural predictions both at the single recording site and neural representation level [29–32]. However, these results have not been extended to action perception and dynamic stimuli.
Here we take advantage of recent advances in artificial systems for action processing to test the hypothesis that invariant recognition drives the representations of action sequences computed by visual cortex. We do so by comparing representations obtained with biologically plausible artificial systems and those measured in human subjects through Magnetoencephalography (MEG) recordings [33]. In this paper we show that, within the Spatiotemporal Convolutional Neural Networks model class [10,12,34,35], deliberate modifications that result in better performing models on invariant action recognition, also lead to empirical dissimilarity matrices that better match those obtained with human neural recordings. Our results suggest that discriminative tasks, and especially those that require generalization across complex transformations, alongside the constraints imposed by the hierarchical organization of visual processing in human cortex, determined which representations of action sequences are computed by visual cortex. Importantly, we quantify the degree of overlap between neural and artificial representations using Representational Similarity Analysis [32]. This measure of agreement between two encodings, does not rely on a one-to-one mapping between neural signal sources and their artificial counterpart, but rather, exploits similarity structures directly in the representation spaces to establish a measure of consensus. Moreover, by highlighting the role of robustness to nuisances that are orthogonal to the discrimination task, our results extend the scope of invariant recognition as a computational framework for understanding human visual intelligence to the study of action recognition from video sequences.
We filmed a video dataset showing five actors, performing five actions (drink, eat, jump, run and walk) at five different viewpoints (Fig 1). We then developed four variants of feedforward hierarchical models of visual cortex and used them to extract feature representations of videos showing two different viewpoints, frontal and side. Subsequently, we trained a machine learning classifier to discriminate video sequences into different action classes based on each model’s output. We then evaluated the classifier’s accuracy in predicting the action content of new, unseen videos.
The four models we developed to extract representations of action sequences from videos were instances of Spatiotemporal Convolutional Neural Networks (ST-CNNs), currently the best performing artificial perception systems for action recognition [34] and were specifically designed to exhibit a varying degree of performance on invariant action recognition tasks. ST-CNN architectures are direct extensions of the Convolutional Neural Networks used to recognize objects or faces in static images [27,36], to input stimuli that extend both in space and time. ST-CNNs are hierarchical models that build selectivity to specific stimuli through template matching operations and robustness to transformations through pooling operations (Fig 2). Qualitatively, Spatiotemporal Convolutional Neural Networks detect the presence of a certain video segment (a template) in their input stimulus; detections for various templates are then aggregated, following a hierarchical architecture, to construct video representations. Nuisances that should not be reflected in the model’s output, like changes in position, are discarded through the pooling mechanism [26].
We considered a basic, purely convolutional model, and subsequently introduced modifications to its pooling mechanism and template learning rule to improve performance on invariant action recognition [36]. The first, purely convolutional model, consisted of convolutional layers with fixed templates, interleaved by pooling layers that computed max-operations across contiguous regions of space. In particular, templates in the first convolutional layer contained moving Gabor filters, while templates in the second convolutional layer were sampled from a set of action sequences collected at various viewpoints. The second, Unstructured Pooling model, allowed pooling units in the last layer to span random sets of templates as well as contiguous space regions (Fig 3B). The third, Structured Pooling model, allowed pooling over contiguous regions of space as well as across templates depicting the same action at various viewpoints. The 3D orientation of each template was discarded through this pooling mechanism, similarly to how position in space is discarded in traditional CNNs (Fig 3A) [2,37]. The fourth and final model employed backpropagation, a gradient based optimization method, to learn convolutional layers’ templates by iteratively maximizing performance on an action recognition task [36].
The basic, purely convolutional model we used as a starting point has been shown to be a reliable model of biological motion processing in human visual cortex [10,12]. The modifications we introduced aimed to improve its performance on a challenging invariant action recognition task. In particular, structured and unstructured template pooling mechanisms have been analyzed and theoretically motivated in recent years [2,3]. Moreover, these pooling mechanisms have successfully applied to robust face and object recognition [37]. Finally, backpropagation, the gradient based optimization method used to construct the last model, is widely used in computer vision systems [36], and recently it has been applied to vision science [29,31]. While prima facie this method might not be relevant to brain science (see Discussion), we found here, that the representations obtained with this technique better match human brain data.
We used these models to recognize actions in video sequences in a simple three-steps experimental procedure: first we constructed feedforward hierarchical architectures and used them to extract feature representations of a number of video sequences. We then trained a machine learning classifier to predict the action label of a sequence based on each feature representation. Finally, we quantified the performance of the classifier by measuring prediction accuracy on a set of new unseen videos. The procedure just outlined was performed using three separate subsets of the video dataset described above, one for each step. In particular, constructing spatiotemporal convolutional models requires access to video sequences to sample, or learn, convolutional layers’ templates. The subset of videos used for this particular purpose was called the embedding set. Likewise, training and testing a classifier requires access to model responses extracted from action sequences; the videos used in these two steps were organized in a training set and a test set. There was never any overlap between the test set and the union of training and embedding set.
Specifically, we sought to evaluate the four models based on how well they could support discrimination between the five actions in our video dataset both across and within changes in viewpoint. To this end, in Experiment 1, we trained and tested the classifier using model features extracted from videos captured at the same viewpoint while in Experiment 2, we trained and tested the classifier using model features computed from videos at mismatching viewpoints (e.g. if the classifier was trained using videos captured at the frontal viewpoint, then testing would be conducted using videos at the side viewpoint).
We used Representational Similarity Analysis (RSA) to assess how well each model feature representation, as well as an ideal categorical oracle, matched human neural data. RSA produces a measure of agreement between artificial models and brain recordings based on the correlation between empirical dissimilarity matrices constructed using either the model representation of a set of stimuli, or recordings of the neural responses these stimuli elicit (Fig 6) [32]. We used video feature representations extracted by each model from a set of new, unseen stimuli to construct model dissimilarity matrices. We also constructed dissimilarity matrices using Magnetoencephalograpy (MEG) data from the average of eight subjects viewing the same action video clips. The MEG data consisted of magnetometer and gradiometer recordings from 306 sensors, averaged over a 100ms window centered at the time when action identity was best decoded from these data in a separate experiment [33] (see Materials and Methods). Finally, we constructed a dissimilarity matrix using an action categorical oracle, a simulated ideal observer able to perfectly classify video sequences based on their action content. In this case, the dissimilarity between videos of the same action was zero and the distance across actions was one.
We observed that end-to-end trainable models (model 4) produced dissimilarity structures that better agreed with those constructed from neural data than models with fixed templates (Fig 7). Within models with fixed templates, model 3, constructed using a Structured Pooling mechanism to build invariance to changes in viewpoint, produced representations that agree better with the neural data than models employing Unstructured Pooling (model 2) and purely convolutional models (model 1). The category oracle did not match the MEG data as well as the highest performing models (models 3 and 4), suggesting that improving performance on the action recognition task does not trivially improve matching with the neural data.
We have shown that, within the Spatiotemporal Convolutional Neural Networks model class and across a deliberate set of model modifications, feature representations that are more useful to discriminate actions in video sequences in a manner that is robust to changes in viewpoint, produce empirical dissimilarity structures that are more similar to those constructed using human neural data. These results support our hypothesis that performance on invariant discriminative tasks drives the neural representations of actions that are computed by our visual cortex. Moreover, dissimilarity matrices constructed with ST-CNNs representations match those built with neural data better than a purely categorical dissimilarity matrix. This highlights the importance of both the computational task and the architectural constraints, described in previous accounts of the neural processing of action and motions, to build quantitatively accurate models of neural data representations [39]. Our findings are in agreement with what has been reported for the perception of objects from static images, both at the single recording site and at the whole brain level [29–31], and identify a computational task that explains and recapitulates the properties of the representations of human action in visual cortex.
We developed the four ST-CNN models using deliberate modifications to improve the models’ feature representations to invariant action recognition. In so doing, we verified that structured pooling architectures and memory based learning (model 3), as previously described and theoretically motivated [2,3], can be applied to build representations of video sequences that support recognition invariant to complex, non-affine transformations. However, empirically, we found that learning model templates using gradient based methods and a fully supervised action recognition task (model 4), led to better results, both in terms of classification accuracy and agreement with neural recordings [31].
The five actions in our dataset were selected to be highly familiar, include both goal-directed hand-arm movements and whole body movements, and span coarse (run vs. eat) as well as fine (drink vs. eat) action discriminations. While the five actions we considered are far from exhaustive, they allow us rank the performance of our four different models on invariant action recognition. Importantly, we show that our top-performing models capture non-trivial aspects of the neural representations of these actions, as shown by the fact that the ST-CNN models match MEG data better than a categorical oracle.
A limitation of the methods used here is that the extent of the match between a model representation and the neural data is appraised solely based on the correlation between the empirical dissimilarity structures constructed with neural recordings and model representations. This relatively abstract comparison provides no guidance in establishing a one-to-one mapping between model units and brain regions or sub-regions and therefore cannot exclude models on the basis of biological implausibility [30]. In this work, we mitigated this limitation by constraining the model class to reflect previous accounts of neural computational units and mechanisms that are involved in the perception of motion [10,21,22,40,41].
Furthermore, the class of models we developed in our experiments is purely feedforward, however, the neural recordings were maximally action discriminative 470ms after stimulus onset. This late in the visual processing, it is likely that feedback signals are among the energy sources captured by the recordings. These signals are not accounted for in our models. We provide evidence that adding a feedback mechanism, through recursion, does not improve recognition performance nor correlation with the neural data (S1 Fig). We cannot, however, exclude that this is due to the stimuli and discrimination task we designed, which only considered pre-segmented, relatively short action sequences.
Recognizing the actions of others from complex visual stimuli is a crucial aspect of human perception. We investigated the relevance of invariant action discrimination to improving model representations’ agreement with neural recordings and showed that it is one of the computational principles shaping the representation of human action sequences human visual cortex evolved, or learned to compute. Our deliberate approach to model design underlined the relevance of both supervised, gradient based, performance optimization methods and memory based, structured pooling methods to the modeling of neural data representations. While memory-based learning and structured pooling have been investigated extensively as a biologically plausible learning algorithms [2,37,42,43], if and how primate visual cortex could implement gradient based optimization or acquire the necessary supervision remains, despite recent efforts, an unsettled matter [44–46]. Irrespective of the precise biological mechanisms that could carry out performance optimization on invariant discriminative tasks, computational studies point to its relevance to understanding neural representations of visual scenes [29–31]. Recognizing the semantic category of visual stimuli across photometric, geometric or more complex changes, in very low sample regimes is a hallmark of human visual intelligence. By building data representations that support this kind of robust recognition, we have shown here, one obtains empirical dissimilarity structures that match those constructed using human neural data. In the wider context of the study of perception, our results strengthen the claim that the computational goal of human visual cortex is to support invariant recognition by broadening it to the study of action perception.
The MIT Committee on the Use of Humans as Experimental Subjects approved the experimental protocol. Subjects provided informed written consent before the experiment. Approval number: 0403000026.
We collected a dataset of five actors performing five actions (drink, eat, jump, run and walk) on a treadmill at five different viewpoints (0, 45, 90, 135 and 180 degrees between the line across the center of the treadmill and the line normal to the focal plane of the video-camera). We rotated the treadmill rather than the camera to keep the background constant across changes in viewpoint (Fig 1). The actors were instructed to hold an apple and a bottle in their hand regardless of the action they were performing, so that objects and background would not differ between actions. Each action/actor/view was filmed for at least 52s. Subsequently the original videos were cut into 26 clips, each 2s long resulting in a dataset of 3,250 video clips. Video clips started at random points in the action cycle (for example a jump might start mid-air or before the actor’s feet left the ground) and each 2s clip contained a full action cycle. The authors manually identified one single spatial bounding box that contained the entire body of each actor and cropped all videos according to this bounding box. The authors who collected the videos identified themselves and the purpose of the videos to the people being video recorded. The individuals agreed to have their videos taken and potentially published.
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10.1371/journal.ppat.1007188 | Retrograde axonal transport of rabies virus is unaffected by interferon treatment but blocked by emetine locally in axons | Neuroinvasive viruses, such as alpha herpesviruses (αHV) and rabies virus (RABV), initially infect peripheral tissues, followed by invasion of the innervating axon termini. Virus particles must undergo long distance retrograde axonal transport to reach the neuron cell bodies in the peripheral or central nervous system (PNS/CNS). How virus particles hijack the axonal transport machinery and how PNS axons respond to and regulate infection are questions of significant interest. To track individual virus particles, we constructed a recombinant RABV expressing a P-mCherry fusion protein, derived from the virulent CVS-N2c strain. We studied retrograde RABV transport in the presence or absence of interferons (IFN) or protein synthesis inhibitors, both of which were reported previously to restrict axonal transport of αHV particles. Using neurons from rodent superior cervical ganglia grown in tri-chambers, we showed that axonal exposure to type I or type II IFN did not alter retrograde axonal transport of RABV. However, exposure of axons to emetine, a translation elongation inhibitor, blocked axonal RABV transport by a mechanism that was not dependent on protein synthesis inhibition. The minority of RABV particles that still moved retrograde in axons in the presence of emetine, moved with slower velocities and traveled shorter distances. Emetine’s effect was specific to RABV, as transport of cellular vesicles was unchanged. These findings extend our understanding of how neuroinvasion is regulated in axons and point toward a role for emetine as an inhibitory modulator of RABV axonal transport.
| Rabies virus (RABV) and alpha herpesviruses (αHV) (e.g. herpes simplex virus) evolved to enter the nervous system efficiently each time they infect a host. In most mammals, RABV reaches the brain, causing a fatal encephalitis. Whereas, αHV remain in the peripheral nervous system in a quiescent but reactivatable state. Despite distinct clinical outcomes, both RABV and αHV must invade axons and repurpose the axon transport machinery to travel long distances toward the neuronal cell bodies where virus replication occurs. How virus particles hijack the transport machinery and how axons respond to and regulate infection are questions of significant interest. We investigated how axonal RABV transport is regulated by exposing axons to interferons or protein synthesis inhibitors, both of which restrict transport of αHV particles. Unlike αHV infection, exposure of isolated axons to interferons has no effect on RABV neuroinvasion. However, RABV transport is blocked by axonal exposure to the translation elongation inhibitor, emetine, via a mechanism that does not depend on protein synthesis inhibition. The effect of emetine is not due to a global inhibition of axon transport because emetine does not limit axonal transport of cellular vesicles. Therefore, emetine may be a novel inhibitory modulator of RABV axonal transport.
| Unlike most nervous system pathogens, which are either accidental or opportunistic, some neuroinvasive viruses have evolved strategies to enter and exit the nervous system. One successful strategy is that of rabies virus (RABV), a neuroinvasive human and animal pathogen of the Rhabdoviridae family. A zoonotic rabies infection begins with the bite of an infected animal into a muscle, followed by spread of virus particles through peripheral nervous system (PNS) somatic motor neurons and into the central nervous system (CNS). Spread of the infection from the CNS to salivary glands facilitates transmission to other hosts [1]. Nevertheless, the end result of CNS infection is a fatal encephalitis in humans and most mammals. A distinct but equally effective strategy is used by alpha herpesviruses (αHV) of the Herpesviridae family (e.g. human herpes simplex virus type 1 and 2 (HSV-1 and 2) and swine pseudorabies virus (PRV)) in their natural and non-natural hosts. These viruses replicate in peripheral epithelia prior to invading the innervating PNS neurons where they establish life-long latent infections. Unlike RABV, αHV rarely spread to the CNS in immunocompetent natural hosts. However, latent αHV infections undergo stress-induced reactivation, which can lead to peripheral herpetic lesions (e.g. cold sores) that facilitate inter-host spread. Despite their distinct clinical pathologies, both RABV and αHV must invade axons, something most viruses do not do. In addition, the infectious particles must travel long distances to reach the viral replication sites in the PNS/CNS cell bodies. How these distinct neuroinvasive viruses infect the nervous system efficiently remains a question of significant interest.
Due to the highly polarized nature of PNS neurons, axons function as molecular highways for long distance transport of various cellular cargos including proteins, RNAs, vesicles, and organelles [2]. Active transport between axon termini and cell bodies relies on microtubule-based molecular motors. In axons, cargos are transported away from the cell body, in the anterograde direction, by various microtubule plus-end directed kinesin motors. Cargos are transported toward the cell body, in the retrograde direction, by a single microtubule minus-end directed dynein motor that works in conjunction with dynein regulatory factors (e.g. dynactin, Lis1, and NudE/NudEL) [3,4]. For efficient axonal invasion, virus particles must co-opt the existing axonal transport machinery for long-distance retrograde transport, a process that has not been fully elucidated.
The axon is a neuronal sub-compartment capable of sensing external stimuli and mounting independent responses to local insults from the peripheral tissues including axon injury, infection, and exposure to trophic factors or pro-inflammatory cytokines [5–8]. Recently, it was shown that αHV re-purpose the retrograde axon injury signaling response for efficient virus particle transport in sympathetic superior cervical ganglia (SCG) axons. To do this, αHV infection induces rapid local translation of a subset of repressed axonal mRNAs without support from the connected cell bodies. Pharmacological inhibition of local protein synthesis restricts axonal αHV infection [7]. Furthermore, exposure of SCG axons to the pro-inflammatory cytokines, interferon-beta (IFNβ) or interferon-gamma (IFNγ), induced local antiviral responses in axons that limited the number of αHV particles transported in the retrograde direction [8]. These findings and others suggest that axons are independent sensors of peripheral αHV infection and raise the question if axons detect and respond to RABV infection in a similar manner.
RABV enters axons primarily by clathrin-dependent receptor-mediated endocytosis, which occurs when the viral envelope glycoprotein (G) binds to one of three known cell surface receptors: neural cell adhesion molecule (NCAM), p75 neurotrophin receptor (p75NTR), and n-acetylcholine receptor (nACHR) [9]. Viral particles in endosomes are transported retrograde until endosome acidification causes a conformational change in the G protein [10]. This change leads to fusion of the viral and endosome membranes and release of nucleocapsids into the cytosol. A majority of evidence suggests that RABV particles are released primarily in the cell body after long distance retrograde transport inside endosomes [11,12]. Although the RABV phosphoprotein (P) on the nucleocapsid was reported to bind directly to the LC8 dynein light chain [13,14], this interaction was not required for retrograde transport of RABV or CNS infection [15].
RABV, similar to αHV, might alter the axonal environment to re-purpose the axon transport machinery. Gluska et al. showed that RABV hijacks the neurotrophin signaling pathway in sensory dorsal root ganglia (DRG) axons by binding to and internalizing with the nerve growth factor (NGF) receptor, p75NTR [16]. Interestingly, RABV-containing endosomes moved retrograde with greater velocity and processivity than NGF-containing endosomes. However, the mechanism leading to faster retrograde transport of RABV-carrying vesicles is not well understood, and it is not clear whether pharmacological agents could act locally in PNS axons to regulate or block RABV transport. An additional complication was that previous studies were conducted with attenuated vaccine strains, which may have different transport properties than pathologic neurovirulent strains due to differences in their glycoproteins.
In this report, we extend the current understanding of RABV axonal invasion and transport by using a RABV P-mCherry-expressing recombinant derived from a neurovirulent strain (CVS-N2c) that is more neurotropic and less neurotoxic than the attenuated strains used previously [17]. Using a tri-chamber neuron culture system that physically separates axons from cell bodies, we show that RABV particles enter SCG axons and are transported efficiently in the retrograde direction. Unlike αHV infection, exposure of isolated axons to IFNβ or IFNγ has no effect on RABV neuroinvasion. Again, unlike axonal infection by αHV, RABV infection does not stimulate significant local protein synthesis. However, RABV transport is blocked by axonal exposure to the translation elongation inhibitor, emetine. Interestingly, emetine is the only protein synthesis inhibitor that blocked retrograde infection: Axonal treatment with cycloheximide or puromycin had no effect. The minority of RABV particles that do move in the retrograde direction in the presence of emetine, move with slower velocities and travel shorter distances. We find that the effect of emetine is not due to a global inhibition of axon transport because emetine does not limit the proportion of moving Rab5- or Rab7-positive vesicles in axons. Therefore, emetine must inhibit retrograde RABV transport by a mechanism that is independent of cytosolic protein synthesis inhibition, and it may be a novel inhibitory modulator of RABV axonal transport. In summary, these findings reveal that axons of PNS sympathetic neurons use distinct mechanisms to detect and respond to αHV and RABV.
RABV axon transport studies were conducted previously with attenuated vaccine strains (i.e. SAD B19) [18]. Attenuated strains display reduced neurotropism compared to virulent strains due to differences in the glycoprotein, which dictates the axon entry and retrograde transport properties of RABV [19,20]. To analyze neuroinvasion events by virulent RABV, we constructed a recombinant derived from the neuroinvasive CVS-N2c parental strain (Fig 1A) [21]. The recombinant expresses a RABV phosphoprotein-mCherry fusion protein (P-mCherry) that is incorporated into nucleocapsids to facilitate real-time visualization and tracking of single RABV particles. This P fusion was shown to be functional recently [22]. Prior to constructing the recombinant virus, the G gene was deleted from the N2c strain to create a spread-deficient virus (ΔG) that, when propagated on a G-complementing cell line, can infect a single round of cells but cannot spread to uninfected cells.
RABV(ΔG) P-mCherry was recovered from cloned cDNA that was transfected into Neuro2A mouse neuroblastoma (N2a) cells expressing the CVS-N2c G protein as described previously [23]. We refer to the G-complemented virus as RABV P-mCherry.
The protein composition of sucrose-purified RABV P-mCherry particles was verified using SDS-PAGE and western blotting with anti-RABV G, N, M, and P antibodies (Fig 1B). We next determined if sufficient P-mCherry protein was incorporated into nucleocapsids to visualize individual RABV particles by fluorescence microscopy. Diluted supernatants from RABV P-mCherry-infected, G-expressing N2a cells were spotted on glass coverslips. We observed red fluorescent punctae of relatively uniform shape from infected cell supernatants (Fig 1C, see also S1 Fig).
To determine the proportion of fully enveloped virions in the virus stock, particles on coverslips were stained with an antibody recognizing the RABV G protein on the envelope surface and green Alexa Fluor 488-conjugated secondary antibody. We observed green fluorescent punctae that were similar in shape to the red punctae in our virus stocks. 68% of the red and green punctae co-localized, suggesting that the majority of P-mCherry containing particles in the virus stock are intact, enveloped virions (Fig 1C).
We next determined the average number of P-mCherry protein copies per particle by comparing the particle brightness of RABV P-mCherry and a pseudorabies virus (PRV; swine αHV) recombinant expressing a capsid protein (pUL25)-mCherry fusion (S1A Fig). Because pUL25 is incorporated in precisely 60 copies in αHV capsids [24], we could extrapolate the P-mCherry protein copy number by comparing the fluorescence emission intensities between virus recombinants [25] (S1B Fig). RABV P-mCherry nucleocapsids contained approximately 116 copies of P-mCherry protein on average (S1C Fig). Thus, RABV nucleocapsids incorporate sufficient copies of P-mCherry protein for effective visualization of single virus particles.
We routinely model axonal invasion by neuroinvasive viruses using primary rodent sympathetic superior cervical ganglia (SCG) of the autonomic nervous system [26]. SCG ganglia contain a homogenous population of neurons that grow robust axons in the presence of NGF, which enables reproducible and well-controlled axonal infections [27]. However, to our knowledge, only a single study has investigated RABV infection in SCG in vitro [28]. Therefore, we first determined if SCG neurons are indeed susceptible to RABV infection. Dissociated SCG were infected with RABV P-mCherry and imaged for P-mCherry expression at 48 hours post infection (hpi). The P-mCherry signal was present in large punctae throughout the cytoplasm of dissociated SCG cell bodies (Fig 1D). Fixed cells were stained with FITC-conjugated anti-RABV targeting RABV nucleoprotein (N). We observed near complete co-localization between P-mCherry and N protein. Cytoplasmic inclusion bodies containing the RABV P and N proteins are characteristic of rabies replication compartments, suggesting that SCG neurons are susceptible and permissive to RABV infection in vitro.
The tri-chamber compartmented neuron culture system separates axons from cell bodies by two physical barriers. This system enables directional infections that mimic the natural route of nervous system invasion, where virus particles enter axons and undergo retrograde-directed transport toward distant cell bodies.
SCG are grown in tri-chambers as described previously [29] (Fig 2A). Briefly, SCG cell bodies are dissociated and seeded in the S (soma) compartment. Cell bodies extend long axons that penetrate beneath the two barriers of the M (methocel/middle) compartment and into the N (neurite) compartment. To visualize and count the cell bodies that extend axons through to the N compartment, a green lipophilic dye (DiO) is added to the N compartment axons. This dye diffuses along axons to label the connected cell bodies in green (Fig 2A and 2B).
We first determined how many RABV particles must be added to axons to establish an infection in the cell bodies in the S compartment. We measured RABV infection based on viral protein expression in the cell bodies. Specifically, we used fluorescence microscopy to detect expression of the P-mCherry fusion protein. We infected axons with 100, 101, 102, 103, 104, 105, or 106 focus forming units (ffu) of RABV P-mCherry and calculated the percentage of connected cell bodies that became infected based on P-mCherry expression. No P-mCherry expression was observed in the cell bodies when axons were infected with 100 ffu. When axons were infected with 101, 102, and 103 ffu, P-mCherry was expressed in a small percentage of the connected cell bodies (0.01 ± 0.03%, 0.2 ± 0.2%, and 2.4 ± 1.3%, respectively) (Fig 2C). When axons were infected with 104, 105, and 106 ffu, P-mCherry was expressed in 25.2 ± 8.4%, 84.8 ± 4.7%, and 84.9 ± 2.0% of the connected cell bodies, respectively. In all conditions, the percentage of infected cell bodies did not increase after 72 hpi, confirming that the virus is spread-deficient. Therefore, a specific threshold of particles (≥ 104 ffu) must be exceeded to establish an efficient retrograde RABV infection. The maximum cell body infection is reached when axons are infected with 105 ffu, above which, the infection is saturated. All future axonal infection experiments were conducted using 105 ffu of RABV P-mCherry.
To monitor the dynamics of retrograde RABV infection in SCG in tri-chambers, we calculated the percentage of infected cell bodies starting at 20 h and up to 168 h after axonal infection with 105 ffu of RABV P-mCherry. The percent of cell bodies expressing P-mCherry increased significantly between 20 and 24 hpi (from 38.3 ± 10.3% to 56.0 ± 6.6%; p = 0.01) and between 24 and 48 hpi (from 56.0 ± 6.6% to 79.2 ± 8.7%; p = 0.0006) (Fig 2D). The maximum percent of infected cell bodies was observed at 48 hpi with no significant increase at 72 hpi (84.0 ± 7.0%) or 168 hpi (86.7 ± 8.8%). To verify the P-mCherry fusion in infected cells, we collected cell bodies at 72 h post axonal infection and used western blotting with anti-mCherry antibody. We observed a major band at the expected size for the P-mCherry fusion protein (~61 kDa) but no band for the unfused mCherry protein (~27 kDa) (Fig 2E). Thus, RABV enters SCG axons and moves retrograde to infect approximately half of the connected cell bodies within 24 h. The infected cell bodies express the intact P-mCherry fusion protein.
We have shown previously that pre-exposure of SCG axons to either type I (IFNα/β) or type II (IFNγ) interferon reduces the axonal transport of αHV particles but not lysotracker-positive vesicles [8]. Because RABV and αHV particles use distinct axon entry/transport mechanisms (in endosomes (RABV) vs. vesicle-independent (αHV)), we determined if exposing axon termini to these pro-inflammatory cytokines would also restrict retrograde infection by RABV. Axons in the N compartment were pretreated with IFNβ or IFNγ for 24 h prior to infection with RABV P-mCherry. At 5 hpi, DiO was added to the axon compartment to label the connected cell bodies (Fig 2A). At 24 h post axonal infection, there was no significant difference in P-mCherry expression in the cell bodies regardless of whether axons were pretreated with IFNβ, IFNγ or untreated (Fig 3A). P-mCherry was expressed in 47.0 ± 5.3% of the connected cell bodies in the control condition and in 50.8 ± 9.0% and 50.8 ± 9.9% of the connected cell bodies in the IFNβ and IFNγ pretreated conditions, respectively (Fig 3B).
To verify that IFNβ and IFNγ were active, we added IFNβ or IFNγ directly on the cell bodies in the S compartment 24 h prior to axonal infection. Consistent with previous reports of interferon inhibiting RABV replication [30,31], we observed a statistically significant decrease in P-mCherry expression in cell bodies that were directly pretreated with either IFNβ or IFNγ. The percentage of infected cell bodies decreased from 47.0 ± 5.3% (untreated) to 33.4 ± 10.9% (p = 0.0114) and 8.83 ± 6.8% (p < 0.0001) when cell bodies were exposed to IFNβ or IFNγ, respectively (Fig 3B). IFNβ pretreatment of cell bodies did not completely abolish RABV infection, suggesting that RABV P-mCherry maintains its IFN antagonist activity [32,33].
As an additional control for IFN activity, we measured phosphorylation of STAT1 (signal transducer and activator of transcription 1), a downstream effector of the IFN response. IFNβ or IFNγ was added to the N compartment, and after a 24 h treatment, the N and S compartments were lysed separately and analyzed by western blot. Consistent with previously published results [8], axonal IFNβ treatment induced phosphorylation of STAT1 only in axons, whereas axonal IFNγ treatment induced accumulation of phosphorylated STAT1 in the distant cell bodies (S2 Fig).
To confirm that axonal IFN exposure has no effect on retrograde RABV infection, we used time-lapse video microscopy with high temporal resolution (> 10 frames/sec) to track the number of RABV particles transported retrograde into the M compartment in the presence or absence of IFNβ or IFNγ between 2–4 h post-axonal infection (Fig 4A). We first confirmed the identity of the transported P-mCherry particles in M compartment axons by anti-RABV N staining (S3 Fig). We then visualized and counted the tracks of moving RABV particles using maximum intensity projections of each field of view (FOV) along the M compartment barrier (Fig 4B). We constructed kymographs to visualize the displacement of individual RABV particles over time during 15 second movies (Fig 4C). In all treatment conditions, particles moved with similar kinetics and relatively constant velocities. There was no significant difference in the average number of particles moving retrograde per FOV across the untreated (5.4 ± 4.7 particles), IFNβ pretreated (4.7 ± 3.4 particles) and IFNγ pretreated (5.0 ± 4.2 particles) axons (Fig 4D). Unlike the effect on αHV axonal infection, exposure of axons to IFNβ or IFNγ does not reduce retrograde RABV infection in the connected cell bodies nor does it alter axonal transport dynamics of RABV particles.
We previously reported that retrograde αHV infection requires local protein synthesis in axons, and αHV transport is reduced by treatment of isolated axons with protein synthesis inhibitors [7]. To determine if inhibition of axonal protein synthesis blocks retrograde RABV infection, N compartment axons were pretreated with the protein synthesis inhibitor, emetine, 1 h prior to RABV infection (see Fig 2A). Emetine-containing media was removed 5 hpi. We observed a remarkable decrease in the percentage of P-mCherry-positive cell bodies in the emetine-treated samples when compared to untreated samples, and this effect was dose-dependent (Fig 5A and 5B; see also S4A Fig). At 24 hpi, the percentage of infected cell bodies was 29.4 ± 8.5% in the untreated condition versus 14.0 ± 6.7%, 4.5 ± 2.0%, and 0.01 ± 0.02% in the 10 μM, 50 μM, or 100 μM emetine-treatment conditions, respectively (S4A Fig).
In axons treated with 100 μM emetine, retrograde infection was essentially blocked at 24 hpi (Fig 5B). The percentage of infected cell bodies remained significantly decreased for 72 hpi when compared to the control. However, a smaller proportion of cell bodies did become infected at later time points. In the emetine treated condition, P-mCherry was expressed in 12 ± 9% of the connected cell bodies at 48 hpi (vs. 63.0 ± 11.3% in 48 hpi untreated), and 20 ± 12% of the connected cell bodies at 72 hpi (vs. 68.4 ± 9.1% in 72 hpi untreated).
We verified that the observed decrease in retrograde infection was due to local effects of emetine on isolated axons and not due to diffusion of the drug to the connected cell bodies. N compartment axons were pretreated with emetine, but in this case, S compartment cell bodies were directly infected with RABV at 1 h post axonal emetine treatment. At 24 hpi, there was no significant difference in the percentage of infected cell bodies between the control and emetine-treated conditions (80.21 ± 12.5% and 83.14 ± 6.9%, respectively) (S4B Fig). Thus, when emetine is added to the N compartment, it acts locally in axons to block retrograde RABV infection in a dose-dependent manner.
To rule out the possibility that axonal emetine treatment has toxic effects, we first examined the morphology of axons and cell bodies at 24 h after axons had been treated with emetine for 6 h. Regardless of whether axons were treated with 100 μM emetine or untreated, the axons and cell bodies were intact and healthy with no apparent toxicity (e.g. no varicosity formation or blebbing) (S5A Fig). We then used SYTOX, a nucleic acid stain that is impermeant to live cells, to further analyze the percentage of live versus dead cell bodies after axons were treated with 100 μM emetine for 6 h. After a 6 h treatment, we counted a similar percentage of dead cell bodies in the emetine-treated condition (3.9% dead cells) versus the untreated (3.2% dead cells) (S5B and S5C Fig). We also imaged the cells at 24 h after a 6 h treatment with emetine in the N compartment (i.e. emetine was washed out at 6 h) and found no increase in cell death after 24 h (3.5% dead cells). By contrast, when emetine was added directly to the cell bodies in the S compartment, we observed a substantial increase in the percentage of dead cells after a 6 h treatment (14.4% dead cells) and a longer 24 h treatment (46.5% dead cells). Therefore, exposing isolated axons to 100 μM emetine for 6 h causes no apparent toxicity.
Because emetine is a translation elongation inhibitor, we hypothesized that the early steps of RABV entry and/or retrograde transport in axons require local, axonal protein synthesis to proceed efficiently. To test this hypothesis, we exposed axons to two other well-known protein synthesis inhibitors and measured the extent of retrograde RABV infection. Axons in the N compartment were treated with either the translation elongation inhibitor, cycloheximide (CHX) or the polypeptide chain terminator, puromycin, 1 h before axonal infection. Unexpectedly, neither of these inhibitors blocked retrograde RABV infection. In the CHX- and puromycin-treated conditions, 24.6 ± 10.9% and 28.5 ± 6.9% of the connected cell bodies became infected compared to 30.4 ± 8.4% in the control at 24 hpi (Fig 5C). By contrast, emetine treatment reduced the percentage of infected cell bodies to 0.4 ± 0.5%.
To confirm that the protein synthesis inhibitors were active at the concentrations tested, we added emetine, CHX or puromycin to cell bodies in the S compartment and infected the N compartment with RABV 1 h post inhibitor treatment. Retrograde RABV infection was completely abolished when each of these drugs was added directly to the S compartment, suggesting that CHX and puromycin actively block viral replication in cell bodies despite having no local effect in axons (Fig 5C).
We further confirmed translation inhibition by assessing the phosphorylation state of eIF2α (eukaryotic initiation factor 2 alpha) in dissociated SCG treated with emetine, CHX, or puromycin for 6 h. The level of phosphorylated eIF2α increased in each drug-treated condition as compared to the non-treated condition (Fig 5D). Because retrograde RABV infection is blocked exclusively by emetine but not by other protein synthesis inhibitors, we conclude that emetine inhibits axonal infection by an alternative mechanism that is independent of cytosolic protein synthesis inhibition.
We used video microscopy to determine if emetine blocks retrograde infection by limiting the number of RABV particles transported toward the cell bodies. We infected N compartment axons with RABV P-mCherry in the presence or absence of emetine and imaged along the M compartment barrier between 2 and 4 hpi to count the number of particles moving toward the cell bodies (Fig 6A). In the presence of emetine, we observed a significant decrease in the number of particles moving retrograde per FOV in the M compartment. We counted 5.0 ± 3.9 moving particles/FOV in the untreated axons versus 1.5 ± 0.9 in the emetine pretreated axons (p < 0.0001) (Fig 6B).
To determine whether emetine inhibits global axonal transport, we tested emetine’s effect on the transport of vesicles containing Rab5 (Ras-related protein 5) or Rab7, which localize to early and late endosomes, respectively [34]. Cell bodies in the S compartment were transduced with adenoviruses expressing either Venus-Rab5 or Venus-Rab7 fusion protein (Fig 6C). At 4 days post transduction, emetine was added to the N compartment (in the absence of RABV infection), and axons in the N compartment were imaged at 2–4 h post treatment. We calculated the percentage of Rab5 or Rab7 particles moving per axon and found no significant difference between the emetine pretreated and untreated axons (Fig 6D). The percent of Rab5-positive vesicles moving per axon was 41 ± 13% in the control versus 42 ± 16% in the emetine treated axons. For Rab7, the percent of vesicles moving per axon was 52 ± 12% (control) versus 45 ± 13% (emetine). Thus, emetine specifically affects RABV and does not reduce transport of cellular vesicles.
We next determined whether emetine affects axon entry of RABV particles. If emetine’s primary effect was on RABV entry, we expected that retrograde infection would be unaltered if emetine was added to the N compartment after viral entry occurred. To define the window of time for the majority of RABV entry events to occur, we measured how long virus inoculum must be in contact with axons to establish an efficient retrograde infection in the cell bodies within 24 h. Axons in the N compartment were incubated with RABV P-mCherry inoculum for 1 min, 10 min, 30 min, 60 min, 120 min, or 300 min (S6A Fig). The percentage of infected cell bodies was 0% after 1 min, 3.1 ± 1.1% after 10 min, 14.0 ± 5.2% after 30 min, 25.7 ± 4.4% after 60 min, 25.1 ± 1.1% after 120 min, and 28.6 ± 4.7% after 300 min (S6B Fig). Incubations longer than 60 min did not significantly increase the percentage of infected cells. Therefore, 60 min provides a suitable time window for the majority of axon entry events to occur.
We found that retrograde RABV infection in the cell bodies was almost completely abolished regardless of whether emetine was added to axons prior to or after infection. When emetine was added to axons 1 h after infection, we still observed a significant decrease in the percentage of infected cell bodies from 32.3 ± 12.9% (untreated) to 2 ± 1% (p < 0.0001) (Fig 7A). Findings suggest that the primary antiviral effect of emetine is not on RABV entry into axons.
We next determined if emetine alters the dynamics of RABV transport by modifying the velocity or transport distance of moving particles. We visualized tracks of RABV particles moving in the retrograde direction by maximum intensity projections of each FOV in the M compartment at 2–4 h after N compartment infection (Fig 7B). From individual tracks, we constructed kymographs to characterize the motion of particles during 15 second movies (163 frames) (Fig 7C). The velocity of particle movement is represented by the slope of the track in the kymograph. In the emetine-treated condition, kymograph analysis indicated periods of decreased slope (closer to zero) with more frequent stalling as compared to the control track (Fig 7C).
To understand how emetine affects the velocity of moving RABV particles across the entire population, we parsed individual particle tracks from M compartment movies into segments of constant velocity and calculated the average instantaneous velocity for each segment. RABV particles moved slower in the emetine pretreated condition (0.76 ± 0.02 μm/s) versus the control condition (1.0 ± 0.01 μm/s) (Fig 7D). To determine how far RABV particles traveled in the retrograde direction in the absence and presence of emetine, we measured the length of each particle track. RABV particles traveled shorter distances in the emetine condition (12.5 ± 0.6 μm) versus the control (18.3 ± 0.4 μm) (Fig 7E). Taken together, emetine treatment significantly reduces the number of RABV particles transported toward the cell bodies. For the minority of particles that still move in the retrograde direction in the presence of emetine, they move with altered transport dynamics, including slower velocities and shorter distances.
Nervous system infection is usually a dead end for many viruses; the host dies and viral transmission is terminated. Yet viruses of the Rhabdoviridae family and the alpha herpesvirinae subfamily, have evolved strategies that exploit neuronal biology to efficiently infect the nervous system and still preserve host-to-host transmission. Recent work has revealed that RABV hijacks the neurotrophin signaling pathway, and αHVs repurpose the axon damage response to invade peripheral nervous system axons [7,16]. Both strategies achieve a common goal: the efficient dynein-mediated retrograde transport of viral particles from axon terminus to cell body where viral replication ensues. Due to the extreme spatial separation of axons from cell bodies, the axon is a neuronal sub-compartment that senses external stimuli and mounts independent local responses. An outstanding question is do such axonal responses regulate or limit axonal infection by two distinct neuroinvasive viruses?
Previous studies have investigated RABV axonal transport using attenuated vaccine strains (SAD B19) or RABV G-pseudotyped vesicular stomatitis virus and lentivirus [11,12,16,18,35]. In this study, we extend the current understanding of RABV axon transport dynamics by use of a neurovirulent CVS-N2c-derived RABV P-mCherry-expressing recombinant. We characterize RABV infection in SCG sympathetic neurons grown in compartmented neuronal cultures, in which the physical separation of cell bodies enables selective infection or treatment of isolated axons. Although somatic motor neurons are predominantly infected by RABV in vivo [36,37], the autonomic motor neurons of the SCG are a homogenous population that provides a good model of the early steps of neuroninvasion due to highly reproducible and quantifiable axonal infections. To date, sensory dorsal root ganglia (DRG) or ventral spinal cord neurons have been used for RABV axonal transport studies. However, unlike SCG, spinal cord neurons exhibit limited axonal projections in compartmented cultures [12], and DRG are heterogenous with subpopulations of unmyelinated neurons that display resistance to RABV infection [38]. We established an ex vivo model of RABV infection in compartmented SCG neurons to facilitate high resolution imaging of RABV axon transport as well as concurrent biochemical analyses of RABV-infected axons.
Using this system, we showed that SCG axons do not regulate RABV and αHV invasion in the same way. We previously reported that treatment of axons with either type I IFN (IFNβ) or type II IFN (IFNγ) was sufficient to limit retrograde αHV infection by reducing the percentage of moving αHV particles by at least 50%. Exposure of axons to IFNβ induced a strictly local axonal response where STAT1 was phosphorylated and retained in axons. Whereas, exposure of axons to IFNγ induced a long distance signaling response where phosphorylated STAT1 was present in the connected cell bodies but not the axons. Importantly, axonal IFN exposure had no effect on the transport of cellular lysosomes, suggesting that vesicular transport was unaffected [8].
As expected, proinflammatory cytokine pretreatment of neuronal cell bodies moderately suppressed RABV infection in this compartmented system. However, exposure of isolated axons to IFNβ or IFNγ, had no effect on RABV neuroinvasion. Why does axonal IFN exposure restrict αHV transport but have no effect on RABV? The most apparent explanation is that RABV is shielded from the axonal inflammatory response due to endosome-mediated transport. Previous studies in hippocampal neurons [39], motor neurons [35], and dorsal root ganglia (DRG) neurons [12,16] showed that clathrin-mediated endocytosis is the primary entry mechanism for RABV particles. In contrast, the primary entry mechanism for αHV is membrane fusion followed by release of non-enveloped viral capsids and tegument proteins into the axoplasm [40]. αHV particles interact directly with dynein and are not transported inside vesicles [41]. As a result, αHV are likely to be more overtly susceptible to IFN-induced axonal responses.
The contrasting effects of axonal IFN exposure on RABV and αHV likely reflect differences in how these viruses evolved with their hosts. Unlike RABV which is 100% lethal after infection of most mammals, αHV establish a lifelong latent infection in the PNS ganglia of the host. Given the importance of latency establishment for αHV persistence, it is possible that αHV evolved to exploit the innate immune response to bias the infection mode toward latency. Interestingly, axonal IFN exposure reduces the number of αHV particles transported toward the cell bodies, which leads to silencing of herpesviral genomes in the neuronal nuclei [42,43]. Such exposure of isolated axons to IFN is biologically relevant for αHV because the initial replication in mucosal epithelia triggers the production and release of proinflammatory cytokines that bathe the innervating axon termini. Perhaps RABV encounters a different inflammatory milieu than αHV in its natural host, giving rise to distinct evolutionary pressures that affect how these viruses respond to innate defenses.
Previous studies have shown that axons respond not only to inflammatory cytokine exposure, but also to damage, axon guidance cues, and αHV infection by local translation of specific pools of mRNAs [5,7,44]. However, axonal RABV infection did not induce detectable new protein synthesis, and there was no effect on RABV infection when we treated axons with cycloheximide or puromycin. By contrast, we found that emetine dramatically blocked retrograde RABV infection in a dose-dependent manner. We first observed that axonal emetine pretreatment strongly limited infection in the connected cell bodies, reducing the percentage of infected cells from 30% to less than 0.5%. Importantly, retrograde infection was still blocked when emetine was added to axons 1 h after infection, suggesting that emetine functions after RABV particles enter axons. Furthermore, axonal emetine pretreatment caused a 70% decrease in the number of particles moving in the retrograde direction into the middle compartment. The few particles that still moved in the presence of emetine, moved an average of 25% slower and traveled approximately 5.5 μm less than particles in untreated samples. The effect of emetine is not due to a global inhibition of axon transport because in the absence of RABV infection, emetine does not limit the proportion of moving Rab5- or Rab7-positive vesicles in axons. Emetine was the only protein synthesis inhibitor that blocked infection, suggesting that protein synthesis inhibition is not the primary mechanism by which emetine inhibits retrograde RABV infection.
A number of groups have recently reported new antiviral roles for emetine. Emetine was shown to inhibit viral nucleic acid synthesis in cultured cells infected with the RNA viruses, PPRV (peste des petits ruminants virus) and NDV (Newcastle disease virus), or the DNA viruses, BPXV (buffalo poxvirus) or BHV-1 (bovine herpes virus). Entry of NDV-1 and BHV-1 was also inhibited by emetine [45]. Furthermore, emetine inhibited human cytomegalovirus (HCMV) replication by disrupting the HCMV-induced interaction between p53 and the E3-ubiquitin ligase, MDM2. Importantly, this effect was not attributed to protein synthesis inhibition because translation was not inhibited at the concentrations used [46].
Recent reports, particularly in the cancer literature, have introduced emetine as an important modulator of cellular signaling pathways. In a screen of ~2800 clinically approved drugs, emetine was found to block NF-кB (nuclear factor-kappa B) activation via inhibition of IкBα phosphorylation in human cervical cancer cell lines [47]. A separate study showed that emetine promotes splicing of Bcl-x to a pro-apoptotic variant in several tumor cell lines, and this action is dependent on the emetine-induced activation of protein phosphatase 1 (PP1). Notably, the authors concluded that this effect of emetine on splicing was not likely to be dependent on protein synthesis inhibition because several other protein synthesis inhibitors did not have analogous effects [48]. In human non-small-cell lung cancer cells (NSCLC), emetine treatment inhibited NSCLC migration and invasion via selective down-regulation of matrix metalloproteases that degrade the extracellular matrix. The mechanism underlying this effect of emetine was an increased phosphorylation of p38 MAPK (mitogen-activated protein kinase) and decreased phosphorylation of ERK (extracellular signaling regulated kinase) [49]. Interestingly, ERK1/2-mediated phosphorylation of the dynein intermediate chain was reported to enhance dynein recruitment to signaling endosomes and to promote retrograde axonal transport of these organelles [50].
We propose that emetine alters local signaling events in axons that are triggered by RABV infection and required for efficient retrograde transport of RABV (Fig 7F). Gluska et al. 2014 recently reported that retrograde transport of RABV was faster than transport of nerve growth factor (NGF), even though RABV exploited the endogenous NGF entry/transport route mediated by p75 neurotrophin receptor (p75NTR). The authors suggest that the increased RABV transport velocity is due either to recruitment of extra motors via receptor clustering at the membrane or to alteration of intra-axonal signaling pathways (e.g. JNK, RhoA, Stathmin, NfкB) downstream of receptor binding. We observed that emetine had no effect on virion entry but decreased the transport velocity of retrograde moving RABV particles. Perhaps an emetine-induced modulation of axonal signaling events prevents effective cargo-motor loading or motor recruitment to the abnormally large RABV-containing endosomes (Fig 7F). RABV-induced axonal signaling events and the effects of emetine on axonal signaling are currently under investigation.
In summary, we show that axons have unique responses to two different viral infections. Unlike what was previously reported for αHV, axonal protein synthesis is not required for RABV neuroinvasion, and RABV transport is not hindered by exposure of axons to interferon. Although both RABV and αHV repurpose the existing axonal transport machinery to establish efficient nervous system infection, the regulation of this repurposing is distinct between these two viruses. Furthermore, we show that exposure of axons to emetine efficiently blocks RABV infection in the distant cell bodies. Although we have not fully elucidated the mechanism, to our knowledge, this is the first study to show antiviral effects of emetine against RABV infection. These findings extend our current understanding of how viral neuroinvasion by two distinct viruses is regulated and point toward a novel role for emetine as an inhibitory modulator of RABV axonal transport.
Timed-pregnancy Sprague-Dawley rats (Rattus norvegicus) were obtained from Hilltop Labs Inc. (Scottsdale, PA). E17 rat embryos were harvested for isolation of superior cervical ganglia (SCG).
SCG neurons were cultured in tri-chambers as described previously [26]. Briefly, SCG were trypsin-digested, mechanically dissociated, and seeded in the S (soma) compartment of the tri-chamber. Neurons extend axons beneath two physical barriers through the M (methocel/middle) compartment and into the N (neurite) compartment. Primary neurons were maintained in Neurobasal media (ThermoFisher; 21103049) + 50X B-27 supplement (ThermoFisher; 17504044) + 100X Penicillin-Streptomycin-Glutamine (ThermoFisher; 10378016) + 80 ng/ml NGF (ThermoFisher; 13257019) with media change every 5–7 days as needed. Neurons were grown at 37°C, 5% CO2 for 3–4 weeks prior to infection or drug treatment.
Neuro2A (N2a) mouse neuroblastoma cells (American Type Culture Collection (ATCC)), N2c G expressing N2a cells (NG cells) (Matthias Schnell Lab), and pig kidney epithelial cells (PK15) (ATCC) were grown at 37°C, 5% CO2 and maintained in DMEM + 10% FBS + 1% Penicillin-Streptomycin (PS). RABV P-mCherry was recovered and propagated on N2c G-expressing N2a cells. PRV recombinants mCherry-UL35 (960) and UL25-mCherry were propagated on PK15 cells. When cell lines were infected with virus, FBS was reduced to 2% in the infection media. Virus strains used in this study include RABV N2cΔG P-mCherry (described below), PRV mCherry-UL35 (PRV 960) [51], and PRV UL25-mCherry [25].
Fluorescein isothiocyanate (FITC)-conjugated anti-Rabies monoclonal globulin recognizing the RABV N protein (FUJIREBIO Diagnostics, Inc. Malvern, PA; 800–092) was used at dilutions of 1:200 for immunofluorescence (IF) and 1:1000 for western blot (WB). Anti-Rabies virus glycoprotein antibody [1C5] (Abcam; ab82460) was used at 1:100 for IF. Rabbit polyclonal anti-RABV N (raised against purified ribonucleoprotein particles) was made by the Schnell Lab and used at 1:1000 for WB. Monoclonal anti-RABV P antibody was a gift from Danielle Blondel [52] and used at 1 μg/ml for WB. Polyclonal anti-RABV M (raised against a SLQTQRSEEDKDSSL peptide from the C-terminus of M) was made by the Schnell Lab and used at 1:2000 for WB. Anti-RABV G for WB was a mixture of 4 human monoclonal antibodies (4C12, 10H5, 8C5, 4H3) used at 1 μg/ml each. This was a gift from Scott Dessain at Lankenau Institute of Medical Research. DAPI (4’,6-diamidino-2-phenylindole) stain was used at 1:1000 for IF. Alexa Fluor 488-conjugated goat anti-mouse IgG (H+L) secondary antibody (Thermo Fisher; R37120) was used at 1:1000 dilution for IF. Anti-phosphorylated STAT1 (Cell Signaling Technology; 7649S) and anti-phosphorylated eIF2α (CST; 9721S) were used at 1:1000 for WB. Anti-β-Actin (Sigma; A1978) was used at 1:10,000 for WB. Horseradish peroxidase-conjugated anti-mouse and anti-rabbit secondary antibodies (KPL; 31430 and 65–6120) were used at 1:10,000 for WB.
Emetine dihydrochloride hydrate (Sigma Aldrich; 45160) was used at 100 μM (dissolved in water) unless otherwise specified. Cycloheximide (CHX) (Sigma Aldrich; C7698) was used at 100 μg/ml (dissolved in DMSO), and Puromycin (Invivogen; ant-pr) was used at 10 μg/ml (dissolved in DMSO). Emetine, CHX, or Puromycin were added 1 h prior to infection unless otherwise specified. For the untreated/ no treatment controls, the appropriate solvent (water or DMSO) was added 1 h prior to infection unless other timing is specified. Recombinant rat IFN-gamma protein (R&D Systems; 585-IF-100) and recombinant rat IFN-beta (PBL Assay Science; 13400–1) were used at 500 U/ml (retrograde infection) or 1000 U/ml (particle tracking). IFN treatment was initiated 24 h prior to infection. SYTOX Green Nucleic Acid Stain (Thermo Fisher; S7020) was used at 5 nM for 10 min to label dead cells. Fluorescent lipophilic dyes (DiO Green—Thermo Fisher; D275 and DiI Red—Thermo Fisher; D282) were used at 1:1000 dilution to label cell bodies with axonal connection to the N compartment.
The RABV recombinant was derived from the highly neuroinvasive and neurotropic CVS-N2c parental strain [21]. PCR primers were designed to amplify 3 DNA fragments: 1) RABV P, 2) mCherry, and 3) RABV M with the intergenic region (IGR) after M, including an XmaI restriction site. Primers were designed to facilitate insertion of DNA fragments into a pCAGGS mammalian expression vector [53] in the following order: P, mCherry, M-IGR. The following primers were used for PCR amplification:
N2c P: 5’-TTGGCAAAGAATTCGTCTCCGTACGACCATGAGCAAGATCTTTGTTA-3’ (fwd) and 5’-GAGCCGTCGCCGGAGCAGGATGTATAGCGATTC-3’ (rev);
mCherry: 5’-TACATCCTGCTCCGGCGACGGCTCTGGCATGGTGAGCAAGGGCGAG-3’ (fwd) and 5’-GAAAACTCGGTTACTTGTACAGCTCGTCCATG-3’ (rev);
N2c M-IGR: 5’-GTACAAGTAACCGAGTTTTCGAACTCAGTC-3’ (fwd) and 5’-GGGAAAAAGATCTCGTCTCGCTAGCCTTCCCGGGGTCTTTTGAG-3’ (rev)
PCR products contained 15–40 bp of overlapping ends to facilitate fragment assembly. The stop codon of P was mutated, and mCherry was fused to the C-terminus with a 5 amino acid spacer sequence (GGG GAC GGC TCT GGC) inserted between the end of P and the start of mCherry. PCR products were ligated into pCAGGS vectors using NEBuilder HiFi DNA Assembly Cloning Kit (New England Biolabs; E5520S) following the recommended protocol for assembly of DNA inserts into linear vectors. Briefly, the digested pCAGGS vector and the 3 PCR products were added to the HiFi DNA Assembly master mix in equimolar amounts (0.1 pmol/fragment) and incubated at 50°C for 1 h. The ligation product was transformed into NEB5α competent cells (NEB; C29871) following the manufacturer’s protocol, and minipreps were prepared from single colonies. P-mCherry-M-IGR inserts were verified by restriction digest and DNA sequencing. P-mCherry expression was confirmed by fluorescence microscopy after transient transfection of the mammalian expression vector into N2a cells.
Verified inserts were digested out of the pCAGGS subcloning vector by cutting at the SpeI site (C-terminus of P) and XmaI cloning site (flanking the 5’end of the glycoprotein gene), and inserts were gel purified. The P-mCherry-M-IGR insert was ligated into the SpeI/XmaI-digested N2c RABV genomic cDNA using T4 ligase (NEB; M0202S) overnight at room temperature (RT). Ligated genomes were transformed into NEB5α cells, and single colonies were picked for mini preps.
For RABVΔG, N2c RABV genomic cDNA was digested with SpeI and NheI (cloning site flanking the 3’-end of the glycoprotein gene) to remove the genomic region from the C-terminus of P to the end of the G gene. P-mCherry-M-IGR was digested out of the pCAGGS expression vector and re-ligated into the SpeI/NheI-digested N2c genomic cDNA. P-mCherry insertion and G deletion were confirmed by restriction digest, and maxi preps were produced from verified clones prior to virus recovery.
The RABV recombinant was recovered on a complementing cell line expressing N2c G (NG cells). The cells were made by transfecting N2a cells with plasmid vectors pTET-off (Clontech; 631017) and pTRE2Hyg (Clontech; 631014) containing the N2c glycoprotein gene. Clones were selected with Geneticin and Hygromycin. Resistant clones were cultured in the absence of doxycycline and screened by immunofluorescence staining with polyclonal antibody against the RABV glycoprotein. Two clones that stained positive for RABV-G were selected (NG7 and NG13) for setting up virus recovery and for large scale virus growth. Briefly, NG13 cells in 6 well dishes were transfected with 1.7 μg of full length genomic cDNA plus 2.5 μg of a mix of pTIT-N, pTIT-P, pTIT-L, and pCAGGS-T7 combined in a 4:2:1:2 ratio, respectively. Plasmids (pTIT) expressing the SAD B19 N, P, and L proteins were provided by Matthias Schnell [23]. The DNA transfection reagent, X-tremeGENE 9 (Roche; 06365809001) was used following the recommended protocol. Following overnight incubation at 34°C, the transfection reagent was replaced with fresh infection media (DMEM +2% FBS + 1% PS). Transfected cells were passaged once in 6 well dishes and then transferred to T-175cm2 flasks for the remainder of the virus recovery period. The cells were kept at 34°C for the duration of virus recovery and passaged as needed with media change every 4 days.
Red fluorescent foci were visible as early as 8 days post transfection, and all cells expressed P-mCherry by approximately 21 days post transfection. Supernatants were collected for virus stocks at days 21, 24, and 28 post transfection. Supernatants were spun for 20 min at 3000 rpm (4°C) to pellet cell debris. Supernatants were filtered through 0.45 μm filters and concentrated using Amicon Ultra -15 Centrifugal Filter Units (100KDa NMWL) (Millipore; Z740210). Concentrator units were spun for 20 min at < 4000 x g. Concentrated virus stocks were kept at 4°C for immediate use or -80°C for long term storage. Each virus stock was titered in triplicate on N2a cells to determine the concentration (ffu/ml).
Concentrated virus stocks were overlaid on a 20% sucrose cushion in either 13.2 ml (Beckman Coulter; 344059) or 38 ml (Beckman Coulter; 344058) thin wall, ultra-clear tubes. Tubes were spun in a Beckman Coulter ultracentrifuge at 25,000 rpm for 1.5 h using the either the SW 41 Ti (13.2 ml tubes) or SW 32 Ti (38 ml tubes) swinging bucket rotor (107,000 x g). The supernatant was aspirated, the remaining liquid decanted, and the tubes were spun upside down in 50 ml conical tubes to remove any residual liquid. Pellets were resuspended in PBS at 4°C overnight. 6% sucrose was added for cryopreservation.
Adenoviral vectors were constructed by Gateway recombination into pAd/CMV/V5-DEST vectors (Invitrogen; V49320). GFP was fused to the N-terminus of Rab5 or Rab7 as described previously [54].
Dissociated SCG neurons or cell bodies from the S compartments were lysed in RIPA light buffer (50 mM Tris-HCL, pH 8.0; 150 mM NaCl; 5mM EDTA; 1% (v/v) NP40; 0.1% (w/v) SDS; 0.1% (v/v) Triton X-100) supplemented with 1 mM DTT and protease inhibitor cocktail (Sigma Aldrich; P1860). Lysates were incubated on ice for 20 min and centrifuged at 10,000 rpm (4°C) for 5 min. Supernatants were mixed with 5x laemmli buffer and heated for 10 min at 90°C. Axons from N compartments were directly lysed in 2x laemmli and heated. Proteins were separated by SDS-PAGE on 4–12% NuPAGE Bis/Tris gels. Proteins were transferred to nitrocellulose membranes (GE Healthcare; 45-004-002) using a Trans-Blot SD semi-dry transfer cell (Bio-Rad). Membranes were blocked in 5% non-fat dry milk powder in PBS-T (PBS-Tween, 0.1%) for 1 h at RT. Primary antibody was diluted in 1% milk powder in PBS-T. Membranes were incubated with primary antibody overnight at 4°C followed by PBS-T washes (3 x 10 min). Horseradish peroxidase-conjugated anti-mouse and anti-rabbit secondary antibodies (KPL) were diluted in 1% milk powder in PBS-T. Secondary antibody was added to the membrane for 1 h at RT followed by three PBS-T washes. Chemiluminescent substrate, Supersignal West Pico or West Dura (Pierce), was added to the membrane for 5 min. Protein bands were visualized by exposing the blot on HyBlot CL autoradiography film (Denville scientific; E3018). For detection of viral proteins in sucrose purified particles, proteins were separated by SDS-PAGE on 10% polyacrylamide Tris/Glycine gels. Primary anti-RABV antibodies were diluted in 10% BSA, and secondary antibodies were diluted in PBS-T + 5% milk powder.
For immunofluorescence staining of P-mCherry-positive RABV particles, supernatants were collected from G-expressing N2A cells (NG cells) at 7 d post RABV P-mCherry infection (MOI 0.1). Following filtration, concentration, and purification (20% sucrose cushion in PBS), supernatants were spotted on glass coverslips and left to adsorb to the glass for 15 min at 37°C. Particles were blocked without fixation for 1 h in DMEM + 10% FBS followed by 1 h staining with 1° antibody recognizing the RABV G protein and Alexa Fluor 488-conjugated 2° antibody (green). Particles were washed 3 times with DMEM for 5 min per wash prior to and after 2° antibody staining.
For immunofluorescence of dissociated SCG or M compartment axons, cells were fixed in 4% PFA at RT for 10 min, permeabilized in 0.25% Triton X-100 for 20 min at RT, and blocked in 3% BSA-PBS for 1 h. FITC-conjugated anti-RABV (N) antibody was diluted in 3% BSA-PBS and added to cells for 1 h followed by three 5 min washes in PBS + 0.05% Tween. DAPI was added to dissociated SCG in the third wash for 5 min.
Unless otherwise specified, N compartment axons were either untreated (appropriate solvent added to control), pretreated with IFNβ or IFNγ for 24 h, or pretreated with protein synthesis inhibitors for 1 h prior to axonal infection with RABV P-mCherry (see Fig 2A). N compartment axons were infected with 105 ffu of RABV P-mCherry unless otherwise specified. At 5 hpi, green lipophilic dye (DiO) was added to the N compartment to label the connected cell bodies in the S compartment. Using live cell imaging, the entire S compartment was tile imaged at designated times post axonal infection for P-mCherry expression (red) and DiO staining (green). Total numbers of green and dual-colored cell bodies were counted manually using NIS Elements Advanced Research software (Nikon). The average number of DiO-positive cell bodies counted per chamber (n = 150 chambers) was 1981 cells. The % infected cell bodies refers to the percentage of DiO-positive cell bodies that were also P-mCherry-positive.
For protein synthesis inhibitor experiments, the inhibitors and virus inoculum were washed out and removed from the N compartment at 5 hpi, at which point the N compartment was replaced with fresh neurobasal media. For the IFN experiments, IFN and virus inoculum were not removed from the N compartment, and media was not replaced throughout the duration of the experiment.
For RABV particle tracking, N compartment axons were either untreated (control), pretreated with IFNβ or IFNγ for 24 h, or pretreated with 100 μM emetine 1 h prior to RABV P-mCherry infection in N (105 ffu). Time lapse imaging was used to visualize and count the number of RABV particles that moved from N into M between 2–4 hpi. Each movie captured one field of view (FOV) for 15 seconds (sec) at > 10 frames/sec. For Rab5 and Rab7 particle tracking, S compartment cell bodies were transduced with adenoviruses expressing either Venus-Rab5 or Venus-Rab7. At 4 days post transduction, the N compartment axons were either untreated or treated with 100 μM emetine (in the absence of RABV infection). At 2–4 h post emetine treatment, Rab motility was recorded in N compartment axons. The percentage of moving Rab particles to total particles (moving + stationary) was calculated per axon for a minimum of 22 axons per condition.
Imaging was conducted on a Nikon Eclipse Ti inverted epifluorescence microscope using a Photometrics CoolSNAP ES2 CCD camera or an Andor iXon3 EMCCD camera (particle tracking). Images and movies were processed using NIS Elements Advanced Research software (Nikon) and Fiji Image J [55]. Comparative images were captured with the same exposure times. Brightness and contrast adjustments were applied to the entire image, and alternations were applied equally across comparative images. For particle intensity analysis, the Fiji threshold feature was used to identify red particles. The analyze particle feature was then used to measure the maximum gray value for each red particle identified in the threshold. Particle intensity (S1B Fig) represents the mean maximum gray value across all particles analyzed for each recombinant strain. To determine the percentage of particles containing G, measurements were redirected from the red threshold to the green (anti-G) channel. A maximum gray value was obtained in the green channel at the location of each particle in the red channel. A red particle was considered dual-colored, if the maximum green intensity was above the background signal. To analyze particle transport, Fiji Z project was used to construct maximum intensity projections of individual fields of view that contained moving viral particles. Each maximum intensity projection was combined into a stack with its original movie file using the Fiji concatenate feature. The transport distance of each particle was measured by manually tracing the course of each track in the concatenated stack using the Fiji segmented line feature. Particles were excluded from the transport distance analyses if they entered or exited the field of view during the 15 s movie. Kymographs were created from single particle tracks using the Fiji KymographBuilder plugin. For velocity measurements, particle tracks were separated into segments of constant velocity as described previously [56].
All data were analyzed with Graphpad Prism 7.04 (GraphPad Software, La Jolla California USA, www.graphpad.com). Figure legends specify the statistical test applied for each analysis, as well as the measure of dispersion about the mean and the number of replicates used. Experiments were repeated three times unless otherwise specified. Standard deviation (SD) is reported as the error for all measurements in the results section except for particle velocities and particle track lengths. For those population level measurements, the standard error of the mean (SEM) is reported. Differences were considered statistically significant when p values were less than 0.05 (*p < 0.05, **p < 0.01, *** p < 0.001, ****p < 0.0001).
All animal work was conducted in accordance with the Institutional Animal Care and Use Committee (IACUC) of Princeton University Research Board. The IACUC approved all animal experiments (protocol # 1947). Animals were euthanized by carbon dioxide inhalation, as recommended by the American Veterinary Medical Association (AVMA) guidelines on euthanasia. All personnel adhered to applicable federal, state, local, and institutional laws and policies governing ethical animal research. This includes the Animal Welfare Act (AWA), the Public Health Service Policy on Humane Care and Use of Laboratory Animals, the Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research and Training, and the Health Research Extension Act of 1985.
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10.1371/journal.pntd.0005264 | Severe Fever with Thrombocytopenia Syndrome in South Korea, 2013-2015 | Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that was recently identified in China, South Korea and Japan. The objective of the study was to evaluate the epidemiologic and clinical characteristics of SFTS in South Korea.
SFTS is a reportable disease in South Korea. We included all SFTS cases reported to the Korea Centers for Disease Control and Prevention (KCDC) from January 2013 to December 2015. Clinical information was gathered by reviewing medical records, and epidemiologic characteristics were analyzed using both KCDC surveillance data and patient medical records. Risk factors for mortality in patients with SFTS were assessed. A total of 172 SFTS cases were reported during the study period. SFTS occurred throughout the country, except in urban areas. Hilly areas in the eastern and southeastern regions and Jeju island (incidence, 1.26 cases /105 person-years) were the main endemic areas. The yearly incidence increased from 36 cases in 2013 to 81 cases in 2015. Most cases occurred from May to October. The overall case fatality ratio was 32.6%. The clinical progression was similar to the 3 phases reported in China: fever, multi-organ dysfunction, and convalescence. Confusion, elevated C-reactive protein, and prolonged activated partial thromboplastin times were associated with mortality in patients with SFTS. Two outbreaks of nosocomial SFTS transmission were observed.
SFTS is an endemic disease in South Korea, with a nationwide distribution and a high case-fatality ratio. Confusion, elevated levels of C-reactive protein, and prolonged activated partial thromboplastin times were associated with mortality in patients with SFTS.
| Severe fever with thrombocytopenia (SFTS) is an emerging infectious disease that was first discovered in China in 2009. Subsequently, SFTS has also been found in South Korea and Japan. Here, we report the epidemiologic and clinical characteristics of 172 confirmed SFTS cases in South Korea that occurred since the first case was reported in 2013. SFTS was prevalent throughout South Korea, except for in urban areas. The incidence was relatively low in the western and southwestern rice field areas and the scarcely populated eastern mountainous area. Hilly areas were the major endemic regions. The incidence was increasing annually, and the case fatality ratio was 32.6%. A mental status of confusion, elevated levels of C-reactive protein, and prolongation of activated partial thromboplastin time were associated with mortality in patients with SFTS. Two outbreaks of nosocomial SFTS transmission were noted.
| Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease that is caused by a novel SFTS virus (SFTSV) which was first reported in China in 2011 [1]. China’s neighboring two countries, South Korea and Japan, have also reported the infection [2, 3]. The virus is transmitted to humans through tick bites, and Haemaphysalis longicornis is known to be a main vector [1, 4]. The clinical manifestations of SFTS include fever, myalgia, vomiting, diarrhea, thrombocytopenia and leukopenia. In severe cases, multi-organ dysfunction may occur [5]; the case fatality ratio (CFR) has been reported to be 6.3–30% [6, 7].
In South Korea, the first patient with SFTSV infection was identified in 2012 [2]. Subsequently, an epidemiologic study reported that 35 cases of SFTS, with a CFR of 45.7%, occurred in 2013 [8]. The objective of this study was to characterize the epidemiologic and clinical findings of all SFTS patients since the first case was reported in South Korea.
SFTS has been a reportable disease in South Korea since 2013. We included all SFTS cases reported to the Korea Centers for Disease Control and Prevention (KCDC) from 2013 to 2015 [9, 10]. Confirmatory tests of SFTSV infection were performed at KCDC by detecting M segment gene of SFTSV RNA using one-step reverse transcription polymerase chain reaction (RT-PCR) or antibody tests with immunofluorescence assay (IFA) to detect the seroconversion of paired sera for anti-SFTSV immunoglobulin G, as previously described [8, 11].
To collect clinical data, we reviewed the medical records of patients who had available epidemiologic information, clinical manifestations and laboratory findings. Epidemiologic characteristics were also supplemented by reviewing the epidemiology investigation records provided by the KCDC.
Demographic factors, date of onset, history of tick bite, presence of bite wound, and comorbidity were included. History of tick bite was self-reported and collected from medical records. The locations of possible exposure to SFTSV were determined considering the patient’s history of outdoor activities or residential scope in one month prior to the onset of illness [8]. We used the KCDC data for the location of SFTS acquisition. Each case was coded according to its geographic location at the district level and was positioned on a map of South Korea (http://www.gadm.org). The national and regional incidences of SFTS per 100,000 person-years from 2013 to 2015 were calculated using the national census (http://kosis.kr). We divided the clinical course of SFTS into three stages by week, and a comparison of clinical and laboratory features was performed for the fatal and non-fatal cases occurring in each period. Worst values were selected for the data in each patient if there were multiple measurements during the unit period.
Meningoencephalitis was defined as a white blood cell count of the cerebrospinal fluid >5 cells/mm3. Myocarditis was determined by an abnormal electrocardiography (ECG), serum levels of troponin or creatine phosphokinase (CK) fractions and an echocardiogram. Arrhythmia was defined as either a new onset during the course of illness or a previously undiagnosed new case. Acute kidney injury was defined as serum creatinine levels ≥2 mg/dl and 2 times the baseline levels [5, 12]. The Acute Physiology and Chronic Health Evaluation (APACHE II) score was also calculated [13]. Severe thrombocytopenia was defined as platelet count <50x103/mm3 in view of its implication for the critical threshold that a risk of spontaneous bleeding increases [5, 14]. The prolongation of activated partial thromboplastin time (aPTT) was defined as aPTT >60 sec, indicating >50% of upper normal value (reference value of aPTT <40 sec) [5, 15].
Statistical analyses were performed using Pearson’s chi-square test or Fisher’s exact test to analyze the relationships between categorical variables and severity of disease. Two-sample t-tests or Mann-Whitney U-tests were used to compare the continuous variables between fatal and non-fatal cases. Longitudinal analysis for serial clinical feature and laboratory parameters was performed using a multivariable generalized estimating equation with binomial variable and linear mixed model with continuous variable. The risk factors for mortality in patients with SFTS were analyzed by binary logistic regression. P values <0.05 were considered statistically significant. Variables having P values <0.05 in the univariate analysis were used for a multivariate stepwise logistic regression analysis. We sought sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of single variable from 1st week after the onset of illness which was significant in univariate analysis. We also analyzed sensitivity, specificity, and C-statistics of combined variable from 1st week after the onset of illness which were significant in the multivariate analysis (SPSS 22.0; SPSS Inc., Chicago, IL, USA).
This study was approved by the institutional review board (IRB) of Boramae Medical Center (#15-2015-123). All the institutions participating in the clinical network also obtained approval from their IRBs. Personal information was de-identified before collection and the anonymized data were processed by different analyzers. All clinical investigations were conducted according to the principles expressed in the Declaration of Helsinki.
A total of 170 SFTS cases were reported to the KCDC during the study period of 2013–2015 [9]. Of the 170 SFTS cases, 161 were confirmed by RT-PCR and 9 patients by IFA. We also included 2 additional cases that had a negative conventional RT-PCR but a positive real-time RT-PCR. Therefore, a total of 172 cases of SFTS were included in this study.
The yearly incidence of SFTS was 36 cases in 2013 (including one case in 2012), 55 cases in 2014, and 81 cases in 2015. The case fatality ratio was 47.2% (17/36) in 2013, 32.7% (18/55) in 2014, and 25.9% (21/81) in 2015, with an overall CFR of 32.6% (56/172). The seasonal distribution of the SFTS cases is shown in Fig 1. Most of the cases occurred between May and October. There were no cases from December to March. The geographical distribution of the SFTS cases is shown in Fig 2. SFTS occurred throughout South Korea, with the exception of urban areas. The incidence was relatively low in the western and southwestern rice field areas and the scarcely populated eastern mountainous areas. Hilly areas were the major endemic regions. The overall incidence was 0.11 cases/105 person-years. Specifically, Jeju province (23 cases, 1.26 cases/105 person-years) showed the highest incidence rate, followed by Gangwon (26 cases, 0.54 cases/105 person-years), Gyeongbuk (33 cases, 0.41 cases/105 person-years), and Jeonnam province (13 cases, 0.23 cases/105 person-years) (Fig 2).
Of the 120 patients, 37 (31.1%) were placed on mechanical ventilation. Acute renal failure (14.2%), meningoencephalitis (13.8%), new-onset arrhythmia (11.8%), and myocarditis (4.2%) were common complications during the hospital course (Table 1). Forty-six patients died, resulting in a CFR of 38.3%. The median time from onset of illness to death was 9.5 days (IQR, 7–15 days); 41.3% (19/46) of the non-surviving patients were male, and the median age was 73.5 years (IQR, 66–79 years). Of the surviving patients, 56.8% (42/74) were male, and the median age was 66 years (IQR, 52–74 years), which was lower than that of the fatal group (p < 0.001) (Table 1). The median time from the onset of illness to hospital discharge was 16 days (IQR, 13–23 days) in the non-fatal group.
Among the clinical parameters in the 1st week, dyspnea, gastrointestinal bleeding, and confusion were associated with death (Table 2). In terms of the categorical clinical features, the frequency of central nervous symptoms was higher in the fatal group (p = 0.025) (S3 Table). In the univariate analysis of laboratory parameters in week 1, severe thrombocytopenia (<50×103/mm3) was more common in the fatal group (43.1% vs 70.6%, p = 0.012). Anemia, increases in serum alkaline phosphatase, AST (>400 IU/L), ALT (> 200 IU/L) and CRP (mg/dL), and prolongation of prothrombin time (PT) (INR ≥1.3) and aPTT (>60 sec) were associated with death (Table 2).
APACHE II scores were higher in the fatal group than in the non-fatal group (15 vs 23, p <0.001).
Changes of clinical and laboratory variables over 3 weeks between the two groups were significantly different for the variables of confusion (p < 0.001), respiratory and cardiovascular symptoms (p = 0.022), platelet (p < 0.001), AST (p = 0.005), CRP (p < 0.001), serum creatinine (p < 0.001), and LDH (p < 0.001) (S2, S3 and S4 Tables & Fig 3). In the multivariate regression analysis, a mental state of confusion, elevated levels of CRP, and the prolongation of aPTT were associated with mortality (Table 2). The single variable for the highest value of sensitivity (leukopenia, 97.1%), specificity (prolongation of PT, 94.3%), PPV (prolongation of PT, 78.6%), and NPV (elevated ALT, 80.0%) was not uniform (S5 Table). With combined variables, ‘confusion + aPTT >60 sec’ showed the maximal C-statistics value of 0.786 (95% confidence interval, 0.625–0.948) (S6 Table).
This study showed that SFTS occurred throughout South Korea. The overall incidence of SFTS in South Korea was 0.11 cases per 105 person-years, which was lower than that in China (0.12–0.73 cases per 105 person-years) [16]. The median age of 69.0 years in our patients is higher than that in China, 57.6 years [17], which reflects the difference of aging populations between two countries, especially in rural areas. We also showed that summer was a period of peak transmission. A previous study suggested that H. longicornis was the predominant species (90.8%) and was widely distributed in a nationwide surveillance of ticks [18]. Although H. longicornis is the major vector, other tick species such as Haemaphysalis flava, Amblyomma testudinarium and Ixodes nipponensis have also been reported to carry SFTSV in South Korea [19, 20]. The peak transmission in summertime may be attributable to the seasonal life cycle of H. longicornis [18, 21] and the increased rates of outdoor activity during this season. The western and southwestern areas of South Korea showed a relatively low incidence of SFTS, which might be related to the low SFTSV infection rate in ticks [18]. This is evidenced by the contrasting finding of a high incidence of tsutsugamushi in the same area, which is closely related to people’s outdoor activity and is one of the most common notifiable infectious diseases in South Korea [22, 23]. The main epidemic season of tsutsugamushi in South Korea is autumn, when chigger mites begin sucking blood from mammals to meet the developmental needs of their life cycle. Several SFTS patients were found to be infected in urban areas. However, 4 cases in Seoul and 5 cases in Wonju were from nosocomial outbreaks. All other cases reported in urban areas were from major cities such as Daegu, Ulsan, Busan and Gwangju, and these cases occurred in the rural suburbs of the city border. Therefore, no endemic cases in inner city were found in South Korea.
The overall CFR in our study was 32.6% which was compared with 12.2% reported recently in China [6]. Although the CFRs of consecutive three years has been decreasing, the CFRs may still be exaggerated. Since SFTS is a newly emerging infectious disease in South Korea, more inclusive screening criteria and education will continue to find more patients with milder presentation. But the patients with mild presentations may have so short or no viremic period that real-time RT-PCR for serum cannot prove the presence of SFTSV effectively. The easy accessibility to well-performing serologic method is needed to help the diagnosis for such patients group. The annual increase of SFTS incidence may be interpreted as a result of increased surveillance or awareness otherwise not detected in the past. We need to follow the trend of SFTS incidence further.
The clinical course of our patients was consistent with the 3 previously reported clinical stages of fever, multi-organ dysfunction and convalescence [5]. Our study identified fever, thrombocytopenia and leukopenia in more than 90% of the patients in the 1st stage, and high fever lasted for a median of 6–11 days. Chinese data showed a high-fever period of 5–11 days [7]. In the 2nd stage, biomarkers including AST, ALT and aPTT were elevated to maximum levels. However, there were notable differences in these values between the fatal and non-fatal groups (Fig 3). During the convalescence stage, the clinical symptoms of SFTS patients began to resolve from 8 to 11 days after the onset of illness, and the laboratory parameters gradually returned to their normal ranges. In China, the convalescence stage began approximately 11–19 days after disease onset, and the biochemical measurements returned to normal within approximately 3–4 weeks in survivors [7]. The overall types of clinical parameters and their changes over time were similar in South Korea and China.
In our study, the median time from the onset of illness to death was 9.5 days. The levels of important biomarkers including hemoglobin, platelets, PT, aPTT, serum ALP, AST, ALT and CRP and clinical parameters such as age, dyspnea, gastrointestinal bleeding and confusion all showed significant differences between the fatal and non-fatal groups. Previous studies found that age, neurologic manifestations, hemorrhagic signs, thrombocytopenia, and elevations of AST, CK or LDH were related to mortality in patients with SFTS in univariate analyses [5, 24]. Neurologic manifestations, thrombocytopenia, prolongation of aPTT, hypoalbuminemia and hyponatremia were significant prognostic factors in multivariate analyses [6, 21, 25]. In our study, confusion, elevated levels of CRP, and prolonged aPTT were associated with the death of patients with SFTS in the multivariate analysis. Overall, mental status and hemorrhagic tendency seemed to be the predominant factors closely related to prognosis. We sought the sensitivity/specificity and PPV/NPV for single or combined variables which were significant in the univariate and multivariate analysis. Confusion and aPTT were important predictors for death. Elevated CRP as either continuous or categorical (>3 mg/dL) variable was one of significant predictors for death from several analyses in our study. Secondary infections like pneumonia or catheter related bloodstream infection might complicate the fatal patients. 31.1% of patients underwent mechanical ventilation during the hospital course. But we couldn’t collect precise data for the infectious complications.
Two hospital outbreaks of SFTS occurred, and 9 healthcare workers (HCWs) were infected [11, 26]. In one hospital, 4 of the 27 HCWs who had contacted with an index patient during cardiopulmonary resuscitation were diagnosed with SFTS via seroconversion [11]. In another hospital, 5 of the 27 HCWs who had been exposed to blood and body fluids were also diagnosed with SFTS via seroconversion [26]. All of the healthcare workers had mild or asymptomatic infections. Nosocomial outbreaks were also reported in China, and not only doctors and nurses but also family members and mortuary beauticians were infected. Therefore, standard precautions should be strictly implemented when caring for suspected SFTS patients [27–29].
Several diseases should be mentioned in the differential diagnosis in South Korea. Human granulocytic anaplasmosis (HGA), which is a tick-borne disease, has a similar clinical presentation including fever, thrombocytopenia and leukopenia. The first human case of HGA in South Korea was reported in 2014 [30]. A serologic survey of the blood samples submitted for SFTS tests showed a 2.2% positivity for anaplasmosis [31]. A. phagocytophilum has been detected in H. longicornis, I. nipponensis and I. persulcatus ticks [32, 33]. Human monocytotrophic ehrlichiosis also has a similar clinical presentation. Ehrlichiosis was reported in an active duty soldier [34], and its causative agent, Ehrlichia chaffeensis, was identified in ticks in South Korea [32]. Hemophagocytic lymphohistiocytosis (HLH) is an aggressive and life-threatening disease which is triggered commonly by infection. Its major diagnostic criteria include fever (≥38.5°C), thrombocytopenia, neutropenia, hyperferritinemia (>3,000 ng/mL), CNS symptoms, hepatitis, coagulopathy and hemophagocytosis in bone marrow [35]. These findings were not uncommon in our data (S3 & S4 Tables). Although we did not observe bone marrow findings, hemophagocytosis in the bone marrow of SFTS patients has been reported as one of the key findings [36–38]. As a cause of secondary HLH, SFTS needs to be considered in endemic areas.
Specific antiviral therapies are urgently needed considering the high fatality and widespread prevalence of SFTS in northeast Asian regions. This study did not analyze the effects of antiviral treatment for SFTS because of the limited number of cases. Several regimens have been tested based on individual physician’s decisions, including a combination of plasma exchange and ribavirin administration [39], plasma exchange followed by convalescent plasma therapy [40], and combination of intravenous immunoglobulin and corticosteroid [41]. Ribavirin showed in vitro antiviral effects against SFTSV in a dose-dependent manner [42]. However, a clinical study found that ribavirin monotherapy was not effective [43]. Recent studies suggest that favipiravir and a combination of ribavirin and interferon may be effective in treating SFTS infection [44, 45].
This study has some limitations. As the study data were retrospectively collected from many study sites, the clinical variables affecting the risk factors for death could be incompletely assessed. But most of the SFTS patients were referred to infectious disease physicians, and usually the patients had intensive medical scrutiny with frequent laboratory evaluations. We presented the epidemiologic features of all 172 patients who had SFTS in a period of 3 years. However, in the clinical analysis, we excluded 52 patients. The two groups (120 vs 52 patients) showed significant differences in age distribution and CFR. This is because the group of 52 patients included the nosocomial outbreak cases, which occurred in younger ages and had a mild presentation.
In conclusion, SFTS is a prevalent endemic disease in South Korea that has a high case-fatality ratio. The clinical manifestations were similar to those reported in China. Confusion, elevated levels of C-reactive protein, and prolonged activated partial thromboplastin times were associated with death in patients with SFTS. The development of effective therapeutics for SFTSV infection is urgently needed.
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10.1371/journal.pcbi.1004043 | Synaptic Plasticity Enables Adaptive Self-Tuning Critical Networks | During rest, the mammalian cortex displays spontaneous neural activity. Spiking of single neurons during rest has been described as irregular and asynchronous. In contrast, recent in vivo and in vitro population measures of spontaneous activity, using the LFP, EEG, MEG or fMRI suggest that the default state of the cortex is critical, manifested by spontaneous, scale-invariant, cascades of activity known as neuronal avalanches. Criticality keeps a network poised for optimal information processing, but this view seems to be difficult to reconcile with apparently irregular single neuron spiking. Here, we simulate a 10,000 neuron, deterministic, plastic network of spiking neurons. We show that a combination of short- and long-term synaptic plasticity enables these networks to exhibit criticality in the face of intrinsic, i.e. self-sustained, asynchronous spiking. Brief external perturbations lead to adaptive, long-term modification of intrinsic network connectivity through long-term excitatory plasticity, whereas long-term inhibitory plasticity enables rapid self-tuning of the network back to a critical state. The critical state is characterized by a branching parameter oscillating around unity, a critical exponent close to -3/2 and a long tail distribution of a self-similarity parameter between 0.5 and 1.
| Neural networks, whether artificial or biological, consist of individual units connected together that mutually send and receive parcels of energy called spikes. While simply described, there is a vast space of possible implementations, instantiations, and varieties of neural networks. Some of these networks are critically balanced between randomness and order, and between death by decay and death by explosion. Selecting just the right properties and parameters for a particular network to reach this critical state can be difficult and time-consuming. The strength of connections between units may change over time via synaptic plasticity, and we exploit this mechanism to create a network that self-tunes to criticality. More specifically, the interplay of opposing forces from excitatory and inhibitory plasticity create a balance that allows self-tuning to take place. This self-tuning takes relatively simple spiking units and connects them in a way that creates complex behavior. Our results have implications for the design of artificial neural networks implemented in hardware, where parameter tuning can be costly, but may provide insight into the critical nature of biological networks as well.
| The mammalian cortex presents a challenging complex system for the study of information processing, behavioral adaptation, and self-organization. At rest, a state in which there is no obvious sensory input or motor output, neural activity in the cortex is predominantly spontaneous, or ongoing. At the single neuron level, resting activity has been characterized as persistent and irregular firing of action potentials, or spikes. A well-known aspect of cortical spiking is that, at rest, the correlation between distant, single neuron spiking is very low [1]. Persistent asynchronous background activity (PABA), however, is typically interpreted as a largely independent activity. Independence does not seem concomitant with the cortex as a complex system, which typically displays interactions among most system elements and long-range structure as detailed below. Demonstrations regarding the exquisitely high sensitivity of cortical networks to the addition of even a single spike [2] have further fueled the debate concerning robust cortical computation in the presence of apparently uncorrelated contributions from single neurons [2, 3].
Other research, however, has demonstrated that spontaneous cortical activity in vitro [4–6] and in vivo [1, 7, 8] at the population level manifests as precisely organized spatiotemporal cascades of activity termed neuronal avalanches. For critical networks, the scale-invariance of avalanche sizes is reflected by a power-law with exponent −3/2. Such a power-law is expected when cortical networks are balanced so that spiking activity neither tends to increase nor decrease, a state quantified by the critical branching ratio σ = 1 [4].
Theory predicts that networks with critical dynamics optimize numerous aspects of information processing [9]. Specifically, experiment and modeling show maximized information capacity and transmission [5], maximized number of metastable states [10, 11], optimized dynamic range [12, 13], and optimum variability of phase synchrony [6]. The ubiquity of scale-invariance in nature, combined with its advantages for information processing, suggests that each of the foregoing properties would be beneficial for neuronal models and artificial systems, i.e. physical embodiments of neuronal networks, as well [14, 15]. In all of these cases, the networks in question are critical and not merely balanced.
Recently, both conservative [16] and non-conservative [17] neural networks featuring short-term synaptic plasticity (STP) have been demonstrated to be critical, or display neuronal avalanches. Likewise, neural networks that incorporate long-term synaptic plasticity, such as spike-timing dependent plasticity (STDP), have displayed balanced networks as well [18, 19]. These models, however, did not exhibit self-generated PABA [3, 20] and were not capable of self-tuning to criticality after being steered away from it by strong perturbations. A neural network that was capable of self-tuning to stable regimes based on short-term plasticity was described in [21], however that network did not exhibit critical dynamics and did not include long-term STDP that can create lasting changes to synaptic conductances. We show that it is possible for a single neural network to exhibit four of the above properties simultaneously, namely PABA, self-tuning due to short-term plasticity (as in [16, 17]) and long-term plasticity (as in [18–19, 22]) and critical balance (as in [16, 17]). We also show that such a network undergoes a lasting change in synaptic strengths, thereby effective connectivity, suggesting the capability of learning. Combining all of these properties into a single system greatly diminishes the need for external controls in order to establish the desirable network dynamics and behavior.
To that end, we demonstrate in a 10,000 neuron network model with 20% inhibitory neurons including AMPA and GABA-receptor dynamics, how a network self-tunes to criticality. This deterministic network spontaneously displays PABA and undergoes changes in network dynamics and structure due to short- and long-term synaptic plasticity in the response to perturbations, while self-tuning back to a critical regime. We believe that this capability will lead to the realization of future synthetic physical systems that self-tune to be optimally sensitive in response to multi-scaled stimuli and adapt to changing environmental conditions, thus paving the way for synthetic intelligent systems [14–15, 23, 24].
A 10,000 neuron network with both excitatory (80%) and inhibitory (20%) neurons was simulated for 900 s in 1 ms time-steps. The simulator itself [25] was based on the dynamics and feature set of a specific neuromorphic hardware implementation [15, 26]. These features are short term plasticity (STP), spike-timing dependent plasticity (STDP), and AMPA and GABA-receptor kinetics. For details of the network model and simulation parameters see Materials and Methods.
The simulation began by injecting Poisson-distributed spikes at a 300 Hz firing rate into a randomly chosen set of 20 excitatory (E) neurons. After 15 ms, the initializing external drive ceased and the network was left to develop its own internal dynamics. The effect of varying these initial perturbation parameters is not well known, and is a subject open for further study. After the initial 15 ms, the network stabilized to a spontaneous firing mode where it maintained an average firing rate of 31.8 ± 2.6 Hz. Average rate is defined as the number of spikes produced by the network per unit time, divided by the number of neurons.
Spiking was asynchronous and irregular as quantified by both pairwise correlations between spike-trains and the coefficient of variation of inter-spike-intervals. Specifically, pairwise correlation distributions from spontaneous activity in the model were centered around 0, as shown in Fig. 1b. Such weak correlation in spiking in our model is in line with the weak pairwise correlations in spiking found in ongoing activity of awake monkeys that demonstrate neuronal avalanche organization in the local field potential [1, 27] (Fig. 1a). They are also similar to near zero-mean correlations found for spiking in vivo in both rats and monkeys, and simulations of large balanced networks that do not exhibit continuous synaptic plasticity [28, 29]. While the firing rate achieved in our model is higher than observed in mammalian neuronal networks [3, 20], we find that as network size increases, firing rate decreases to below 5 Hz for networks larger than 100,000 neurons (Fig. 1c).
In order to quantify the irregularity of spiking in the network, we calculated distributions of the coefficient of variation, C o V = σ μ, of inter-spike-intervals (ISIs). That is, the ratio of the standard deviation of a series of ISIs to the mean ISI. If the standard deviation is greater than the mean, i.e. if CoV ≥ 1, the ISIs are considered irregular. Fig. 1d shows the distribution of firing rates for the network. For a range of ISIs between 3 ms and 1000 ms, equivalent to a range between 300 Hz and 1 Hz, the distribution of CoV is as shown in Fig. 1e. This CoV distribution is heavily skewed and centers near 2.5, demonstrating highly irregular spiking. If the range of ISIs is restricted further to those between 10 ms and 1000 ms (100 Hz to 1 Hz) (Fig. 1f), then the distribution of CoV peaks slightly below unity, suggesting an exponential distribution of ISIs close to that expected from a Poisson process. Thus, a considerable contribution to spike irregularity originates from action potential bursts at 100–300 Hz.
Treating the firing rate of the network as a dynamical system in its own right, it is possible to estimate fixed points in order to identify stable (and unstable) firing rates. Assuming a Langevin model for the firing rate, Fig. 2b shows a reconstruction of the deterministic dynamics [30, 31]. Firing rates greater than 50 Hz exist entirely in the first 500 ms of the simulation, when the network goes through a stabilization period. During this period, firing rates visit a sequence of multi- and meta-stable states until settling into a stable fixed point near 30 Hz. More study is required to know whether the fixed points at greater firing rates still exist or have been annihilated due to bifurcations, a bifurcation being a change in the number or type of fixed points. It is worth noting that a stable and unstable fixed point pair near 10 Hz (see Fig. 2b inset) is very near a bifurcation.
Even though the spiking behavior of the network is classified as uncorrelated, i.e. asynchronous and irregular, there might still be identifiable structure. Specifically, causal spikes, or spikes that cause other spikes, can be grouped into avalanches of particular sizes; the inflation or deflation of causal spikes as they propagate through the network can be balanced or unbalanced; and fluctuations in ISI can be correlated or uncorrelated. If these three measures take on particular values, the network is said to be in a critical state, which we detail below.
The first measure is based on avalanche sizes, where an avalanche is identified as a set of contiguous spiking events. If a neuron spikes without any input from a presynaptic neuron, it is the beginning of an avalanche. If a neuron spikes due to incoming spikes, the new spike is a member of the same avalanche as the spiking presynaptic neurons. In a causally closed network, this definition only makes sense for a subgraph of the network, where inputs that start avalanches are allowed to come from neurons outside of the subgraph.
Avalanche size is defined as the number of spikes that belong to an avalanche, and the distribution of sizes is particular to the properties of the network. If this distribution follows a power-law, it will produce a straight line when plotted on a log-log scale. The slope of this line, λ, is related to the power-law exponent.
It has been observed, both experimentally and following from theory [4], that neuronal networks behaving in the critical regime have λ = −3/2. Using an avalanche tracking algorithm (see Methods), we measured the avalanche size distribution for a sliding 100 s window, producing estimates for λ over time, which were further grouped into sliding windows of 20 λ estimates to produce histograms over time (Fig. 2a). After about 300 s into the simulation, λ ≈ −3/2. In our analysis, 12.5% of neurons were randomly sampled by the avalanche tracking algorithm (see Methods). Random sampling produces a relatively normal distribution of λ estimates. After 500 samples for the period of time between 500 s and 600 s, the distribution of corresponding λ estimates had a mean of -1.620 with standard deviation 0.0700.
The second measure assessed the branching ratio σ of the network. The branching ratio σ measures the average ratio of postsynaptic spikes to presynaptic spikes. If σ < 1, or is sub-critical, the spiking activity in the network decays. If σ > 1, or is super-critical, then spiking activity in the network grows. A branching parameter σ = 1 signifies a stationary network where, on average, the number of spikes received by a neuron results in about the same number of spikes emitted by postsynaptic neurons [32]. The branching ratio over the course of the simulation was measured and is plotted in Fig. 2c. Oscillations of σ about unity indicate that the network is stable in the context of critical branching. Such stability is a necessary, but not sufficient, condition for the more subtle property of criticality as measured by avalanche size distributions above.
As a third measure of criticality, the network simulation was analyzed using detrended fluctuation analysis (DFA) [8, 33], which measures how the variance of fluctuations in spiking activity changes over changes in measurement scale (see Methods). DFA estimates a scaling exponent α, which, when in the range 0.5 < α < 1, indicates positive long-range correlations in the fluctuations. Results of DFA for individual spike-trains are presented in Fig. 2d as a distribution of scaling exponents with mean α = 0.68 (SD = 0.061). This distribution shows that scaling exponents are spread among the range that coincides with correlated fluctuations.
A network could be balanced, or positioned, at a critical state by, among other possibilities, the adjustment of network parameters or input properties. In order for a network to seek criticality, instead of merely being positioned there, it must be able to alter itself. The ability of the simulated network to alter itself was tested by subjecting a subset of excitatory neurons to externally applied perturbations. Perturbations were organized into a series of 10 pulses of 300 Hz spiking, each lasting 500 ms and separated by 500 ms of silence. Three such perturbations were applied, at 300, 400, and 500 s, respectively.
The three criticality analyses were repeated to confirm that criticality was reattained after such perturbations. These three measures, λ, σ, and α are shown for the perturbed simulation in Fig. 5. Together, they show that the network did indeed reattain criticality. As a point of interest, the deterministic dynamics of the network firing rate were again reconstructed, this time showing the dynamics present during external perturbation. The stable point reached during a perturbation is visible as a stable fixed point near 100 Hz. Notably, the perturbations have appeared to push the low firing rate fixed point near 10 Hz through a bifurcation (see Fig. 5b inset). Care must be taken with this interpretation, however, as the data only represent an approximation of deterministic dynamics.
We may also note the effect of constant random perturbations. Such input to the network would provide starting points for new avalanches throughout the whole simulation. Providing a constant 1 Hz input of Poisson-distributed spikes causes the network to tune towards criticality faster (possibly due to increased activity of STDP), but does not alter avalanche size distributions. Another method of disturbing the network is to add variation to the network parameters in Table 1. We added 1% noise to each parameter throughout a simulation and observed that self-tuning to criticality was still manifested.
As argued above, self-tuning to criticality requires change within the network, which is most readily effected by altering synaptic conductances. Fig. 6 shows the changing distribution of these synaptic “weights” over the course of the simulation. While the effect of perturbations on E and I synaptic weights is evident visually, by the three ridges in the weight-time plot respectively (Fig. 6a,c) it can be further quantified by comparing the perturbed and unperturbed weights using a simple mean square error measure (see Methods). This measure shows that each perturbation caused strong change to the synaptic conductances in both E and I weights, which outlasted the perturbation. Thus, these perturbations also significantly changed the effective network topology as well.
Speaking more directly to topology, it is possible to define an in-degree, the number of incoming connections to a neuron, using a synaptic weight threshold. In this manner, a connection was considered present if its strength had a value of at least 0.1. Applying this threshold, the unperturbed simulated network began (Fig. 7a) with a mean excitatory in-degree of 80.07 (SD = 8.862) and a mean inhibitory in-degree of 20.06 (SD = 4.471). Since pre and post neurons for all connections were chosen using the same random selection procedure, in- and out-degrees were approximately equal. After 300 s of simulation time (Fig. 7b) the mean in-degree of excitatory synapses dropped significantly to 15.19 (SD = 3.845, t(15998) = 600.7, p < 0.001). Inhibitory in-degree remained largely unchanged at 19.93 (SD = 4.341, t(15998) = 1.901, p = 0.057). Fig. 7c shows the in-degree time-series for both perturbed and unperturbed cases.
It is unclear from the relatively stable mean in-degree depicted in Fig. 7c whether the degree of connectivity between neurons is statically or dynamically stable. That is, the stability of the mean could be due to slowly changing connectivity, or connectivity could be changing rapidly while maintaining a near constant mean value. This question was addressed by examining which and how often synapses transitioned between strong and weak (see Methods). The occurrence of these so-called “flips” is summarized in Fig. 8, showing that connectivity is most likely the result of rapidly changing synapses that combine to a mean value that changes over a slower time-scale.
Here we show how synaptic plasticity allows neuronal networks to attain a number of desirable network dynamics and properties. First, it helps to produce PABA, persistent asynchronous background activity, which acts as a foundation for more specific behavior. Second, this foundational activity takes on characteristics of so-called critical networks. Lastly, synaptic plasticity enables the critical network, once established, to remain critical in the face of perturbations.
Spiking in the simulated network is only weakly correlated and irregular, shown by examining pairwise correlations between spike-trains and coefficient of variation within spike-trains. By restricting the range of ISIs considered, it can be concluded that spike bursts are responsible for a high coefficient of variation in otherwise Poisson-like spiking. Spiking with bursts can result from tonic input [35], but in this network bursts are favored by a Vreset voltage that is higher than Vrest. After a spike, membrane voltages are decreased to Vreset, which gives integration a head-start toward reaching the spiking threshold. This mechanism produces spikes on a time-scale near the refractory period of the neuron (see Fig. 12). Another possible source of bursts, most likely on a longer time-scale, is the action of STDP to create a bimodal distribution of synaptic strengths, which could take the place of manually increasing the J parameter in [35]. In any case, spike bursts contribute to high dimensional network activity, which increases input separability and dynamical memory capacity[35]. Avalanche size distributions, branching ratio, and correlated spike-interval fluctuations leading to DFA α > 0.5 show that the apparently irregular spiking activity is consistent with a critical network. A hallmark of self-organizing systems is a composition of relatively “dumb” units connected together and constrained by “interaction dominant dynamics” [36]. In the case of the simulated network presented above, the connection strength between units is altered by synaptic plasticity, effectively changing the network topology. The individual units, however, remain primarily unchanged. The structure of the network self-organizes such to combine uncorrelated units in a balanced way to produce network-level behavior that meets several criteria for criticality. This approach is different from other robust, balanced networks that rely on pre-constrained synaptic weights [37] and do not address the more subtle features of criticality, such as power-law avalanche sizes. In contrast, the network presented here adapts to perturbations with lasting changes to synaptic conductances (see Fig. 6) while maintaining the ability to self-tune towards a critical state.
The balance created is especially evident when following the reaction of the network to a single extra spike. Since the network dynamics are fully deterministic, we were able to follow two parallel realities for the network: one in which a particular spike occurred and one in which it didn’t. What results are two spike histories that begin to diverge exponentially, meaning that the network is sensitive to small changes in state. Since the network is finite, the spike-vector difference settles at a new value near the expected difference for two random spike-vectors. Balance in the network is also present at the level of excitatory and inhibitory currents. These currents are observed to balance each other, leaving the resultant current near zero. The level at which the currents balance is slightly excitatory, which makes sense for a spontaneously active network.
This last type of balance points towards the key mechanism for self-tuning. There are a limited number of ways that current into a neuron can be altered. In the present model, those ways are limited to changing synaptic conductance, via STDP, or changing synaptic efficacy, via STP.
It cannot be overstressed that both excitatory and inhibitory long-term plasticity are important, as it is the interplay between these two effects that results in the balanced, critical network achieved above. Networks without inhibitory STDP fail to reach this state for any of a large set of possible parameters. Even otherwise balanced networks without inhibitory STDP succumb to runaway positive feedback when stimulated by strong perturbations [38].
Fig. 9b shows a drift away from criticality after inhibitory STDP is switched off. A close inspection of the size distributions reveals that they contain a large amount of large avalanches, i.e. global bursts. Such global bursts tend to interrupt temporal correlations and spatial heterogeneity by globally depleting network resources. This interpretation is further supported by the finding that while a balance between excitatory and inhibitory current is maintained, the net positive current has increased making the network too excitable. Our simulations suggest that inhibitory STDP allows the network to respond rapidly enough to transient over-excitability to prevent resource depletions, which is crucial to maintain long-term temporal correlations in the system.
It is not only a practical matter that inhibitory STDP is required, but there are deep connections to self-organizing systems as well. Self-organization, especially self-organized criticality, is usually the result of two opposing effects, often some mutually-referring function of each other [39–42]. Here, excitatory and inhibitory STDP play these roles, and together produce various forms of compensatory feedback, depending on temporal differences between pre- and postsynaptic spikes [43, 44]. Fig. 11 shows a schematic description of how these two STDP functions combine to create a balanced network, in the hopes of attacking the “how” and “why” of the mechanisms involved. Further study on these points is required. We can discriminate roughly 4 different types of feedback depending on these temporal differences.
The inhibitory STDP function is symmetrical, supporting an increase in synaptic conductance, i.e. synaptic inhibition, for closely timed pre- and postsynaptic spikes regardless of their order. In contrast, the excitatory STDP function is anti-symmetric and biased towards depressing action. Together, averaged over the firing activity of a network, these two STDP functions combine such that along the Δt = tpre − tpost time-line, there are four qualitative regions: proximal causal and anti-causal, for those spikes that occur relatively close together, and distal causal and anti-causal, for those that occur farther apart. The difference in symmetry between E-STDP and I-STDP causes these regions to behave asymmetrically at the population level. In the Balanced regime, causal spikes that occur close to each other lead to a similar strong increase in excitation and inhibition. In the Accelerated Potentiation regime, where causal spikes occur at larger temporal distance, excitatory potentiation dominates whereas inhibitory STDP is absent or slightly negative. These leads to a temporal tightening of these causal spikes. In contrast, Pruning affects anti-causal spikes that are close in time. The decrease in excitatory drive and strong increase in inhibition for these spike pairs should loosen their temporal tightness and greatly reduce their probability of occurrence. Finally, in the Decelerated Depression regime, we encounter anti-causal spikes that are far-apart in time. In this regime E-STDP slightly dominates leading to reduction of the corresponding excitatory synapses, tempered by slight decreases in inhibition. The combined effect of the symmetry breaking is to foster balance among neurons that form networks of causal spiking, quickly reduce those that are strongly anti-causal, and maintain all other connections at a low, but non-vanishing level.
It is not surprising that inhibitory plasticity leads to balanced networks, as this phenomenon has been shown many times before [22, 43, 44]. The stabilizing nature of inhibitory plasticity is not the main issue here, but rather how that stability allows tuning towards critical spiking behavior. Furthermore, a stable network is not necessarily a critical one, as stability is necessary but not sufficient for criticality. Running the parameter search described in Code S1 on networks without plasticity results in some balanced networks that are not critical. Stability itself might come from or be enhanced by another homeostatic mechanism, such as synaptic scaling [45], however such mechanisms normally occur on much longer time-scales than the duration of the simulations presented above. Future investigations could focus on such questions.
The co-existence of PABA and self-tuning to criticality in our model is consistent with several experimental observations of the mammalian cortex. The cortex is spontaneously active both during development and in a fully developed cortex and this activity is asynchronous with low firing rates [28]. Neuronal avalanches are observed in animals [1], humans [7, 8], and in vitro cell cultures [4–6]. This suggests that natural neuronal activity exhibits PABA and has a tendency, on average, to operate close to its critical state.
While the model presented here takes many of its features from biological networks, and as just argued, shares important behaviors as well, there are still many differences. These differences reflect themselves in the model parameters that best show self-tuning. For instance, there is empirical evidence that the ratio of τF to τAMPA sits near 20 [46]. For this model, however, that ratio sits near 2. As such, this model is not intended to be a biological model in itself, but to encapsulate the driving dynamics for spiking networks that exhibit critical branching and avalanches. Networks of different size, topology, and with more or fewer biological features are expected to have different parameter sets for optimal self-tuning. Here, the qualitative dynamics of STDP and STP, and their role in critical networks, have been clarified. Furthermore, they have been quantified for a specific neuromorphic implementation of an E-I neuronal network using LIF neurons, two different STDP rules, and synaptic short-term depression. When considering neuromorphic systems in general, biological networks may be taken as a special case. Discovering which mechanisms in the general case are responsible for desired behavior, such as self-tuning to a critical state, provides insight into their presence in biological systems, but may also help to direct the design of large-scale artificial neuromorphic systems.
The recurrent neuronal network (Fig. 12a) consisted of 10,000 neurons composed of 8000 excitatory (E) neurons and 2000 inhibitory (I) neurons with a connection probability of 1%. Neurons were simulated as single compartments with leaky integrate-and-fire (LIF) dynamics. For this non-conservative network, synaptic input currents were modeled as exponentially decaying functions with a temporal time course approximating excitatory α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid (AMPA) and inhibitory gamma-aminobutyric acid (GABA) postsynaptic currents (Fig. 12b). Each combination of pre- and postsynaptic connections based on neuronal type were modeled, resulting in two excitatory (E → E, E → I) and two inhibitory (I → I, I → E) types of synapses. All synapses in the model continuously exhibited both short- and long-term plasticity. A notable aspect of our model is that its dynamics are completely deterministic and there is no explicit source for asynchronous or irregular firing such as external and irregular input or probabilistic synaptic transmission or action potential generation. Instead, PABA in the model emerged from intrinsic, deterministic dynamics. As shown, this deterministic design of the network allows for a detailed and precise analysis of the network response to extremely small changes such as the addition or removal of a single spike.
Two types of plasticity were implemented in the network. Short-term plasticity (STP), which transiently changes synaptic efficacy as a function of spike frequency in the presynaptic neuron (Fig. 12c) was simulated using a phenomenological model [47, 48] that combines short-term synaptic facilitation and depression. Long-term synaptic plasticity was implemented based on spike timing-dependent plasticity (STDP) rules at the level of network connectivity. In STDP, the temporal relationship between the arrival of synaptic input at a postsynaptic neuron and the action potential generation in the presynaptic neuron determines the magnitude and direction of the change at that particular synapse [22, 49–52]. We implemented STDP for excitatory synapses using the well established asymmetrical function (Fig. 12d). For inhibitory synapses (Fig. 12e), we used a recently reported symmetrical function [34, 53] (Fig. 12e).
We first performed a parameter search (see Discussion for comments about self-tuning) to identify neuronal networks that exhibit PABA with relatively low firing rates when compared to other simulated networks of this type, e.g. [17].
The role of a parameter search in a model that claims to be self-tuning is worthy of further discussion. At first, any parameter search seems to be at odds with the concept of “self-tuning”. The search, however, is at a broad level that identifies networks that subsequently have the self-tuning property. That is, there are at least two levels at which manual parameter tuning might take place. Here, a parameter search is performed at one level, such that no manual tuning is needed at the other level. The benefit provided by this process is that the parameter tuning that is done is not problem specific, but yields networks that self-tune at the problem level.
While plasticity allows some amount of self-tuning, the parameters of a network must be within particular ranges for it to do so. To find reasonable values for the parameters in Table 1, we employed a biased random-walk searching algorithm. This search was primarily used to find networks with lower firing rates than would otherwise have occurred for random or arbitrary selection of parameters, as well as lower firing rates than other simulated networks of this type, e.g. [17]. As such, recent (r) and maximum (rmax) firing rates were placed into a rate performance index, R r a t e = r 2 + r m a x 2.
The eight parameters comprising the search space were: four parameters for STDP (g e x c m a x, g i n h m a x, β, A+), two STP time constants for facilitation (τF) and depression (τD), and two receptor kinetic parameters τAMPA and τGABA for the neuron [48]. Networks with various combinations of these eight parameters received a 15 ms lasting initialization of Poisson-distributed spikes at 300 Hz to a randomly chosen set of 20 E neurons, and left to run for 900 s.
During the search, each parameter was perturbed by Gaussian noise with standard deviation equal to 2% of the parameter value. If such a perturbation resulted in an R less than the current best, those parameters became the new starting point for perturbations. Typically, a good region in the parameter space was found within 100 iterations. The parameter search algorithm is described in Code S1.
The first attribute to guide the search was to ensure that the network exhibited PABA. The criterion for determining if a network exhibited PABA was based on measuring the duration of time for which the average spiking activity of the network was non-zero after a brief external initialization with Poisson spikes. We selected networks that exhibited PABA for the full 900 s after initialization. We filtered the selected networks further on the basis of the second attribute that the average firing rate remained less than 35 Hz with a peak firing rate less than 100 Hz.
We then evaluated the resulting networks for criticality using three measures: a branching parameter σ, the avalanche power-law exponent λ, and the distribution of correlated fluctuations parameter α. We arrived at the final set of candidate networks that simultaneously have an average branching ratio σ ≈ 1, critical exponent λ ≈ −3/2 for avalanche activity and a correlated fluctuations parameter in the range 0.5 < α < 1. The results of these tests on each combination of parameters tried in the search are shown in Fig. 13.
The output of a neural network exhibiting PABA is a relatively dense set of spike trains. The continuous nature of PABA makes analysis of spike propagation a non-trivial task. For instance, many avalanches, as described below, might overlap in a way that makes their detection difficult. In the paragraphs that follow, the techniques used to analyze such data are described. Unless otherwise specified, we used the following conventions: a neuron i is taken to generate a spike train Xi(n), where Xi(n) = 1 when there is a spike at time-step n, and Xi(n) = 0 otherwise. Synaptic connections form a graph with matrix G, where Gij = 1 if neuron i has a post-synaptic connection to neuron j. A particular spike is referenced as a tuple (i,n) implying Xi(n) = 1. As a convenience, also let X(n) = {i : Xi(n) = 1}, i.e. the set of neurons that spiked at time-step n.
Each neuron in the network receives input from many other neurons, each with an associated synapse. In turn, each synapse maintains what amounts to a connection strength or weight. These weights change over time due to the effects of STDP. Synaptic efficacy, on the other hand, is an instantaneous property affected by STP. Both of these changing synaptic parameters affect the strength of connection between two neurons, but they do so in different ways and might be more or less active at different times. One way to capture this difference is to examine the entropy of each measure over time.
For neuron i and synapse j, there are synaptic weights g and synaptic efficacies ux. Entropy over a time window Δt was computed for each neuron at each time-step nΔt. For quantity y, which can either be g or ux, the quantity’s distribution within the time-step must be determined
h k ( t ) = ∫ t t + Δ t ∫ b k b k + 1 δ ( x − y j ( τ ) ) d xd τ(18) p k ( t ) = h k ∑ k h k(19) so that hk(t) is the number of times bk ≤ yj < bk+1 within the time-window beginning at t, which can be expressed as a probability pk(t). Given a probability distribution, it is straightforward to compute the Shannon entropy for the quantity y over time,
H
y
(t)=−
∑
k=1
K
p
k
(t)
log
2
p
k
(t)
(20) where K is the total number of bins. We used this measure of Shannon entropy to track the respective contributions of STDP and STP by computing a difference in entropy ΔH as
Δ H ( t ) = H g ( t ) - H u x ( t )(21) where Hg represents entropy due to STDP, while Hux represents entropy due to STP. Furthermore, we may reserve Hg for excitatory STDP and additionally take the difference of inhibitory STDP and STP factor, Hz(t) − Hux, or excitatory and inhibitory STDP, Hg(t) − Hz(t).
Synaptic weights were compared between pulsed and non-pulsed conditions using a simple mean square error (MSE). That is, for a weight vector w1, containing the weights of all of the synapses in the first network, and w2, containing the same for the second, the distance between the weights was calculated using
M S E = ∥ w 1 - w 2 ∥ 2 N(22) where N is the total number of synapses.
A second way to measure synaptic weight evolution was to identify times at which individual synapses transition from high weight to low weight, or vice versa. High weight for neuron i was defined to be gi > 0.9, and low weight gi < 0.1. Transitioning from one regime to the other is termed a flip, and represents a particular synaptic connection being turned on or off. To determine flipping points, each synaptic weight was tracked over time at a resolution of 1000 ms. Time points at which a synapse left the high or low regime were recorded. If at a subsequent time a synapse entered the other regime, a flip was recorded at that time.
Given a first-order dynamical system,
x ˙ ( t ) = h ( x ) + Γ ( t )(23) h(x) and zero-mean stochastic component Γ(t), it is possible to recover the deterministic component using an ensemble average [30, 31]
〈
x
˙
(
t
)〉 = h(
x
)
(24)
Approximation of the ensemble average from a discrete time-series y, sampled from dynamical system x, can be achieved by binning the range of y into bins b0,..,bn. For each data point ym in bin k, bk ≤ ym < bk+1, take the mean Δy(k) = ⟨ym+1 − ym⟩. The resulting mapping from k to Δy approximates h(x), the deterministic component of x. From this point, zero-crossings of Δy denote stable (negative slope) or unstable (positive slope) fixed points in the dynamics of x. This interpretation cannot be made with confidence if the system undergoes bifurcations (a change in the type or number of fixed points) during the analyzed time or contains more dimensions than are represented by y.
Ongoing LFP (1–100Hz) and extracellular spike (300 - 3000 Hz; offline sorted; Plexon Offline sorter; PCA based) activities were recorded using microelectrode arrays (BlackRock; 400 mm inter-electrode distance; shank length: 1 mm) chronically implanted in the premotor cortex of two monkeys (Macaca Mulatta), sitting in a primate chair, alert, but not engaging in any task. Measurements were taken for 20–30 minutes. The power law in avalanche sizes for this resting activity was published in [27].
|
10.1371/journal.pgen.1006167 | Dnmt3a Regulates Proliferation of Muscle Satellite Cells via p57Kip2 | Cell differentiation status is defined by the gene expression profile, which is coordinately controlled by epigenetic mechanisms. Cell type-specific DNA methylation patterns are established by chromatin modifiers including de novo DNA methyltransferases, such as Dnmt3a and Dnmt3b. Since the discovery of the myogenic master gene MyoD, myogenic differentiation has been utilized as a model system to study tissue differentiation. Although knowledge about myogenic gene networks is accumulating, there is only a limited understanding of how DNA methylation controls the myogenic gene program. With an aim to elucidate the role of DNA methylation in muscle development and regeneration, we investigate the consequences of mutating Dnmt3a in muscle precursor cells in mice. Pax3 promoter-driven Dnmt3a-conditional knockout (cKO) mice exhibit decreased organ mass in the skeletal muscles, and attenuated regeneration after cardiotoxin-induced muscle injury. In addition, Dnmt3a-null satellite cells (SCs) exhibit a striking loss of proliferation in culture. Transcriptome analysis reveals dysregulated expression of p57Kip2, a member of the Cip/Kip family of cyclin-dependent kinase inhibitors (CDKIs), in the Dnmt3a-KO SCs. Moreover, RNAi-mediated depletion of p57Kip2 replenishes the proliferation activity of the SCs, thus establishing a role for the Dnmt3a-p57Kip2 axis in the regulation of SC proliferation. Consistent with these findings, Dnmt3a-cKO muscles exhibit fewer Pax7+ SCs, which show increased expression of p57Kip2 protein. Thus, Dnmt3a is found to maintain muscle homeostasis by epigenetically regulating the proliferation of SCs through p57Kip2.
| How muscle homeostasis is maintained is not completely elucidated yet. Epigenetic disorders such as Beckwith-Wiedemann syndrome, which causes hypergrowth of skeletal muscles and rhabdomyosarcoma, indicate that epigenetic regulations such as DNA methylation, contribute to this homeostasis control. DNA methylation is mediated by DNA methyltransferases, such as Dnmt3a and Dnmt3b, which are de novo DNA methyltransferases. The role of DNA methylation in somatic stem cells is not completely understood, although it has been shown to be indispensable in differentiation of primordial germ cells and embryonic stem cells. In this report, we investigated the role of Dnmt3a in muscle satellite cells by analyzing Dnmt3a-conditional knockout (cKO) mice in which Dnmt3a loci are deleted utilizing Cre-recombinase driven by Pax7 or Pax3 promoters that are specifically activated in the muscle precursor lineage. The loss of Dnmt3a in cKO mice causes decreased muscle mass and significantly impaired muscle regeneration. Moreover, Dnmt3a loss also results in a striking loss of proliferation of SCs, which is caused by mis-expression of a cyclin-dependent kinase inhibitor, p57Kip2. Therefore, our findings suggest that DNA methylation plays an essential role in muscle homeostasis.
| Myogenic differentiation program has been extensively studied as a model of tissue differentiation since the discovery of MyoD [1]. Although much is known about the gene cascade of myogenesis [2,3], the epigenetic mechanisms that regulate the physiological and pathological condition of skeletal muscles remain unknown [4].
Gene expression is regulated by both genetic and epigenetic mechanisms. DNA methylation is an epigenetic modification, which usually occurs at CpG sites [5]; the cytosine residues at CpG sites are methylated to 5-methyl-cytosine. This DNA methylation is mediated by a group of DNA methyltransferases (Dnmt) [6]. Among them, Dnmt3a and Dnmt3b catalyze de novo DNA methylation, and Dnmt1 mediates the maintenance of DNA methylation [7–9]. Accumulating evidence suggests that DNA methylation by Dnmt proteins in the promoter regions is associated with gene silencing, thus linking DNA methylation to gene suppression [6,10]. Recent studies have also clarified the roles of DNA methylation in gene bodies and intergenic regions in enhancing gene expression [11–14].
We previously reported that a transcriptional repressor Rp58, which has been known to bind Dnmt3a [15], is a direct target of MyoD and has an essential role in skeletal myogenesis [16], in which DNA methylation at the promoter of myogenic genes is implicated [17].
Dnmt3a-null mice die within 3 to 4 weeks after birth, and deletion of Dnmt1 or Dnmt3b leads to early embryonic lethality [9,18,19], indicating that DNA methylation has a critical role in embryogenesis and postnatal homeostasis. The Dnmt1-mediated maintenance of DNA methylation is necessary for self-renewal of the hematopoietic, mammary, mesenchymal and skin stem cells [20–23]. On the other hand, Dnmt3a and Dnmt3b coordinately generate DNA methylation profiles in differentiating stem cells, resulting in determination of distinct cell fates. In embryonic stem cells, concomitant deletion of Dnmt3a and Dnmt3b leads to a loss of differentiation capacity [24].
The precise role of de novo DNA methylation by Dnmt3a and Dnmt3b in muscle SCs, however, remains to be characterized. Hematopoietic stem cells null for Dnmt3a and/or Dnmt3b, progressively lose differentiation potential [25,26] and self-renewal capacity [27]. Neural stem cells deficient for Dnmt3a show impaired differentiation and increased cell proliferation [28], and Nestin-Cre-mediated deletion of Dnmt3a causes motor neuron defects and premature death of the mice [29]. Dnmt3a-deficient osteoclast precursor cells do not differentiate into osteoclasts efficiently [30]. However, little is known about the functions of Dnmt3a in the muscle SCs.
Proper muscle development and regeneration require coordinated gene expressions in embryonic muscle precursor cells and adult SCs [2,4]. The embryonic muscle precursor cells originate from dermomyotome, a dorsal part of the somite, which gives rise to myotome and dermatome. During embryogenesis, muscle precursor cells expressing Paired box 3 (Pax3) transcription factor appear in dermomyotome. These Pax3+ cells are myogenic progenitor cells and a portion of them also express Pax7. Most of the Pax3+/Pax7+ cells, and Pax3+/Pax7- cells are defined as myoblasts in later stages and develop into skeletal muscles. A small fraction of the Pax3+/Pax7+ cells becomes quiescent and settle in as SCs in postnatal skeletal muscles [31–33]. The myoblasts express muscle regulatory factors (MRFs) such as Myf5, MyoD, Myogenin (Myog) and Mrf4, and then differentiate and fuse with each other to form myotubes, which mature into myofibers [34]. Pax3-null mice are devoid of all limb muscles [35].
In the muscle tissues, SCs are located on the surface of myofibers, inside the ensheathing basal lamina, and regulated by both extrinsic and intrinsic factors [36–38]. In the steady state, SCs maintain quiescence and express Pax7 [31]. Upon muscle injury, they are activated and proliferate to form muscle fibers for regeneration [39]. Upon activation, Pax7 expression is rapidly lost and the MRFs are induced during regeneration. SCs are also responsible for postnatal muscle growth [40], and age-related muscle decline is associated with functional impairment of SCs [38].
The number of tissue precursor cells increases during organ development and tissue regeneration. The precise mechanism underlying the proliferation of SCs is not fully understood. Cell cycle is regulated by a set of cell cycle factors, including Cyclins, Cyclin-dependent kinases (CDKs), and CDK inhibitors (CDKIs). CDKIs, the negative regulators of cell cycle, comprise two families, namely the INK4 and the Cip/Kip families. Members of the INK4 family (p16INK4a, p15INK4b, p18INK4c and p19INK4d) inhibit CDK4 and CDK6, whereas Cip/Kip members (p21Cip1, p27Kip1, and p57Kip2) mainly inhibit CDK2 and CDK4 [41]. Among them, p57Kip2 (also called as Cdkn1c) is reportedly important to maintain the hematopoietic stem cells in a non-proliferative state [42,43]. The p57Kip2 is located at an imprinted locus and loss-of-function mutations in p57Kip2 cause Beckwith-Wiedemann syndrome, an overgrowth disorder which is characterized by increased organ sizes including that of muscles [44,45], and gain-of-function mutations cause undergrowth disorders such as Silver-Russell syndrome [46–48].
Here, we show an indispensable role of Dnmt3a in muscle SCs by utilizing muscle precursor cell-specific Dnmt3a deletion in mice, and identify p57Kip2 as a critical target gene of Dnmt3a for the proper proliferation of SCs.
To assess the role of DNA methylation in muscle development, we analyzed muscle precursor cell-specific Dnmt3a cKO mice. We established a mouse line in which Dnmt3a gene was deleted by Cre recombinase driven by a Pax3 promoter (Fig 1A). The efficiency of deletion in tibialis anterior muscles of cKO mice was approximately 70% at the genomic DNA level (Fig 1B), and over 90% at the mRNA level in tibialis anterior, gastrocnemius, paraspinal muscles and diaphragm (Fig 1C); Dnmt3b expression level was unaffected (S1A Fig). The Dnmt3a-cKO mice exhibited significantly smaller body sizes than WT littermates at 8- to 12-week old (Fig 1D), although they were born at normal Mendelian ratios, and were viable. The Dnmt3a-cKO mice weighed less than WT controls and the difference was more prominent in females (Fig 1E). No apparent skeletal deformity was observed using X-ray whole body imaging (Fig 1F). Muscle tissues were hypoplastic in Dnmt3a-cKO mice (S1B Fig). Computed Tomography (CT) scan of distal hindlimbs revealed significantly reduced muscle mass in the Dnmt3a-cKO mice compared to WT controls (Fig 1G and 1H), and the difference was more prominent in females (Fig 1G and 1H). Histological analysis of the gastrocnemius muscles revealed that myofibers in Dnmt3a-cKO muscles were narrower than WT myofibers (Fig 1I and 1J). Median myofiber cross sectional area (CSA) of the Dnmt3a-cKO muscles was significantly smaller than that of the WT muscles (Fig 1K). Growth retardation and decreased muscle mass in Dnmt3a-cKO mice persisted at later stages as well and growth did not catch up with WT littermates. These findings indicate that the loss of Dnmt3a in muscles leads to reduced muscle mass. The relatively well- maintained muscle tissue patterns prompted us to investigate the status of muscle differentiation. Gene expression analysis in muscles did not reveal any significant differences in myogenic gene expression between Dnmt3a-cKO and WT muscles (S1C Fig), suggesting that Dnmt3a deletion does not affect myogenic differentiation. These findings suggest that the loss of Dnmt3a in the Pax3+ myogenic precursor cells leads to decreased muscle mass in mice.
The finding that Dnmt3a-cKO muscles are hypoplastic implied that the potential of muscle precursor cells to grow organs had reduced. To investigate the role of muscle SCs in recreating muscle tissues, we probed muscle regeneration in the cKO mice (Fig 2A). The tibialis anterior muscles were injected with cardiotoxin (CTX) to induce tissue injury. Histological analysis of the muscles 7 days after the CTX treatment revealed smaller regenerated myofibers with a central nucleus, in the Dnmt3a-cKO muscles than in the WT muscles (Fig 2B and 2C). Median regenerative myofiber CSA of Dnmt3a-cKO muscles was significantly smaller than that of WT muscles (Fig 2D). These findings indicate that muscle regenerative capacity is impaired in Dnmt3a-cKO mice. Since the loss of Dnmt3a causes decreased muscle formation in adult mice also, it implies that Dnmt3a loss impairs the function of adult SCs.
To gain a mechanistic insight into how loss of Dnmt3a leads to a functional impairment of the SCs, we performed an in vitro analysis of the muscle SCs. We isolated SCs from Pax3-Cre; Dnmt3a-cKO mice and WT littermates and cultured the cells in growth conditions. The proliferation of Dnmt3a-cKO SCs was impaired relative to that of WT SCs, indicating that Dnmt3a is required for SCs to re-enter the cell cycle (S2A and S2B Fig). Because Pax3 is expressed during development, we considered that there may be an effect of Pax3-dependent Dnmt3a deletion during the development of SCs.
In our evaluation of the non-muscle effects of the Pax3 promoter-dependent Dnmt3a mutation, we found that Pax7-KO mice, which completely lack SCs, exhibit growth retardation and thin myofibers, indicating that dysfunction in SCs leads to growth retardation [40]. Accordingly, we considered that the Dnmt3a-cKO mouse phenotype was attributable to impaired SC function. To eliminate the possible developmental deficit of SCs and non-muscle effects, we utilized a tamoxifen-inducible Pax7-CreERT2 system and generated Pax7-CreERT2; Dnmt3aflox/flox mice for later analyses. Pax7-Cre; Dnmt3a-KO SCs were isolated from Pax7-CreERT2; Dnmt3aflox/flox mice after tamoxifen injection (Fig 3A). Dnmt3a KO efficiency was over 99% both at the genomic DNA level (Fig 3B) and mRNA level (Fig 3C). The morphologies of the isolated Dnmt3a-KO SCs were indistinguishable from those of WT SCs (Fig 3D, Day 1). However, Dnmt3a-KO SCs showed a striking loss of expansion in culture and their growth rate was significantly lower than that of WT SCs (Fig 3D and 3E). To explore whether the impaired expansion of Dnmt3a-KO SCs was caused by decreased proliferation of the SCs, we performed phosphorylated histone H3 (PHH3-Ser10) immunostaining of the SCs. The frequency of the PHH3-Ser10+ Dnmt3a-KO SCs was significantly lower than that of WT SCs (Fig 3F and 3G). We also performed 5-ethynyl-20-deoxyuridine (EdU) incorporation assay. EdU+ cells were significantly less frequent in Dnmt3a-KO SCs than in WT SCs (S4 Fig). These findings suggest that cell proliferation is impaired in Dnmt3a-KO SCs. With regard to apoptosis, we immunostained proliferating Pax7-Cre; Dnmt3a-cKO and WT SCs with a cleaved Caspase-3 antibody. The frequency of cleaved Caspase-3-positivity was very low in Dnmt3a-cKO SCs and not statistically different from that in WT SCs. These results suggest that the loss of expansion observed in Dnmt3a-KO SCs was attributable not to activated apoptosis but to decreased proliferation (S5 Fig). To examine the influence of the Dnmt3a deletion on the differentiation capacity of SCs, myogenic differentiation was induced by serum starvation. The number of cells was strictly adjusted so that differentiation was induced at the same confluency in both Dnmt3a-KO and WT SCs. The Dnmt3a-KO SCs showed no apparent morphological differences from WT SCs (S6A Fig). Also, the expression of myogenic genes was not different significantly, indicative of the unaffected myogenic differentiation capacity of the Dnmt3a-KO SCs, compared to the WT SCs (S6B Fig). Collectively, loss of Dnmt3a leads to decreased proliferation of muscle SCs.
To elucidate the mechanism of how Dnmt3a regulates the proliferative capacity of SCs, we performed transcriptome analysis of Dnmt3a-KO SCs. To minimize the potential developmental differences in the SCs of the Dnmt3a-cKO mice, we established a temporal deletion of Dnmt3a by infecting Dnmt3aflox/flox SCs with adenovirus expressing Cre-recombinase (Ax-Cre). The Dnmt3a deletion efficiency was approximately 70% at the mRNA level (Fig 4A). Consistent with the gene expression analysis in the Pax7-dependent deletion of Dnmt3a, the expression of myogenic genes was not significantly altered in the Ax-Cre-mediated Dnmt3a-KO SCs (S7A Fig). Among cell-cycle related genes, the expression of p57Kip2, a negative regulator of cell cycle, increased in the Ax-Cre Dnmt3a KO SCs without induction of differentiation (Fig 4B). The increased expression of p57Kip2 was also observed in the Pax7-dependent Dnmt3a-KO SCs (Fig 4C), and it continued even after differentiation (Fig 4C). Immunostaining with a p57Kip2 antibody showed significantly higher intensities of fluorescence in Pax7-Cre; Dnmt3a-cKO SCs than in WT SCs, suggesting enhanced expression of p57Kip2 in the Pax7-Cre; Dnmt3a-cKO SCs (Fig 4D and 4E). According to RT-qPCR analysis of Pax7-Cre; Dnmt3a-KO and WT SCs for all of the other CDKIs, the expression level of p16INK4a was only elevated by Dnmt3a loss (S8 Fig). But the difference of p16INK4a expression between Dnmt3a-KO and WT SCs was much smaller than that of p57Kip2. Therefore, we considered p57Kip2 as a primary candidate of a causative factor of impaired proliferation of Dnmt3a-KO SCs. Collectively, loss of Dnmt3a leads to elevated expression of p57Kip2 in SCs.
To determine whether the mis-expression of p57Kip2 in Dnmt3a-KO SCs is attributable to alteration of DNA methylation, we performed a bisulfite sequencing analysis in the Pax7-dependent Dnmt3a-KO and WT SCs. It was found that the p57Kip2 promoter region was extremely hypomethylated in the undifferentiated Dnmt3a-KO SCs (Fig 5A and 5B), suggesting that the extent of DNA methylation in the promoter region underlies p57Kip2 expression. Since we confirmed by lineage tracing that pure Pax7+ cells were isolated by the single myofiber culture method (S9 Fig), the difference in DNA methylation levels between Dnmt3a-KO and WT SCs did not seem to be due to contamination by non-myogenic cells. To examine whether p57Kip2 is a functional target of Dnmt3a in regulating the proliferation of SCs, we tested the effect of p57Kip2 depletion in the Dnmt3a-KO SCs. The cell proliferation defect was partially but significantly rescued by p57Kip2 knockdown (Fig 5C and 5D). In line with these data, the decreased frequency of PHH3+ Dnmt3a-KO SCs was also partly rescued by p57Kip2 knockdown (Fig 5E), indicating that Dnmt3a regulates the proliferation of SCs by controlling the expression of p57Kip2. Accordingly, our findings suggest that the decreased proliferation of SCs is, at least partly, due to mis-expression of p57Kip2 caused by DNA hypomethylation. DNA hypomethylation of the p57Kip2 promoter in the Dnmt3a-KO SCs prompted us to suppose that it is a methylation target of Dnmt3a. To assess the recruitment of Dnmt3a to the p57Kip2 regulatory region, a ChIP-qPCR analysis was performed with Dnmt3a in undifferentiated proliferating WT SCs. The p57Kip2 regulatory region was enriched with Dnmt3a at a similar level as the H1foo promoter, which is DNA-methylated except in oocytes (S10A Fig). The primers for the ChIP in the H1foo locus were designed on the basis of Dnmt3a2-ChIP-seq data by Baubec et.al [49] (S10B Fig). The housekeeping gene Rps18 promoter, which is consistently DNA hypomethylated, was not enriched with Dnmt3a. These findings suggest that the p57Kip2 regulatory region is a direct methylation target of Dnmt3a in SCs. In contrast to p57Kip2, the p16INK4a promoter region was not enriched in the Dnmt3a ChIP (S10A Fig), suggesting that this region is not a direct target of Dnmt3a. Taken together, p57Kip2 is a methylation target of Dnmt3a and regulates proliferation of SCs.
To extend our in vitro findings to an in vivo context, we checked p57Kip2 expression in the Pax3-Cre; Dnmt3a-cKO muscles. Immunostaining with a p57Kip2 antibody in single myofibers revealed a higher level of p57Kip2 protein expression in Dnmt3a-cKO muscles (Fig 6A). We further performed costaining of Pax7 and p57Kip2 in Dnmt3a-cKO and WT myofibers. The expression of p57Kip2 was very weak in the WT Pax7+ SCs (Fig 6B). In contrast, p57Kip2 was costained with Pax7 in the cKO myofibers, indicating that expression of p57Kip2 is indeed enhanced in the SCs (Fig 6B). Bisulfite sequencing analysis revealed significant hypomethylation at the promoter region of p57Kip2 in the Dnmt3a-cKO muscles (Fig 6C and 6D), corroborating the findings in the Pax7-Cre; Dnmt3a-KO SCs. Since p57Kip2 is also mis-expressed in the Dnmt3a-cKO muscles, this implies that Dnmt3a regulates p57Kip2 expression through epigenetic mechanisms, both in vitro and in vivo.
Our findings indicate that Dnmt3a loss impairs muscle regenerative capacity and reduces proliferative capacity of SCs. To determine whether the impaired muscle regeneration was a result of impaired SC proliferation, we assessed the frequency of SCs expressing Pax7 in both the unperturbed and the regenerating muscles. The frequency of Pax7+ cells in all nucleated cells in unperturbed Pax3-Cre; Dnmt3a-cKO muscles was not significantly different from that in WT muscles (Fig 7A and 7B). However, in the regenerating muscles, Pax7+ cells were less frequent in the Dnmt3a-cKO mice than in the WT mice (Fig 7A and 7B). Pax7/Laminin costaining demonstrated that most of these Pax7+ cells were located inside the basal lamina of the regenerated myofibers (S11 Fig). Next, to examine whether the lower frequency of Pax7+ cells in the Dnmt3a-cKO regenerating muscles was caused by decreased proliferation of the SCs, phospho-histone H3 (Ser10) immunostaining was performed in the regenerating tibialis anterior muscles. Immunostaining at 7 days after CTX injection revealed that PHH3+ cells were less frequent in the Dnmt3a-cKO than WT mice (Fig 7C and 7D). These results suggest that the SCs are not wasting in the uninjured muscles of Dnmt3a-cKO mice, but that their ability to proliferate after injury is impaired, leading to defects in their regenerative capacity. Immunostaining with a p57Kip2 antibody showed that p57Kip2+ cells were more frequent in the Dnmt3a-cKO than in the WT regenerating muscles (S11B and S11C Fig). The behavior of SCs was explored by Pax7/MyoD-costaining and Myog immunostaining in regenerating muscles. The ratios of MyoD+Pax7+ cells to MyoD-Pax7+ cells were lower in Dnmt3a-cKO regenerating muscles than in the WT (S11D and S11E Fig), suggesting SC activation is impaired in Dnmt3a-cKO muscles. Myog+ cells were less frequent in Dnmt3a-cKO regenerating muscles compared to those in the WT (S11F and S11G Fig). This lower frequency of Myog+ cells does not necessarily indicate impaired differentiation capacity as a result of the Dnmt3a deletion, because Dnmt3a-cKO reduced the number of proliferating SCs, which produce the differentiating SCs. Taken together, these results suggest that the SCs are not wasting in the uninjured muscles of Dnmt3a-cKO mice but their proliferation after injury is impaired, leading to the defects in the regenerative capacity.
In summary, Dnmt3a regulates the proliferation of muscle SCs, thereby influencing the growth of SCs in culture and the regenerative capacity of skeletal muscles. Hence, Dnmt3a maintains muscle homeostasis by regulating the functions of SCs through the epigenetic regulation of p57Kip2.
In this study, we have shown that loss of Dnmt3a in the Pax3-expressing cell lineage leads to reduced body size and muscle mass in mice. Although Pax3-Cre; Dnmt3a-cKO mice exhibited grossly normal tissue patterns, they had thinner myofibers, unproportionally decreased muscle mass and impaired muscle regeneration, suggesting that Dnmt3a contributes to the function of SCs that are responsible for postnatal muscle growth and regeneration. Pax7-/- mice which completely lack SCs display similar phenotypes to those of Dnmt3a-cKO mice, namely decreased muscle mass and reduced myofiber diameter, although the overall organization of myofibers appears normal [40]. The phenotypes of Pax7-/- mice are attributable to a lack of SC fusion during the postnatal period [40]. We also identified p57Kip2 as an essential downstream target of Dnmt3a for methylation and a causative candidate gene for the functional deficits in Dnmt3a-cKO SCs. This is corroborated by the finding that p57Kip2 knockdown ameliorates the decreased proliferation of the Dnmt3a-cKO SCs. Dnmt3a deletion in SCs impairs proliferation through the mis-expression of p57Kip2, resulting in quantitative insufficiency of SCs similar to that in Pax7-/- mice (Fig 8).
Roles of p57Kip2 in regulating body and organ sizes have been elucidated in the context of human overgrowth and undergrowth disorders. p57Kip2-deficient mice have phenotypes similar to the manifestations of Beckwith-Wiedemann syndrome (BWS), an overgrowth disorder [50,51]; in addition, p57Kip2 activity is lower in BWS patients [44,52]. Silver-Russell syndrome (SRS) is a heterogeneous disorder characterized by pre- and post-natal growth retardation [53,54]. IMAGe syndrome is another undergrowth disorder characterized by intrauterine growth retardation, metaphyseal dysplasia, adrenal hypoplasia and genital anomalies [55]. Loss-of-function mutations of p57Kip2 have been identified in BWS patients [44], and gain-of-function mutations in the Proliferating cell nuclear antigen (PCNA)-binding domain of p57Kip2 have been identified in growth retardation syndromes such as SRS and IMAGe syndrome [46–48].
It is well known that genomic imprinting is controlled by DNA methylation and that p57Kip2 is paternally imprinted. DNA methylation at the imprint center is maintained by Dnmt1, a maintenance DNA methyltransferase, but Dnmt1 alone is not able to maintain all of the DNA methylation loci, especially in CpG-rich regions [24,56]. Therefore, there is a possibility that maintenance DNA methylation deficits besides de novo DNA methylation is caused by Dnmt3a deletion, resulting in the progressive loss of genomic imprinting. However, we think the mis-expression of p57Kip2 in Dnmt3a-KO SCs is not a result of lost genomic imprinting because the imprint center is not located in the p57Kip2 promoter and because p57Kip2 is expressed only from the methylated maternal allele [52]. Considering this regulatory mechanism, the expression of p57Kip2 should be decreased as a result of loss of genomic imprinting. In our Dnmt3a-KO SCs, p57Kip2 expression level was lower than that of the WT, which implies that there was no change in genomic imprinting.
If the cell population is perfectly homogeneous, the DNA methylation level of a CpG site should be either 100% or 0%. Isolated SCs in our experiments are all Pax7-positive (S9 Fig), but their differentiation status after in vitro culture is not perfectly homogeneous. We consider some SCs might not get out of quiescence and others might be beginning spontaneous differentiation, and therefore the DNA methylation levels of WT SCs at the p57Kip2 promoter were not 100%. In fact, during culture of isolated myofibers, some SCs divide asymmetrically into two types of cells that are distinctively fated to self-renew or to differentiate [57]. Hence, SCs are considered heterogeneous population composed of stem cells and committed progenitors. A certain proportion of SCs may divide asymmetrically even when cultured on dish. In addition, a DNA methylation level of the p57Kip2 promoter was not 0% even in Dnmt3a-KO SCs. This might be because Dnmt3b incompletely compensates the influences of Dnmt3a deletion.
Although our findings reveal an essential role of p57Kip2 in the undifferentiated SCs, p57Kip2 is also known to be a target of MyoD, which promotes muscle differentiation [58]. We also observed a further increase in the expression of p57Kip2 after myogenic differentiation, coincident with the cell cycle deceleration in the differentiating SCs. Our findings suggest that Dnmt3a-KO prematurely triggers the induction of p57Kip2 in the undifferentiated SCs, which results in a reduced number of SCs forming mature myofibers.
The decrease in body size and muscle mass of Dnmt3a–cKO mice were more severe in females. We could not identify the reason for this gender difference; previous studies of Dnmt3a deletion in other tissues have not reported such gender-dependent severity of phenotypes. However, female mice show more severe phenotypes of several heart diseases [59,60]. In the mdx mouse model of Duchenne cardiomyopathy, aged female mice display more severe cardiomyopathy [61]. Although the detailed reasons for such differences are not clear, it is possible that the female muscular tissues are more susceptible to a specific pathological condition.
Another epigenetic regulatory mechanism, histone modification is also known to regulate SC functions. Histone deacetylase inhibitors increase muscle cell size by promoting cell fusion without affecting cell proliferation [62]. On the other hand, conditional ablation of Polycomb-repressive complex (PRC2) subunit EZH2 in Pax7+ cells results in impaired SC proliferation and reduced muscle mass with small myofibers [63]. Taken together, it is suggested that multiple epigenetic mechanisms coordinately regulate SC functions and control the tissue size of skeletal muscles.
Thus, the loss of Dnmt3a in muscle progenitor cells leads to premature expression of a CDKI, p57Kip2, which causes decreased proliferation of the SCs, leading to smaller body size and disproportionately reduced muscle mass in mice. Our findings indicate that there are several potential mechanisms for size regulation. Firstly, DNA methylation, which specifies the sets of genes to be expressed in a certain context, influences body size. Secondly, the number of tissue stem cells, which is balanced between self-renewal and differentiation commitment, might influence body and organ sizes. There is an increased incidence of rhabdomyosarcoma among BWS patients [64,65], which implies that deteriorated size regulation leads to tumorigenesis. Our current understanding of the mechanisms regulating body and organ size is limited; however, further elucidation of the size control machinery may lead to novel therapeutic approaches for cancer that target these mechanisms.
In this study, we show that Dnmt3a regulates proliferation of muscle SCs by methylating the p57Kip2 locus and suggest that this Dnmt3a-p57Kip2 axis forms the basis of size-control mechanisms in muscle tissues. Further elucidation of the underlying relation between DNA methylation and body and organ size control, will provide novel insights for developing new therapeutic approaches for some of the incurable human disorders.
We used mice in our research. The mice were anesthetized by intraperitoneal injection of pentobarbital or inhalation of isoflurane. Cervical dislocation was used as a euthanasia method. All animal experiments were approved by the Institutional Animal Care and Use Committee at Tokyo Medical and Dental University (approval number; 0160127A).
Dnmt3a-flox mice were kindly provided by Dr. M. Okano. Dnmt3a-floxed allele was previously described [66]. Pax3-Cre mice and Pax7-CreERT2 mice were purchased from the Jackson Laboratory (Bar Harbor, ME). Pax3-Cre allele and Pax7-CreERT2 allele were previously described [67,68]. Genomic DNA was isolated from muscle tissues using DNeasy Blood & Tissue Kit (Qiagen, Hilden, Germany) according to the manufacturer’s instructions. Gene deletion efficiency was calculated by genomic DNA qPCR. Relative genomic DNA level was determined by the standard curve method. All primer sequences are listed in S1 Table.
Computed tomography (CT) scan of distal hindlimbs was performed using Latheta LCT-200 (Hitachi Aloka Medical, Tokyo, Japan). Mice were anesthetized by isoflurane inhalation during the scan. The image data were analyzed using Latheta software (Hitachi Aloka Medical, Tokyo, Japan), and muscle and bone cross-sectional volume were calculated. The slice of each limb where the muscle cross-sectional area was the greatest was selected for muscle volume evaluation, for each mouse.
Muscle tissues of 8- to 12week-old mice were frozen in isopentane cooled in liquid nitrogen. Frozen tissues were sectioned using a cryostat CM3050S (Leica, Wetzlar, Germany) at 10 μm thickness and mounted on MAS-coated slide glasses (Matsunami Glass, Osaka, Japan). The CSA of myofibers were measured in at least five fields of view using ImageJ software (National Institutes of Health, Bethesda, MD). For Hematoxylin-Eosin (HE) staining, muscle sections were fixed in 4% paraformaldehyde (PFA) in phosphate buffered saline (PBS) at room temperature for 10 minutes, then immersed in Mayer’s Hematoxylin Solution (Wako, Osaka, Japan) for 5 minutes, followed by washing under running water for 10 minutes. After staining with 1% Eosin Y Solution (Wako, Osaka, Japan) for 1 minute, they were sequentially immersed in 70%, 95% and 100% ethanol for 30 seconds, 1 minute and 3 minutes, respectively. Finally, they were washed thrice in xylene for 3 minutes each and embedded in Entellan Neu (Merck KGaA, Darmstadt, Germany).
Dnmt3a-KO SCs were harvested from 6- to 8-week-old Pax7-CreERT2; Dnmt3aflox/flox mice. Tamoxifen (Sigma, St Louis, LA) was administered to the mice intraperitoneally at the dose of 100 μg/body weight (g) for 5 consecutive days. After seven days of the first tamoxifen administration, the mice were sacrificed to harvest gastrocnemius muscles, and SCs were isolated as previously described [69,70]. Briefly, single myofibers were obtained by collagenase digestion and cultured in primary cultured myocyte growth medium (pmGM) consisting of Dulbecco’s modified Eagle’s medium (DMEM; Sigma, St Louis, LA) with 20% fetal bovine serum, 1% penicillin/streptomycin (Life Technologies, Grand island, NY), 2% Ultroser G (Pall, New York, NY), 1000 U/ml mouse leucocyte inhibitory factor (LIF; AMRAD Biotech, Victoria, Australia) and 10 ng/ml human basic fibroblast growth factor (bFGF; PeproTech EC, London, UK) on type I collagen-coated dishes (Sumilon, Tokyo, Japan) at 37°C under 10% CO2 in a humidified chamber. SCs migrated from the myofibers in 4 to 5 days. For analyzing growth of SCs, isolated SCs were cultured in pmGM. To induce myogenic differentiation, SCs were cultured in DMEM with 2% horse serum.
Frozen muscles were sectioned at 10 μm thickness and mounted on MAS-coated slide glasses (Matsunami Glass, Osaka, Japan). Single myofibers were isolated by collagenase digestion as previously described [69,70], and plated on MAS-coated slide glasses (Matsunami Glass, Osaka, Japan). Sections or myofibers were dried in the air and then fixed in 4% PFA in PBS at room temperature for 10 min. For immunocytochemistry, cultured cells are fixed in 4% PFA in PBS at room temperature for 10 min. After permeabilization with 0.1% Triton X-100 in PBS for 20 min, they were blocked with 1% Bovine serum albumin (BSA) in PBS for 1 hour and incubated with primary antibodies at 4°C overnight. The following antibodies were used: anti-Pax7 (described previously [71]), anti-MyoD (BD Pharmingen 554130, 1:100), anti-Myog (Santa Cruz sc-576, 1:50), anti-Phospho-Histone H3 (Ser10) (Cell Signaling #9701, 1:400), anti-active Caspase-3 (Abcam ab2302,1:200), anti-p57Kip2 (Santa Cruz sc-8298, 1:100), anti-p57Kip2 (Cell Signaling #2557, 1:500) and anti-Laminin 2 alpha (Abcam ab11576, 1:500). After the primary antibody incubation, sections were incubated with secondary antibodies conjugated with Alexa Fluor 488 or 594 (Life Technologies, 1:1000). Finally, they were mounted in VectaShield with DAPI (Vector Laboratories, CA, USA). The mean intensity of fluorescence signals in each cell was calculated using ImageJ software (National Institutes of Health, Bethesda, MD).
SCs were harvested as described above and cultured in pmGM for about 7 days to expand enough for the assay. One day after a passage to adjust confluency, they were cultured in medium containing 10 μM EdU for 3 hours for EdU labeling. EdU incorporation was assessed using Click-iT Plus EdU Alexa Fluor 488 Imaging Kit (Life Technologies, Grand island, NY).
Total RNA was isolated from the homogenized muscle tissues using ISOGEN (Nippon Gene, Tokyo, Japan) according to the manufacturer’s instructions. One μg of total RNA was used to synthesize cDNA. Reverse transcription was performed using ReverTra Ace (Toyobo, Osaka, Japan) following the manufacturer’s instructions. qPCR was performed by Thermal Cycler Dice Real Time System II (Takara Bio, Japan) using Thunderbird SYBR qPCR Mix (Toyobo, Osaka, Japan) and the relative expression levels were detected by the ΔΔCt method. All primer sequences are listed in S1 Table.
Microarray analysis (Affymetrix) was performed with RNA samples derived from the WT- and Dnmt3aflox/flox-SCs infected with Ax-Cre (MOI 30) at 0, 12, 24, 48, 72 and 96 hours of differentiation in vitro. The data were normalized and z transformed for the hierarchical clustering analysis utilizing Multiple Experiment Viewer [72].
Bisulfite conversion of the isolated genomic DNA was performed by CpGenome Turbo Bisulfite Modification Kit (Millipore, Billerica, MA) according to the manufacturer’s instructions. Bisulfite-treated DNA was amplified by PCR using Quick Taq HS DyeMix (Toyobo, Osaka, Japan). All primer sequences are listed in S1 Table. PCR products were cloned into T-Vector pMD20 (Takara Bio, Shiga, Japan) and sequenced with the M13 reverse primer from at least 12 clones.
Fifty microliters of 0.03 mg/ml cardiotoxin (CTX; Sigma, St Louis, LA) was injected into the bilateral tibialis anterior muscles of 8- to 12-week-old mice, after making skin incisions to expose the fascia on bilateral hindlimbs under anesthesia. The mice were sacrificed 7 to 14 days after CTX injection, and the injured muscles were harvested for histological analysis and gene expression analysis.
p57Kip2 knockdown was achieved by p57Kip2 siRNA transfection. SCs were disseminated on type I collagen-coated dishes at a density of 0.1 × 105 cells/ml. After verifying cell adherence to the dishes, siRNA was transfected at a final concentration of 20 nM, using Lipofectamine RNAiMAX Transfection Reagent (Invitrogen, Carlsbad, CA) according to the manufacturer’s instructions. SCs were counted daily, starting from day 1 after transfection. MISSION siRNA targeting murine p57Kip2 was supplied by Sigma-Aldrich (St. Louis, MO). p57Kip2 siRNA duplexes of the following RNA sequences were used: 5’-GUGCUGAGCCGGGUGAUGATT-3’; 5’-UCAUCACCCGGCUCAGCACTT-3’. AllStars Negative Control siRNA (Qiagen, Hilden, Germany) was used for the mock transfection control.
Approximately 1.0 × 107 proliferating SCs for each antibody were fixed with 1% formaldehyde at room temperature for 10 minutes. The cell lysates were sonicated with a Covaris S2 sonicator to shear DNA. Dynabeads Protein A (Invitrogen, Carlsbad, CA) conjugated with 10 μg of each primary antibody was added, followed by incubation at 4°C overnight. The beads were washed 5 times with RIPA buffer (0.2% NP-40, 0.2% Na-deoxycholate, 0.16 M LiCl, 10 mM EDTA, 20 mM HEPES-KOH, pH 7.6) and eluted with elution buffer (1% SDS, 50 mM EDTA, 100 mM Tris-HCl, pH 8.0). The eluate was incubated at 65°C overnight to reverse the crosslinking, followed by incubation at 55°C for 1 hour in the presence of proteinase K. DNA was purified using a MinElute PCR Purification Kit (Qiagen, Hilden, Germany) and quantified by real-time PCR (Thermal Cycler Dice Real Time System II (Takara Bio, Japan)). All primer sequences are listed in S1 Table.
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10.1371/journal.pbio.2006767 | The Cdk8/19-cyclin C transcription regulator functions in genome replication through metazoan Sld7 | Accurate genome duplication underlies genetic homeostasis. Metazoan Mdm2 binding protein (MTBP) forms a main regulatory platform for origin firing together with Treslin/TICRR and TopBP1 (Topoisomerase II binding protein 1 (TopBP1)–interacting replication stimulating protein/TopBP1-interacting checkpoint and replication regulator). We report the first comprehensive analysis of MTBP and reveal conserved and metazoa-specific MTBP functions in replication. This suggests that metazoa have evolved specific molecular mechanisms to adapt replication principles conserved with yeast to the specific requirements of the more complex metazoan cells. We uncover one such metazoa-specific process: a new replication factor, cyclin-dependent kinase 8/19–cyclinC (Cdk8/19-cyclin C), binds to a central domain of MTBP. This interaction is required for complete genome duplication in human cells. In the absence of MTBP binding to Cdk8/19-cyclin C, cells enter mitosis with incompletely duplicated chromosomes, and subsequent chromosome segregation occurs inaccurately. Using remote homology searches, we identified MTBP as the metazoan orthologue of yeast synthetic lethal with Dpb11 7 (Sld7). This homology finally demonstrates that the set of yeast core factors sufficient for replication initiation in vitro is conserved in metazoa. MTBP and Sld7 contain two homologous domains that are present in no other protein, one each in the N and C termini. In MTBP the conserved termini flank the metazoa-specific Cdk8/19-cyclin C binding region and are required for normal origin firing in human cells. The N termini of MTBP and Sld7 share an essential origin firing function, the interaction with Treslin/TICRR or its yeast orthologue Sld3, respectively. The C termini may function as homodimerisation domains. Our characterisation of broadly conserved and metazoa-specific initiation processes sets the basis for further mechanistic dissection of replication initiation in vertebrates. It is a first step in understanding the distinctions of origin firing in higher eukaryotes.
| Efficient and well-regulated DNA replication origin firing is central to ensure complete and accurate genome duplication before cell division. We here use bioinformatics and cultured human cells to understand the role of the essential origin firing factor Mdm2 binding protein (MTBP). We prove that MTBP is orthologous to yeast Sld7. Their homologous N-terminal domains bind the orthologous firing factors Treslin/TICRR and Sld3, respectively. The homologous C termini may constitute MTBP/Sld7 homodimerisation domains. Because Sld7 was the only yeast core origin firing factor for which no metazoan orthologue had been found, our work proves that the complement of core firing factors is conserved from yeast to man. We also find that MTBP contains a central region that is not found in yeast, and that is important for replication in human cells, showing that—despite fundamental conservation—higher eukaryotes have also evolved specific origin firing processes. The Cdk8/19-cyclin C transcription kinase that has not previously been implicated in DNA replication binds the central MTBP region, and this is required for complete DNA replication and proper chromosome segregation in human cells. Our work suggests that MTBP helps integrate universal conserved eukaryotic origin firing processes into the complex metazoan cells.
| Eukaryotic cells must faithfully replicate their genomic DNA exactly once before each cell division in order to ensure genetic homeostasis through successive cell generations. Initiation of DNA replication is a major step of replication control by the cell cycle and the DNA damage checkpoint, ensuring faithful genome duplication in normal and adverse conditions [1, 2]. In higher eukaryotic cells, we know little about the molecular mechanisms and regulation of replication initiation. Yeast cells have served as a model for understanding initiation in eukaryotes. In the first initiation step, origin licensing, that occurs in the G1 cell cycle phase, prereplicative complexes (pre-RCs) form dependently on the origin recognition complex (ORC), cell division cycle 6 (Cdc6), and Cdc10-dependent transcript (Cdt1). Pre-RCs are inactive double hexamers of the Mcm2-7 helicase, comprising the six minichromosome maintenance (Mcm) proteins 2–7, loaded on origin DNA. In S phase, high activity of S phase cyclin-dependent kinase (S-CDK) and dumbbell former 4 (Dbf4)–dependent kinase (DDK) induces origin firing, the conversion of pre-RCs into two bidirectional replisomes, each containing a Mcm2-7 hexamer, Cdc45, and the go-ichi-ni-san (GINS) complex. This Cdc45-Mcm2-7-GINS (CMG) complex is the active replicative helicase [3]. DDK phosphorylates pre-RCs, which recruits the synthetic lethal with Dpb11 3/7 (Sld3-Sld7) complex to pre-RCs, which in turn interacts with and recruits Cdc45 [4–6]. S-CDK facilitates origin firing by phosphorylating Sld3 and Sld2 [7–9]. Phospho-Sld3 interacts with DNA polymerase binding 11 (Dpb11), which is part of the preloading complex (pre-LC) [10]. This interaction is thought to locate pre-LCs at origins. Pre-LCs also contain Sld2, polymerase epsilon, and the GINS tetramer. Pre-LC formation depends on S-CDK that phosphorylates Sld2 to induce the interaction of Sld2 with Dpb11 [11]. Formation of active replisomes further requires Mcm10, whose function is less clear [12, 13].
Biochemical reconstitution demonstrated that these yeast core initiation factors are sufficient to initiate bidirectional DNA replication [14–16]. Metazoan orthologues for each of the yeast core factors have previously been identified, except for Sld7. Sld7-deleted cells are viable but have severe replication defects [17]. In vitro replication initiates in the absence of Sld7 [6]. Whether the dispensability of Sld7 in vitro reflects an indirect role of Sld7 in origin firing in vivo or whether it is a consequence of the special biochemical in vitro conditions is unclear. Sld7 forms a stable complex with Sld3 in vivo and in vitro [17, 18]. Yeast cells require the interaction of Sld7 with Sld3 to replicate efficiently, because Sld7 binding mutants of Sld3 are sensitive to hydroxyurea, just like sld7Δ cells [17].
Conservation of most, but potentially not all, origin firing factors between yeast and metazoa suggested that although many fundamental initiation processes are conserved, processes that are specific for higher eukaryotes might have evolved. All metazoan orthologues of core yeast firing factors are essential for replication, as studies in Xenopus egg extracts and human cells have shown, but their molecular functions are largely unexplored [19–24]. The Sld3 orthologue, Treslin/TICRR (Topoisomerase II binding protein 1 (TopBP1)–interacting replication stimulating protein/TopBP1-interacting checkpoint and replication regulator) [25], utilises conserved domains for S-CDK–dependent interaction with the Dpb11 orthologue TopBP1 [24, 26, 27].
The Mdm2 binding protein (MTBP) protein was the last metazoan firing factor identified and described to be required for firing in human cells [28]. It did not fit a universal model of eukaryotic replication because, despite our extensive efforts, no homology with yeast initiation proteins was detected. MTBP is reminiscent of Sld7 in its binding to Treslin/TICRR/Sld3. This binding appears essential for replication as MTBP nonbinding Treslin/TICRR mutants did not facilitate replication. These functional similarities of MTBP and Sld7, and similarities in protein sequence and structure of the C termini [29] led to the hypothesis that MTBP performs Sld7-like functions in metazoans. However, no statistically significant evidence for orthology between MTBP and Sld7 has been provided.
We here employed various approaches to search for remote homologies in the MTBP and Sld7 proteins. These revealed MTBP to possess two Sld7-homologous regions in its N and C termini, and a metazoa-specific region separating these two homology domains. We show that the Sld7-homologous domains are required for proper replication origin firing in human cells. We thus incontrovertibly demonstrate orthology between MTBP and Sld7. This fills the last gap in the list of metazoan core origin firing factors, establishing a universal framework of eukaryotic replication initiation. Despite this conservation, metazoa have also evolved specific initiation processes, because the metazoa-specific middle domain of MTBP proved to be required for proper DNA replication. This domain apparently harbours more than one activity important for replication. Cyclin-dependent kinase 8/19–cyclinC (Cdk8/19-cyclin C), a protein that was not previously implicated in DNA replication, with roles in controlling transcription [30], binds the metazoa-specific MTBP domain. This interaction was required for complete genome replication and, consequently, for normal chromosome segregation. We hypothesise that the metazoa-specific binding of Cdk8/19-cyclin C to MTBP helps integrate the conserved initiation principles into the special requirements of the more complex metazoan cells to achieve well-regulated origin firing to guarantee genome stability.
Human MTBP (hMTBP) is surprisingly devoid of known domain homologues. To identify its domain architecture, we initiated an exhaustive computational sequence analysis. We identified three domains that are conserved in MTBP orthologues across most of the animal kingdom.
Two of these domains proved conserved in yeast Sld7 (Fig 1A). For this we employed iterative profile-based sequence similarity searches [31] of the UniRef50 database [32]. Focusing first on the most C-terminal of these regions, we found that its sequences are statistically significantly similar to the C terminus of Saccharomyces cerevisiae Sld7 of known tertiary structure (protein data bank [PDB] identifier, 3×38) [18] (E-value = 0.012; probability = 94.1%) (Fig 1B).
This step employed HHpred searches against the PDB70 profile database [33] and a C-terminal conserved region, amino acids 824–896 of hMTBP, as input. Consistent with these C-terminal domains of MTBP and Sld7 being homologous, secondary structure predictions of a three-helix bundle for the former agree with the published Sld7 crystal structure [18, 34]. Thus, the C-terminal domains of MTBP and Sld7 have evolved from a common ancestral sequence. We call them the Sld7-MTBP C-terminal domain (S7M-C). Our findings provide the statistical evidence required to support a recent proposal of MTBP-Sld7 C-terminal domain homology [29].
Two complementary approaches then provided evidence for MTBP and Sld7 sharing an N-terminal homologous domain. The first approach queried the protein homology/analogy recognition engine V 2.0 (phyre2) server with MTBP sequences and returned low confidence alignments with yeast Sld7 (S1A Fig) [18]. We term these amino acids the MTBP-phyre2 and Sld7-phyre2 region. Six amino acids in the Sld7-phyre2 region directly contact Sld3 to form Sld7-Sld3 dimers (L45, V48, 52-KLPL-55 of Zygosaccharomyces rouxii Sld7; S1A Fig, blue asterisks; S2 Fig) [18], and four of them are conserved in MTBP (V306, I309, L314, P315) with respect to their chemical properties. These MTBP amino acids are among the most highly conserved residues in this region across animals (S1B Fig).
We tested next if these amino acids in the MTBP-phyre2 region are important for binding to Treslin/TICRR. We deleted the phyre2 region (amino acids V295-T329) of hMTBP (MTBP-Δphyr2) and tested its interaction with endogenous Treslin/TICRR in cell lysates after transient transfection of MTBP-Flag into 293T cells. Flag immunoprecipitation (IP) (see Table 1 for all antibodies used) of wild-type (WT) MTBP-Flag (MTBP-WT), but not MTBP-Δphyr2, co-purified Treslin/TICRR (Fig 2A, lanes 1 and 2). A quintuple point mutant (MTBP-5m) exchanging the MTBP-phyre2 region amino acids V306, I309, D313, L314, and P315 against alanine (D313) or aspartate (all others) also showed no detectable binding to Treslin/TICRR (lane 3). These five residues map to Sld3-contacting amino acids in Sld7 (Figs 1C and S2). MTBP-Δphyr2 and MTBP-5m were specifically defective in binding to Treslin/TICRR but bound Cdk8, a new MTBP interactor, whose function in replication we discuss below, as well as MTBP-WT, suggesting that the mutants are not misfolded. To assess further the folding quality of the MTBP-5m protein, we tested its migration behaviour in gel filtrations. We found that MTBP-WT and MTBP-5m eluted indistinguishably from each other as sharp peaks and did not form aggregates (S3 Fig). MTBP-5m localises predominantly to the nucleus, like MTBP-WT (S4 Fig).
We realised that in some gels Treslin/TICRR in samples from cell lysates formed multiple bands representing different phosphorylation forms, whereas MTBP-bound Treslin/TICRR usually showed only one band (Fig 2B left panel). To test if MTBP-bound Treslin/TICRR is phosphorylated we ran lambda phosphatase (PPase)-treated and untreated lysates side by side with MTBP immunoprecipitates on an SDS polyacrylamide gel to compare gel mobility of Treslin/TICRR. MTBP-bound Treslin/TICRR showed a PPase-dependent mobility shift (S5 Fig).
To fine-map the interaction between MTBP and Treslin/TICRR, we then mutated amino acids V306 to S316 in the MTBP-phyre2 region individually. MTBP-Flag IP showed that four of the five amino acids aligning with Sld3-interacting amino acids, V306, I309, L314, and P315 (S2 Fig), contribute to Treslin/TICRR binding (Fig 2A, lanes 4, 7, 12, 13). Mutating other amino acids that were not aligned with Sld3-interacting Sld7 amino acids had variable effects. Mutating the most conserved, L311 (lane 9), showed a considerable reduction of Treslin/TICRR binding. Mutating the moderately conserved M308 (lane 6) had mild effects, whereas mutating the moderately conserved Q307 (lane 5) and the weakly conserved K310 and S312 (lanes 8 and 10) did not alter Treslin/TICRR interaction. All mutants bound Cdk8 normally. These data show that the phyre2 regions of Sld7 and MTBP have a conserved function, the binding to Sld3/Treslin/TICRR.
To identify homology between MTBP and Sld7 N termini, we then used the newly identified MTBP-phyre2 region as an anchor point for similarity searches based on amino acid sequences and secondary structures. This revealed extensive sequence similarities between amino acids V295 and G426 in the N terminus of MTBP and the full length of the N-terminal domain of Sld7 (Figs 1C and S2A). These similarities are sufficient to predict homology because their alignment score greatly exceeds those expected from alignments with random sequences (E = 3.0 × 10−5). We term these domains in Sld7 and MTBP Sld7-MTBP N-terminal (S7M-N) domains. Independent evolution of shared domain architectures is rare [35] and highly unlikely for functionally similar proteins. Our evidence thus strongly indicates that metazoan MTBP and fungal Sld7 are orthologues.
Regions outside MTBP-phyre2 are also important for interaction with Treslin/TICRR. Itou and colleagues showed that the S7M-N domain of Sld7 contains a second cluster of Sld3-contacting amino acids (Fig 1C) [18]. The corresponding positions in the S7M-N domain of hMTBP are amino acids C413–G426, and were also required for interaction with Treslin/TICRR (Fig 2B lanes 5–11). MTBP-Q423-G426 (mutated in MTBP-m3–6) and C413 (MTBP-m1) map to positions in Sld7 that directly contact Sld3 (S2 Fig). A415 (MTBP-m2) probably affects the stability of its β-strand (S2 Fig). Finally, deleting amino acids D2 to S55, D2 to S31 or D2 to V8 from the N terminus also resulted in MTBP mutants that failed to bind Treslin/TICRR (Fig 2B lanes 1–5). MTBP-ΔD2-V8 and mutants of the region between amino acids C413 and G426 do not show signs of protein misfolding. Their Cdk8 binding ability (Fig 2B) and migration behaviour in gel filtrations were indistinguishable from MTBP-WT (S3 Fig). MTBPm1–6 localises predominantly to the nucleus, like MTBP-WT (S4 Fig). We conclude that various regions in the N terminus of MTBP contribute to Treslin/TICRR binding, and termed it Treslin/TICRR binding domain (TresBD).
We next investigated if the two Sld7-like domains of MTBP, S7M-N and S7M-C, are important for DNA replication. We used an established RNA interference (RNAi) system [28] (see Table 2 for all small interfering RNAs [siRNAs] used) to replace MTBP with siRNA-resistant MTBP-WT or mutant MTBP transgenes (Fig 3A). Relative replication speed was then determined by 5-bromodeoxyuridine (BrdU) labelling and flow cytometry (Fig 3B and 3C, gating strategy for all flow cytometry experiments, including raw data for quantification in S1 Data). In control cells expressing no transgene MTBP-RNAi (siMTBP) reduced the BrdU signal 2.9-fold compared with control RNAi (siCtr) (Fig 3C and S1 Data), indicating effective suppression of replication. siRNA-resistant MTBP-WT rescued replication in siMTBP-treated cells almost completely (1.2× reduction).
In contrast, Treslin/TICRR nonbinding S7M-N mutants, MTBP-5m (phyre2 region) and MTBP-m1–6 (amino acid C413–G426 region), did not rescue replication. These mutants’ BrdU–propidium iodide (PI) profiles were indistinguishable from siMTBP-treated control cells. BrdU signals were reduced 3.0-fold (MTBP-5m) and 2.6-fold (MTBP-m1–6). We sought to use minimally invasive single point mutations to confirm these conclusions that were drawn from the MTBP-5m and m1–6 combination mutants. The three non-Treslin/TICRR-binding MTBP mutants I309D, L314D (both mutated in 5m) and A415Q (mutated in m1–6) did not support replication over background, whereas cells expressing the Treslin/TICRR-binding proficient D313A replicated normally (S6 Fig and S1 Data).
To test which step of DNA replication was defective in Treslin/TICRR nonbinding MTBP mutants, we tested if origin licensing and origin firing occurred in cells expressing these mutants. Immunoblotting of chromatin fractions indicated normal licensing, as judged by Mcm5 (pre-RC subunit) signals (Figs 3D and S7 and S1 Data). In contrast, Cdc45 and Sld5 (GINS subunit) antibodies indicated that CMGs did not form in the presence of the MTBP mutants, unlike with MTBP-WT. Together, we conclude that the Treslin/TICRR-binding S7M-N region is essential for MTBP function in replication at the origin firing step.
These conclusions are consistent with earlier results showing that Treslin/TICRR mutants that do not bind MTBP are replication incompetent [28]. We reported before that binding of Treslin/TICRR to MTBP is required for normal cellular levels of MTBP [28] (S8E and S8F Fig and S1 Data). Consistently, the Treslin/TICRR binding-deficient MTBP-5m and m1–6 transgenes, but not other mutants that can bind Treslin/TICRR, expressed to lower levels than MTBP-WT (Figs 3A and S8A and S6A and S1 Data). Cycloheximide shut-off experiments with MTBP-5m and m1–6 confirmed that these degrade faster than MTBP-WT (S8B Fig). Lower MTBP levels cannot be the sole reason for the lack of replication in MTBP-5m and m1–6 cells because partial siMTBP-mediated knock-down experiments showed that strong replication defects required suppression of MTBP signals to less than approximately 15%, whereas the 5m and m1–6 mutants have approximately 40% and 35% of MTBP signals left compared with endogenous and transgenic MTBP-WT (S8A, S8C and S8D Fig and S1 Data). We then tested if elevating the expression levels of the MTBP-5m and m1–6 to MTBP-WT levels rescues their replication deficiency. To this end we selected stable Hela-Kyoto cell clones generated by random transgene integration into the genome that expressed MTBP-5m, m1–6, or WT at comparable levels. Replication analysis showed very low if any replication activity of the MTBP mutants (S9 Fig and S1 Data), indicating Treslin/TICRR binding to MTBP has essential roles in replication distinct from or in addition to stabilising MTBP levels.
The S7M-C domain is also important for replication. Two MTBP mutants lacking the C-terminal 150 (MTBP-ΔC150) or 81 amino acids (MTBP-ΔS7M-C) showed defects in rescuing replication. BrdU signals were reduced by 1.7-fold in MTBP-ΔC150 and 1.8-fold in MTBP-ΔS7M–C (Fig 3F and S1 Data). Because these replication defects were milder compared with the Treslin/TICRR-binding mutants, we quantified replication of these mutants more thoroughly. Averaging multiple independent experiments confirmed that both mutant cell lines replicated significantly less than MTBP-WT–expressing lines (39% and 27% of WT) (Fig 3G and S1 Data). Both mutant proteins bound Treslin/TICRR normally (S10A Fig). Moreover, chromatin immunoblots showed that, whereas licensing occurred normally in cells expressing MTBP-ΔS7M-C, origin firing (CMG formation) was impaired, albeit not as much as in Treslin/TICRR-nonbinding MTBP mutants (Figs 3D and S7 Fig and S1 Data). Together, we conclude that the S7M-C domain is required for MTBP function in replication origin firing. The S7M-C function is distinct from Treslin/TICRR binding, consistent with a recent report [29]. The Sld7-Sld3 crystal structure suggested that the S7M-C domain of Sld7 mediates Sld7 dimerisation [18]. The relevance of Sld7 dimerisation for replication in yeast cells was not tested. If the S7M-C domain of MTBP mediates replication by homodimerisation, fusing a homodimerisation domain to MTBP that lacks the S7M-C domain should rescue the capability of this MTBP mutant to induce replication. We attached MTBP-ΔC150 and MTBP-ΔS7M-C with their C termini to the homodimerising glutathione S transferase (GST) tag. Cells expressing MTBP-ΔC150-GST and MTBP-ΔS7M-C-GST transgenes replicated to near WT levels, 80% and 99%, respectively, whereas fusing the nondimerising green fluorescent protein (GFP) tag had little effect (Figs 3G and S10B and S1 Data). The slightly better replication by the GFP fusion (49%) over MTBP-ΔC150 (39%) might stem from its higher expression level. The most parsimonious explanation of the replication rescue by fusing GST is that GST replaces a dimerisation activity of the C terminus, and there is no other essential replication function of the C terminus. However, alternative, more complicated scenarios cannot be excluded.
We hypothesised that the Sld7-homologous domains of MTBP confer the core replication activities common across eukaryotes, whereas the metazoa-specific MTBP middle region may mediate metazoa-specific roles in replication, or may have nonreplicative functions. To test the relevance of the MTBP middle domain for replication, we sought to delete the whole middle domain but retain the activities of the S7M termini. The smallest TresBD fragment of MTBP that was biochemically well behaved (was soluble and showed no unspecific binding to control pull-down beads) and retained full Treslin/TICRR binding activity contained amino acids M1 to Y513 (S11A Fig). We fused to it amino acids L705 to K904, Y770 to K904, or Q810 to K904 (Fig 4A). We call these mutants metazoan Sld7s (mSld7s) because they contain little more than the Sld7-equivalent MTBP termini. All three mSld7 versions supported replication poorly compared with MTBP-WT (Fig 4B and S1 Data) but better than cells carrying no transgene, cells expressing the TresBD (amino acids M1–D515) (Fig 4C and 4D and S1 Data), and cells that lack TresBD activity (Fig 3A–3C and S1 Data).
This shows that the Sld7-equivalent termini cooperate in mSld7s to mediate replication, albeit to sub-WT levels. Because MTBP-ΔA516-H769 and ΔA516-G809 lack an nuclear localisation sequence (NLS) around amino acid D740, a simian virus 40 (SV40)–NLS had been fused to the C terminus of the C-terminal GFP tag. All mSld7 versions bound Treslin/TICRR. ΔA516–P704 and ΔA516–H769, but not ΔA516–G809, showed some reduction of Treslin/TICRR association compared to WT (S11B Fig). Because the C-terminal 388 amino acids of MTBP (amino acids A516–K904) are dispensable for Treslin/TICRR binding (S11A Fig), this indicates that the artificial domain boundaries in these two mutants may affect folding of the N-terminal TresBD to some degree. The relatively weak Treslin/TICRR binding of MTBP-ΔA516–P704 is probably the reason for the slightly lower expression of this transgene. We do not think, however, that lower Treslin/TICRR binding capacity and lower MTBP protein levels are critical for the low replication in these mutant cells, because the Treslin/TICRR binding capacities of the mSld7 versions do not correlate with their replication-promoting activities (Figs 4B and S11B). We conclude that the metazoa-specific middle domain of MTBP and the Sld7-homologous domains cooperate to support DNA replication in human cells.
We found, using purification of MTBP-Flag-GFP and mass spectrometry, that the Cdk8 and Cdk19 kinases and their activating subunit cyclin C co-purify with MTBP. Cdk8 and Cdk19 show extremely high sequence identity. In our experiments they behaved identically, and we do not functionally distinguish between them. Endogenous MTBP co-purified Cdk8, cyclin C, and Cdk19, and anti-cyclin C antibodies precipitated Cdk8, Cdk19, and MTBP (Fig 5A). The interaction occurred in lysates from asynchronous cells and from cells synchronised in G1, S, or mitosis (Fig 5B). The slightly lower MTBP amounts in G1 cell lysates suggested that the interaction with Cdk8/19-cyclin C might require cell cycle CDK kinase activity, but adding Cdk2-Cyclin A to G1 lysates did not promote the interaction. Treslin/TICRR also co-purified with anti-Cdk8 antibodies from all cell cycle stages (Fig 5B).
Moreover, transiently transfected Treslin/TICRR-WT and mutants of the Treslin/TICRR termini, the Sld3/Treslin domain (STD), and the TopBP1 interaction domain coimmunoprecipitated Cdk8/19-cyclin C (Fig 5C). In contrast, two deletion mutants of the Treslin/TICRR M domain that are partially (ΔM1) or strongly (ΔM2) deficient in MTBP interaction showed proportional defects in Cdk8 binding. These interaction studies suggested that MTBP, CDK8/19-cyclin C, and Treslin/TICRR form a protein complex, with MTBP bridging Treslin/TICRR and the Cdk8 kinase (Fig 5H). Corroborating this interpretation, a C-terminal MTBP-A516–K904 fragment, but not the TresBD fragment, interacted with Cdk8/19-cyclin C (S12B(i) Fig), placing the binding site for the kinase between MTBP amino acids A516 and K904.
TopBP1 is also part of the Treslin/TICRR-MTBP-Cdk8/19-cyclin C complex. IP of endogenous TopBP1 from cell lysates co-purified Treslin/TICRR, MTBP, and a faint but specific signal for Cdk8 (Fig 5D). Adding Cdk2-cyclin A to the lysate to enhance the binding of Treslin/TICRR to the N-terminal triple breast cancer type 1 susceptibility protein C terminal repeat (BRCT) repeat domain of TopBP1 [26] increased the signals for Treslin/TICRR, MTBP, Cdk8, Cdk19, and cyclin C, indicating that TopBP1 indirectly binds MTBP-Cdk8/19-cyclin C via Treslin/TICRR (Fig 5H).
Cdk8-cyclin C forms the kinase module of the mediator of transcription together with mediator of transcription subunits 12 and 13 (Med12 and Med13). However, Med12 and -13 do not associate with the MTBP-bound fraction of Cdk8/19-cyclin C: whereas anti-Cdk8 antibodies co-purified MTBP, Med12, and Med13 (Fig 5E), the same amount of Cdk8 purified with anti-MTBP antibodies did not contain Med12 and Med13 detectably. We conclude that Cdk8/19-cyclin C forms distinct protein complexes with the mediator of transcription and the origin firing regulator Treslin/TICRR-MTBP-TopBP1.
We then mapped the interaction site for Cdk8/19-cyclin C in MTBP-A516–K904. Binding studies using truncated MTBP fragments in cell lysates showed that amino acids R595 to P704 of MTBP are required and sufficient for binding the Cdk8 kinase (Figs 5F and S12A and S12B). The R595–P704 region contains a particularly well-conserved metazoa-specific domain (Fig 5H; blue oval; amino acids 636–693, S12D Fig). Deleting only this domain (MTBP-ΔT635–P704) abrogated Cdk8 binding, as did a mutant lacking amino acids R595 to L634 (Fig 5F). A series of MTBP point mutants subsequently showed that amino acids between L601 and E605 as well as L620 and T635 were important for MTBP binding to Cdk8/19-cyclin C (Fig 5G and S12C). In order to create a maximally Cdk8/19-cyclin C binding-deficient MTBP mutant for subsequent functional analyses, we combined seven amino acid exchanges between amino acids L620 and T635 to generate MTBP–Cdk8 binding mutant (Cdk8bm) (Fig 5G). MTBP-Cdk8bm bound Treslin/TICRR normally and behaved like MTBP-WT in gel filtrations (S3 Fig) and nuclear localisation (S4 Fig).
Next, we asked whether Cdk8/19-cyclin C, which is not amongst the yeast core firing factors, is a novel replication initiation factor in vertebrate cells. Because Cdk8 and Cdk19 may act redundantly, we knocked down cyclin C (siCycC; cyclin C siRNA) to test an involvement in DNA replication. BrdU–flow cytometry analysis revealed a mild reduction of replication speed in siCycC-treated HeLa-Kyoto and also in U2OS cells (S13A–S13C Fig and S1 Data). Because Cdk8/19-cyclin C regulates the transcription of many genes as part of the mediator complex, eliminating Cdk8/19-cyclin C from cells will generate complex phenotypes, leading to difficulties in differentiating primary and secondary defects. Therefore, we sought to separate mediator-dependent from -independent replication functions of Cdk8/19-cyclin C. To this end we tested if the binding of the Cdk8/19 kinase to MTBP is required for DNA replication. Because MTBP does not interact with mediator subunits (Fig 5E), Cdk8 kinase nonbinding MTBP mutants should reflect mediator-independent functions of the kinase. BrdU-incorporation analysis of siMTBP-treated cells expressing the Cdk8/19-cyclin C nonbinding MTBP-Cdk8bm and -ΔR595–P704 mutants showed a reduction of replication rescue of around 23% to 77% and 17% to 83%, respectively, compared with MTBP-WT (Fig 6A and 6B and S1 Data). This decrease of replication was more moderate than in siCycC cells (S13B and S13C Fig and S1 Data).
We sought to confirm this mild replication reduction by a more sensitive method. Compromised replication typically increases the frequency of fragile metaphase chromosome sites (FS). FS are under-replicated chromosome regions. A control experiment confirmed that FS frequency can be used to monitor replication defects by lower MTBP activity. HeLa cells treated with siMTBP, which reduces replication severely, displayed 5.6-fold more FS than siCtr-treated cells (S13D Fig and S1 Data). siMTBP-treated cells expressing the Cdk8/19-cyclin C binding-deficient MTBP mutants showed about two times more FS than siCtr-treated cells and siMTBP-treated cells expressing MTBP-WT (Fig 6C and S13E and S13F and S1 Data). We conclude that cells expressing Cdk8/19-cyclin C binding-deficient MTBP enter mitosis with incompletely replicated chromosomes. Such premature mitotic entry should increase aberrant mitotic figures. We fixed and stained siCtr- or siMTBP-treated MTBP-WT and Cdk8 binding-deficient cell lines with anti-tubulin antibodies and double stranded DNA (dsDNA) stain to visualise mitotic spindles and chromosomes. siMTBP-treated MTBP-Cdk8bm and -ΔR595–P704 cells showed more anaphases with DNA that was not pulled to the cell poles together with the remaining chromatin mass (Fig 6D(i) and S1 Data). Subsequent cytokinetic abscission was delayed, as judged by more cells that had already formed interphase nuclei but that were still connected by a thin connection containing the spindle midbody (ii). Delayed abscission upon incomplete replication has been described [36]. Occasionally, these connections contained Hoechst-positive structures. In line with chromosome segregation errors, we also found a higher frequency of micronuclei in MTBP-Cdk8bm and -ΔR595–P704 cells (iii). MTBP-ΔR595–P704 appears to have slight dominant effects, as suggested by the mutant cell lines showing a minor increase of aberrant anaphases upon siCtr treatment. We did not observe a compromised spindle assembly checkpoint response of MTBP-Cdk8bm and -ΔR595−P704 cells, as was reported for MTBP knock-down cells [37]. Nevertheless, we cannot exclude that a direct role of MTBP in mitosis contributes to the observed phenotypes.
That the observed DNA replication defect of cells expressing Cdk8/19-cyclin C nonbinding MTBP was mild was confirmed by the similar cell cycle distributions of siMTBP-treated MTBP-Cdk8bm and MTBP-WT cells (Fig 6E and S1 Data). Also, the temporal replication program was not grossly affected in MTBP-Cdk8bm cells. Classification of replication patterns using GFP–proliferating cell nuclear antigen (PCNA) in live cells expressing MTBP-WT and -Cdk8bm showed a normal appearance and order of early, mid–, and late–S phase replication patterns (Fig 6F and S1 Data). Also, in-detail inspection of mid–and late–S phase patterns revealed no differences (Fig 6G and S1 Data).
We conclude from Fig 6 that cells expressing Cdk8/19-cyclin C nonbinding MTBP mutants have moderate defects in genome replication and enter mitosis prematurely with under-replicated chromosomes.
The metazoa-specific MTBP middle region appears to harbour at least one more activity in addition to Cdk8/19-cyclin C binding that is required for replication. This activity is situated outside amino acids R595 to P704. MTBP-Cdk8bm and -ΔR595–P704, which are both maximally Cdk8/19-cyclin C binding deficient, show less severe defects in replication (around 20% reduction) than the mSld7 mutants (around 70%) that have larger deletions in the middle domain.
Our characterisation of MTBP as an origin firing factor that integrates conserved and metazoa-specific initiation processes presents an early step into understanding the specifics of higher eukaryotic replication in a field that has focused on showing that the principles of origin firing are conserved with yeast. We establish MTBP as the metazoan orthologue of yeast Sld7. Furthermore, we describe that MTBP also has metazoa-specific features that are important for replication. We identified Cdk8/19-cyclin C as a novel interactor of the metazoa-specific middle domain of MTBP and discovered that this binding is required for proper replication in human cells. Our work suggests that MTBP and its partners Treslin/TICRR-MTBP and TopBP1 function as a platform that integrates evolutionarily conserved and metazoa-specific replication-regulating signals to mediate complete and accurate replication of the genome.
The orthology between MTBP and Sld7 described here is the final proof that all core factors for origin firing are conserved across fungi and animals (S14 Fig). The set of eukaryotic factors sufficient for conversion of pre-RCs into bidirectional replisomes with leading and lagging strand synthesis was defined by biochemical reconstitution with yeast proteins [16, 38, 39]. Whether these factors are also sufficient in higher eukaryotes now requires in vitro reconstitution of metazoan initiation from purified components.
Despite conservation of the core origin firing factors in eukaryotes, diversions of origin firing process occur in some species, indicating some evolutionary plasticity. For example, in fission yeast and Caenorhabditis elegans, Sld7/MTBP has not been found. Furthermore, the TopBP1 equivalent of the BRCT3/4 repeat domain of yeast Dpb11 that facilitates origin firing by interacting with CDK-phosphorylated Sld2 does not have an essential replication role in Xenopus egg extracts [24]. Instead, TopBP1-BRCT3 is essential [24]. Its role is unknown but it probably does not bind CDK-phosphorylated recombination helicase Q like 4 (RecQ4), metazoan Sld2, because TopBP1-BRCT3 lacks key amino acids for phosphopeptide binding [40].
Metazoan cells are more complex than yeasts and will probably require more sophisticated regulation of replication initiation. The metazoa-specific domains of MTBP and of other firing factors like Treslin/TICRR [25] may help integrate the conserved fundamental initiation processes into metazoan cells. Metazoa have less precisely defined origins than yeast, but they face the same problems as any cell with multiple replication start sites. Genome duplication needs to be complete before mitosis to minimise genome instability. This necessitates that no inter-origin distance is bigger than the amount of DNA that can be replicated in S phase. To avoid large random gaps, appropriately located origins must fire with the right timing. The organisation of metazoan replication in replication factories, in which neighbouring origins fire with near synchrony, may help prevent large random gaps [41]. The factory organisation may also help facilitate differential regulation of origin firing between different factories upon DNA damage. In these circumstances so-called dormant origins (that are inactive in normal growth conditions) fire in actively replicating factories after DNA damage, whereas origin firing becomes inhibited in nonreplicating factories. Both regulations help prevent genetic instability upon replication stress [42].
MTBP is a promising candidate for mediating some of these regulations, as it forms a main regulation platform of origin firing in eukaryotes together with Treslin/TICRR/Sld3 and TopBP1/Dpb11. All complex members are subject to regulations by various pathways to mediate appropriately timed and localised replication initiation. These regulations mediate (1) S phase specificity of replication via CDK and DDK [5, 8, 9], (2) replication timing of centromeres and other genome regions [43–45], (3) inhibition of origin firing upon DNA damage by S phase checkpoint kinases [46–48], and (4) firing inhibition in response to low cellular glucose levels involving the silent information regulator (SIRT1) deacetylase [49, 50]. These regulations all involve Sld3/Treslin/TICRR, Dpb11/TopBP1, or Sld2/RecQ4, and at least some are conserved between yeast and man [26, 27, 51]. How MTBP activity is controlled is unknown. Several posttranslational modification sites in MTBP were identified by high-throughput approaches but have not been functionally investigated, among them potential ataxia teleangiectasia mutated related/mutated (ATR/M) and CDK sites. These are good candidates to mediate cell cycle and checkpoint regulations. Cdk8/19-cyclin C binding to MTBP could also be involved in mediating firing control (see below).
We show that the S7M-N domain of MTBP mediates Treslin/TICRR binding. As with yeast Sld7 and Sld3, complex formation of MTBP with Treslin/TICRR is important for MTBP’s replication function, as several nonoverlapping point and deletion mutants of MTBP that are deficient in Treslin/TICRR binding failed to support replication in HeLa cells. The same was true for MTBP interaction-deficient mutants of Treslin/TICRR [28]. Treslin/TICRR binding-deficient MTBP mutants, for example MTBP-5m and m1–6, showed reduced MTBP levels in cell lysates and had lower levels of endogenous Treslin/TICRR. The lower levels of the MTBP mutants were at least partly due to their higher degradation rate compared with MTBP-WT (S8A and S8B Fig). This corroborates earlier RNAi experiments suggesting that Treslin/TICRR and MTBP stabilise each other’s cellular levels [28] (S8C and S8F Fig and S1 Data): Treslin/TICRR RNAi reduced MTBP levels and vice versa, and this was rescued by RNAi-resistant proteins. Mutual stabilisation is also seen with Sld3 and Sld7, and it is important for replication in yeast [17]. Lower Treslin/TICRR levels may make a small contribution to the replication defects in human cells expressing Treslin/TICRR nonbinding MTBP mutants. However, Treslin/TICRR stabilisation cannot be the only function of MTBP in replication, because (1) deleting the MTBP middle and the S7M-C domains results in a fully Treslin/TICRR–binding-proficient MTBP1-515 protein that is, however, incapable of inducing replication over background, (2) elevating the expression levels of the non-Treslin/TICRR–binding MTBP mutants to MTBP-WT did not rescue replication in human cells, (3) restoring Treslin/TICRR levels in the absence of MTBP binding was insufficient to restore replication in Treslin siRNA (siTreslin)–treated cells [28] and in MTBP-immunodepleted Xenopus egg extracts [29], and (4) our partial RNAi-depletion experiments with Treslin/TICRR showed that strong reduction of replication—as observed in MTBP-5m and m1–6 cell lines—is only achieved by depleting Treslin/TICRR to levels well below the levels in these MTBP mutant lines (S8A, S8E and S8F Fig and S1 Data).
The S7M-C domain of MTBP—albeit not essential for replication—is required for normal replication levels in HeLa cells. Its function is distinct from Treslin/TICRR binding because MTBP-ΔS7M-C bound Treslin/TICRR as well as MTBP-WT. In yeast, the S7M-C domain mediated Sld7 dimerisation in the crystal structure to form dimers of Sld3-Sld7 heterodimers [18]. How important Sld7 dimerisation is for replication in yeasts was not addressed. We found that attaching an artificial dimerisation domain in the form of GST to S7M-C deletion mutants of MTBP rescues the ability of these mutants to induce replication in Hela cells. Sld3/Treslin-Sld7/MTBP dimerisation could help ensure that both MCM helicases in pre-RCs become activated, but never only one, which could generate initiation intermediates that jeopardise genetic stability. We cannot formally exclude that GST rescues replication in cells expressing S7M-C MTBP deletion mutants by virtues other than homodimerisation that it does not share with the GFP control tag. We found no biochemical evidence of S7M-C–dependent MTBP dimerisation by binding studies using pulldowns with MTBP fragments expressed in 293T cells. This indicates that S7M-C–mediated MTBP dimerisation may be weak or spatiotemporally regulated, e.g., by posttranslational modification.
Three lines of evidence support the conclusion that Cdk8/19-cyclin C has a role in genome replication that requires its binding to the metazoa-specific middle domain of MTBP. (1) Cdk8/19-cyclin C forms a protein complex with the MTBP-Treslin/TICRR-TopBP1 replication initiation regulator. (2) Cells expressing deletion or point mutants of MTBP that are deficient in binding to Cdk8/19-cyclin C show compromised replication. (3) RNAi depletion of cyclin C also compromises replication.
We believe that the function of Cdk8/19-cyclin C in replication is distinct from its classic role as the kinase module of the mediator of transcription because Cdk8/19-cyclin C forms distinct complexes with MTBP-Treslin/TICRR-TopBP1, or the kinase module of the mediator subunits Med12 and Med13. Moreover, in contrast to RNAi depletion of cyclin C knock-down of Med12, which shares many mediator-related functions with Cdk8/19-cyclin C [52, 53], did not lead to replication defects (S13G and S13H Fig and S1 Data).
The exact role in the replication of Cdk8/19-cyclin C in complex with MTBP-Treslin/TICRR-TopBP1 is unclear. Cdk8/19-cyclin C nonbinding MTBP mutants replicate slightly slower than MTBP-WT cells. Apparently, the replication problems in the mutant cell lines are severe enough to cause unreplicated gaps in the genome that become evident as fragile metaphase chromosomes and aberrant mitotic figures. Compromised replication was previously shown to induce a checkpoint that delays cytokinetic abscission [36]. This checkpoint is thought to provide cells in which abscission is not possible normally, with an opportunity to nevertheless achieve division and avoid tetraploidy [54, 55]. Consistently, we find delayed abscission in our mutant MTBP cell lines.
Due to these phenotypic analyses, we believe that insufficient replication forks are generated in cells with Cdk8/19-cyclin C binding-deficient MTBP, resulting in replication gaps. One model is that Cdk8/19-cyclin C binding to MTBP is generally required for maximal activity of MTBP to promote origin firing. Alternatively, a specific subset of firing events could be controlled by Cdk8/19-cyclin C. This could be a positive control, so that inefficient firing of CDK8-activated origins causes replication gaps, or a negative control, so that ectopic firing of CDK8-inhibited origins causes replication gaps indirectly, for example, by promoting replication-transcription collisions or by depleting certain genomic regions of dormant origins. We cannot exclude that Cdk8/19-cyclin C binding to MTBP has roles in transcription or other processes that contribute to the observed phenotype.
All cell lines were maintained in DMEM (Life Technologies) supplemented with 10% FBS (Life Technologies) and 1% penicillin/streptomycin antibiotics (Life Technologies) in 5% CO2 conditions at 37°C.
Stable MTBP transgene–expressing HeLa Flip-In T-Rex cell lines [56] were generated according to the manufacturer’s instructions (Invitrogen) using pcDNA5-FRT-TO-Flag3-Tev2-GFP or pcDNA5-FRT-TO-Flag3-Tev2. Cell pools containing at least 50 individual clones were used to average out clonal variation.
Stable MTBP transgene-expressing HeLa Kyoto cell lines were generated using pIRESpuro3-Flag-AcGFP.
For cell synchronisations, HeLa-Flp-In T-Rex or U2OS cells were treated with 2 mM thymidine for 24 h. For S phase populations, double thymidine-arrested cells were harvested in thymidine or released for 3 h before harvesting. G2/M phase populations were released from thymidine arrest for 10 h and for G1 phase populations for 14 h. For release, cells were washed twice in PBS before incubation in normal medium. For mitotic blocks, unsynchronised cells or cells released from thymidine were treated with 0.1 μg/mL nocodazole overnight before harvesting by mitotic shake-off. For nocodazole release into G1 phase, mitotically arrested cells were washed twice in PBS before incubation in medium lacking nocodazole. Only attached cells were harvested by trypsinisation for G1-synchronised populations.
Cells (293T) were transfected using standard calcium phosphate transfection. In brief, an approximately 60%–70% confluent 10-cm dish of 293T cells was transfected with 13 μg plasmid DNA using 83 μL 2 M CaCl2, 566 μL sterile H2O, and 667 μL 2×HBS (50 mM HEPES, 10 mM KCl, 2 mM dextrose, 280 mM NaCl, 1.5 mM Na2HPO4). The medium was changed 24 h after transfection, and cells were cultivated for another 48 h before harvesting.
HeLa Flip-In T-Rex cell lines carrying transgenes or control cells were treated with 1 μg/mL doxycycline roughly 2 h prior to transfection to induce expression. siRNA transfections were carried out using RNAiMAX (Life Technologies) according to the manufacturer’s guidelines. In brief, 1.5 × 105 cell per 6-cm tissue culture dish were transfected with 20 nM control siRNA (GL2) or siRNA against MTBP (1:1 mix of MTBP1 and MTBP2) using 10 μL RNAiMAX in 5 mL volume.
For MTBP and Treslin/TICRR immunoblots following RNAi, whole cells were boiled in 3× Laemmli sample buffer, separated by SDS-PAGE, and blotted on nitrocellulose membranes before detection with anti-MTBP and anti-Treslin/TICRR antibodies.
Seventy-two hours after RNAi transfection, cells were pulse labelled using 10 μM BrdU for 30min. Cells were then harvested and fixed with −20°C cold methanol for 24 h. The fixed cells were treated with 2 M HCl/0.5% Triton X-100 for 30 min before washing extensively with PBS/0.5% Triton. Incorporated BrdU was detected using FITC-coupled mouse anti-BrdU in 1% BSA/PBS/0.01% Triton X-100 for 1 h according to the manufacturer’s instructions. After washing once with PBS/0.01% Triton X-100, the DNA was stained with 25 μg/mL PI in the presence of 100 μg/mL RNaseA. Flow cytometry analysis was performed using a FACSCalibur flow cytometer (BD Biosciences) with a logarithmic setting of the FL1-H channel for FITC detection and a linear setting of the FL2-H channel for PI detection. Data analysis was performed using the Kaluza Analysis 1.3 software (Beckman Coulter). Cell aggregates were excluded based on their high FL2-W signal. BrdU-PI profiles were generated as density plots. Reduction of BrdU incorporation upon siRNA treatment was analysed by gating S phase cells in BrdU/PI dot plots and visualised as BrdU histogram overlays. For quantifications of the efficiency of replication rescue by MTBP transgenes, the BrdU signal of BrdU-positive S phase cells was background-subtracted using the signal intensity of BrdU-negative G1 and G2/M phase cells to calculate the replication-dependent BrdU signal. This value was divided by the BrdU replication signal of control siRNA–treated cells. Visualisation as bar diagrams and statistics was done using GraphPad Prism 5 (GraphPad Software).
To assess the frequency of fragile chromosomes, cells were treated for 2 h with 0.08 μg/mL colcemide. After mitotic shake-off, cells were treated with 1% sodium citrate for 10 min and fixed by washing three times with methanol/acetic acid (3:1). Droplets of cells in fixation solution were dropped on glass slides from a height of about 150 cm. After drying at RT for 24 h, cells were Giemsa stained using 1:20 Giemsa (Merck 109204) in 4 mM NaHPO4, 5 mM KHPO4. Fragile chromosomes were imaged using conventional light microscopy (40× oil lens) using the Zeiss ZEN 2.3 software. The fractions of fragile chromosomes were assessed manually. For quantification, around 8,000 chromosomes per sample were analysed.
HeLa Flip-In T-Rex cell lines stably expressing AcGFP-PCNA were seeded 24 h after siRNA treatment into 4-well glass bottom plates (ibidi 80427). Imaging was done using an Andor/Nikon spinning disk confocal microscope at 37°C and 5% CO2 in imaging medium (DMEM, Life Technologies) supplemented with 10% FBS, 1% penicillin/streptomycin antibiotics (Life Technologies). Excitation was at 475 nm laser wavelength. A frame rate of one image per 30 min was used for each 48-h imaging session. Three Z sections per frame were taken. For image processing, maximal intensity projections were made, and Fiji software was used to generate movies. Movies were analysed visually to assess replication patterns. To analyse the temporal replication program, S phase patterns were classified into early, mid–, and late–S phase patterns and quantified. Early S phases were from the first appearance of replication foci to the first frame, with mid–S phase patterns defined as nuclear rim and/or perinucleolar replication. Late patterns were all frames with distinctive large bright replication foci. Quantifications represent the fraction of early, mid, and late patterns of the total number of S phase frames. For detailed analysis of mid/late replication patterns, three classes of patterns were defined: (A) fragmented or complete nuclear rim staining, (B) small intranuclear foci, and (C) bright large foci in the nuclear interior or at the rim. Frames showing a single pattern or combinations of patterns were quantified and normalised to the total frame number.
Cells on glass coverslips were fixed with 4% paraformaldehyde for 10 min at RT. After permeabilisation and blocking in PBS, 0.1% BSA, 0.1% Triton X-100 for 1 h, anti-tubulin and anti-mouse Alexa 488 antibodies were used at dilutions of 1:2,000 and 1:100, respectively, in blocking buffer (PBS, 0.1% BSA) in distinct 1-h incubation steps to stain tubulin. A total of 1 μg/mL dsDNA stain (Hoechst 33258; Sigma, B2883) in blocking buffer was used to stain DNA. After mounting in FluorSave Reagent (Calbiochem, 345789), cells were imaged using a Zeiss Axio Observer7 microscope. Eleven Z sections were taken and analysed visually using the Fiji software. Anaphases were classified as ‘aberrant’ if they showed a DNA signal between the separating DNA masses. For quantification, aberrant anaphases were normalised to the total number of anaphases. Cells with cytokinesis delays showed a thin connection containing a spindle midbody between two daughter cells that had already formed clear interphase nuclei with decondensed chromatin. Micronuclei were defined as DNA masses in interphase cells that were clearly distinct from the main nucleus. For quantification of cytokinesis delays and micronuclei, their number was normalised to the total number of cells.
Hela Flip-In cells were grown on glass coverslips. Transient transfection was performed using Lipofectamine 2000 (Life Technologies) according to the manufacturer’s instructions. Cells were fixed 48 h after transfection using 4% paraformaldehyde for 15 min at RT. After three washing steps with PBS, cells were permeabilised and blocked with PBS, 0.1% BSA, 0.1% Triton X-100 for 1 h at RT. Subsequently, cells were incubated for 1 h at RT with the primary antibodies (anti-FLAG M2 1:1,000 or anti-GFP 1:250) diluted in blocking buffer. After three washing steps using PBS with 0.1% BSA, cells were incubated with anti-mouse Alexa 488 (1:250) and 1 μg/mL dsDNA stain (Hoechst 33258; Sigma, B2883) for 1 h at RT. After three washing steps and mounting using Roti-Mount FluorCare (Carl Roth, HP19.1), image acquisition was performed using the Zeiss Axio Observer 7.
For generation of the monoclonal antibody against human Treslin/TICRR (clone 30E7: N-His-Treslin [amino acids 260–791]), MTBP (clone 12H7: N-His-MTBP [amino acids M1–I284]), and clone 4H9 (N-His-MTBP [amino acids F102-Y513]), approximately 50 μg of His-tagged fusion protein dissolved in PBS was emulsified in an equal volume of incomplete Freund’s adjuvant, and Wistar rats (MTBP antigens) or C57BL/6J mice (Treslin antigen) were immunised subcutaneously (s.c.) and intraperitoneally (i.p.). Six weeks after immunisation, a 50-μg boost injection was applied i.p. and s.c. 3 d before fusion. Fusion of the splenic B cells and the myeloma cell line P3X63Ag8.653 was performed using polyethylene glycol 1500 according to standard protocols [57]. Hybridoma supernatants were tested by solid-phase ELISA using the His-fusion proteins and verified by western blotting. Monoclonal hybridoma cell lines of the Treslin/TICRR- and MTBP-reactive supernatants were cloned twice by limiting dilution. The IgG subclasses were determined with ELISA assay as MTBP clones 12H7 and 4H9: rat IgG2b and Treslin/TICRR clone 30E7.
The following nomenclature was used for Treslin/TICRR domain mutants: ΔCIT, the N-terminal 264 amino acids of Treslin/TICRR were deleted; ΔM1, amino acids 265–408 were deleted; ΔM2, amino acids 409–593 were deleted; ΔSTD, amino acids 717–792 were deleted; 2PM, the two essential CDK sites (T969A; S1001A) were mutated to alanine; ΔC-terminal, The C-terminus was deleted from amino acid 1057.
Overexpressed Flag-purified MTBP-3Flag was loaded onto a Superdex 200 column (2.4 mL 3.2/300). FPLC was performed at +4°C at a 30 μL/min flow rate with a 100-μL fraction size in running buffer (20 mM HEPES, pH 8.0, 200 mM NaCl, 0.5 mM TCEP, 10 mM NaF, 0.1% TritonX-100). A total of 1/15 of each fraction was analyzed via western blot. Gel filtration standard (1511901, BioRad) was used to determine apparent molecular weights.
Chromatin-enriched fractions were purified from Hela Flp-In cells via lysis in 10 mM HEPES, pH 7.0, 100 mM NaCl, 300 mM sucrose, 3 mM MgCl2, 0.5% Triton, 5mM ß-mercaptoethanol, complete EDTA-free protease inhibitor cocktail, and 5 μg/mL cytochalasin D. Chromatin was harvested by centrifugation at 3,200 rpm for 4 min at 4°C in table-top centrifuge and washing the chromatin pellet in lysis buffer three times, involving 5 min of incubation in washing buffer.
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10.1371/journal.ppat.1003575 | Aspergillus Galactosaminogalactan Mediates Adherence to Host Constituents and Conceals Hyphal β-Glucan from the Immune System | Aspergillus fumigatus is the most common cause of invasive mold disease in humans. The mechanisms underlying the adherence of this mold to host cells and macromolecules have remained elusive. Using mutants with different adhesive properties and comparative transcriptomics, we discovered that the gene uge3, encoding a fungal epimerase, is required for adherence through mediating the synthesis of galactosaminogalactan. Galactosaminogalactan functions as the dominant adhesin of A. fumigatus and mediates adherence to plastic, fibronectin, and epithelial cells. In addition, galactosaminogalactan suppresses host inflammatory responses in vitro and in vivo, in part through masking cell wall β-glucans from recognition by dectin-1. Finally, galactosaminogalactan is essential for full virulence in two murine models of invasive aspergillosis. Collectively these data establish a role for galactosaminogalactan as a pivotal bifunctional virulence factor in the pathogenesis of invasive aspergillosis.
| Invasive aspergillosis is the most common mold infection in humans, predominately affecting immunocompromised patients. The mechanisms by which the mold Aspergillus fumigatus adheres to host tissues and causes disease are poorly understood. In this report, we compared mutants of Aspergillus with different adhesive properties to identify fungal factors involved in adherence to host cells. This approach identified a cell wall associated polysaccharide, galactosaminogalactan, which is required for adherence to a wide variety of substrates. Galactosaminogalactan was also observed to suppress inflammation by concealing β-glucans, key pattern associated microbial pattern molecules in Aspergillus hyphae, from recognition by the innate immune system. Mutants that were deficient in galactosaminogalactan were less virulent in mouse models of invasive aspergillosis. These data identify a bifunctional role for galactosaminogalactan in the pathogenesis of invasive aspergillosis, and suggest that it may serve as a useful target for antifungal therapy.
| The incidence of invasive mold infections due to the fungus Aspergillus fumigatus has increased dramatically in hematology patients receiving intensive cytotoxic chemotherapy or undergoing hematopoietic stem cell transplantation [1]. Despite the advent of new antifungal therapies, the mortality of invasive aspergillosis (IA) remains 60–80% [2]. There is therefore a pressing need for novel therapeutic strategies to treat or prevent IA. A better understanding of the pathogenesis of IA is one approach that may inform the development of new therapeutic targets.
Adherence of A. fumigatus to host constituents is thought to be an early and critical step in the initiation of colonization and infection [3]. Upon inhalation, A. fumigatus conidia rapidly adhere to pulmonary epithelial cells and resident macrophages before being internalized and germinating within host cells [4], [5], [6]. Following germination, filamentous hyphae remain in intimate contact with host epithelial, endothelial and immune cells and can induce tissue injury and inflammatory responses. Inhibition of these adherence events may provide a useful therapeutic strategy to reduce morbidity and mortality of A. fumigatus mediated disease.
Despite the fact that hyphae play such a critical role in the pathogenesis of invasive aspergillosis, the fungal ligands governing adherence of A. fumigatus hyphae to host constituents are largely unknown. A bioinformatic analysis of potential adhesins of A. fumigatus has identified several candidate proteins involved in mediating adhesion to host constituents [7], but the adherence of mutant strains that lack these proteins has not been reported. Carbohydrate constituents of the cell wall have been recently implicated in adherence events [8] although their role in mediating adherence to host constituents has not been studied.
We recently identified a fungal regulatory protein, MedA, which governs fungal adhesion to host cells and basement membrane constituents and biofilm formation [9]. In addition, we found that a strain deficient in StuA, a previously described developmental transcription factor [10], was similarly deficient in the formation of adherent biofilms. In contrast deletion of ugm1, which encodes a UDP-galactose mutase required for the production of galactofuranose, has been reported to result in a strain of A. fumigatus with increased adherence to epithelial cells [11]. Here, we report that carbohydrate analysis of these mutants revealed that the ΔmedA and ΔstuA mutants were defective in galactosaminogalactan (GAG) production whereas the Δugm1 mutant hyperproduced GAG. A comparative transcriptome analysis of the ΔmedA and ΔstuA regulatory mutants identified a gene encoding a putative UDP-glucose-epimerase, designated uge3, which was dysregulated in both the ΔstuA and ΔmedA mutants. Disruption of uge3 resulted in a complete block in GAG synthesis, and markedly decreased adhesion to host cells and biofilm formation. The Uge3 deficient strain was also attenuated in virulence and induced a hyperinflammatory response in a corticosteroid treated mouse model. The absence of GAG in hyphae resulted in an increased exposure of cell wall β-glucan and in higher levels of dectin-1 binding, in association with the release of higher levels of pro-inflammatory cytokine by dendritic cells. Blocking dectin-1 with an anti-dectin-1 antibody, or pre-incubating hyphae with Fc-dectin-1 blocked this increased cytokine production. Suppression of inflammation in mice treated with cyclophosphamide and cortisone acetate resulted in further attenuation of virulence of the Δuge3 mutant, although the degree of reduction in fungal burden was similar in neutropenic and corticosteroid treated mice. Collectively these data identify GAG as a multifunctional virulence factor that mediates adherence of A. fumigatus, cloaks β-glucan and suppresses host inflammatory responses in vivo.
Previously, a mutant deficient in UDP-galactofuranose mutase (ugm1) was reported to have increased adherence to host cells and abiotic substrates, while an A. fumigatus mutant deficient in the regulatory factor MedA had markedly impaired adherence to multiple substrates and was defective in biofilm formation [9]. To identify other mutants with alterations in adhesion, we screened a collection of regulatory mutants including mutants deficient in StuA, BrlA, AcuM and DvrA [10], [12], [13], [14] for their ability to form biofilms on plastic surfaces. Using this approach, we found that the ΔstuA mutant previously described by our group [10] was also markedly impaired in the formation of adherent biofilms on plastic (Fig. 1A, 1B). We hypothesized that these differences in the adherence properties of these three strains might stem from alterations in expression of a single adhesion factor. To test this hypothesis, we performed an analysis of the cell wall carbohydrate composition of these mutant strains in comparison to their respective complemented strains and wild-type A. fumigatus. The cell walls of the hypoadherent ΔmedA and ΔstuA strains, but not the hyperadherent Δugm1 strain, were found to contain a significant reduction in N-acetyl galactosamine (GalNAc) (Fig. 1C). Since N-acetyl galactosamine is a key component of galactosaminogalactan (GAG), a glycan found within the amorphous cell wall and extracellular matrix of A. fumigatus during infection [15], [16], these results suggested that GAG is involved in the differential adhesive properties of these mutants. Consistent with this hypothesis, culture supernatants from the ΔmedA mutant contained no detectable GAG, while only trace amounts of GAG were found in supernatants from the ΔstuA mutants (Fig. 1D). In contrast, culture supernatants from the Δugm1 mutant contained markedly increased GAG as compared to the wild-type and ugm1 complemented strains.
To test the hypothesis that GAG mediates A. fumigatus adherence, we examined the ability of a suspension of extracellular GAG harvested from wild-type hyphae to rescue the adherence defects of the ΔstuA and ΔmedA hyphae. The addition of supplemental GAG resulted in a dose dependent increase in adherence to tissue culture treated plates (Fig. 1E). The addition of exogenous GAG also increased the adherence of wild-type A. fumigatus, although to a lesser extent than was seen with the adhesion deficient mutants treated with GAG. Collectively these results suggested that GAG was responsible for the adherence of A. fumigatus to plastic and other substrates.
Since MedA and StuA control the expression of hundreds of genes, to remove any pleiotrophic effect and to test the specific role of GAG production in adherence, we sought to identify the specific genes required for GAG synthesis. Whole genome microarray analysis of the ΔmedA strain was performed during hyphal growth and development, and compared with the wild-type and medA complemented strain. Genes that were significantly dysregulated in the ΔmedA strain were then compared with the list of previously identified ΔstuA dependent genes [10]. Ten genes were identified as being significantly dysregulated in both mutant strains (Fig. 2A). Among these genes was Afu3g07910, predicted to encode a UDP-glucose epimerase. Given the role of glucose epimerases in the biosynthesis of galactose and galactosamine, this gene, designated uge3, was selected for further study. Real-time RT-PCR confirmed that uge3 expression was reduced in both the ΔstuA and ΔmedA strains (Fig. 2B).
To test the role of Uge3 in the synthesis of GAG, a Δuge3 mutant strain was constructed. Deletion of uge3 had no observable effects on growth or morphology including conidiation, conidia size, germination, and radial hyphal growth on solid media in a wide variety of conditions including: minimal media, nutrient rich media (YPD), pH range 4.5 to 8.5, varying iron concentrations (from 0 to 30 µM), and microaerophilic or normoxic conditions (Fig. S1). Scanning electron microscopy of Δuge3 mutant hyphae revealed a complete loss of surface decoration and of intercellular matrix (Fig. 3A). Cell wall analysis of the Δuge3 mutant strain demonstrated an undetectable level of N-acetyl galactosamine (Fig. 3B), and no GAG was detected in culture filtrates from this strain (Fig. 3C). The production of soluble galactofuranose was unaffected (Fig. 3D). When compared with wild-type A. fumigatus, a slight increase in cell wall GlcNAc content was observed in the Δuge3 mutant (Fig. 3B), as well as a minimally increased resistance to the anti-chitin agent nikkomycin but not to the chitin binding agent calcofluor white (Table 1). However, complementation of the Δuge3 mutant with an intact allele of uge3 had no effect on these observations, despite completely restoring N-acetyl galactosamine and GAG synthesis. Collectively these results suggest that Uge3 is necessary for the production of GAG.
To test the hypothesis that GAG was required to mediate A. fumigatus adherence to substrates, we compared the adherence of the Δuge3 mutant with the wild-type and uge3 complemented strains to a variety of substrates. The Δuge3 mutant strain exhibited a near complete absence of adherence to all substrates tested, including plastic, pulmonary epithelial cells and fibronectin (Fig. 4A, 4B). As it was observed with the ΔstuA and ΔmedA mutant strains, preincubation of either plastic plates or hyphae of the Δuge3 mutant with a suspension of wild-type GAG produced a dose dependent increase in adherence to plastic (Fig. 4C, 4D). This increased adherence was not observed when the wells or hyphae were supplemented with a suspension of zymosan, a β-glucan-rich fungal cell wall preparation, suggesting that the increased adherence is specific to GAG. Scanning electron microscopy of Δuge3 hyphae incubated with a suspension of GAG demonstrated a partial restoration of the cell wall decoration seen in wild-type hyphae, suggesting that the Δuge3 mutant was able to bind extracellular GAG (Fig. 4E). To confirm that GAG directly binds to epithelial cells, we tested the ability of a suspension of GAG isolated from wild-type A.fumigatus to bind directly to A549 cells. FITC conjugated Soy Bean Agglutinin (SBA) was used to quantify GAG binding. This lectin is specific for terminal GalNAc residues, and does not bind to GAG deficient uge3 mutant hyphae (Fig. 5A). Using this approach, purified GAG was observed to bind to A549 epithelial cells in a dose dependent manner (Fig. 5B). Collectively these results demonstrate that GAG is required for adherence to, and injury of epithelial cells, and suggest that GAG is an important adhesin of A. fumigatus.
Deletion of uge3 also completely blocked the ability of A. fumigatus to induce pulmonary epithelial cell injury as measured by a chromium release assay (Fig. 6). Restoration of uge3 expression in the Δuge3 mutant completely restored the ability of A. fumigatus to adhere to host constituents and damage epithelial cells, confirming the specificity of these observations and suggesting that GAG is necessary for adherence to host constituents and subsequent induction of epithelial cell injury.
To determine if blocking GAG synthesis and fungal adherence alters virulence, we compared the virulence of the Δuge3, wild-type A. fumigatus and the uge3 complemented strain in a corticosteroid treated mouse model of invasive aspergillosis. Mice infected with the Δuge3 mutant strain survived significantly longer than mice infected with either the wild type of uge3-complemented strain (Fig. 7A), although this effect was modest. Consistent with the increased survival of mice infected with the Δuge3 mutant, these mice were found to have a significantly reduced pulmonary fungal burden after four days of infection as compared with mice infected with wild-type A. fumigatus (Fig. 7B). Histopathologic examination confirmed that infection with the Δuge3 mutant strain produced fewer and much smaller fungal lesions than did the wild-type A. fumigatus (Fig. 7C). Surprisingly, despite the lower abundance of hyphae in pulmonary lesions of mice infected with the Δuge3 mutant, these lesions contained more neutrophils than did those of mice infected with wild-type A. fumigatus. These results suggest that infection with the Δuge3 mutant strain induced an increased host inflammatory response.
GAG has been localized to the amorphous outer layer of the fungal cell wall [15]. We therefore hypothesized that extracellular GAG might mask the surface exposure of other fungal pathogen-associated molecular pattern (PAMP) molecules such as β-1,3 glucan, and as a result the increased inflammatory response seen during infection with the Δuge3 mutant might be a consequence of unmasking of these PAMPs. To test this hypothesis, we performed immunofluorescent microscopy to compare the binding of recombinant Fc-dectin-1 [17] to the Δuge3 mutant and wild-type A. fumigatus. Consistent with previous reports [18], we found that binding of Fc-dectin-1 to swollen conidia could be detected in both strains (Fig. 8A). However, during germination and hyphal growth, there was much more intense staining of Δuge3 mutant hyphae as compared with the wild-type parent strain, in which Fc-dectin-1 binding decreased over time. In contrast, total β-1,3 glucan content, as assessed by aniline blue staining and release of soluble β-1,3 glucan in the culture supernatant, was not different between the wild-type and the Δuge3 mutant strains (Fig. 8B, 8C). Similarly, sensitivity of the Δuge3 mutant to the β-1,3 glucan synthase inhibitor casofungin was unchanged from the wild-type parent strain (Table 1). Therefore, the increased binding of Fc-dectin-1 to the Δuge3 cells was due to greater surface exposure of β-1,3 glucan rather than increased synthesis of this glycan.
To test if the increased exposure of β-glucan, or other fungal cell wall PAMPs, on the surface of Δuge3 hyphae might induce an increased inflammatory response by immune cells, we determined the cytokine response of bone marrow derived dendritic cells (BMDDCs) upon co-culture with wild-type or Δuge3 hyphae. After 6 hours of co-incubation, BMDDCs infected with the Δuge3 mutant strain produced significantly higher levels of pro-inflammatory cytokines, including TNF-α, KC, MIP-1α, IL-6, and a trend to higher IL-12 levels, as compared to BMDDCs infected with the wild-type strain (Fig. 9). In addition, a trend to lower levels of the anti-inflammatory cytokine IL-10 produced by BMDDCs infected with the Δuge3 mutant as compared with hyphae of wild-type A. fumigatus was observed, although this difference was not statistically significant.
To confirm these results and determine if this increased pro-inflammatory response induced by the Δuge3 mutant was mediated by increased binding to dectin-1, we examined the ability of an anti-dectin-1 neutralizing antibody and Fc-dectin-1 to block the increase in TNF-α production by BMDDCs in response to hyphae of the Δuge3 mutant strain. Pre-incubation of BMDDCs with a monoclonal anti-dectin-1 antibody completely blocked the increased TNF-α production by BMDDCs in response to hyphae of the Δuge3 mutant strain (Fig. 10). Similarly, pre-incubating hyphae of the Δuge3 mutant strain with Fc-dectin-1 completely blocked the increased TNF-α production by BMDDCs. Collectively these results support the hypothesis that GAG inhibits host inflammatory responses in part by masking of PAMPs such as β-glucan.
The results of these in vitro and in vivo studies suggest that the unmasking of fungal PAMPs in the absence of GAG induces an increased inflammatory response to hyphae that is detrimental to the host. To test this hypothesis, the virulence of the Δuge3 mutant and wild-type strain was compared for their virulence in highly immunosuppressed mice treated with both corticosteroids and cyclophosphamide. In this model, A. fumigatus infection does not induce a detectable cellular or cytokine inflammatory response during the neutropenic period [19]. In these highly immunosuppressed mice, the Δuge3 mutant strain exhibited markedly attenuated virulence as compared with the wild-type parent strain (Fig. 11A). This difference in virulence was unlikely related to differences in the initial infectious inoculum, since the fungal burden was similar between mice infected with the wild-type and Δuge3 mutant and sacrificed one hour after infection (a median of 1900 vs. 1850 colony forming units per animal, respectively). Mice infected with the Δuge3 mutant strain had a reduction in pulmonary fungal burden that was similar in magnitude to that seen in the non-neutropenic mouse model (Fig. 11B). Histopathologic examination of lungs after 5 days of infection confirmed an absence of infiltrating leukocytes surrounding the sites of wild-type A. fumigatus infection (Fig. 11C). These data suggest that the increased inflammatory response induced by the Δuge3 strain in non-neutropenic mice is non-protective and increases mortality, because inhibiting inflammation in the highly immunosuppressed mouse model was associated with improved survival.
To confirm this hypothesis, we compared the inflammatory response during infection with the wild-type and the Δuge3 mutant in both the non-neutropenic model and the highly immunosuppressed models. To minimize the effects of differences in fungal burden between strains, mice were studied earlier in the course of disease, after three days of infection. In non-neutropenic immunosuppressed mice, a significantly lower fungal burden was again observed in mice infected with the Δuge3 mutant strain as compared with those infected with wild-type A. fumigatus (Fig. 12A). Relative to this lower fungal burden, Δuge3 mutant strain was found to induce significantly higher pulmonary myeloperoxidase levels (MPO) suggesting that this mutant has a higher capacity to mediate pulmonary leukocyte recruitment as compared with wild-type A. fumigatus (Fig. 12A). Similarly, the relative induction of pulmonary TNF-α, as well as the ability to induce pulmonary injury, as measured by LDH levels in BAL fluid, was significantly greater with the Δuge3 mutant than with wild-type A. fumigatus (Fig. 12A). In contrast, in the highly immunosuppressed mouse model there was no significant difference in pulmonary fungal burden, myeloperoxidase content, TNF-α levels or LDH release between mice infected with the wild-type and with the Δuge3 mutant strain at this earlier time point (Fig. 12B). Further, these measures of inflammation were significantly lower in these highly immunosuppressed mice as compared with non-neutropenic animals. Collectively these data suggest that in non-neutropenic mice, infection with the Δuge3 mutant stimulates a non-protective hyper-inflammatory response.
In A. fumigatus, GAG is a heterogeneous linear polymer consisting of α1–4 linked galactose and N-acetylgalactosamine residues in variable combination [16]. GAG is secreted and also a component of both the amorphous cell wall and extracellular matrix. In addition, GAG has been detected in lung lesions of experimentally infected animals [15]. The present study adds significantly to our understanding of the biosynthesis and function of this fungal polysaccharide.
First, the results of our in vitro studies strongly suggest that GAG is the principal mediator of A. fumigatus adherence and plays a key role in biofilm formation. The mechanism by which this carbohydrate mediates adherence to substrates and binds to hyphae remains undefined. Although specific host or fungal lectins may mediate binding of Aspergillus GAG, binding to plastic is clearly independent of host receptors and must be mediated by physicochemical interactions such as charge or hydrophobicity. Further, the lack of competition observed when GAG in suspension was added to wild-type hyphae would argue against a receptor-ligand interaction. Overall, these data are most consistent with a model in which GAG functions as a glue that mediates attachment between hyphae and substrates in a highly promiscuous fashion. The findings of this study add to the growing body of evidence implicating polyhexosamine glycans as key adhesion factors for microorganisms. Work from the 1970's identified polygalactosamine compounds from Neurospora crassa and Bipolaris sorokiniana and suggested that they could potentially play a role in the adherence of fungal spores to glass surfaces 20,21. Similarly, a large number of gram positive and gram negative bacterial biofilms contain polysaccharide intercellular adhesin (PIA), a homopolymer of N-acetylglucosamine, which mediates adherence between bacteria and the surfaces they colonize [22]. Although composed of a different amine sugar, the similarities between these mechanisms of adherence are striking. The adhesive characteristics of PIA are in large part governed by de-acetylation of N-acetyl glucosamine residues. PIA differs from A. fumigatus GAG in which the galactosamine residues have been reported to be uniformly acetylated [16]. Nevertheless, these data suggest that the use of polyhexosamine glycans is a widespread microbial adherence strategy, and could potentially serve as a useful target for the development of antimicrobial strategies with broad applicability.
The present results suggest that Uge3 is a key enzyme in the GAG biosynthetic pathway. The absence of N-acetyl galactosamine in the cell wall of the uge3 mutant strain and the absence of effects on galactofuranose synthesis suggest that this enzyme functions in the production of N-acetyl galactosamine, although experimental validation of this hypothesis is required. A minimal increase in the GlcNAc content of the cell wall of the Δuge3 mutant was also noted. Although this finding could suggest accumulation of substrate in the absence of conversion to GalNAc, it is unclear if this is a significant finding. A similar increase in GlcNAc content was also seen in the uge3 complemented strain despite a restoration of GalNAc and GAG synthesis. Similarly, we observed no difference in susceptibility to classic cell wall perturbing agents between the Δuge3 mutant and complemented strains, suggesting that the increased GlcNAC seen in both strains does not contribute to the marked reduction of adherence and virulence that was seen only in the Δuge3 mutant. Although these data suggests Uge3 therefore mediates synthesis of the N-acetyl galactosamine component of GAG, the pathways responsible for the production of galactose for the synthesis of GAG remain unknown. It is possible that Uge3 also mediates the interconversion of UDP-glucose to UDP-galactose, as epimerases with dual substrate affinity have been described [23], however the normal levels of galactose in the Δuge3 mutant strain argue against this hypothesis. Alternately, galactose synthesis may be dependent on one of the other two putative epimerases identified within the A. fumigatus genome.
The results of this and previous studies strongly suggest that GAG modulates immune responses in vivo. Previous work has suggested that GAG may be recognized by the host as a PAMP and mediate immunosuppression. Fontaine et. al. observed that a urea-soluble fraction of GAG induced neutrophil apoptosis in vitro, and that vaccination of mice with a soluble fraction of GAG enhanced the progression of invasive aspergillosis in immunocompetent and immunosuppressed mice in association with increasing Th2 and Th17 responses [16]. The experiments described here add substantially to these findings by testing the effects of live organisms deficient in GAG production in both non-neutropenic and highly immunosuppressed leukopenic mice. Our findings of increased local inflammation surrounding the Δuge3 strain support the role of GAG as an immunosuppressive molecule. The increased inflammatory response to GAG-deficient hyphae was not protective, but rather attenuated the survival advantage in mice infected with this strain when compared with highly immunosuppressed animals. These findings add support to the growing body of literature suggesting that non-protective inflammatory responses can increase mortality during infection with A. fumigatus [24], [25], [26].
In addition to its direct effects on the immune system, GAG likely also modulates host immune responses through cloaking β-glucan and possibly other PAMPs on the surface of hyphae. Masking of β-glucan and other cell wall PAMPs by the hydrophobin RodA has been previously demonstrated in conidia, and is thought to play an important role in immune evasion [27]. These cell wall β-glucans and other PAMPs are then exposed when the hydrophobin layer is shed during germination. However, studies examining β-glucan exposure during the growth and development of A. fumigatus hyphae have found that, as hyphae mature, the recognition of β-glucan exposure by dectin-1 decreases when compared with swollen conidia and early germinated hyphae [18]. Our results suggest that the production of GAG by maturing hyphae may account for this reduced exposure of β-glucan, and as in the case of conidia, results in an attenuation of inflammatory responses. A similar immune evasion strategy has been reported in the dimorphic fungus Histoplasma capsulatum, in which surface expression of α-(1,3)-glucan has been shown to mask exposure of β-glucan and reduce inflammatory responses [28]. This modulation of β-glucan exposure is the natural converse to the effects of the echinocandin antifungals, in which increasing the exposure of β-glucan is postulated to increase host inflammatory responses and improve fungal killing [29], [30].
The findings of this study also suggest that GAG-mediated adherence may play a role in virulence. Suppression of inflammation in mice infected with the Δuge3 mutant resulted in a reduced pulmonary fungal burden and increased survival of the mice infected with the Δuge3 mutant as compared to mice infected with the wild-type A. fumigatus. It is therefore possible that this attenuated virulence and reduced fungal burden reflects the impaired ability of this mutant to adhere to, and form colonies in the lung, rather than alterations in immune mediated fungal killing. Alternately, loss of GAG may render hyphae more susceptible to host killing by microbicidal peptide or other neutrophil-independent host defences, or result in a unique growth defect seen under in vivo conditions.
These studies suggest that anti-GAG strategies could be useful in the therapy of invasive aspergillosis. Importantly, however, our data would suggest that blocking GAG function would likely be a superior approach to inhibiting the synthesis of GAG, in order to avoid potentially increasing the inflammatory response, and potentially mortality, attributable to unmasking β-glucan or other PAMPs.
A. fumigatus strain Af293 (a generous gift from P. Magee, University of Minnesota, St. Paul, MN) was used as the wild-type strain for all molecular manipulations. The ΔmedA, Δugm1 and ΔstuA mutants and their corresponding parent and complemented strains were described previously [10]. Except where indicated, strains were propagated on YPD agar and at 37°C while exposed to light as previously described [9]. Liquid growth media were synthetic Brian medium [31], Aspergillus Minimum Medium (AspMM) [32], and RPMI 1640 medium (Sigma-Aldrich) buffered with 34.53 g of MOPS (3-(N-morpholino)propanesulfonic acid, Sigma-Aldrich) per liter, pH 7.0 as indicated. When noted, pH and/or iron concentration were modified in AspMM, in order to generate a pH from 4.5 to 8.5, and a [Fe2+] from 0 to 30 µM. Microaerophilic growth was performed using YPD or AspMM, incubated in a candle jar.
The type II pneumocyte cell line CCL-185 (lung epithelial cells A549) was obtained from the American Type Culture Collection, and was grown in DF12K medium containing 10% foetal bovine serum, streptomycin (100 mg/litre) and penicillin (16 mg/litre) (Wisent).
Bone marrow derived dendritic cells (BMDDCs) were prepared by flushing femurs and tibias of 6–8 week old C57BL/6 mice. Bone marrow cells were then cultured with culture media supplemented with either J558L culture supernatants or rGM-CSF, as previously described [33], [34]. Marrow cells were plated at a density of 4×105 cells/ml in petri dishes containing 10 ml of culture media. For J558L supernatant-supplemented cultures, on days 3 and 6, cells were fed an additional 1 ml J558L culture supernatant per dish, and on day 8 with 4 ml of culture media with 30% J558L culture supernatant per dish. BMDDCs were used in fungal interaction experiments after 11 days of culture. For rmGM-CSF-supplemented cultures, on day 3 marrow cells were fed an additional 4 ml of culture media, and used in experiments on day 9. BMDDC differentiation was confirmed by flow cytometry via CD11c expression (data not shown).
In vitro, expression of the genes of interest was quantified by relative real-time RT-PCR analysis as previously described [38]. The primers used for each gene are shown in Table S1. First strand synthesis was performed from total RNA with Quantitec Reverse Transcription kit (Qiagen) using random primers. Real-time PCR was then performed using an ABI 7000 thermocycler (Applied Biosystems) Amplification products were detected with Maxima® SYBR Green qPCR system (Fermentas) . Fungal gene expression was normalized to A. fumigatus TEF1 expression, and relative expression was estimated using the formula 2−ΔΔCt, where ΔΔCt = [(Cttarget gene)sample−(CtTEF1)sample]/[(Cttarget gene)reference−(CtTEF1)reference]. To verify the absence of genomic DNA contamination, negative controls were used for each gene set in which reverse transcriptase was omitted from the mix.
Cell wall extraction was performed as previously described [39]. Alkali soluble (AS) and alkali insoluble (AI) fractions were extracted as previously described [40]. Monosaccharides were determined by gas chromatography after hydrolysis, reduction and peracetylation of the AI and AS fractions [41] with meso-inositol as internal standard. Both hexose and hexosamine concentrations were expressed as percentages of the total cell wall.
The ability of strains to form biofilms was tested by inoculating 6- well culture plates with 105 conidia in 1 mL of Brian broth. After incubation at 37°C for 24 h, the plates were washed, fixed and stained as previously described [9].
The capacity of the various strains of A. fumigatus to adhere to plastic, fibronectin and epithelial cells was analyzed using our previously described method [9]. Six-well culture plates were prepared with confluent monolayers of A549 epithelial cells, adsorbed with 0.01 mg/ml of fibronectin overnight, or left untreated and then infected with 200 germlings of the strain of interest in each well and incubated for 30 minutes. Following incubation, wells were washed 3 times with 4 mL of PBS in a standardized manner, and overlaid with YPD agar for quantitative culture. The adherence assays were performed in triplicate on at least three separate occasions.
For carbohydrate supplementation experiments, either the plastic plate or the germlings were coated with the carbohydrate of interest. Briefly, the appropriate amount of extracellular GAG isolated as above, or of zymosan (Sigma-Aldrich), was resuspended in 2 mL of PBS by sonication, added to the wells of a 6-well non-tissue culture plate and incubated overnight before being washed and used in the adherence assay described above. For adherence assay with glycan treated fungus, germlings were incubated for 1 hour, at room temperature, in a dilution of GAG or zymozan in PBS; then rinsed three times to remove non-adherent carbohydrate before being tested for adherence as described above.
To measure the adherence of purified GAG to A549 epithelial cells, monolayers of A549 cells were grown to confluence in 96-well plate (Nunclone, Inc) then fixed in 4% paraformaldehyde. Cells were incubated with varying concentrations of GAG suspended in PBS, washed and then stained with fluorescein conjugated Soybean Agglutinin (Vector Labs). Binding was quantified by measuring fluorescence at 495 nm using Spectramax (Molecular Devices). To confirm the specificity of Soybean Agglutinin (SBA) for GalNAc residues of GAG, hyphae of Af293 wild-type, Δuge3, and Δuge3::uge3 were grown on poly-d-lysine coated glass coverslip for 12 hours, fixed with 4% paraformaldehyde, co-incubated with fluorescein conjugated SBA, and imaged by confocal microscope at 488 nm (Olympus).
Caspofungin (Merck) and nikkomycin X (Sigma-Aldrich) were diluted in sterile deionized H2O. Calcofluor white (Sigma-Aldrich) was diluted in a solution of 0.8% KOH and 83% glycerol. Antifungal susceptibility testing was performed in accordance with the CLSI M38-A document for broth dilution antifungal susceptibility testing of filamentous fungi [44] as previously described [45]. Final dilutions of antifungals were prepared in RPMI 1640 buffered with MOPS. 100 µL of drug stock was added to 100 µL of 105 conidia/mL solution per well. Plates were examined after 24 and 48 hours of incubation and the minimal inhibitory concentration (MIC) was determined by visual and microscopic inspection resulting in 100% growth inhibition while the minimal effective concentration (MEC) was determined by visual and microscopic inspection resulting in abnormal growth.
The extent of damage to epithelial cells caused by the various strains of A. fumigatus was determined using a minor modification of our previously described method [46]. Briefly, A549 cells were loaded with chromium by incubating monolayers grown in 24-well tissue culture plates with 3 µCi of 51Cr at 37°C in 5% CO2 for 24 hours. Excess chromium was removed by washing with HBSS. The labelled A549 cells were then infected with 5×105 conidia in 1 ml serum free DF12K medium. After a 16 h incubation, the medium above the cells was retrieved. The cells were then lysed with 6 N NaOH and the lysate collected. The 51Cr content of the medium and lysates was then measured in a gamma counter and the extent of epithelial cell damage was calculated. Each strain was tested in triplicate on three separate occasions, and all results were corrected for spontaneous chromium release by uninfected epithelial cells.
A. fumigatus conidia were germinated for 9 h in non-tissue culture treated six-well plates in 2 ml phenol red-free RPMI 1640 medium at a concentration of 1.5×106 conidia/well and allowed to germinate. Next, 1.5×106 BMDDCs were then added to each well in 1 ml of RPMI 1640 medium. As a positive control BMDDCs were incubated with 3 µg/ml lipopolysaccharide (purified from S. minnesota, Invitrogen). Following 6 hrs co-incubation, culture supernatants were collected. Total cytokine analysis in culture supernatants was performed using the Mouse Cytokine 20-Plex Panel (Invitrogen), as per manufacturer's instructions, and analyzed using xPONENT analysis software. To investigate neutralization of either dectin-1 or β-glucan, BMDDCs or fungi were co-incubated for 1 h with 10 µg/ml mouse anti-dectin-1 (Invivogen) or 10 µg/ml Fc-dectin-1 recombinant protein (a generous gift from G. D. Brown), respectively. BMDDCs were added to fungi at a MOI of 1∶2. As controls, BMDDC or 5 µg/ml zymosan (Sigma-Aldrich) were co-incubated with fungi or BMDDC, respectively. TNF-α analysis in culture supernatant was performed using the Mouse TNF alpha ELISA Ready-SET-Go kit (eBiosciences).
The virulence of the indicated A. fumigatus strains was tested in two different murine models of invasive pulmonary aspergillosis. In the first model, male BALB/C mice were immunosuppressed by administering 10 mg of cortisone acetate (Sigma-Aldrich) subcutaneously every other day, starting on day −4 relative to infection and finishing on day +4, for a total of 5 doses [47]. In the second model, the mice were immunosuppressed with cortisone acetate, 250 mg/kg subcutaneously on days −2 and +3, and cyclophosphamide (Western Medical Supply), 250 mg/kg intraperitoneally on day −2 and 200 mg/kg on day +3 [48], [49]. For each fungal strain tested, groups of 11–13 mice were infected using an aerosol chamber as previously described [48]. An additional 8 mice were immunosuppressed but not infected. To prevent bacterial infections, enrofloxacin was added to the drinking water while the mice were immunosuppressed. Mice were monitored for signs of illness and moribund animals were euthanized. All procedures involving mice were approved by the Los Angeles Biomedical Research Institute Animal Use and Care Committee, and followed the National Institutes of Health guidelines for animal housing and care. In both models, differences in survival between experimental groups were compared using the log-rank test.
The mouse studies were carried out in accordance with the National Institutes of Health guidelines for the ethical treatment of animals. This protocol was approved by the Institutional Animal Care and Use Committee (IACUC) of the Los Angeles Biomedical Research Institute at Harbor-UCLA Medical Center (Animal Welfare Assurance Number A3330-01).
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10.1371/journal.ppat.1003564 | Phosphorylation of Calcineurin at a Novel Serine-Proline Rich Region Orchestrates Hyphal Growth and Virulence in Aspergillus fumigatus | The fungus Aspergillus fumigatus is a leading infectious killer in immunocompromised patients. Calcineurin, a calmodulin (CaM)-dependent protein phosphatase comprised of calcineurin A (CnaA) and calcineurin B (CnaB) subunits, localizes at the hyphal tips and septa to direct A. fumigatus invasion and virulence. Here we identified a novel serine-proline rich region (SPRR) located between two conserved CnaA domains, the CnaB-binding helix and the CaM-binding domain, that is evolutionarily conserved and unique to filamentous fungi and also completely absent in human calcineurin. Phosphopeptide enrichment and tandem mass spectrometry revealed the phosphorylation of A. fumigatus CnaA in vivo at four clustered serine residues (S406, S408, S410 and S413) in the SPRR. Mutation of the SPRR serine residues to block phosphorylation led to significant hyphal growth and virulence defects, indicating the requirement of calcineurin phosphorylation at the SPRR for its activity and function. Complementation analyses of the A. fumigatus ΔcnaA strain with cnaA homologs from the pathogenic basidiomycete Cryptococcus neoformans, the pathogenic zygomycete Mucor circinelloides, the closely related filamentous fungi Neurospora crassa, and the plant pathogen Magnaporthe grisea, revealed filamentous fungal-specific phosphorylation of CnaA in the SPRR and SPRR homology-dependent restoration of hyphal growth. Surprisingly, circular dichroism studies revealed that, despite proximity to the CaM-binding domain of CnaA, phosphorylation of the SPRR does not alter protein folding following CaM binding. Furthermore, mutational analyses in the catalytic domain, CnaB-binding helix, and the CaM-binding domains revealed that while the conserved PxIxIT substrate binding motif in CnaA is indispensable for septal localization, CaM is required for its function at the hyphal septum but not for septal localization. We defined an evolutionarily conserved novel mode of calcineurin regulation by phosphorylation in filamentous fungi in a region absent in humans. These findings suggest the possibility of harnessing this unique SPRR for innovative antifungal drug design to combat invasive aspergillosis.
| Invasive fungal infections are a leading cause of death in immunocompromised patients. Translating molecular understanding into tangible clinical benefit has been difficult due to the fact that fungal pathogens and their hosts have similar physiology. The calcineurin pathway is an important signaling cascade in all eukaryotes, and calcineurin inhibitors are powerful immunosuppressants that have revolutionized medicine. Through both genetic and pharmacologic inhibition, we have established that calcineurin is vital for invasive fungal disease. Although the currently available calcineurin inhibitors are active in vitro against the major invasive fungal pathogens, they are also immunosuppressive in the host, limiting therapeutic effectiveness. Here we defined an evolutionarily conserved novel mode of calcineurin regulation by phosphorylation in filamentous fungi that is responsible for virulence in the opportunistic human pathogen, Aspergillus fumigatus. This phosphorylation occurs on a cluster of four serine residues located in a unique serine-proline rich domain of calcineurin that is absent in humans. This finding of a new fungal-specific mechanism controlling hyphal growth and virulence represents a new potential target for antifungal drug therapy.
| Invasive fungal infections are a leading cause of death in immunocompromised patients [1]. With a 40–60% mortality rate, invasive aspergillosis, caused by the filamentous fungus Aspergillus fumigatus, is the most frequent fungal cause of mortality [2]. Through both genetic and pharmacologic inhibition, we have established that the conserved phosphatase calcineurin is necessary for invasive fungal disease [3], [4]. Although currently available calcineurin inhibitors FK506 and cyclosporine A are active in vitro against A. fumigatus [5], they are also immunosuppressive in the host, limiting therapeutic effectiveness. Our goal is to translate fungal biology into tangible clinical benefit by identifying targets that specifically inhibit fungal calcineurin, resulting in fungal killing without suppressing the immune system of the host.
Calcineurin is a Ca2+/calmodulin (CaM)-dependent protein phosphatase comprised of a catalytic A and regulatory B subunit heterodimeric complex [6]. Calcineurin is activated after Ca2+/CaM binds to calcineurin A at the CaM-binding domain (CaMBD), adjacent to the calcineurin B binding helix (CnBBH) in its regulatory domain and displaces the auto-inhibitory domain (AID) [6], [7].
Although calcineurin is conserved from yeasts to human, it exhibits diverse roles in different cell types, evidenced by modulating immune responses, impacting muscle development, neuronal plasticity and cell death in mammalian cells [8]–[11], and influencing cation homeostasis, morphogenesis, cell wall integrity, mating, and stress responses in yeasts [12]–[15]. In the fission yeast Schizosaccharomyces pombe, calcineurin participates in morphogenesis by affecting septal positioning, spindle body organization, and membrane trafficking [16], [17]. In the pathogenic yeasts Candida albicans and Cryptococcus neoformans, calcineurin regulates growth at alkaline pH and elevated temperature, membrane stress, and virulence [18]–[20]. In filamentous fungi, calcineurin is important for cell cycle progression, hyphal branching, stress adaptation, sclerotial development and formation of the infectious appressorium in a plant pathogen [21]–[25].
As a protein phosphatase, calcineurin is known to dephosphorylate specific substrates [26]. However, few reports have focused on phosphorylation of calcineurin as a mechanism of its own activation. King and Huang [27] first reported that bovine brain calcineurin contains sub-stoichiometric amounts of covalently bound phosphate, suggesting calcineurin regulation by phosphorylation. While bovine calcineurin phosphorylation by CK1 yielded no change in resultant activity [28], its phosphorylation by CaM Kinase II and PKC in the CaMBD (S411) inactivated it and decreased its affinity for substrates [29]–[31]. Although this phosphorylation was inhibited upon CaM binding [29], it did not significantly alter the binding of CaM [32]. Recently, calcineurin from S. pombe was shown to be activated after phosphorylation by the check point kinase Cds1 at the similarly positioned serine residue within the CaMBD (S459), and at another site at the C-terminus (S521) [33].
We and others have previously determined that calcineurin is required for hyphal growth and virulence of A. fumigatus [3], [34]. We subsequently showed that the calcineurin complex (CnaA and CnaB) localizes at both the hyphal tips and septa to direct proper hyphal growth and regular septum formation, and that the regulatory subunit (CnaB) is essential for activation of the catalytic subunit (CnaA) in vivo [35], [36].
Here we performed mutational analyses in the functional domains of A. fumigatus CnaA to investigate those required for hyphal growth, CnaA septal localization, phosphatase function, and virulence. We uncovered six novel findings, including (i) the linker between the CnBBH and CaMBD, contains a region unique to filamentous fungi (completely absent in humans), that is rich in serine and proline residues (404-PTSVSPSAPSPPLP-417; designated “SPRR” for Serine Proline Rich Region) and is phosphorylated in vivo at all 4 clustered serine residues (S406, S408, S410 and S413), (ii) complementation of the A. fumigatus ΔcnaA mutant strain with calcineurin A homologs from other fungi defined a filamentous fungal-specific phosphorylation of the SPRR in CnaA, suggesting its evolutionarily conserved importance in fungal hyphal growth, (iii) GSK-3β, CK1, CDK1 and MAP kinase as potential kinases that phosphorylate the SPRR, implicating their role in the regulation of A. fumigatus CnaA, (iv) mutations in the SPRR did not affect septal localization of CnaA but resulted in significant hyphal growth and virulence defects, implicating the importance of calcineurin phosphorylation for its function in A. fumigatus and its possibility as a new antifungal target, (v) CaM is not required for septal localization of CnaA but is required for its function at the hyphal septum, and (vi) the PxIxIT substrate binding motif in CnaA is required for its localization at the hyphal septum.
To characterize domains required for CnaA activity and septal localization, we generated A. fumigatus strains expressing a series of truncated cnaA cDNAs (cnaA-T1, cnaA-T2, cnaA-T3 and cnaA-T4) under the control of its native promoter in the ΔcnaA mutant strain (Figure 1A). While the expression of cnaA-T1, containing only the catalytic domain (1–347 aa), did not complement the hyphal growth defect of the ΔcnaA mutant strain and mislocalized CnaA in the cytoplasm (Figure 1B and 1C), expression of cnaA-T2 that included the CnBBH region (1–400 aa) showed partial growth recovery, indicating that this fragment may bind to CnaB in vivo and partially function by less efficiently localizing at the septum (Figure 1B and 1C). However, expression of cnaA-T3 (1–425 aa), containing the linker region spanning 23 aa between the CnBBH and CaMBD (Figure 1A; indicated in red), completely restored hyphal growth and efficiently localized CnaA at the septum (Figure 1B and 1C). This indicated that the CaMBD and AID are not required for septal targeting of CnaA. Complete hyphal growth recovery observed in the CnaA-T3 strain also suggested the possibility of a constitutively active calcineurin due to the absence of the AID. Expression of cnaA-T4, including the CaMBD but not the AID (1–458 aa), also completely restored hyphal growth and properly localized CnaA at the septa (Figure 1B and 1C). Expression of all the constructs was confirmed by Western analysis (Figure 1D).
Our previous studies have shown that the “paradoxical effect” (attenuation of the antifungal activity of the echinocandin drug caspofungin at elevated concentrations) is calcineurin-mediated, and that paradoxical growth is abolished in the ΔcnaA mutant strain lacking calcineurin activity [37]. While the CnaA-T1 strain showed no paradoxical growth (Figure 2), the CnaA-T2 strain exhibited partial recovery of paradoxical growth only at 4 µg/ml of caspofungin. In comparison to the wild-type, the CnaA-T3 and CnaA-T4 strains displayed more sensitivity to 0.25 µg/ml caspofungin, indicating less calcineurin activity, but showed almost wild-type equivalent paradoxical growth recovery at 4 µg/ml caspofungin. Concordant with these findings, the CnaA-T1 and CnaA-T2 strains (Figure 1E) showed a significant reduction in calcineurin activity (86% and 80%, respectively), and the CnaA-T3 strain showed only a 28% decrease in activity. Inclusion of the CaMBD in the CnaA-T4 strain restored wild-type level of calcineurin activity (Figure 1E). The growth restoration of the CnaA-T3 and CnaA-T4 strains may also be attributed to constitutively active calcineurin due to the truncation of the C-terminal autoinhibitory domain. Taken together, these results indicated that the major determinants/residues for hyphal growth restoration in the CnaA-T3 and CnaA-T4 strains and CnaA septal targeting may be present in this newly described linker region between the CnBBH and the CaMBD of CnaA. However, it is possible that targeting CnaA to the hyphal septum occurs either independently or by binding of the linker region to other unknown protein(s).
Because the truncated forms of CnaA revealed the importance of the linker region between the CnBBH and the CaMBD for hyphal growth and septal localization of CnaA, we performed multiple sequence alignments of A. fumigatus CnaA with homologs from other organisms. This alignment confirmed a high degree of conservation within the catalytic domain, CnBBH, and the CaMBD across different species (data not shown). However, the linker region showed marked variation in different species (Figure 3A and S1). There are no structural data available on fungal calcineurins, so we modeled the A. fumigatus CnaA-CnaB complex based on the available human calcineurin complex structure [38]. Our calcineurin modeling attempts did not reveal any known structure in this linker region, as it seemed to be highly disordered. Inside the 23-residue A. fumigatus linker region there is a specific 14 residue domain (404-PTSVSPSAPSPPLP-417) that is relatively conserved in filamentous fungi and completely absent in the human calcineurin α-catalytic subunit (Figure 3A). We designated this as the “Serine-Proline Rich Region” (SPRR), based on the preponderance of serine and proline residues. A phylogenetic tree constructed from an alignment of the CnBBH-linker-CaMBD domains from diverse organisms showed that, although the CnBBH and CaMBD were nearly identical for all species, the linker, and specifically the SPRR, clearly distinguished the filamentous fungi from other species (Figure 3B). This suggested that the SPRR could be evolutionarily important for filamentous hyphal growth.
The A. fumigatus SPRR has little homology with the region in the yeast S. cerevisiae, and, importantly, the region is absent in human calcineurin. We transformed the A. fumigatus ΔcnaA mutant strain with CnaA homologs from human (CnAα) and S. cerevisiae (CNA1) and found no hyphal growth recovery and cytosolic CnaA localization (data not shown). We then complemented our A. fumigatus ΔcnaA mutant strain with CnaA counterparts from other phylogenetically distinct fungi belonging to the phyla basidiomycota and zygomycota (Figure 4A and 4B). CNA1 from C. neoformans minimally restored the hyphal growth defect (Figure 4B) but septal localization was seen, indicating that C. neoformans CNA1 contained the determinants required for septal localization but not hyphal growth. Western analysis confirmed the expression of C. neoformans CNA1 (Figure 4D). Next, we constructed a chimera (CNAFCNA) consisting of the N-terminal C. neoformans CNA1 catalytic domain and the C-terminal regulatory domain of A. fumigatus cnaA, including the SPRR (Figure 4B). This chimera completely restored hyphal growth, indicating that the SPRR is important for regulating hyphal growth.
We also complemented the A. fumigatus ΔcnaA mutant strain with calcineurins from a phylogenetically unrelated zygomycete fungus, Mucor circinelloides, which grows as extended hyphae but lacks septation (Figure 4B). M. circinelloides has three calcineurin A homologs (designated as MccnaA, MccnaB and MccnaC; Lee SC et al, communicated) and we utilized the genes encoding MccnaA and MccnaC that showed variability in the SPRR (Figure 4A). Although both McCnaA and MccnaC localized to the septum, MccnaC expression showed greater recovery of the hyphal growth defect (Figure 4B). Clustal alignment with the A. fumigatus CnaA SPRR revealed that MccnaC contained 3 prolines and a serine residue, but MccnaA had only a single serine residue (Figure 4A), suggesting that the partial growth complementation observed with MccnaC may be due to partial homology to the A. fumigatus SPRR. To confirm this, we transformed the A. fumigatus ΔcnaA mutant strain with cnaA homologs from closely related filamentous fungi Magnaporthe grisea and Neurospora crassa that have greater homology in the SPRR (Figure 3A and 4C). Both M. grisea and N. crassa calcineurins fully complemented the growth defect, restored calcineurin activity (data not shown) and also localized to the hyphal septa (Figure 4C). Western analysis confirmed the expression of the respective calcineurins from M. circinelloides, M. grisea, and N. crassa (Figure 4D). While calcineurin activity was decreased by ∼70% in the C. neoformans CNA1 complemented strain, concomitant with the decreased radial growth, expression of the chimera (CNAFCNA), which contained the SPRR, completely restored both calcineurin activity and hyphal growth (Figure 4E). Although the McCnaC partially complemented the hyphal growth defect, that replacement strain possessed less calcineurin activity. Taken together, these results indicated that SPRR is important for regulating proper hyphal growth, calcineurin activity, and CnaA septal localization.
The concentration of serine and proline residues in SPRR may create a hydrophobic environment, and the PPLP-motif, predicted to be a WW-domain protein binding motif, may contribute to protein-protein interactions [39]. Phosphorylation at serine or threonine residues that precede a proline, referred to as proline-directed phosphorylation, is known to play an essential role in the regulation of cellular processes such as cell proliferation and differentiation [40]. The two proline residues P409 and P414, preceded by serine residues at positions 408 and 413, respectively, prompted us to examine the phosphorylation of A. fumigatus CnaA. We found that the isolated A. fumigatus CnaA-EGFP fusion protein reacted with the anti-phosphoserine antibody, indicating that CnaA was phosphorylated in vivo (data not shown). Further phosphoproteomic analyses by LC-MS/MS identified 6 serine residues phosphorylated in A. fumigatus CnaA (Figure 5), including all four serine residues clustered in the SPRR at positions 406, 408, 410 and 413 (Figure 5A and 5B), and two additional serine residues in the C-terminus at positions 537 and 542 (Figure 5C). Validation of phosphorylation site localization was performed using the AScore algorithm (Table 1). Furthermore, we also identified that the calcineurin regulatory subunit, CnaB, which was co-purified with CnaA, was also phosphorylated at two serine residues (Ser21 and Ser33) at its N-terminus (Figure S2 and Table 1).
To investigate if phosphorylation of the evolutionarily conserved filamentous fungal SPRR is also a conserved feature, we isolated M. grisea and N. crassa CnaA from the two complemented A. fumigatus strains and analyzed their in vivo phosphorylation status. Two phosphorylations (positions 432 and 436) were detected in the M. grisea CnaA SPRR, and a single phosphorylated serine residue (position 423) was detected in the N. crassa CnaA SPRR (Figure S3A and S4). Additionally, we also identified the phosphorylation of a serine residue (Ser577) in the C-terminus of M. grisea CnaA (Figure S3B). Because we also noted partial hyphal growth complementation with the M. circinelloides CnaC construct, which contains a single serine residue (position 404) in the region aligning with the A. fumigatus CnaA SPRR, we verified its phosphorylation status in vivo and found that this serine residue was phosphorylated, along with another serine residue at position 499 in the C-terminus (Figure S5A and S5B). These results confirmed that CnaA phosphorylation at the SPRR is a unique and conserved mechanism in filamentous fungi.
In order to determine the potential kinase(s) that may phosphorylate the CnaA SPRR we scanned this region using Scansite 2.0, NetPhos 2.0, and NetPhosK 1.0 programs. These analyses suggested that the amino acids surrounding S406 and S413 of CnaA form a potential consensus sequence for phosphorylation by the proline-directed kinases, such as glycogen synthase kinase (GSK-3), cyclin dependent kinase 1 (CDK1), and mitogen activated protein kinase (MAP Kinase). Casein kinase I (CK1) was also predicted to phosphorylate the SPRR. Based on this prediction, we performed in vitro phosphorylation assays using the purified recombinant CnaA regulatory domain (AfRD; regulatory domain spanning 395–482 aa of CnaA, including the SPRR and the CaMBD) from A. fumigatus and various combinations of the kinases. The phosphorylation reactions were processed for mass spectrometry after proteolytic digestion to identify the phosphorylated residues. As shown in Table 2, GSK-3β and CK1 alone phosphorylated the S413 and S406 residues, respectively. Because GSK-3β recognizes two substrate motifs characterized by either primed or non-primed phosphorylation sites at serine/threonine-proline rich motifs, and a majority of GSK-3 substrates are formed via prior phosphorylation by an additional kinase at position P+4 (pS/TXXXpS/T) [41], a combination of the two kinases was also tested. Interestingly, GSK-3β and CK1 together phosphorylated all 4 clustered serine residues (S406, S408, S410 and S413) within the SPRR.
Next, to determine the role of GSK-3β and CK1 in the phosphorylation of S406, S408, S410 and S413 in vivo, we treated the CnaA-EGFP expression strain with GSK-3β and CK1 specific inhibitors, GSK-3β inhibitor VII and D4476, respectively. Both the GSK-3β inhibitor VII and D4476 showed a growth inhibitory effect in a concentration range of 0.5–0.75 µM (data not shown). The CnaA-EGFP fusion protein was isolated after treatment with 0.75 µM each of GSK-3β inhibitor VII and D4476 and analyzed for its phosphorylation status by mass spectrometry. Surprisingly, treatment with GSK-3β and CK1 inhibitors resulted in the dephosphorylation of only S406, but S408, S410 and S413 were phosphorylated, suggesting the possibility of S406 as a target for GSK-3β and CK1 in vivo, while other kinases may be involved in phosphorylating the S408, S410 and S413 residues in vivo. To investigate this possibility, we performed in vitro phosphorylation reactions in presence of the other potential proline-directed kinases, CDK1 and MAP kinase. As shown in Table 2, we found that CDK1 alone phosphorylated 2 serine residues at positions 406 and 408 in the SPRR. Although we could not identify any sites phosphorylated in the presence of MAP kinase alone, a mixture of CDK1 and MAP kinase phosphorylated S406, S408, S410 and S413. Based on these results, it is possible that more than one kinase is responsible for regulating CnaA by phosphorylation at the SPRR in vivo and further work on the interaction of these enzymes with CnaA, specifically addressing the timing of phosphorylation of CnaA by these enzymes, will lead to a more definitive understanding of the role of these kinases in the regulation of CnaA.
Since the immunosuppressant FK506 inhibits calcineurin activity by binding to the immunophilin FKBP12, we also examined the phosphorylation of CnaA and CnaB in the presence of FK506 to correlate phosphorylation versus activity. The FK506-treated sample showed a 2-fold decrease in the phosphorylation of S406 in the CnaA SPRR and a 1.2- and 1.8-fold increase in the phosphorylation of S537 and S542, respectively, in the C-terminus compared to the untreated control (Figure 6 and S6). While CnaB was phosphorylated at S21 and S33 residues under basal conditions, FK506 also significantly reduced the phosphorylation at S33 (Figure 6 and S7). These results suggest a previously unknown link between FK506-FKBP12-mediated inhibition of calcineurin activity and CnaA phosphorylation, including in the novel SPRR. Based on a recent report on the inactivation of GSK-3 by calcineurin inhibitors, cyclosporine A and tacrolimus (FK506) in renal tubular cells [42], and our result demonstrating the phosphorylation of CnaA by GSK-3β and CK1, it is possible that FK506 inhibits the activity of GSK-3β, resulting in its inability to phosphorylate CnaA.
Ca2+/CaM binds to the CaMBD to displace the AID, resulting in calcineurin activation [7]. In mammalian calcineurin, phosphorylation at S411 near the CaMBD resulted in its inactivation [29], while phosphorylation at the same residue in S. pombe (S459) activated calcineurin [33]. However, filamentous fungal calcineurins do not show conservation of this phosphorylation site in the CaMBD (Figure S1). A recent study on the structural basis for the activation of human calcineurin by CaM revealed that the intrinsically disordered CaMBD, along with ∼25 to 30-residues adjacent to the AID, adopts an α-helical structure upon Ca2+/CaM binding [43]. Since the SPRR is located close to the N-terminal end of the CaMBD (Figure S1), we examined if the phosphorylation of the four serine residues in the SPRR influences Ca2+/CaM binding to CnaA or imparts a change in structural content following Ca2+/CaM binding. To test this, we utilized the purified AfCaM and the CnaA regulatory domain (AfRD; regulatory domain spanning 395–482 aa of CnaA, including the SPRR and the CaMBD) from A. fumigatus. We also expressed another recombinant AfRD in which we mutated the four SPRR serine residues to glutamate to mimic phosphorylated status, designating this as AfRD-4SE. The CD spectra of both complexes (AfRD+AfCaM and AfRD-4SE+AfCaM) indicated essentially identical secondary structural content revealing that conformational changes that occur upon Ca2+/CaM binding appear to be unaffected by the phosphorylation (Figure 7). Although glutamate residues may not accurately mimic the phosphorylated state, these results suggest that phosphorylation in the SPRR does not alter the structure of the calcineurin complex, but there is a significant increase in α-helical content. The CD spectrum for AfCaM bound to AfRD has more α-helix than the mathematical sum of the individual AfCaM and AfRD spectra, as evidenced by the stronger negative bands at 222 nm and 208 nm (Figure 7). This is consistent with earlier observations of the human regulatory domain bound to Ca2+/CaM [43]. This suggests that the AfRD is disordered, and then folds upon binding to AfCaM, and we speculate that the phosphorylation-dependent activation of CnaA is independent of Ca2+/CaM binding.
We next mutated the 4 phosphorylated serine residues in the SPRR to alanine (CnaAmt-4SA; S406A, S408A, S410A and S413A) to block phosphorylation and also the 4 serine residues to glutamate (CnaAmt-4SE) to mimic a fixed phosphorylated state in vivo. In comparison to the wild-type strain expressing CnaA-EGFP, only the CnaAmt-4SA strain exhibited a growth defect (Figure 8A, 8B, and 8D; GMM panel), but CnaA septal localization remained unaltered (Figure 8C). To verify if the slightly increased cytosolic staining seen in the CnaAmt-4SA strain is due to protein instability, we performed Western analysis of the extracts obtained from the strains. As shown in Fig. 8E (upper panel), all the mutated constructs were stably expressed, indicating the possibility that the higher cytosolic distribution seen in the CnaAmt-4SA could be a consequence of this mutation, leading to some amount of CnaA mislocalization. While the control strain and the CnaAmt-4SE strain showed complete hyphal growth, the CnaAmt-4SA strain had very poor growth in liquid media (Figure 8D). This indicated that the phosphorylation of the 4 serine residues is required for calcineurin-mediated regulation of hyphal growth but is not required for CnaA septal localization. Moreover, the CnaAmt-4SA strain showed hyphal growth recovery in the presence of sorbitol, indicative of osmotic stress and cell wall defects (Figure 8A; SMM panel) similar to our other calcineurin mutant strains [3], [36], [37], [44]. The CnaAmt-4SA strain was also hypersensitive to caspofungin (Figure 8A; GMM+Caspofungin panel). Supporting these observations, calcineurin activity was also decreased by ∼70% in the CnaAmt-4SA strain compared to the wild-type strain, indicating that phosphorylation plays an important role in the regulation of calcineurin activity (Figure 8E; lower panel). Although the CnaAmt-4SE strain showed complete recovery of hyphal growth, its activity was also decreased by ∼25%, indicating the possibility of active phosphorylation-dephosphorylation events required to fully control calcineurin activation and function. Although the substitution of glutamate residues for phosphorylated serines does not perfectly mimic phosphorylation in vivo, the CnaAmt-4SE strain indeed showed wild-type comparable hyphal growth, but only a slight decrease in calcineurin activity. It is possible that a threshold level of calcineurin activity is sufficient for regular hyphal growth.
Because we noted a significant growth defect of the CnaAmt-4SA strain (Figure 8), we examined its virulence in our persistently neutropenic murine inhalational model of invasive aspergillosis. The mortality associated with CnaAmt-4SA strain infection (Figure 9A) was significantly lower (10%) in comparison to the wild-type strain (90%) (P<0.0001) indicating that phosphorylation of the four serine residues clustered in this novel SPRR is critical for calcineurin function and virulence. Consistent with the survival data, lung histopathologic studies revealed both decreased inflammation as well as a near absence of hyphal growth in the mice infected with the CnaAmt-4SA strain compared to those infected with the wild-type strain (Figure 9B).
Binding studies with the human calcineurin-NFAT complex previously revealed the PxIxIT motif as a common binding site for calcineurin on its substrates [38]. In S. cerevisiae, mutation of the calcineurin residues (N366 I367 R368) in contact with the PxIxIT motif resulted in defective substrate interaction [45]. Recent structural studies of Ca2+/CaM bound to a 25-residue peptide spanning the CaMBD in the human calcineurin catalytic subunit also revealed that R408, V409, and F410 play a major role in rigidity and stabilization of the central helix of CaM bound to calcineurin [46]. To investigate the role of these specific domains for CnaA septal localization and calcineurin function in A. fumigatus, we mutated the PxIxIT-binding NIR residues to alanines (NIR-AAA), as well as the critical Ca2+/CaM-binding RVF residues in the CaMBD to alanines (RVF-AAA; Figure 10A and 10B). The NIR-AAA mutation only partially restored hyphal growth and completely mislocalized CnaA, indicating that septal localization of CnaA occurs through binding to other protein(s) (Figure 10C and 10D). On the contrary, the RVF-AAA mutation had partial hyphal growth restoration but did not affect CnaA septal localization (Figure 10C and 10D), supporting our CnaA truncation results. Western analysis confirmed that both mutations maintained protein stability (Figure 10E). The observed growth defect with the RVF-AAA mutation may be due to the inability of CaM to bind to CnaA, and as a result the AID remains bound to the regulatory domain, leading to continued inhibition of calcineurin activity. Although CaM localizes at the hyphal tip and septum in A. nidulans [47], and we confirmed this in A. fumigatus (data not shown), these results, coupled with our truncational analyses, confirmed that CnaA localization at the septum is CaM-independent, yet CaM is required to activate CnaA to completely restore hyphal growth.
Critical regions controlling calcineurin function in S. cerevisiae have been identified by substitution of V385 with an aspartic acid that disrupted the interaction between the catalytic and the regulatory subunit, and also by random mutagenesis of three residues (S373, H375, and L379) that led to loss of calcineurin activity but did not disrupt calcineurin A binding to Ca2+/CaM or to calcineurin B [48]. To examine if any of these mutations would affect the septal localization or function of A. fumigatus CnaA, we mutated V371 to aspartic acid (V371D) and the T359, H361, and L365 to proline, leucine and serine (THL-PLS), respectively. Both mutations had a significant effect on hyphal growth, but neither affected CnaA septal localization (Figure 10C and 10D). The V371D mutation confirmed our previous finding [36] that, although CnaB is not required for CnaA septal localization, it is required for CnaA function and growth. The THL-PLS mutation had an effect on the catalytic activity and therefore it is possible that although CnaA is localized at the hyphal septum it is catalytically inactive. We confirmed the stability of each mutation by Western analysis (Figure 10E). The reduction in calcineurin activity due to these mutations (Figure 10F) and the lack of paradoxical growth recovery (Figure S8) established that catalytic site residues and CnaB-binding activity of CnaA do not influence its septal localization, yet catalytically active calcineurin is required at the hyphal septum to direct proper hyphal growth.
Calcineurin inhibitors are promising new antifungal candidates due to their unique mode of action from other antifungal classes (e.g., polyenes, triazoles, echinocandins), efficacy against emerging resistant strains, and synergism with existing antifungals [4]. However, currently-used calcineurin inhibitors complex with immunophilins leading to host immunosuppression [49]. Although calcineurin has been well studied in several organisms and its functional domains described, few studies have focused on mutations in its key domains in vivo, and none have examined phosphorylation as a mechanism of calcineurin function in a human pathogen. By deleting the C-terminal regulatory domains of CnaA which led to progressive defects in hyphal growth (Figure 1 and 2), we identified a unique fungal-specific 23 residue linker domain between the CnBBH and the CaMBD, containing the novel and evolutionarily conserved SPRR (Figure 3A). Inclusion of the SPRR showed full recovery of hyphal growth, concomitant increase in calcineurin activity, and clear localization of CnaA to the hyphal septum (Figure 4).
To evaluate the conservation of this linker region, we examined it in 22 eukaryotes selected based on divergence and to include model organisms and pathogens affecting both humans and plants (Figure S1). Phylogenic analysis showed that the linker containing the SPRR clearly distinguished the filamentous fungi (Figure 3B). Based on the CnaA-CnaB molecular model we created (Figure 10A), the SPRR is present outside the core binding region between CnaA and CnaB, and the preponderance of proline and serine residues in this linker creates a hydrophobic environment which could lead to binding to other as of yet unknown proteins.
To determine the importance of the SPRR for CnaA function in vivo, we performed complementation tests in our A. fumigatus ΔcnaA mutant strain with cnaA homologs from human, S.cerevisiae, C. neoformans and M. circinelloides, all of which lacked similarity within the SPRR (Figure 4A and 4B). While neither human nor S.cerevisiae CNA, which possess an overall 56% and 50% similarity to A. fumigatus CnaA, respectively, complemented the hyphal growth defect (data not show), C. neoformans CNA1, which exhibits 67% similarity to A. fumigatus CnaA, localized to the septum but did not restore hyphal growth (Figure 4B). Interestingly, only M. circinelloides CnaC, which had partial similarity to the A. fumigatus SPRR, and exhibits 59% similarity to A. fumigatus CnaA, partially complemented the hyphal growth defect and localized CnaA at the septum (Figure 4A and 4B). Domain swapping of the C. neoformans CNA1 C-terminus with the A. fumigatus cnaA, to include the SPRR in the chimera, completely restored hyphal growth and localized CnaA at the septum (Figure 4B), revealing that the SPRR is required for calcineurin function in regulating proper hyphal growth. To further confirm this, calcineurins from more closely related ascomycete filamentous fungi, such as N.crassa and M. grisea, each of which exhibit overall similarity of 80% and greater conservation in the SPRR, were used for complementation of the A. fumigatus ΔcnaA strain. The respective complemented strains showed proper septal localization and complete hyphal growth recovery (Figure 4C). Taken together, although these results clearly indicated the importance of the SPRR for calcineurin function, we cannot exclude the possibility that some minor variations in other regions of calcineurin may also limit the ability of calcineurins from other species to fully complement the A. fumigatus ΔcnaA strain.
Our phosphoproteomic analyses provided unequivocal evidence of A. fumigatus CnaA phosphorylation in vivo at the unique SPRR that is specific to filamentous ascomycetes (Figure 5 and Table 1). Phosphorylation at the SPRR serine residues in proximity to the proline residues may induce secondary structure conformation in the molecule facilitating binding to other proteins. Moreover, as this SPRR is non-conserved in the yeasts and is completely absent in the human calcineurin α-subunit, it may have been acquired during evolution by diverging from the phylum basidiomycota.
As mentioned earlier to confirm if phosphorylation at the SPRR is also important for other filamentous fungi, we performed similar complementation analyses with the closely related filamentous fungi N. crassa and M. grisea, which contain 4 and 5 serine residues in their SPRR, respectively, and both complemented hyphal growth (Figure 4C). Phosphoproteomic analyses confirmed the phosphorylation of serine residues from those fungi within the SPRR (Figure S3 and S4), demonstrating a unique feature of calcineurin function via a conserved phosphorylation in this novel domain found only in filamentous fungi. Mutation of the 4 serine residues in the A. fumigatus SPRR to block phosphorylation of CnaA caused increased branching and reduction in hyphal growth, confirming the significance of this phosphorylation (Figure 8). Most importantly, we also found that the CnaAmt-4SA strain was defective in virulence (Figure 9), strengthening our results of the requirement for phosphorylation at the SPRR for calcineurin activation and function in vivo.
Phosphorylation was reduced in both the catalytic and the regulatory calcineurin subunits following treatment with FK506 (Figure 6, S6 and S7). It is possible that the FK506-FKBP12 complex may indirectly cause an inhibitory effect on the kinase that phosphorylates calcineurin at the SPRR and also at the N-terminus of CnaB. Based on the phosphorylation of residues in both the SPRR and the C-terminus, we speculated that more than one kinase is responsible. Although mammalian calcineurin was shown to be phosphorylated at S411 in the CaMBD by PKC and CaM kinase II 29–31, and the same residue at position S459 in S. pombe was phosphorylated by Cds1 check point kinase [33], this serine residue is not conserved among filamentous fungal calcineurins. Based on our identified phosphorylation sites in A. fumigatus CnaA and the SPRR, we were able to predict the phosphorylation of this region by potential proline-directed kinases such as GSK-3, CDK1 and MAP kinase. By in vitro phosphorylation assays, using the enzymes GSK-3β and CK1, we identified that all 4 serine residue in the SPRR were phosphorylated. Additionally, S413 and S408 are both flanked by downstream proline residues, which represent typical GSK-3 phosphorylation sites. While GSK-3β alone phosphorylated S413 in the SPRR, CK1 alone phosphorylated S406, and a combination with GSK-3β led to phosphorylation of other serine residues (S408 and S410), revealing that the prephosphorylation of S406 residue by CK1 may trigger the subsequent phosphorylation of S408 and S410. Interestingly, a previous study showed that the yeast Mck1 protein kinase belonging to the GSK-3 kinase family stimulated calcineurin activity by phosphorylating Rcn1, and in the absence of GSK-3 kinase, calcineurin activity was fully inhibited revealing a allosteric mechanism of calcineurin regulation by Rcn1 [50]. We further validated our in vitro phosphorylation data by examining the phosphorylation status of CnaA in vivo after treating with inhibitors for GSK-3β and CK1, which confirmed the absence of phosphorylation at S406 only but not the other serine residues, leading to the notion that other kinases may also phosphorylate the CnaA SPRR in vivo. The observed phosphorylation of CnaA SPRR by CDK1 and MAP kinase in vitro strengthens this possibility. However, further analyses are required to specifically understand how these enzymes actually regulate CnaA.
Since the CaMBD is in close proximity to the SPRR, we hypothesized that phosphorylation in the SPRR caused conformational changes or altered binding between Ca2+/CaM and the calcineurin complex. Circular dichroism studies revealed that conformational changes that occur after Ca2+/CaM binding remained unaffected between unphosphorylated and phosphomimetic SPRR constructs (Figure 7), indicating that CaM-mediated activation of calcineurin is independent of SPRR phosphorylation. This is intriguing when considering that calcineurin phosphorylation at S411 in Rat slightly decreased the affinity of calcineurin for Ca2+/CaM, causing its inactivation [29], and phosphorylation at the same residue (S459) in S. pombe activated calcineurin [33].
Surprisingly, even though Ca2+/CaM is well known to bind to calcineurin and localize at the hyphal tips and septum [47], the septal localization of CnaA was not impaired by the deletion of the C-terminus (Figure 1), indicating that CaM is not involved in CnaA septal localization. We confirmed this by mutating key residues (RVF-AAA) within the CaMBD (Figures 10 and 11A), which did not cause CnaA mislocalization from the septum but affected hyphal growth, indicating the requirement of CaM for calcineurin activity and growth but not for septal localization.
Mutation of V371 (V371D), located at the beginning of the long helix that forms the bulk of the CnaB binding interface of CnaA, would place a charged residue in an unfavorable hydrophobic environment and result in destabilization of the calcineurin heterodimer (Figure 11B). Mutation of N352, I353, and R354 (NIR-AAA) residues that lie in the substrate recognition β strand of CnaA (Figures 12A and 12B) [38], [45], revealed that CnaA localizes at the septum (Figure 10) by likely binding to other proteins, and its septal localization is important for hyphal growth. Neither the V371D mutation, nor the triple mutation of the residues T359, H361, and L365 (THL-PLS) that lie in a stretch of amino acids connecting the last β strand of the catalytic core domain of CnaA with the helical CnaB binding domain (Figures 10, and 12C, 12D and 12E), altered the CnaA septal localization. This reconfirmed that CnaA localizes to the septum independent of CnaB, validating our previous findings [36]. The septal localization aspect of calcineurin complex is specific to filamentous fungi. Although the exact mechanism of how these mutations affect calcineurin function at the hyphal septum is unknown, we expect that the availability of the crystal structure for A. fumigatus calcineurin in the future would help us in better understanding the consequence of these mutations. Based on our previous report [36], we presume that the localization and activity of the calcineurin complex at the septum is necessary to maintain proper septum formation through proper cell wall assembly at the septum by regulating the enzymes involved in β-glucan and chitin synthesis, apart from also regulating the cell wall repair mechanisms at the hyphal septa under stress. In contrast to the yeast cells, filamentous fungi proliferate by hyphal tip extension and form septa that divide the hyphal compartments at regular intervals; during these two very important processes, active cell wall biosynthesis is required. Based on our previous findings and also our present results, we think the calcineurin phosphorylation is important for not only its activation but also for its interaction with other substrates that are necessary for cell wall biosynthesis. Our findings on A. fumigatus CnaA are broadly significant because of the conservation of this unique SPRR among filamentous fungi, and phosphorylation in this region is a newly described mode of calcineurin regulation for growth and virulence. Future studies directed towards understanding the phosphorylation status of CnaA at different stages of growth, stress conditions, and also identifying phosphorylation-dependent interactions of the calcineurin complex with other substrates in vivo will help reveal the exact mechanism of calcineurin-mediated regulation of hyphal growth in filamentous fungi. In addition, given the importance of CnaA phosphorylation at the SPRR for its activity, function, and virulence in this pathogen, and the absence of the SPRR in human calcineurin, future antifungal drug targeting to combat invasive aspergillosis could exploit the SPRR.
Animal studies at Duke University Medical Center were in full compliance with all of the guidelines of the Duke University Medical Center Institutional Animal Care and Use Committee (IACUS) and in full compliance with the United States Animal Welfare Act (Public Law 98-198). Duke University Medical Center IACUC approved all of the vertebrate studies under the protocol number A-038-11-02. The studies were conducted in the Division of Laboratory Animal Resources (DLAR) facilities that are accredited by the Association for Assessment and Accreditation of Laboratory Animal Care (AAALAC).
Strains are listed in Table S1. ΔcnaA mutant strain (ΔcnaA::A. parasiticus pyrG) [3], was used for transformations. Isogenic A. fumigatus wild-type strain (AF293) or the strain expressing cnaA-egfp under its native promoter [36] were used as control strains for radial growth assays and paradoxical growth testing [36], [37]. Axioskop 2 plus microscope equipped with AxioVision 4.6 imaging software was used for fluorescence microscopy [36]. For ΔcnaA mutant complementation analyses, the respective truncated or mutated cDNAs encoding cnaA amplified by using pUCGH-cnaA as a template with primers listed in Table S2 were cloned in the plasmid pUCGH-cnaApromo and transformants selected in the presence of hygromycin B [36] were verified by Southern analysis.
The calcineurin A encoding cDNAs from C. neoformans, M. grisea, N. crassa and M. circinelloides were amplified from respective cDNA libraries. All genes were amplified using the respective templates and primers listed in Table S2 and cloned into the pUCGH vector as previously described [36]. For cloning human CNA-α subunit the plasmid, pET15b CnA CnB (obtained from Addgene), was used as a template. S. cerevisiae genomic DNA was used as a template to amplify S. cerevisiae CNA1. The plasmids were sequenced to verify for accuracy prior to their transformation into the A. fumigatus ΔcnaA mutant strain. Transformants were selected in the presence of hygromycin B (150 µg/ml).
Preparation of cell extracts and Western detection were performed as described earlier [36]. Cell extracts from 24 h cultures were assayed for calcineurin phosphatase activity [36] using p-nitrophenyl phosphate as substrate at 405 nm. The difference of absorbance values between the amounts of p-nitrophenol released in the strains versus the ΔcnaA ΔcnaB double mutant control strain represented the phosphatase activity mediated by calcineurin. Each experiment consisted of two biologic replicates, with each assay consisting of 6 technical replicates; data are presented as mean ± SD of nanomoles of pNPP released/min/mg protein.
Total cell lysates were extracted and normalized to contain ∼10 mg protein in each sample before GFP-Trap® affinity purification (Chromotek) and processed for TiO2 phosphopeptide enrichment and mass spectrometry as previously described [51]. The dried phospho-peptide enriched samples were resuspended in 10 µl of 2% acetonitrile, 0.1% formic acid, 10 mM citric acid and subjected to chromatographic separation on a Waters NanoAquity UPLC equipped with a 1.7 µm BEH130 C18 75 µm I.D.×250 mm reversed-phase column. The mobile phase consisted of (A) 0.1% formic acid in water and (B) 0.1% formic acid in acetonitrile. Following a 5 µl injection, peptides were trapped for 5 min on a 5 µm Symmetry C18 180 µm I.D.×20 mm column at 20 µl/min in 99.9% A. The analytical column was held at 5% B for 5 min then switched in-line and a linear elution gradient of 5% B to 40% B was performed over 90 min at 300 nl/min. The analytical column was connected to a fused silica PicoTip emitter (New Objective, Cambridge, MA) with a 10 µm tip orifice and coupled to an LTQ-Orbitrap XL mass spectrometer. In some experiments the analytical column was connected to a fused silica PicoTip emitter (New Objective, Cambridge, MA) with a 10 µm tip orifice and was coupled to a Waters Synapt G2 QToF mass spectrometer through an electrospray interface operating in a data-dependent mode of acquisition. The instrument was set to acquire a precursor MS scan in the Orbitrap from m/z 400–2000 with r = 60,000 at m/z 400 and a target AGC setting of 1e6 ions. In a data-dependent mode of acquisition, MS/MS spectra of the three most abundant precursor ions were acquired in the Orbitrap with r = 7500 at m/z with a target AGC setting of 2e5 ions. Max fill times were set to 1000 ms for full MS scans and 500 ms for MS/MS scans with minimum MS/MS triggering thresholds of 5000 counts. For all experiments, fragmentation occurred in the LTQ linear ion trap with a CID energy setting of 35% and a dynamic exclusion of 60 s was employed for previously fragmented precursor ions. When using the Waters Synapt G2 QToF mass spectrometer through an electrospray interface operating in a data-dependent mode of acquisition the instrument was set to acquire a precursor MS scan from m/z 50–2000 with MS/MS spectra acquired for the three most abundant precursor ions. For all experiments, charge dependent CID energy settings were employed and a 120 s dynamic exclusion was employed for previously fragmented precursor ions.
Raw LC-MS/MS data files were processed in Mascot distiller (Matrix Science) and then submitted to independent Mascot database searches (Matrix Science) against a SwissProt (fungus taxonomy) containing both forward and reverse entries of each protein. Search tolerances for LTQ-Orbitrap XL data were 10 ppm for precursor ions and 0.02 Da for product ions, and for Synapt G2 data were 10 ppm for precursor and 0.04 Da for product ions using trypsin specificity with up to two missed cleavages. Carbamidomethylation (+57.0214 Da on C) was set as a fixed modification, whereas oxidation (+15.9949 Da on M) and phosphorylation (+79.9663 Da on S, T, and Y) were considered a variable modification. All searched spectra were imported into Scaffold (Proteome Software) and protein confidence thresholds were set using a Bayesian statistical algorithm based on the PeptideProphet and ProteinProphet algorithms which yielded a peptide and protein false discovery rate of 1%. Phosphopeptide intensities were obtained by generating selected ion chromatograms (20 ppm window around most abundant charge state of precursor ion with seven point Boxcar smoothing) from raw LC-MS data. MS response at peak apex was used for quantitating abundance.
E. coli optimized genes for A. fumigatus calcineurin (AfRD; regulatory domain from 395–482 aa of CnaA including the SPRR and the CaMBD) and calmodulin (AfCaM) were synthesized and ligated into a pUC57. The genes were digested and ligated into the pET303/CT-His vector using XhoI and XbaI. The mutant AfRD4Ser-Glu (AfRD-4SE), containing mutations S14E, S16E, S18E, and S21E, was made using a QuikChange Site-Directed Mutagenesis Kit. AfRD and AfRD-4SE were transformed into E. coli BL-21 (DE3) cells for expression, and purified on a Ni2+/NTA column followed by a calmodulin-sepharose column. AfCaM was purified on a 2-trifluoromethyl-10-aminopropylphenothiazine (TAPP)-sepharose column. Protein concentrations were determined by bicinchoninic acid assay. Circular dichroism (CD) experiments were performed using a Jasco J-810 spectropolarimeter. Sample buffers were composed of 20 mM Tris, 200 mM NaCl, 4 mM EGTA, pH 7.5, and 20 mM CaCl2. Samples contained AfRD, AfCaM, or equimolar concentrations of AfRD and AfCaM. The CD experiments shown were all performed in the presence of an excess of calcium in order to determine the effects of the phospho-mimics on the AfRD conformation when bound by AfCaM. The concentrations of either protein alone per sample ranged from 10–20 µM. In the sample that contained both of AfRD and AfCaM, the total protein concentration per sample ranged from 10–25 µM. Spectra were collected in quartz 1 mm pathlength cuvettes. Samples were scanned from 200–260 nm in 0.5 nm increments at a scanning speed of 50 nm/sec and each spectrum is the average of 4 scans. The raw CD data (in millidegrees) was converted to molar ellipticity. The amount of secondary structure was determined using the CONTIN/LL deconvolution program.
In vitro phosphorylation reactions with GSK-3β, CK1, CDK1/cyclinB and MAP kinase (New England Biolabs) contained 10 µg of recombinant CnaA-AfRD protein, reaction buffer supplied by the manufacturer, and 500 µM ATP in total volume of 50 µl. GSK-3β (2500 U), CK1 (5000 U), CDK1 (100 U) and MAP kinase (500 U) were used for each reaction. The reactions were performed either with single enzymes or combinations of the different enzymes. The reactions were incubated at 30°C for 4 h and processed for mass spectral analysis following digestion with Glu-C and TiO2 phosphopeptide enrichment.
To determine the effect of GSK-3β and CK1 inhibitors on the phosphorylation status of CnaA in vivo, the CnaA-EGFP expression strain was grown in presence of 0.75 µM each of the GSK-3β inhibitor VII (calbiochem) and D4476 (abcam) for a period of 24 h and the isolated CnaA-EGFP fusion protein was subjected to phospho enrichment and mass spectrometry as described earlier [51].
Homology models for the A. fumigatus calcineurin A and B subunits were prepared using the Phyre server. A model of the fungal heterodimer was constructed by superimposing the individually created homology models onto the X-ray structure of the human calcineurin heterodimer bound to a substrate peptide (PDB ID 2P6B) [38]. Superpositions and analysis of mutations were performed using PyMol, Coot and Molprobity. Due to the high level of sequence conservation with the mammalian calcineurin structure already determined, the models are largely superimposable at the Cα backbone level and exhibit good packing overall. We employed this model of A. fumigatus calcineurin to propose a structural basis for the disruption of calcineurin activity observed for the mutants described in the current study.
Six-week-old CD1 male mice (mean weight 22.5 g) were immunosuppressed with both cyclophosphamide and triamcinolone acetonide as previously described [52]. Two groups of 20 immunosuppressed, unanesthetized mice each inhaled an aerosolized suspension of either AF293 wild-type or CnaAmt-4SA strain [52]. Survival was plotted on a Kaplan-Meier curve and log rank was used for pair-wise comparison of survival with statistical significance defined as a two-tailed p<0.05. Histopathological examination of the lungs was performed in two mice in each group that were euthanized on day +7 of infection. Lungs were embedded in 10% neutral buffered formalin and subsequently sectioned and stained with Gomori methenamine silver and hematoxylin-eosin for assessment of histological signs of infection. The animal model and experiments were conducted in accordance with the Animal Care and Use Program of the Duke University Medical Center.
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10.1371/journal.ppat.1002073 | A Freeze Frame View of Vesicular Stomatitis Virus Transcription Defines a Minimal Length of RNA for 5′ Processing | The RNA synthesis machinery of vesicular stomatitis virus (VSV) comprises the genomic RNA encapsidated by the viral nucleocapsid protein (N) and associated with the RNA dependent RNA polymerase, the viral components of which are a large protein (L) and an accessory phosphoprotein (P). The 241 kDa L protein contains all the enzymatic activities necessary for synthesis of the viral mRNAs, including capping, cap methylation and polyadenylation. Those RNA processing reactions are intimately coordinated with nucleotide polymerization such that failure to cap results in termination of transcription and failure to methylate can result in hyper polyadenylation. The mRNA processing reactions thus serve as a critical check point in viral RNA synthesis which may control the synthesis of incorrectly modified RNAs. Here, we report the length at which viral transcripts first gain access to the capping machinery during synthesis. By reconstitution of transcription in vitro with highly purified recombinant polymerase and engineered templates in which we omitted sites for incorporation of UTP, we found that transcripts that were 30-nucleotides in length were uncapped, whereas those that were 31-nucleotides in length contained a cap structure. The minimal RNA length required for mRNA cap addition was also sufficient for methylation since the 31-nucleotide long transcripts were methylated at both ribose-2′-O and guanine-N-7 positions. This work provides insights into the spatial relationship between the active sites for the RNA dependent RNA polymerase and polyribonucleotidyltransferase responsible for capping of the viral RNA. We combine the present findings with our recently described electron microscopic structure of the VSV polymerase and propose a model of how the spatial arrangement of the capping activities of L may influence nucleotide polymerization.
| Using a prototype of the nonsegmented negative strand RNA viruses, vesicular stomatitis virus, we probed the spatial relationship between the RNA dependent RNA polymerase and 5′ mRNA capping and methylation activities of the large polymerase protein. Because the 5′ mRNA processing reactions dramatically impact the nucleotide polymerization activity of the protein, they may function as a quality control step in viral transcription. We developed a means to stall transcription at precisely defined locations following initiation and analyzed the cap status of the stalled transcripts. We show that 30-nt transcripts are uncapped whereas those that are 31-nt long gain are capped and methylated at both guanine-N-7 and ribose-2′-O positions. Combined with our recent work that determined the molecular architecture of the VSV polymerase, this work reveals the spatial relationship within a functional polymerase complex of the polymerase domain and the 5′ mRNA processing domains of the L protein.
| The RNA synthesis machinery of the non-segmented negative-strand (NNS) RNA viruses contains at its core a large polymerase protein (L) that possesses all the enzymatic activities for genome transcription and replication. During transcription, L catalyzes nucleotide polymerization [1]–[3] as well as each step of mRNA cap addition [4]–[9] and polyadenylation [10]. Those activities are intimately coordinated such that failure to cap the mRNA results in the premature termination of RNA synthesis [7], [11]–[13], and failure to methylate the mRNA can result in the hyper polyadenylation of the mRNA [13]–[15]. In this study, we sought to examine how the different L activities are coordinated to ensure the correct synthesis of a capped and methylated mRNA by precise determination of the point at which those 5′ mRNA processing reactions occur during transcription.
Our understanding of the activities of L protein has been largely shaped by studies of a prototype of the NNS RNA viruses, vesicular stomatitis virus (VSV). The NNS RNA virus L proteins are homologous and share six regions of sequence conservation (CRI-VI) [16] that were thought to contain the conserved functions. The RNA dependent RNA polymerase (RdRP) was readily identified by the presence of a set of motifs in CRIII [1]. Consistent with this assignment, substitution of an aspartic acid residue predicted to coordinate a catalytically essential magnesium ion ablates nucleotide polymerization in vitro. Although L protein was known to possess the enzymatic activities for mRNA cap addition, their identity proved difficult to pin down unambiguously. This is because the enzymatic activities themselves are unusual, in that the cap is added by the action of a polyribonucleotidyltransferase (PRNTase) that transfers pRNA onto a GDP acceptor through a covalent L-pRNA intermediate [7], [9], [17] This contrasts with all other known capping reactions which involve an RNA guanylyltransferase that transfers GMP onto a diphosphate RNA acceptor [18]. Substitutions to residues in CRV of VSV L that are conserved throughout all NNS RNA viruses led to the ablation of capping activity in vitro and defined a motif GxxT[N]HR that was essential for capping, implicating CRV as the PRNTase [7]. Subsequently, the conserved histidine was shown to be essential to form the covalent L-pRNA intermediate, further substantiating this assignment [17]. Polymerases that were defective in mRNA cap addition terminated transcription prematurely, further underscoring the link between correct 5′ mRNA processing and nucleotide polymerization [7].
Following cap formation, the cap itself is methylated at guanine-N-7 and ribose-2′-O positions [5], [19]–[25]. Those reactions also differ for VSV since they are catalyzed by a single methyltransferase domain [8]. Moreover, in contrast to the order of cap methylation events in other viruses and organisms, the ribose 2′-O methylation precedes and facilitates the subsequent guanine-N-7 methylation [26]. Sequence alignments identified a methylase like domain in CRVI of L [27], [28], and substitutions within this region block both 2′-O and G-N-7 methylation [6], [8]. Like capping, the act of methylation can also influence the properties of the RdRP, since in some circumstances a failure to methylate the mRNA cap is accompanied by the production of a large polyadenylate tail by excessive stuttering of the polymerase on a U7 tract [13]–[15]. This intimate co-ordination of the 5′ mRNA processing reactions with nucleotide polymerization may help ensure production of correctly modified transcripts, which in turn could minimize triggering of cellular pathways that recognize either uncapped or unmethylated RNA [29].
Recently, we obtained a first view of the molecular architecture of the VSV L polymerase protein [30]. Single particle electron microscopy revealed that the capping machinery of L resides within 3 globular domains that are appended to a core ring-like RdRP domain. Moreover the architecture of L rearranges significantly following complex formation with the essential viral polymerase cofactor P, and this rearrangement likely positions the domains in the correct orientation to ensure the modification of the nascent mRNA chain [30]. In this study, we sought to determine at what stage a VSV mRNA acquires a cap structure. Since the PRNTase and the RdRP are localized within different regions of the L protein, a minimal length of RNA may serve as an important check point regulating the distinct activities. For decades RNA synthesis reactions have been carried out in vitro with VSV using either virus in which the membrane is disrupted with detergent [31], or purified polymerase (L and P) and the N-RNA template [32], [33]. From those reactions, the shortest transcripts that were identified as being capped were 37-nucleotides [34]. It was not clear, however, whether those transcripts were capped during synthesis or had at some level gained access to the PRNTase following release from the polymerase. Recent experiments have shown that short 5-nt transcripts corresponding to the beginning of a VSV mRNA can be capped in trans by L, but such experiments cannot address at what stage during transcription the RNA chain is modified. In the present study, we established a system to provide a “freeze-frame” view of VSV transcription using templates that lacked sites for UTP incorporation. By stalling transcription reactions at precisely defined chain lengths we identify a minimal length at which the transcript becomes capped. This work reveals the spatial arrangements of the capping activities of the VSV L protein in relation to the RNA dependent RNA polymerase domain during active transcription.
The promoter for VSV mRNA synthesis includes the 3′ leader region and the first 10-nt of the conserved gene-start element [35]–[38]. The sequences of those elements specify the incorporation of each of the four NTP's into the nascent RNA chain (Figure 1A), creating a challenge in selecting a nucleotide that can control polymerase stalling. We elected to engineer the template such that it lacked sites for UTP incorporation. We chose UTP because of the known requirement for a high ATP concentration for initiation of RNA synthesis, and because previous work had revealed the essential nature of sites for CTP and GTP incorporation in the cis-acting signals in the mRNA start sequence [11], [39]. To do this, we engineered the leader region and the gene-start element of a non-essential 60-nt gene that was inserted at the leader-N gene junction of an infectious cDNA clone of VSV (Figure 1A). Specifically, we modified the leader sequence such that it lacked adenosine nucleotides except for those at position 48–50 which are typically not transcribed by the polymerase. We generated recombinant viruses in which those mutations were engineered within the leader region, purified the virus and confirmed that the mutations were present in the viral genome (data not shown).
To examine the effect of the leader mutations on viral transcription, we performed transcription reactions in vitro. Briefly, 10 µg of purified virus was incubated with detergent to disrupt the viral membrane and NaCl to liberate the M protein from the RNP core and permit transcription. The detergent-disrupted particles were incubated in transcription buffer containing ATP, CTP, UTP and [32P]-GTP and the products of transcription purified and analyzed by electrophoresis on acid-agarose gels (Figure 1B). Although overall levels of transcription were reduced (compare rVSV (60) and rVSV(A-)60), indicating that the U- leader promoter was less efficient than the wild type sequence, each of the viral mRNAs were synthesized. This result demonstrates that the sequence of the viral leader region could be engineered to eliminate sites of UTP incorporation which forms the basis of the templates to stall transcription within the downstream 60-nt transcriptional unit.
The 60-nt gene sequence was next modified such that the first place the polymerase would encounter sites for UTP incorporation were at positions +11–13, 21–23, 31–33, 41–43, or 51–53 with respect to the first gene-start (Figure 2A). Infectious recombinant VSV was recovered from each of those clones and while all the mutants grew less well than wild type VSV, there we no obvious differences in the plaque morphologies of the mutants (Figure 2B). Sequencing of the genomic RNA confirmed that the first sites for UTP incorporation were at the desired location (Figure 2C and data not shown). To determine whether transcription could be stalled at the inserted adenylates, we used purified recombinant viruses and performed RNA synthesis in the absence of UTP. Briefly, 10 µg of purified virus was disrupted with detergent, incubated in transcription buffer containing ATP, CTP and [32P]-GTP and the products of transcription purified and analyzed by electrophoresis on polyacrylamide gels. The VSV recombinants that were designed to stall transcription at positions +30, 40 and 50, generated RNA of the anticipated length, thus demonstrating that transcription can be specifically stalled by omission of UTP from the reactions (Figure 2D). We did not observe significant quantities of a read-through transcript where polymerase incorporated another nucleotide in place of UTP to generate a full-length 60-nt transcript (Figure 2D). This indicates that the polymerase error rate is insufficient to bypass three sites of UTP incorporation through mis-incorporation of an alternate nucleotide.
Treatment of the RNA products with the cap cleaving enzyme tobacco acid pyrophosphatase (TAP) revealed that the stalled 30-nt transcript was insensitive to cleavage as evidenced by its unaltered mobility, whereas the 40-nt and 50-nt transcripts shifted in mobility by a single nucleotide following cleavage (Figure 2D). This indicates that the 30-nt transcripts were uncapped, whereas the 40 and 50-nt long transcripts were capped. Using a 5′-3′ exonuclease (+Exo) that cleaves uncapped RNA, we further confirmed that the 40-nt and 50-nt transcripts were capped, since those RNAs were resistant to digestion, whereas the 30-nt transcript was sensitive (Figure 2D). As expected, each of the recombinant viruses also produced a leader RNA as evidenced by the collection of transcripts around 47-nt (Figure 2D). Leader RNA synthesized in vitro is known to contain at least 4 distinct 3′ termini which likely accounts for the multiple bands observed for the altered leader sequence [40]. Those leader transcripts lack an mRNA cap structure as shown by their insensitivity to cleavage by TAP, and their hydrolysis by the 5′-to 3′ exonuclease. Collectively, this analysis indicates that a VSV mRNA gains access to the mRNA capping machinery at an RNA chain length of >30-nt, but <40-nt. This experiment, however, could not determine whether the RNA was capped within a range of 30–40 nt or whether capping required a specific chain length.
To more precisely define the point at which the RNA chain gains access to the capping machinery, we constructed a set of recombinant viruses designed to stall transcription at intermediate points between 30 and 40-nt (Figure 3A). Specifically, we recovered viruses designed to stall transcription at 31–37 nts (Figure 3B) and examined the products made by those viruses following transcription reactions performed as above. As expected, each of those viruses generated the characteristic profile of leader RNAs around 47-nt, as well as specifically stalled transcripts (Figure 4A). The mobility of the majority of the transcripts matched the anticipated position of stalling as shown by those RNAs that were 30, 31, 35, 36 and 37-nt long (Figure 4A). The mobility difference between the transcripts produced by the viruses designed to stall transcription at 30 vs. 31-nt appears to be 2-nt, indicating that the 31-nt transcript was fully capped. By contrast, stalling appeared relatively inefficient and somewhat heterogeneous for the viruses designed to stall transcription at positions +33 and +34 extending from the anticipated length (indicated by the *) to 1–2 nt larger. Stalling of transcription at +32 appeared inefficient with only low levels of the stalled transcript visible on the gel (Figure 4A). An enhanced contrast view of the gel is provided to illustrate the low levels of stalled transcripts obtained with the virus designed to stall transcription at +32 (Figure 4A, lower). Although we do not know the reason for this altered stalling efficiency it correlates precisely with the point immediately following mRNA cap addition. The ratio of leader to stalled transcript appears to vary for several of the recombinant viruses. We do not know the basis for this variation, but with the exception of the virus designed to stall transcription at +32, each of the viruses generated sufficient stalled transcripts for us to determine the cap status.
To definitively determine whether those transcripts contained an mRNA cap structure, we compared the mobility of the stalled transcripts before and after TAP digestion. As expected, the leader RNA, and the +30 RNA were insensitive to TAP cleavage indicating that they lack an mRNA cap structure (Figure 4B). By contrast the transcripts stalled at +31, +35, +36 and +37 showed a clear single nucleotide shift in their mobility following TAP cleavage (Figure 4B). Moreover, the collection of transcripts synthesized following stalling at positions +33 and +34 shifted in mobility indicating that they were capped (Figure 4B). Collectively, these data indicate that a VSV mRNA gains access to the capping machinery at a chain length of 31-nt.
The above experiments show that the transcript must be 31-nt long to be capped. To provide further support for this, we took advantage of our previously characterized cap defective polymerase (L-H1227A) [7]. As expected, the transcripts synthesized following stalling of transcription at +30 were uncapped (Figure 5), and identical products were generated by L-H1227A. Using wild type L, stalling of transcription at position +31 and above demonstrated that the transcripts were capped. Consistent with this, the cap defective polymerases generated stalled transcripts that were 1-nt shorter. This experiment confirms the identity of the uncapped stalled transcripts and further supports that a VSV mRNA gains access to the mRNA capping machinery at an RNA chain length of 31-nt. We also noted that the cap defective polymerase appears to stall more readily on the rVSV(A-)32 template. This raises the possibility that the inability to efficiently stall RNA synthesis reflects a transition of the polymerase related to cap addition.
Electron microscopic structural analyses of VSV L indicate that the capping and cap methylation activities reside within distinct globular domains that are connected to a ring-like domain containing the RdRP. The capping enzyme resides in a globular domain that is proximal to the RdRP, whereas the MTase located to a more distal globular domain. The position of the MTase domain showed a degree of variability suggesting a high degree of flexibility within L. Significant structural rearrangements occur in L, following complex formation with the essential cofactor P rendering a straightforward identification of the capping and MTase domains within the active polymerase complex difficult. We therefore sought to determine whether there was an additional length requirement for mRNA cap methylation. To do this, we performed transcription reactions on the engineered templates in the absence of UTP and the presence of [3H]-SAM. The resulting transcripts were purified, analyzed by electrophoresis on a 20% polyacrylamide gel and detected by fluorography (Figure 6A). The 30-nt uncapped transcripts were not labeled by [3H]-SAM, demonstrating that lacked methyl groups at both ribose 2′-O and G-N-7 positions of the cap structure. By contrast the 31-nt and larger transcripts were labeled by [3H]-SAM. To evaluate whether a methyl group was present at both G-N-7 and 2′- O positions, the transcripts were exposed to TAP prior to gel electrophoresis. Following TAP cleavage, approximately 50% of the label remained associated with the transcript (Figure 6B), consistent with the removal of the 7mGp structure. Because this reduction in intensity of labeling was apparent starting at the +31 transcript, the results suggest that there is no distinct length requirement for 2′-O and guanine-N-7 methylation. To quantify this effect further, we measured the amount of label associated with purified RNA before and after TAP cleavage by scintillation counting. To eliminate the released 7mGp we purified RNA that was longer than 20-nt prior to scintillation counting. This analysis demonstrates that 50% of the [3H]-SAM signal is lost upon TAP cleavage, and indicates that the RNA cap structures are guanine-N-7 and ribose 2′-O methylated. Collectively, these data indicate that the RNA chain gains access to the MTase domain at the same length as the PRNTase domain.
In this study, we established a freeze-frame approach to stall VSV transcription using engineered templates that lack sites for UTP incorporation. Using this approach we defined a nascent RNA chain length requirement of 31-nucleotide for the access of the 5′ end to the mRNA capping machinery. Since the RNA chain length for mRNA cap addition is +31-nt and the distance between adjacent phosphates is approximately 3.4 Å, the physical distance that the nascent RNA chain must transit is approximately 100 Å. The requirement for a minimal length for mRNA capping underscores an architectural arrangement of VSV L where a defined distance separates the capping domain from the RdRP. The finding that the capped mRNA does not require further elongation to be methylated is consistent with a more flexible positioning of the methyltransferase domain. We discuss the present observations in light of our earlier work on the molecular architecture of the VSV polymerase, as well as previous functional studies of the impact of capping and cap methylation on mRNA synthesis. Since there are commonalities among the mechanisms of RNA synthesis, this work also likely has implications for the large polymerase proteins of other nonsegmented negative-strand RNA viruses.
The evidence presented here shows that a VSV mRNA must be 31-nt long to gain access to the mRNA capping machinery. This conclusion comes from stalling transcription at precisely that point and evaluating whether the RNA contains an mRNA cap structure. In these experiments, however, the nascent RNA chain remains stalled at 31-nt and the relative time at which the cap is added is not certain. Thus while our experiments demonstrate that the RNA chain can gain access to the PRNTase once it reaches 31-nt, they cannot determine over what range during transcription cap addition actually occurs. This will be impacted, for example, by the rate at which the PRNTase catalyzes transfer of the RNA onto GDP, and indeed the formation of the GDP acceptor. Consequently, it seems likely that cap addition occurs over a range of nucleotide positions and that the earliest possible point of capping is position +31. We and others have reported the existence of abortive uncapped transcripts that range in size up to several 100 nucleotides [7], [11], [12]. We anticipate, however, that since failure to cap leads to premature termination of mRNA synthesis, it seems likely that capping typically occurs within a short window of +31. Consistent with this latter idea it is possible to generate a short capped and polyadenylated transcript from an artificial transcription unit of 60-nt inserted between the leader and N genes of VSV [41]. The presence of uncapped transcripts that range in size up to several 100 nucleotides would then simply reflect the variable termination of transcripts that failed to gain access to the capping apparatus within the optimal window for cap addition. Our study defines the lower limit of that optimal window as 31-nt during transcription. We do not precisely know the upper limit of the window for capping nascent RNA, but since we do not observe large quantities of an uncapped 60-nt mRNA transcript synthesized in vitro [41] it seems likely that the window is quite narrow.
The finding that the transcript gains access to the methyltransferase active site at the same length, +31-nt, demonstrates that there is no further elongation requirement for cap methylation. An earlier study that examined the kinetics of mRNA cap methylation catalyzed by the VSV New Jersey polymerase provided evidence that cap methylation occurs over a nascent RNA chain length window that spanned several 100 nucleotides [42]. It seems likely, therefore, that during transcription the nascent RNA chain continues to grow while the RNA is being capped and methylated and that the precise point at which the chain gets methylated is not critical. Such a scenario therefore implies that the influence of methylation on mRNA polyadenylation, where failure to methylate can result in the production of large polyadenylate tails, is unlikely to be a coupling of the two reactions directly. Rather, the act of methylation itself in some way influences the extent of polymerase stuttering on the U7 tract present at the viral gene-end sequence and not by influencing the cessation of stuttering during the reiterative transcription process. The effects of several mutations in L that ablate mRNA cap methylation were previously examined for their ability to promote hyper polyadenylation. The majority of the methylation defective mutants synthesized mRNA with normal levels of polyadenylate [15]. The synthesis of large polyadenylate may instead be related to the affinity of the various L mutants for the inhibitor SAH, such that the hyperpolyadenylation phenotype may simply reflect increases in the Km for SAM binding rendering the polymerase mutants hypersensitive to levels of SAH.
Although the present study defines 31-nt as a minimal length of mRNA cap addition, it remains possible that there are additional gene-position or sequence dependent effects that may alter the precise position of cap addition for specific genes. For example, the sequence of the transcript may influence the length at which the RNA gets capped simply by influencing the elongation properties of the polymerase, or possibly by favoring specific nascent RNA structures that alter access of the nascent transcript to the capping site. If such gene specific effects occur, they may serve as a means to regulate the amount of full-length transcripts made from specific genes, since failure to cap would lead to premature termination, down-regulating specific gene expression. We have not yet succeeded in extending the freeze-frame methodology to study sequential transcription from the viral genome, consequently, further experiments will be required to investigate this possibility.
Previous experiments have shown that short exogenous synthetic RNA oligonucleotides (5–10 nts) are of sufficient length to be capped when added to VSV L in trans. In the system used in the current study, where capping is evaluated co-transcriptionally, mRNAs shorter than 31 nts fail to be capped by the polymerase. These results indicate that while an exogenously added RNA is able to freely diffuse into the capping active site, the access of an endogenous nascent RNA to that site is regulated. Such a regulation is likely achieved through anchorage of the mRNA 3′ end by the RdRP domain preventing the 5′ end from accessing a physically distant capping active site.
In combination with the recent electron microscopic structures, the present findings provide new insights into the structural and functional organization of the VSV polymerase. EM images of L alone showed the organization of L into a ring domain (90–100 Å) containing the RNA polymerase and an appendage of three globular domains (approximately 45 Å each) containing the cap-forming activities [30]. The capping enzyme maps to a globular domain that is juxtaposed to the ring and the cap methyltransferase maps to one of two more distal and flexibly connected globules. Notably, the position of the globules relative to one another appears rather flexible such that the methylase domain can be positioned adjacent to the RdRP. This arrangement of the capping and methyltransferase domains is consistent with the results of this study: the 5′ end of the mRNA originating from the RdRP domain requires a minimal length to reach the capping apparatus. The length of 100 Å is approximately the distance spanned by 31 nts, and is consistent with the distance between the center of the ring and the proximal globular domain in the appendage. The fact that the same length is sufficient to gain access to the MTase is consistent with the high degree of flexibility exhibited by the distal globular domains. However, it should be emphasized that our EM studies showed that complex formation with P induces a significant conformational rearrangement of L including the loss of the globular features of the appendage (Figure 7). It is this L-P complex that is the active form of the polymerase for RNA synthesis. The L-P complex, however, retains some features of the L protein alone, including a ring-like domain that presumably includes the RdRP activity and an altered appendage that presumably includes the capping machinery (Figure 7). The work presented here supports the idea that in the rearranged appendage, the capping active site is approximately 100Å from the RdRP active site, and the MTase domain is sufficiently flexible to not require further elongation of the mRNA.
Our findings also have implications for the path the RNA chain takes between the RdRP domain and the capping apparatus. Although, the length of the RNA would permit the transcript to traverse from the core ring domain to the PRNTase appended to the ring itself, as well as the flexible cap methylation apparatus of the same L molecule, we cannot exclude the possibility that the cap formation is catalyzed by an adjacent L molecule. Indeed, our electron microscopic examination of the L-P complex provided evidence for polymerase dimers, and genetic experiments with Sendai virus are consistent with the presence of at least a dimeric polymerase complex. It remains unknown, however, whether cap defective VSV polymerase molecules can complement RdRP defective VSV polymerase molecules. If indeed the RNA chain is capped by an adjacent polymerase molecule, such complementation should be achievable. Additionally, by incorporation of thio substituted nucleotides within the nascent RNA chain, it should now be possible to decipher the path that the RNA chain traverses in its transit from the RdRP domain to the PRNTase domain.
During transcription, the capping activities of L are known from biochemical experiments to influence the polymerization activity. Specifically, failure to cap the mRNA leads to premature termination of transcription [7], [11]–[13], and inhibition of methylation of the mRNA cap can lead to hyperpolyadenylation [13]–[15]. We previously investigated the influence of sequence on premature termination by polymerases that are defective in cap addition [13]. Analogous to authentic termination, premature termination was favored at AU rich elements in the template and was significantly suppressed during copying of CG rich templates. This implies that the strength of a hybrid between the nascent strand and template can influence polymerase processivity. Although speculative, a relatively simple mechanism by which capping could influence termination is by regulating the stability of the hybrid between the nascent strand and template in the RdRP domain. Since the capping domain is juxtaposed to the ring, capping itself may induce a tightening of the grip of the polymerase stabilizing the hybrid between the template and nascent mRNA strand thus rendering the polymerase fully processive. The physical sequestration of the 5′ end of the nascent RNA strand within the PRNTase domain and then the cap methylation domain may itself also favor the RNA polymerization reaction becoming fully processive, so that the nascent RNA chain has elongated to the point at which the polymerase can only terminate in response to a highly specialized transcription stop sequence.
The length requirement for mRNA 5′ modification is similar to that previously reported for vaccinia virus [43], RNA polymerase II and the apparent length at which a reovirus transcript gains access to the capping enzyme [44]. In those other cases the capping enzyme is encoded by a separate polypeptide that appears associated with the RNA polymerase either physically in the case of the reovirus polymerase as a structural component of the virus capsid, or by recruitment to the phosphorylated C-terminal domain of RNA polymerase II [45]. For RNA polymerase II it has been shown that the nascent RNA chain is capped over a range of nucleotide positions, in this case the polymerase pauses prior to the act of mRNA cap addition, and the act of cap addition is linked to full polymerase elongation. We do not know whether a similar pausing event occurs during VSV transcription. The properties of the polymerase, however, do appear distinct immediately upon cap addition. This is illustrated by the fact that while we were able to efficiently stall the polymerase at positions +30, +31, +35, +36, +37, +40, +50 during transcription, stalling at positions +32, +33 and +34 result in a ladder of products that range up to 1–2 nucleotides longer than anticipated. Since the cap was just added at position +31, capping itself may render the polymerase somewhat resistant to stalling and result in the production of slightly longer transcripts.
A second possible explanation for the production of stalled transcripts that vary in their precise point of termination relates to the intrinsic ability of the polymerase to stutter on homopolymeric tracts. The best characterized slippage sequence for VSV is that of the gene-end AUACUUUUUUUG in which the AUAC element regulates the extent of stuttering by L on the U7 tract [46]. We considered that the template sequence CCGUUUGUCUUU present at positions 25–36 of the unperturbed 60-nt gene may itself favor some slippage that is enhanced by the insertion of three adenylates at positions 32, 33, and 34. Although the presence of G or C within a U tract blocks slippage during transit of polymerase across a gene-junction [46], the general A/U rich nature of the present sequence may be sufficient to destabilize the elongating polymerase complex in vitro. Definitive assessment of the precise point of termination of the stalled transcripts will require sequencing of these small RNA products.
The ability to stall transcription at specific places on the template, through the use of engineered recombinant templates now provides us an ability to study more precisely the steps of transcription. This methodology should be readily adaptable to probe the length of the nascent RNA chain that is protected by the polymerase during transcription and to determine whether the RNA chain becomes accessible prior to mRNA cap addition. Moreover, combined with our ability to image the L-P complex by electron microscopy, this approach may permit us to examine the polymerase bound to the RNA template at various stages of transcription. Our work also has implications for understanding transcription in other nonsegmented negative-strand RNA viruses in that they likely require a minimal length of transcription prior to cap addition. We anticipate that those lengths will vary for each virus, and as outlined above may perhaps show gene-specific variation. Since the production of triphoshphate RNA itself may serve as an activator of specific cytosolic sensors of the innate immune system, what appears to be a specific and efficient position of mRNA cap addition may also serve to decrease the production of pppRNA transcripts in infected cells. Moreover, since the act of capping serves as a positive regulator of transcriptional elongation this minimal length represents a key check-point in the transcription cycle.
Plasmid pVSV1(+)60, containing an infectious cDNA clone of the VSV genome with a 60-nt long non-essential gene inserted at the leader-N gene junction, was generated as described previously [41]. The adenylates at positions 19, 20, 22, 23, 25, 28, 29 and 37-nt in the VSV genome were replaced using site-directed mutagenesis to generate an (A-) leader region (Figure 1A). In order to introduce three adenylates at positions 11, 21, 31, 32, 33, 34, 35, 36, 37, 38, 41 and 51 in the 60-nt long non-essential gene site-directed mutagenesis was performed. The presence of the mutation was confirmed by sequence analysis. Recombinant VSV was rescued from cDNA by transfection of BHK-21 cells infected with a recombinant vaccinia virus (vTF7-3) that expressed T7 RNA polymerase as described previously [47], [48]. The generated viruses were designated rVSV(A-)-10, -20, -30, -31, -32, -33, -34, -35, -36, -37, -40 and -50, respectively. Cell culture supernatants were collected at 48 to 96 h post transfection, and virus was amplified once in BHK-21 cells. Individual plaques were isolated on Vero cells, and large stocks were generated in BHK-21 cells and purified as described previously [12]. Viral titer was determined by plaque assay on Vero cells, and protein content was measured with the Bradford reagent (Sigma Chemical Co., St Louis, MO). The (A-) leader and 60-nt gene of the purified viruses were sequenced again, and these stocks were used for in vitro transcription reactions.
Viral RNA was synthesized in vitro as described previously [31], [38]. 10 µg of the purified recombinant virus was activated by incubation with detergent for 5 min at room temperature. RNA synthesis reactions were performed in the presence of nucleotide triphosphates (1 mM ATP and 0.5 mM each of CTP and GTP). The reaction mixtures were supplemented with 20 µµCi of [α-32P]-GTP (3,000 Ci mmol-1) (Perkin-Elmer, Wellesley, MA). Total RNA was extracted, purified, and used for secondary manipulations as follows. Where indicated, the RNA cap structure was removed by tobacco acid pyrophosphatase (TAP; Epicenter) as previously described [6]. For exonuclease digestion, total RNA was dephosphorylated using Antarctic phosphatase (New England Biolabs [NEB]), and a single phosphate was added using T4 polynucleotide kinase (NEB) according to the manufacturer's instructions. The resulting RNAs were subsequently treated with Terminator exonuclease (Epicenter) according to the manufacturer's instructions [12]. The products of RNA synthesis were analyzed on a 6% polyacrylamide gel and visualized by a phosphorimager (GE Healthcare; Typhoon).
The N-RNA template was purified from rVSV(A-)-30, -31, -32 and-33 as described previously [6]. Briefly, 4 mg purified virus was disrupted on ice for 1 h in 20 mM Tris-HCl (pH 8.0), 0.1% Triton X-100, 5% glycerol, 5 mM EDTA, 3.5 mM dithioerythritol, 20% dimethyl sulfoxide, and 1.0 M LiCl. The template was recovered by centrifugation (190,000× g, 3.5 h) through a step gradient of 0.25 ml each of 40, 45, and 50% glycerol in TED buffer (20 mM Tris-Cl [pH 8.0], 1 mM EDTA, 2 mM dithioerythritol) supplemented with 0.1 M NaCl. The pellet was resuspended in 0.3 ml of TED buffer plus 10% glycerol and disrupted on ice, except that the Triton X-100 and EDTA concentrations were reduced to 0.05% and 1 mM, respectively. The N RNA was isolated by banding in a 3.6-ml 20 to 40% (wt/wt) CsCl gradient (150,000× g, 2.5 h), recovered by side puncture and diluted fourfold with 10 mM Tris-Cl (pH 8.0), 0.1 mM EDTA. The N-RNA was recovered following centrifugation (150,000× g, 1.5 h) through a 0.5-ml cushion (50% glycerol, TED buffer, 0.1 M NaCl).
Recombinant L was expressed from recombinant baculoviruses in Spodoptera frugiperda 21 cells, and P was expressed in BL21 (DE3) as described previously [6]. At 72 h postinfection, the cells were collected, washed twice with ice-cold phosphate-buffered saline, and recovered by centrifugation. The cells were suspended in lysis buffer (50 mM NaH2PO4, 10% glycerol, 0.2% NP-40, 300 mM NaCl, 10 mM imidazole [pH 8.0]) supplemented with EDTA-free protease inhibitor cocktail (Roche) and 1 mM phenylmethylsulfonyl fluoride and disrupted by sonication. The L and P proteins were purified by Ni-nitrilotriacetic acid–agarose (Qiagen), followed by ion-exchange chromatography as described previously [6].
Reactions were carried out in the absence of rabbit reticulocyte lysate using 5 µg of N-RNA template, 4 µg of purified L, 2 µg of purified P and nucleoside triphosphates (1 mM ATP and 0.5 mM each of CTP and GTP) as described previously [6]. The reaction mixtures were supplemented with 20 µCi of [α-32P]GTP (3,000 Ci mmol−1) (Perkin-Elmer, Wellesley, MA). After 5 h of incubation at 30°C, the RNA was purified by phenol and chloroform extraction and analyzed on 6% polyacrylamide gel and visualized by a phosphorimager (GE Healthcare; Typhoon). For methylation of the transcripts, the same reaction condition was used as described above with the exception that 20 µM 3H-labeled S-adenosyl-L-methionine (3H-SAM) (76 Ci/mmol, Perkin-Elmer, Wellesley, MA) was used for labeling the RNA instead of [α-32P]GTP. After 5 h of incubation at 30°C, the RNA was purified by phenol and chloroform extraction and analyzed on 20% polyacrylamide gel followed by autoradiography.
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10.1371/journal.ppat.1005636 | Comparative Structural and Functional Analysis of Bunyavirus and Arenavirus Cap-Snatching Endonucleases | Segmented negative strand RNA viruses of the arena-, bunya- and orthomyxovirus families uniquely carry out viral mRNA transcription by the cap-snatching mechanism. This involves cleavage of host mRNAs close to their capped 5′ end by an endonuclease (EN) domain located in the N-terminal region of the viral polymerase. We present the structure of the cap-snatching EN of Hantaan virus, a bunyavirus belonging to hantavirus genus. Hantaan EN has an active site configuration, including a metal co-ordinating histidine, and nuclease activity similar to the previously reported La Crosse virus and Influenza virus ENs (orthobunyavirus and orthomyxovirus respectively), but is more active in cleaving a double stranded RNA substrate. In contrast, Lassa arenavirus EN has only acidic metal co-ordinating residues. We present three high resolution structures of Lassa virus EN with different bound ion configurations and show in comparative biophysical and biochemical experiments with Hantaan, La Crosse and influenza ENs that the isolated Lassa EN is essentially inactive. The results are discussed in the light of EN activation mechanisms revealed by recent structures of full-length influenza virus polymerase.
| Segmented negative strand viruses (sNSV) such as Influenza, Lassa or Hantaan viruses are responsible for a large number of severe human infectious diseases. Currently, there are vaccines and antiviral treatments available for influenza but none for the infections caused by other sNSV. All carry out transcription by the cap-snatching mechanism, which requires the action of a metal ion dependent endonuclease (EN), a domain within their large viral polymerases. Here we provide the crystal structure of the Hantaan virus (family Bunyaviridae) and Lassa virus (family Arenaviridae) cap-snatching ENs in complex with manganese and a comparative functional study of their catalytic activity. Despite the high structural homology between the two ENs a few changes in the active site, involving a catalytic histidine, cause a different binding of the metal ions with dramatic consequences for their in vitro activity. Hantaan EN binds the metal ions as Influenza A (family Orthomyxoviridae) and LACV (family Bunyaviridae) ENs and all three are active in vitro. In contrast Lassa virus EN is inactive in the same experimental conditions. We can now classify sNSV into two functionally distinct groups (His+ and His- ENs), providing a broad view of the sNSV cap-snatching ENs properties that will be determinant for the comprehensive development of broad-spectrum antiviral drugs. These results also have implications for the viral transcription regulation in the light of recent studies on full-length sNSV polymerases.
| Segmented negative strand viruses (sNSVs) represent one of the most threatening groups of emerging viruses for global health [1]. They are classified in three main families: Orthomyxoviridae, Bunyaviridae and Arenaviridae with respectively six to eight, three and two genome segments [2]. Seasonal and pandemic influenza A virus (IAV, orthomyxovirus) strains rapidly propagate worldwide with human to human transmission being the key factor for spread. In contrast, arenaviruses (e.g. Lassa virus) or bunyaviruses (e.g. Hantaan, La Crosse, Rift Valley, Crimean Congo Haemorrhagic viruses), as well as some highly pathogenic avian influenza strains, are zoonotic viruses that result in generally limited outbreaks through contact with animal vectors but with high mortality rates and lack of effective treatments. The future spread of some of these infectious agents from their traditional geographical niches due to vector species redistribution arising through climate change is a potential threat [3,4], emphasising the need to develop new, ideally broad-spectrum, drugs against sNSV zoonotic viral diseases.
Despite the diversity in the infectious cycles of sNSVs there are common mechanisms that can be potentially targeted for broad spectrum inhibitors, such as genome and mRNA synthesis by the conserved RNA dependent RNA polymerase (RdRpol) or their characteristic cap-snatching transcription mechanism [5–8]. This mechanism, most extensively characterized for IAV virus, involves the recognition of capped cellular mRNAs by a cap-binding domain located in the polymerase and its subsequent cleavage 10–14 nucleotides downstream by the polymerase’s endonuclease (EN) to provide a primer for viral mRNA transcription [5,9]. The cap-binding and the EN domains were first identified in the IAV hetero-trimeric polymerase and are located in the middle region of the PB2 and the N-terminal region of the PA subunits respectively [10,11]. The recent crystal structures of influenza A and B heterotrimeric polymerases show the relative disposition of these two domains within the full RdRpol domains allowing an integrated structural model for the cap-snatching mechanism to be proposed for orthomyxoviruses [9,12]. Studies on La Crosse (LACV) bunyavirus and Lymphocytic Choriomeningitis (LCMV) arenavirus allowed the structural and functional characterization of the cap-snatching EN domains in the amino terminal region of their monomeric polymerases (L proteins) [13,14] and showed them to be essential for viral transcription. Similar results were subsequently obtained for Lassa arenavirus and the bunyaviruses Rift Valley Fever Virus (RVFV) and Crimean Congo Haemorragic Fever Virus (CCHFV) [15–18]. However the location of the putative cap-binding domain still remains elusive for bunya- and arenaviruses.
The sNSV cap-snatching ENs belong to the PD-D/ExK superfamily of cation dependent nucleases. The available structures of the influenza orthomyxovirus and LACV orthobunyavirus show the canonical conformation of the active site with two divalent metal ions directly coordinated by the acidic conserved residues of the PD and the D/ExK motifs as well as with a conserved histidine (His+ ENs). The two metal ions bind aligned towards the catalytic lysine [14]. The arenavirus EN crystal structures reported to date (LCMV and Lassa) are structurally homologous to LACV EN [13,16], but there are important differences in their active sites. The main divergence is that the metal co-ordinating histidine, conserved in most bunya- and orthomyxoviruses, is replaced by an acidic residue in arenavirus ENs (His- ENs). No metal ions were present in the LCMV EN structure [13] and the Lassa EN structure was reported with two magnesium ions in the active site coordinated by some catalytic residues through bridging water molecules, instead of the direct coordination shown by His+ ENs. The reported ion preference for the catalytic activity also changes, Lassa EN preferring magnesium and LCMV EN or His+ ENs preferring manganese [16].
Here we focus on two sNSV, Hantaan bunyavirus and Lassa arenavirus, that are both transmitted to humans by rodents and can cause severe haemorrhagic fevers with up to 50% fatality rates [19,20]. To demonstrate the presence of a cap-snatching endonuclease domain in hantavirus L proteins we determine the crystal structure of the isolated Hantaan virus EN in complex with Mn2+ ions and characterize its endonuclease activity. By comparing the activity and ion binding with the IAV (Orthomyxoviridae), LACV (Orthobunyaviridae) and Lassa (Arenaviridae) ENs we find that the catalytic histidine present in Hantaan and other His+ ENs correlates with high endonuclease activity. Subsequent structural characterization of the Lassa endonuclease with bound ions reveals an active site with a non-canonical coordination of the catalytic metal ions and this correlates with low intrinsic activity. Therefore the histidine of His+ ENs appears to promote the canonical binding of metal ions in the active site and is a determinant for efficient in vitro catalytic activity. These results are relevant for understanding possible differences in the mechanism of regulation of EN activity and have strong implications in the development of new antiviral drugs targeting transcription of sNSV.
By sequence alignment the Hantaan virus EN is predicted to be at the N-terminus of the L protein [14]. We could express and purify protein constructs comprising residues 1–179 and 1–182 that crystallized with 2 mM MnCl2 as thick plates diffracting to 1.7 Å resolution. The structure was solved by SAD experiment (see Materials and Methods and Table 1). Each protein binds two Mn2+ ions in the active site in a similar fashion to that observed for LACV and influenza [10,14]. A third Mn2+ stabilises the interface between crystallographic symmetry related neighbour proteins (S1A Fig).
The structure of the Hantaan virus cap-snatching EN (molecule A) is shown in comparison with Lassa (form X3, see below), LACV orthobunyavirus (PDB: 2xi7) and IAV H1N1 virus (PDB: 4avq) in Fig 1. Despite sequence identities below 20%, all structures present root mean square deviations of around 3.5 Å for at least 98 residues alignment (Dalilite) [21] (S1 Table). Overall, Hantaan EN conserves the two lobe shape of LACV but with the active site, which lies between the two lobes, being more accessible (S2 Fig). The central β-sheet made by the four conserved strands βa-d is extended by βe until the C-terminus of the construct (Figs 2A, 2B and S1B). LACV EN does not have an equivalent of this βe strand and instead, the 30 C-terminal residues of LACV fold into three α-helices, two integrated into the helical bundle which also includes the N-terminal α-helices (S1B Fig). The central β-sheet is surrounded by the conserved helices αb-e. A specific insertion between helix αc and the β-sheet, consisting of helices αc’ and αc” partially covers the β-sheet on the catalytic side (Figs 1 and 2). The flexible loop linking αb and αc (highlighted in green in Fig 1), harbouring a catalytic acidic residue (see below), plays the same structural and functional role as in IAV and LACV ENs. In Hantaan EN helix αa points outwards compared to the other ENs (Fig 1). This conformation, identical for the two molecules in the asymmetric unit, is stabilized by crystallographic contacts partially mediated by a shared, non-active site Mn2+ ion (S1A Fig), and might also be a consequence of the lack of the C-terminal α-helices stabilising the helical bundle, thus allowing helix αa to move (S1B Fig).
A structure based alignment of sNSV ENs of known crystal structure illustrates not only the overall conservation of secondary structures and catalytic residues but also the specific features of each family (Fig 2A and 2B). The structures show an identical secondary structure organization in the central region, starting from helix αb and ending at strand βc. However the different lengths of helices αc and αd change the overall shape. The longer helix αc and much shorter helix αd of IAV EN confer a globular shape in contrast with the elongated shape of bunya- and arenavirus ENs (Figs 1 and 2). Hantaan EN has a unique two α-helix insertion after αc, whereas IAV has a longer unique insertion between helices αb and αc. The structural alignment of the N- and C-terminal regions is poor because of the different arrangement of terminal alpha helices building the helical bundle around conserved helix αb. The 182 residue long Hantaan construct has only 16 residues after strand βc instead of 45 in IAV or 50 in LACV. Thus our structure lacks part of the helical lobe (see [22], co-submission). However we were not able to express longer constructs with the wild-type sequence in E.coli.
The active site of Hantaan EN structure is configured very similarly to that of LACV and IAV ENs with two divalent cations bound in a canonical way (Fig 3A, 3B and 3C). The two ions (denoted Mn1 and Mn2) were identified as Mn2+ by the anomalous scattering signal detected at their respective positions (S1C Fig). Mn1 is octahedrally coordinated by the side chains of amino acid residues H36, D97 (from the conserved PD motif) and E110, and the main chain carboxyl oxygen of V111. The putative catalytic lysine K124 (see below) is deployed from helix αd as is K134 in IAV (Fig 3A and 3B) whereas the LACV catalytic lysine (K94) is deployed from strand βb (Fig 3C). Mn2 is coordinated by D97 and E54 coming from the conserved flexible loop preceding helix αc. Together all these residues constitute the conserved bunya/orthomyxovirus motif H.PD.D/E.K. The octahedral coordination of each Mn2+ is completed by two and four water molecules for Mn1 and Mn2 respectively, one central water being shared by both ions (S1C and S1D Fig).
To study the ion binding specificity of Hantaan EN we measured the melting temperature (Tm) increase by a Thermal Shift Assay (TSA) in the presence of 2 mM of several metal ions (Fig 4A). This follows previous work showing that divalent metal ion binding increases EN thermal stability [10,14,24]. The Hantaan EN has a Tm of 40.5°C in the absence of metal ions. The Tm increases in the presence of MnCl2 (+5.6°C), CaCl2 (+4.9°C) and MgCl2 (+1.2°C), does not change in the presence of CoCl2 and is slightly lower with NiCl2 (-2°C). To define the specific ion binding preferences of Hantaan EN we compared, in parallel experiments, the stabilisation effect of metal ions on Lassa, LACV and IAV ENs (Fig 4A). The results show that Hantaan EN is generally the least stable, MnCl2 induces the highest stability increase for all ENs, MgCl2 also stabilise all ENs but less so for Hantaan EN and Ca2+ stabilizes Hantaan, IAV and Lassa ENs almost as much as Mn2+, but not LACV EN. The effects of CoCl2 and NiCl2 on Hantaan EN stabilization are similar to LACV and different to IAV and Lassa ENs (Fig 4A). Increase of the MgCl2 concentration from 2 mM to 5 mM further stabilises the Hantaan EN by 1.7°C (Fig 4B).Therefore, each EN has a specific ion stabilization pattern, but with the common feature that the highest Tm shift results from Mn2+ binding.
Subsequent TSA experiments were performed in the presence of 2 mM MnCl2 and 200 μM 2,4-dioxo-4-phenylbutanoic acid (DPBA) a known EN inhibitor [14,25] (Fig 4B). The addition of 2 mM Mn2+ and 200 μM DPBA induces a dramatic increase of the Hantaan EN stability (13.2°C), even larger than found for the other ENs tested (Fig 4B). DPBA has been shown to chelate the two metal ions in the active site in the homologous LACV and IAV endonucleases [14,26]. To confirm that Hantaan EN also binds two metal ions in solution, Isothermal Titration Calorimetry (ITC) experiments were performed to determine the binding affinities to Mn2+ and Mg2+. The Mn2+ binding profile could be fitted by a model that assumes sequential binding of two ions (S3A Fig). The first Mn2+ ion binds with a Kd1 of 48.5±2.6 μM and the second showed a much lower affinity with a Kd2 of 1.1±0.06 mM. The binding to Mg2+ is much weaker making it impossible to calculate reliable affinity values. The calculated affinities could be underestimated because of protein precipitation observed during the ITC experiment. However, these data are consistent with the two metal ion binding in the active site observed in the structure of the Hantaan EN and the stronger binding of Mn2+ ions compared to Mg2+.
We next investigated the ion dependence of the nuclease activity of Hantaan EN using IAV and LACV and Lassa ENs for comparison (Fig 4C). The reactions were carried out at room temperature for one hour with 2 mM of each metal ion, 7.5 μM of G-rich RNA and 10 μM protein. Hantaan EN shows clear nuclease activity with Mn2+ and also some with Co2+, but not with the other ions tested (Mg2+, Ca2+, Ni2+, and Zn2+). DPBA was able to inhibit the reaction with 2 mM MnCl2 at 200 μM. The control ENs, LACV and IAV, behaved as previously described [10,14,24] and showed a similar ion dependence to Hantaan EN. Surprisingly, under the same conditions Lassa EN showed no nuclease activity with any ion. In order to test the substrate specificity the same experiment was carried out in the presence of 2 mM MnCl2 on three different RNAs: unstructured U-rich and G-rich and the structured Alu RNAs (Fig 4D). The Hantaan EN is able to degrade all three RNAs efficiently showing no sequence or secondary structure specificity. Indeed Hantaan EN cleaves the structured Alu RNA more efficiently than LACV EN, which itself is more efficient than IAV EN (as previously reported [14]). Again, Lassa EN showed no activity against any RNA. In all cases no RNA degradation was detected in the absence of MnCl2 or in presence of 200 μM DPBA. The cleavage efficiency of the more structured RNA correlates with higher substrate accessibility to the active site (S2 Fig). The inhibitory effect of DPBA on Hantaan EN was further analysed by titrating increasing amounts of DBPA in EN assays with G-rich and Alu RNA in the presence of 2 mM Mn2+. LACV EN was used as a control. DPBA had a slightly higher inhibitory effect on Hantaan virus, with the IC50 estimated between 15 and 31 μM compared to the 62 μM estimated for LACV, in agreement with previously reported values for LACV (~50 μM, [14]) and with the TSA experiments where DPBA has a higher stability effect on Hantaan than on LACV EN (S4 Fig).
In conclusion, Hantaan EN is thermally stabilized most by Mn2+, consistent with it having the highest binding affinity for this ion. Mn2+ also most efficiently promotes the nuclease activity which is non sequence specific and inhibited by DPBA. The Hantaan EN efficiency for single stranded RNA appears similar to IAV and LACV but it is able to cleave structured RNA more efficiently than either IAV or LACV, probably due to the higher accessibility to the active site. Under the same experimental conditions, Lassa EN showed no nuclease activity at all.
Intrigued by the differences in activity found between ENs from different families and genera we analysed nuclease activity rates with a more sensitive and quantitative real-time assay. This was done by measuring the fluorescence increase upon RNA substrate cleavage using a doubly labelled RNA (see Materials and Methods). For different protein concentrations, initial reaction velocities were determined as the initial slope of the reaction progression as monitored by the fluorescence signal. The linear relationship between the initial reaction velocity (V = ru/min, where “ru” is fluorescence relative units) and protein concentration is shown for Hantaan EN in Fig 5A. From the slope, a specific reaction rate of 14.38 ru min-1 μM-1 was derived. This activity is five-fold lower than for IAV or LACV ENs with rates of 74.88 and 69.45 ru min-1 μM-1 respectively (Fig 5A). The difference could be explained by the lower stability of the truncated Hantaan EN construct. On the other hand, the Hantaan EN is 180-fold more active than Lassa EN, which has a rate of 0.086 ru min-1 μM-1 (Fig 5B).
Since Lassa EN was reported to prefer Mg2+ as catalytic ion we also compared the activity with Hantaan, IAV, LACV and Lassa ENs in the presence of 5 mM MgCl2. Hantaan EN showed fifty-fold lower activity in the presence of 5 mM MgCl2 than with 2 mM MnCl2 at 1 μM of protein concentration and no activity was detected in the absence of metal ions (Fig 5C). IAV and LACV showed at the same protein concentration 20 fold slower activity with 5 mM MgCl2 than with 2 mM MnCl2 (Fig 5E). For 60 μM Lassa EN (compared to typically 1 μM for the other ENs), the activity did not change significantly in the presence of 5 mM MgCl2 and in the absence of metal ions. This suggests that the observed weak Lassa EN activity must be, at least partially, ion-independent, perhaps due to contaminants which become significant at very high protein concentrations (Fig 5D). Concerned by this lack of activity, we measured the ion binding to Lassa EN by ITC. Lassa EN showed two Mn2+ binding sites with Kd values of 21.2 ± 0.7 μM and 120.6 ± 4.7 μM. The interaction with Mg2+ gave a lower signal that could be fitted by a one site model with a much higher Kd of 352.0 ± 7.3 μM (S3B Fig).
In Fig 5F we summarise the quantitative nuclease activity results. Hantaan virus EN has between four and five fold less activity than LACV and IAV and Lassa virus 800 fold less activity in presence of 2 mM MnCl2. The activity drops to 6.5, 5.5 and 2.2% respectively for LACV, IAV and Hantaan upon substituting 2 mM MnCl2 by 5 mM MgCl2. We conclude that Hantaan, together with LACV and IAV ENs are active ENs that preferentially use Mn2+ but can also use Mg2+ with much lower activity rates. By comparison Lassa EN is virtually inactive in the presence of either Mg2+ or Mn2+ metal ions, even if it is able to bind them with similar affinities.
To elucidate the role for the Hantaan EN active site residues in ion binding and catalytic activity the mutants H36A, E54G, D97A, K124A and K127A were produced. We first analysed the effect of mutation on protein stability by TSA with either no ions, 5 mM MgCl2 or 2 mM MnCl2 with and without 200 μM DPBA (Fig 6A). In the absence of ions, removal of negatively charged sidechains (D97 and E54) resulted in a greater than 5°C protein stability increase, whereas removal of positive charges (H36, K124, K127) had a lower effect on stability changes. Mutation of H36 slightly reduced the Mn2+ and Mg2+ stabilization effect but reduced more DPBA stabilisation, to 50% of the wild-type. Mutation of the D97, which coordinates both ions, resulted in a complete lack of ion or DPBA stabilisation. Mutation of E54, in the flexible loop and coordinating Mn2, impairs both the ion stabilization effect and reduces the DPBA super shift. When the putative catalytic lysine K124 and its neighbour K127 were mutated to alanine the stabilization by ions and DPBA was not impaired and even slightly enhanced. These results are consistent with the crystal structure where H36, D97 and E54 are engaged in ion coordination but K124 and K127 are not. Subsequently, ITC experiments were performed with mutants E54G and D97A. E54G shows, instead of the wild-type two ion binding profile, a binding profile consistent with one Mn2+ binding site with a Kd = 387.6 ± 6.2 μM, higher than the wild-type for Mn1 binding (Kd = 48.5 ± 2.6μM, see above) whereas for D97A almost no binding was detected (S3C Fig). This confirms the loss of one binding site (Mn2) and both binding sites (Mn1 and Mn2) upon removal of respectively E54 and D97 side chains, in agreement with the ion coordination observed in the Hantaan EN crystal structure.
To determine the role of the mutated residues in the catalytic activity we carried out EN assays with 2 mM MnCl2 and the three different RNAs in parallel with the wild-type EN (Fig 6B). All mutations abolished Hantaan EN activity. The mutant’s activity was also tested by the more sensitive FRET based EN assay. Again, the mutants showed a dramatic drop of reaction rate compared to the wild-type. Only K127A showed clear EN activity above the other mutants, but still much lower than the wild-type (Fig 6C).
Therefore those active site conserved residues that are observed to coordinate the metal ions in the crystal structure are important in solution for both ion binding and endonuclease activity, as is the putative catalytic lysine K124. A neighbouring residue, K127, is also important for EN activity, possibly in substrate binding, but is not essential since its mutation still allows a low activity rate.
To investigate the cause of the lack of activity of Lassa EN we structurally characterized the Mn2+ ion bound form by X-ray crystallography to see how it differs from the active ENs. Using a construct encompassing residues 1–174 from Lassa L protein (see Materials and Methods) we obtained three different crystal forms: one with no ions bound (crystal form X1), and two with one or two Mn2+ ions bound in the active site (crystal forms X2 and X3 respectively).
The X1 structure was solved by molecular replacement using LCMV EN structure (PDB: 3JSB) and refined at 1.85 Å resolution (see Materials and Methods and Table 1). It is similar to the reported Lassa EN structure in complex with Mg2+ [16]. Despite the presence of 2 mM MnCl2 in the crystallization buffer no anomalous scattering was detected in the active site. The X2 data, solved by molecular replacement using the X1 structure, reached an ultra-high resolution of 1.09 Å, thus providing a highly accurate electron density map of the Lassa EN. Compared with the X1 form, the X2 and X3 forms show a 17 degree rotation between the helical bundle lobe (residues 4–49 and 149–167) and residues 50–148, with the hinge being at the base of helix αb (Fig 7A). The closure of the two lobes slightly changes the orientation of the active site residue E51 (see below and Fig 7B). In the X2 form, data measured close to the manganese absorption edge, showed a 50σ anomalous peak corresponding to one Mn2+ ion in the active. X3 was obtained by co-crystallization with the inhibitor DPBA in the presence of 5 mM MnCl2. The structure was solved from the X2 model and refined to 2.36 Å resolution. Whereas structures X2 and X3 both exhibit the closed form (S5A and S5B Fig), X3 has two Mn2+ ions in the active site (as detected by anomalous scattering, S5C Fig), but no extra density for DPBA.
Figs 1 and 2 compare the fold of the Lassa X3 structure with the other sNSV ENs. Lassa EN has the basic EN fold made by three beta strands (βa-c) and two alpha helices (αc-d) without any insertion. The helical bundle lobe, in comparison with the LACV EN, comprises four long α-helices, the N terminal αpre-a being additional. The active site residues are between the two lobes. The loop connecting αb and αc is also conserved, but instead of approaching the active site, as in Hantaan, LACV and IAV, it is turned outwards and the acidic residue D66, equivalent to the catalytic residues E80 (IAV) D52 (LACV) and E54 (Hantaan), is distanced from the active site (Fig 3D).
Both the X2 and X3 structures share a common Mn2+ site (Mn1) which is coordinated by the D89 and E102 side chains with 2 Å bond distances, but, unlike other ENs, the carbonyl oxygen of the C103 backbone is too distant for a direct interaction. In the X3 structure, a second Mn2+ ion (Mn2) is found in the active site coordinated by D89 and one carboxyl oxygen of E51 at 2.4 Å distance (Fig 3D). This two metal ion binding is additionally stabilised by crystal contacts with N-terminal residue E3 from the neighbouring symmetry related molecule also coordinating Mn2 (Fig 3D). The Mn2+ ion pair in Lassa EN is differently orientated with respect to the catalytic residues than in Hantaan, LACV and IAV ENs, and only four of the eight possible octahedral coordination bonds for the two Mn2+are satisfied (Fig 3). The previously reported Lassa EN structure in complex with Mg2+ ions [16] has an open conformation of the two lobes, similar to the X1 apo-structure (Fig 7C). The active site residues have the same rotamer conformation than in the X3 structure (Fig 7B and 7D) but the helix αb is more open, slightly enlarging the active site. The two Mg2+ ions are both directly coordinated by D89. Furthermore, Mg1 interacts directly with the C103 backbone carbonyl oxygen and indirectly with E51 and E102 by bridging water molecules. However the full canonical ion co-ordination observed in the active His+ ENs is not achieved.
The two Mn2+ ions binding mode reported here for Lassa EN is non-canonical when compared to the mode of ion binding in the His+ ENs. This is likely a consequence of several factors, including the replacement of the histidine by a glutamic acid (E51), the loss of one residue potentially coordinating the metal ions (D66, that is far from the active site) and the observed flexibility of the active site that is able to alter configuration because of the hinge at the base of helix αb. In comparison, Hantaan, LACV and IAV ENs have more constrained active site with higher ion coordination provided by an acidic residue from the flexible loop and the presence of a histidine that helps stabilise the ion binding in an active configuration.
To further investigate these findings on Lassa EN, we mutated to alanine the amino acid residues E51, D89 and E102, which are engaged in ion coordination, D66 from the flexible loop and the putative catalytic lysine K115 (whose side-chain amide group superposes exactly with that of catalytic K137 in IAV EN). TSA experiments again show a stability increase, in the absence of ions, when active site acidic residues are mutated to alanine, presumably due to the loss of electrostatic repulsion between these residues (Fig 8A). The stability increase induced by metal ions and DPBA was severely impaired for the E51A mutation that removes one Mn2 coordination in the X3 Lassa EN structure, and for E102A, that removes one Mn1 coordination, and completely abolished for D89A, which removes the coordination with both Mn1 and Mn2 ions. Mutations D66A and K115A, which do not contact the catalytic ions in the X3 structure, did not significantly affect the ion stabilisation effect. For the D66A and D89A mutants, Mn2+ binding was tested by ITC. The D66A thermogram fits a two ion binding model with affinities of Kd1 = 16.9 ± 0.6 μM and Kd2 = 153.8 ± 62.3 μM, similar to the wild-type protein (Kd1 = 21.2 ± 0.7 μM, Kd2 = 120.6 ± 4.7 μM, see above). D89A resulted in a complete loss of ion binding (S3D Fig). Altogether this data is consistent with the non-canonical two ion binding of Lassa EN observed in the X3 crystal structure.
We also tested whether an E51H substitution could increase the activity of Lassa EN, by potential conversion of a His- to a His+ EN, but observed the same lack of endonuclease activity shown by the wild-type Lassa EN. LCMV, another His- EN, was included in this experiment showing that, in our experimental conditions and like Lassa EN, it lacks activity in comparison with the LACV and Hantaan ENs included in the experiment as positive controls (Fig 8B). Therefore changing the catalytic E51 to a histidine is not enough to confer efficient EN activity to Lassa EN.
Despite the fact that the isolated Lassa EN is almost inactive in vitro, the full length polymerase is clearly able to carry out cap-snatching dependent transcription in the cellular context. Indeed, using a minireplicon system, it has already been confirmed that active site residues D89, E102, D129 [17] and E51 and K115 [16] are essential for cap-dependent transcription. Because of the very low activity of the isolated Lassa EN in vitro we decided to test the activity of new mutants in the full length L protein using the same approach, with the E102A mutant as negative control (Fig 8C and 8D). D66 was mutated to alanine truncating the sidechain, to glutamic to extend the side chain by one carbon, and to asparagine for the removal of the negative charge. All mutants showed about a 50% increase of transcriptional activity confirming that this residue does not play an essential metal-binding role, consistent with the TSA and ITC experiments just described, but unlike the equivalent residues in Hantaan (E54), LACV (D52) and IAV (E80). Consistent with in vitro results, which showed the E51H mutant EN to be inactive (Fig 8B), we also found that in the full-length context this mutant did not support any transcription. Some conservative mutations were performed on the critical catalytic residues (E51D, D89E and K115R) and residues involved in hydrogen bonding close to the active site (K122R and D119T, which makes hydrogen bonds with both K115 and K122). The mutations of the acidic residues showed a certain drop of transcriptional activity, but not as much as the E102A mutation negative control. Interestingly both lysine to arginine mutations did not change the transcriptional activity.
Based on these results we can conclude that the acidic residue on the flexible loop (D66) in Lassa EN is not essential for activity (as it is in the His+ ENs), D51 is not replaceable by histidine, and that even if conservative mutations in the putative catalytic K115 or neighbouring residues (K122 and D119T) do not significantly change the transcriptional activity yields, these residues seems to be important in natural infection as shown by their conservation in sequence alignments of arenavirus ENs (S6 Fig).
The cap-snatching mechanism for transcription is exclusively used by sNSVs. The partial picture provided by the isolated endonucleases and cap-binding domain structures has been dramatically extended recently with the structure determination and biochemical characterization of the heterotrimeric influenza polymerase and the major part of the LACV L protein, allowing the understanding of how the cap-snatching EN domains integrate with the RdRpol domain [9,12,27–30]. Furthermore, in influenza virus polymerase, the EN domain (as well as several other PB2 domains including the cap-binding domain) is connected to the polymerase core through a flexible hinge, which allows it to adopt multiple conformations including those competent for cap-snatching and others where access to the cap-snatching domains is hindered [28,30]. 5' vRNA end binding is required to induce the correct relative positioning of the cap-binding and cap-snatching endonuclease domains to increase the RNA cleavage efficiency by 100-fold compared to the isolated EN domain [30]. This allows, for instance, an efficient EN activity with MgCl2 in the context of the full length polymerase that is not achievable by the isolated domain [9]. This shows that the activity of the cap-snatching endonuclease can be significantly modified in the context of the full length polymerase and this is likely to be also the case in the arena- and bunyavirus L proteins.
Here we demonstrate that hantavirus L proteins also have a cap-snatching endonuclease that shares with LACV bunyavirus and IAV the same configuration of the active site and contributes to define the canonical binding of metal ions in the active sites by which all the catalytic residues of the H.PD.E/D motif are directly coordinating the two metal ions oriented towards the catalytic lysine (Fig 3). The mutation of any of these residues affects the ion binding and, results in the complete loss of catalytic activity, as does mutation of the catalytic lysine. To be able to express the Hantaan EN we had to delete part of its C-terminus, which may impair the activity to some extent, although the measured activity rate is comparable to those of LACV and IAV. Indeed, the wild-type Hantaan EN activity could be much higher, resulting in toxicity for E.coli cells, as observed in the accompanying article ([22], co-submitted) for the full length wild-type Andes virus EN, where only reducing the activity by mutagenesis near the active site made expression possible.
In the case of arenavirus ENs the most significant active site differences correspond to Lassa EN E51, that substitutes the bunya- and orthomyxovirus conserved histidine, and D66, which is not any more engaged in the active site, unlike the equivalent acidic residue in the bunya- and orthomyxovirus EN flexible loop. This study shows that in Lassa EN these differences result in the non-canonical binding of metal ions to the isolated enzyme causing a dramatic drop of endonuclease activity in comparison with the His+ ENs. However the Lassa EN PD-D/E-K catalytic residues are essential for transcription in the minireplicon context, showing that in the full length polymerase the nuclease efficiently cleaves cellular mRNA. Since arenavirus EN active site is more open than His+ ENs the catalytic residues cannot directly coordinate the two metal ions in the same way. Even in the Lassa EN X3 crystal form, were the active site is more closed than X1 form, the open helix αb conformation maintains E51 2 Å away from the Mn1 coordination site (S5D Fig). Therefore we speculate that to achieve the canonical binding required for activity, the Lassa active site needs to be activated for instance by changing E51 and E102 rotamers coupled to a slight movement of helix αb that would close the active site allowing the coordination of Mn1 as shown by the active His+ ENs (S7 Fig). Another possibility is that residues from other parts of the polymerase might contribute to the ion coordination. These changes could be induced by other parts of the L-protein in response for example to vRNA binding (as in IAV) or induced by substrate binding. The shift between an active or an inactive enzyme would provide arenaviruses with an “on and off” transcription switch.
With the addition of the Hantaan EN structure and new results of Lassa EN, this comparative study puts previously reported work on the isolated cap-snatching endonucleases from bunyavirus, orthomyxovirus and arenavirus in a more general context. We find two different kinds of endonucleases, one with the characteristic catalytic histidine (His+) as in orthomyxovirus and bunyavirus, which have efficient endonuclease activity in isolation, and a second, without the histidine (His-) and conserved among arenaviruses which shows very poor activity in vitro. This classification should be taken into account in further development of inhibitor screening assays targeting sNSV ENs. Furthermore, the structure of the active Hantaan EN provides another tool towards the comprehensive development of broad spectrum antivirals against sNSV.
E. coli codon optimised coding sequences were synthesised (Geneart) for residues 1–250 of Lassa polymerase (Lassa 250) and residues 1–182 of Hantaan polymerase (Hanta 182) (UniProt accession code Q6GWS6 and P23456 respectively). A histidine tag and a Tobacco Etch Virus (TEV) cleavage site (MGHHHHHHDYDIPTTENLYFQG-) were added to the amino terminus of all protein constructs. For the final Hantaan constructs a SUMO tag was inserted between the histidine tag and the TEV cleavage site. All protein variants were cloned into a modified pET9a (Novagen) vector as described [14]. Mutagenesis of the proteins expressed in E. coli was performed on Lassa196 and Hanta182. Mutant constructs were obtained by site directed mutagenesis using overlapping oligonucleotides and Pfu DNA polymerase.
Proteins were expressed in Escherichia coli strain BL21 (DE3) in LB media with 25 mM kanamycin at 18°C overnight after induction with 0.2 mM of IPTG. The protein was purified as previously described, removing the histidine or His-SUMO tags by TEV protease resulting in an additional glycine before the first translated methionine of the original sequence. The resulting untagged proteins were concentrated and purified by gel filtration chromatography using a SD75 column (Pharmacia) with lysis buffer (20 mM Tris-HCl pH 8.0, 150 mM NaCl, 5 mM β-mercaptoethanol) for in vitro experiments and crystallization trials. Hantaan required 1mM TCEP in the lysis buffer to avoid aggregation. IAV A/H1N1 EN (PA residues 1–198) and La Crosse EN (LACV-L 1–183) were expressed as described elsewhere [26] [14] and purified as Lassa and Hantaan ENs.
Several protein constructs including the first 179 to 250 aa of the Hantaan L protein N-terminus were tested for expression in E.coli. Only the shorter constructs (179–185 aa) were expressed, but insolubly into inclusion bodies. This could be circumvented by insertion of an N-terminal SUMO tag linked by a TEV cleavage site.
The length of the proteolytically stable amino terminal domain was defined from the purified Lassa 250 protein by limited papain digestion with 1:500 (w:w) papain: protein ratio. Products were characterized by N-terminal sequencing (Edmann degradation) and mass spectrometry (Electrospray). Two papain resistant fragments were obtained with molecular weights of 22.0–22.8 kDa and 25.4 kDa corresponding to the first 191–198 or 222 residues respectively of the Lassa L-protein. Proteins Lassa 190, 192, 196, 205, 221 were subsequently produced for crystallization trials. Based on the first crystal structure obtained, the constructs Lassa 174, 177, 180, 186 were cloned. Finally, the protein construct Lassa 196 was used for in vitro biochemical experiments and Lassa 174 for structural studies.
The influence of manganese, magnesium and DPBA binding on protein stability was measured by thermal stability assays (TSA) [31] at a protein concentration of 7.5 μM in lysis buffer implemented with 2 mM or 5 mM metal ion concentration or 2 mM Mn2+ plus 200 μM DPBA concentration. For nuclease activity experiments, 10 μM of Influenza-PA 1–209, LACV-L 1–183, Lassa 1–196 and Hantaan 1–182 wild-type and mutant proteins were incubated with 10 μM of Alu RNA (110 nucleotides of the Alu domain of Pyrococcus horikoshii SRP RNA) or 15 μM of 44 nucleotides U-rich (5’-GGGCCAUCCU GCUCU4CCCU11CU11-3’) and G-rich (5’-GGGCCAGGAAAGGGAGGAGA AAG11AAAAGG AGAAA-3’) RNAs for 1 or 2 h at room temperature in the same buffer. The metal ion concentration was 2 mM. The reaction was stopped by adding loading buffer, 10 M urea and twofold concentrated Tris-borate-EDTA buffer (TBE). The reaction products were loaded onto 15% acrylamide 8 M urea TBE gels and stained with methylene blue.
For the FRET based real-time quantitative endonuclease activity assays 500 nM of synthetic double labelled RNA, 6-FAM-5′-CUCCUCAUUUUUCCCUAGUU-3′-BHQ1 (IBA), were mixed with the endonuclease proteins. The reaction buffer was 20 mM Tris-HCl pH 8, 150 mM NaCl, 1 mM TCEP and 2 mM MnCl2 or 5 mM MgCl2. The fluorescence increase upon the RNA cleavage was measured in a TECAN (infinite M200 pro) at 26°C using 465 nm excitation and 520 nm emission wavelengths. The initial reactions velocities were determined by the slope of the linear part of the reaction and where the fitting quality for a straight line was above R2 = 0.99.
ITC measurements were performed at 25°C, using an ITC200 Micro-calorimeter (MicroCal, Inc). Experiments comprised 26 injections of 1.5 μL of 2.5–5.0 mM manganese or magnesium solutions into the sample cell containing 200 μL of 100–160 μM of Lassa EN (wild-type, E51A or D89A) or Hantaan EN (wild-type, E54G or D97A). All binding studies were performed in the lysis buffer. For data analysis the heat produced by the metal ion dilution in the buffer was subtracted from the heat obtained in the presence of the protein. Binding isotherms were fitted to a one-site binding or two-site sequential binding model using Origin Software version 7.0 (MicroCal, Inc). The initial data point was routinely deleted.
Hantaan EN crystals were obtained with the NH179 and NH182 constructs at 10 mg/ml in lysis buffer with 2 mM MnCl2 mixed at 1:1 ratio with mother buffer Hepes 0.1 M pH7, 20% PEG 6K, 1 M LiCl2. The crystals grow at 20 0C after 24 h and were frozen with mother buffer plus 30% glycerol and 2 mM MnCl2.
LACV and IAV were expressed and purified as previously described [14] [26]. Protein constructs denoted Lassa 174, 177, 180, 186,190, 192, 196, 205, 221 were expressed and screened for crystallization using a Cartesian robotic system [32]. Only Lassa 174 and Lassa 190 crystallised and Lassa 174 was used for all following work. The first crystals (X1) were obtained by mixing 1:1 ratio protein: reservoir solution of 5 mg/ml Lassa174 protein in lysis buffer with 2 mM MnCl2, and a reservoir composition of sodium citrate 0.191 M, PEG 3350 4%, 0.1 M Hepes pH7 and 0.1 M strontium chloride. Form X2 crystals were obtained with Lassa 174 in lysis buffer with 10 mM GMP and 2 mM MnCl2 and a reservoir composition of 0.1 M MES pH6 and 20% v/v 2-Methyl-2.4-pentanediol. Form X3 was crystallized with Lassa 174 in lysis buffer with 5 mM DPBA and 2 mM MnCl2 and a reservoir composition of 0.005 M magnesium chloride hexahydrate, 0.05 M HEPES-Na pH7 and 25% (v/v) PEG MME 550. All crystals grow at 20°C overnight. The crystals were frozen in liquid nitrogen in the reservoir buffer with 30% glycerol after adding 5 mM MnCl2 and 10 mM of GMP or 5 mM of DPBA for the co-crystallization derived crystals.
The Hantaan EN crystals are of monoclinic space group (P21), with two molecules in the asymmetric unit, and the structure was solved by a SAD experiment performed at 1.77 Å wavelength on beamline ID23-1 (ESRF) with high redundancy (Table 1). The data were processed with XDS and solved with CRANK [33] within the CCP4i package. The three Mn2+ ions and Met94 of each molecule gave the eight anomalous sites enabling structure solution. The structure was refined with a native dataset to Rwork/Rfree of 0.166/0.216 at 1.7 Å resolution obtained at 0.984 Å wavelen sequence alignment sequence alignment gth on ID23-1 with NH182 crystals (Table 1). Molecule A in the asymmetric unit shows density for residues 1–179, whereas molecule B shows residues 1–171 with a gap between residues 165 to 167.
Lassa EN form X1 crystals are of space-group P212121 with two molecules in the asymmetric unit. Data were collected on ID14-4 at the European Synchrotron Radiation Facility (ESRF) to 1.8 Å resolution using a wavelength of 0.939 Å. Data were processed and scaled with the XDS package [34] and subsequent analysis performed with the CCP4i package. Statistics of data collection and refinement are given in Table 1. The structure was solved by molecular replacement using Phaser and the LCMV EN model (PDB: 3jsb) split into two parts (residues 1–60 plus 147–170 and residues 61–145). The resultant map was excellent and could be largely built automatically by ARP/wARP [35]. The structure was refined to Rwork/Rfree of 0.205/0.259 at 1.85 Å resolution using REFMAC [36] (Table 1).
Lassa EN crystal forms X2 and X3 were obtained by co-crystallization with GMP and DPBA respectively. They are of space-group P41212 with one molecule in the asymmetric unit. Two X2 datasets were collected on ID23-1 to 1.09 Å resolution, one at a wavelength of 0.976 Å and another close to the manganese edge (1.892 Å). Data were processed, scaled and the structure solved by molecular replacement using the X1 structure, which only succeeded after searching separately for the two lobes of the EN (residues 1 to 60 and 145 to 170, including most of the alpha helical bundle, and residues 65 to 140 including the β-sheet core of the protein). Data were refined to Rwork/Rfree 0.161/0.179 at 1.09 Å resolution (Table 1). Extra density for manganese is observed in the X2 active site, with a peak > 50 σ in the anomalous difference map calculated from the data collected at the Mn2+ edge. The X3 data were collected at SOLEIL on beamline PROXIMA1 to 2.5 Å resolution at a wavelength of 0.976 Å. The structure was solved from the X2 model and refined to Rwork/Rfree of 0.204/0.288 at 2.36 Å resolution. Extra density for two manganese ions is observed in the active site, consistent with the anomalous difference map. A third Mn2+ also is found bound to His75 on the protein surface. There is no extra density consistent with DPBA or GMP in the active sites of the X2 or X3 structures.
The T7 RNA polymerase-based Lassa virus replicon system was used as described previously [37,38] [17]. Briefly, generation of L genes with single mutations in the endonuclease domain was performed by mutagenic PCR using pCITE-L as a template and Q5 High-Fidelity DNA Polymerase (NEB) for amplification. After purification and spectrophotomeric quantification the PCR-products were directly used for transfection. To make sure the specific mutations were present the PCR-products were sent for sequencing. For Luciferase measurements and RNA extractions BSR-T7/5 cells stably expressing T7 RNA polymerase (kindly provided by Ursula Buchholz and Karl-Klaus Conzelmann) [39] were transfected in a 24 well plate with the following amounts of DNA per well: (a) 250 ng of minigenome expressing Renilla luciferase (Ren-Luc), (b) 250 ng of L gene PCR-product, (c) 250 ng of pCITE-NP expressing NP, as well as (d) 10 ng of pCITE-FF-Luc expressing firefly luciferase (FF-Luc) as an internal transfection control. About 24 hours after transfection, either total cellular RNA was purified for Northern blotting using an RNeasy minikit (Qiagen) or cells were lysed and assayed for FF-Luc and Ren-Luc activity using the dual-luciferase reporter assay system (Promega). To compensate for differences in transfection efficiency and cell density Ren-Luc levels were corrected with the FF-Luc levels resulting in standardized relative light units [sRLU]. For Northern blot analysis, 500 ng of total cellular RNA was separated in a 1.5% agarose-formaldehyde gel and transferred onto a HybondN+ membrane (GE Healthcare). After UV crosslinking and methylene blue staining to visualize 28S rRNA the blots were hybridized with a 32P-labeled riboprobe targeting the Ren-Luc gene. Transcripts of Ren-Luc genes and RNA-replicates of the minigenome were visualized by autoradiography using an FLA-7000 phosphorimager (Fujifilm). To provide proof for expression of L protein mutants in BSR-T7/5 cells the cells were transfected with 500 ng of PCR product expressing C-terminally 3xFLAG-tagged L protein mutants per well in a 24-well plate. To boost the expression levels and thus facilitate detection by immunoblotting cells were additionally infected with modified vaccinia virus Ankara expressing T7 RNA polymerase (MVA-T7) [40]. After cell lysis and electrophoretic separation in a 3 to 8% Tris-acetate polyacrylamide gel proteins were transferred to a nitrocellulose membrane (GE Healthcare), and FLAG-tagged L protein mutants were detected using peroxidase-conjugated anti-FLAG M2 antibody (1:10,000) (A8592; Sigma-Aldrich). Detected bands were visualized by chemiluminescence using Super Signal West Femto substrate (Thermo Scientific) and a FUSION SL image acquisition system (Vilber Lourmat).
The structure factors and PDB models are deposited in the PDB database as PDB: 5IZE for Hantaan EN, PDB: 5IZH for Lassa X1, PDB: 5J1N for Lassa X2 and PDB: 5J1P for Lassa X3.
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10.1371/journal.pntd.0003446 | Conservation and Immunogenicity of Novel Antigens in Diverse Isolates of Enterotoxigenic Escherichia coli | Enterotoxigenic Escherichia coli (ETEC) are common causes of diarrheal morbidity and mortality in developing countries for which there is currently no vaccine. Heterogeneity in classical ETEC antigens known as colonization factors (CFs) and poor efficacy of toxoid-based approaches to date have impeded development of a broadly protective ETEC vaccine, prompting searches for novel molecular targets.
Using a variety of molecular methods, we examined a large collection of ETEC isolates for production of two secreted plasmid-encoded pathotype-specific antigens, the EtpA extracellular adhesin, and EatA, a mucin-degrading serine protease; and two chromosomally-encoded molecules, the YghJ metalloprotease and the EaeH adhesin, that are not specific to the ETEC pathovar, but which have been implicated in ETEC pathogenesis. ELISA assays were also performed on control and convalescent sera to characterize the immune response to these antigens. Finally, mice were immunized with recombinant EtpA (rEtpA), and a protease deficient version of the secreted EatA passenger domain (rEatApH134R) to examine the feasibility of combining these molecules in a subunit vaccine approach.
EtpA and EatA were secreted by more than half of all ETEC, distributed over diverse phylogenetic lineages belonging to multiple CF groups, and exhibited surprisingly little sequence variation. Both chromosomally-encoded molecules were also identified in a wide variety of ETEC strains and YghJ was secreted by 89% of isolates. Antibodies against both the ETEC pathovar-specific and conserved E. coli antigens were present in significantly higher titers in convalescent samples from subjects with ETEC infection than controls suggesting that each of these antigens is produced and recognized during infection. Finally, co-immunization of mice with rEtpA and rEatApH134R offered significant protection against ETEC infection.
Collectively, these data suggest that novel antigens could significantly complement current approaches and foster improved strategies for development of broadly protective ETEC vaccines.
| Infectious diarrhea is one of the leading causes of death among young children in developing countries, and a major cause of morbidity in all age groups. The enterotoxigenic Escherichia coli contribute substantially to this burden of diarrheal illness, and have been a focus of vaccine development efforts for more than forty years following their discovery as a cause of severe diarrheal illness. The heat-labile, and/or heat stable enterotoxins that define ETEC are produced by a diverse population of Escherichia coli. This inherent genetic plasticity of E. coli has made it difficult to identify antigens specific to ETEC that are highly conserved. Therefore, identification of protective antigens shared by many ETEC strains will likely play an essential role in development of the next iteration of vaccines.
| The enterotoxigenic Escherichia coli (ETEC) are among the most common causes of infectious diarrhea worldwide. Importantly, ETEC are disproportionately represented in cases of severe diarrheal illness as well as in deaths due to diarrhea among young children in developing countries [1].
These pathogens cause diarrhea by the elaboration and effective delivery of heat-labile and/or heat-stable enterotoxins to intestinal epithelial cells where they stimulate production of cyclic nucleotides ultimately activating the cystic fibrosis transmembrane regulator (CFTR) with resulting net efflux of fluid into the intestinal lumen[2]. Plasmid-encoded colonization factors (CFs), discovered [3] shortly after these organisms were identified as a causative agent of cholera-like diarrheal illness[4–6], are thought to be essential for effective colonization of the small intestine and required for ETEC pathogenesis.
Following early studies suggesting a pivotal role for these structures[7,8], CF antigens have defined the basis for most subsequent ETEC vaccine efforts [9,10]. However, one factor complicating development of a broadly protective vaccine for ETEC has been the general plasticity of E. coli genomes[11], and the significant antigenic heterogeneity of the CFs. To date, at least 26 antigenically distinct CF antigens have been described[12]. The lack of appreciable cross-protection afforded by these antigens combined with the complex landscape of CFs portrayed in ETEC molecular epidemiology studies continue to complicate rational CF antigen selection[13].
Antigenic heterogeneity, recent failure of LT-toxoid-based vaccine strategies[14,15], as well as the need to optimize the performance of live-attenuated vaccines currently in clinical trials [16–18] have highlighted the need to identify additional virulence molecules that might be targeted in ETEC vaccines. Recent efforts led to the identification of two loci discovered on the same virulence plasmid of ETEC strain H10407 that encodes the CFA/I colonization factor. These include the etpBAC two partner secretion locus responsible for production and export of EtpA[19], a novel adhesin molecule which bridges highly conserved regions of flagellin and the eukaryotic cell surface[20]. Also located on this plasmid is the eatA gene that encodes the EatA serine protease autotransporter molecule[21] capable of degrading EtpA[22] as well as MUC2[23], the major gel-forming soluble mucin in the small intestine[24].
Recent immunoproteomic[25] and transcriptomic [26] analyses of H10407 have also highlighted two chromosomally encoded antigens that are not specific to the ETEC pathovar, but which nonetheless appear to be involved in the pathogenesis of these organisms. Conceivably, these molecules, YghJ[27], a secreted mucin-degrading metalloprotease, and EaeH [28], an adhesin, act in concert with colonization factors and other pathovar-specific virulence proteins like EatA and EtpA to promote toxin delivery.
While emerging data suggests that these novel proteins are highly immunogenic[25] and that EtpA and EatA are protective antigens[29–31] in a murine model of ETEC infection, additional data regarding their conservation among ETEC strains are needed to determine their suitability as vaccine targets. Here we demonstrate that these antigens are broadly represented in a diverse collection of ETEC isolates suggesting that they could be employed to augment existing approaches to ETEC vaccine development.
ETEC strains used in this study are detailed in S1 Dataset. All strains were grown at 37° in Cassamino acids yeast extract media[32] (CAYE: 2.0% Casamino Acids, 0.15% yeast extract, 0.25% NaCl, 0.871% K2HPO4, 0.25% glucose, and 0.1% (v/v) trace salts solution consisting of 5% MgSO4, 0.5% MnCl2, 0.5% FeCl3) from frozen glycerol stocks maintained at −80°C.
Strains from the International Centre for Diarrhoeal Disease Research (icddr,b) in Dhaka were selected based on their associated disease severity using modified WHO guidelines as previously outlined[33]. Expression of individual CFs was determined by dot immunoblotting with monoclonal antibodies specific to each respective CFs (CF-MAb) as previously described [34]. Briefly, 2 μl of a PBS suspension containing ∼106 colony forming units of each ETEC strain was dotted onto nitrocellulose, air-dried, blocked with BSA in PBS, followed by detection with CF-MAbs and goat anti-mouse IgG_HRP conjugate. Bound MAbs were then detected with 4-chloro-1-naphthol chromogen and H2O2.
We screened a total of 181 ETEC available isolates currently maintained as frozen glycerol stocks in our laboratories. The majority of these strains were collected between 1998 and 2011 in Bangladesh, and were obtained from the icddr,b in Dhaka. Complementing this collection were geographically disparate strains associated with severe diarrheal illness including strains from the Amazon region in Brazil [35], and ThroopD, an isolate from a patient with severe ETEC diarrheal illness who presented in Dallas in the 1970s[36]. Strains encoding eatA and etpA were identified by PCR using primers directed against conserved regions of these genes as previous described [37]. Briefly, a small amount of frozen glycerol stock from each strain was introduced with a sterile pipette tip into a PCR mixture containing the respective primers and a master mix. Toxin genotypes were confirmed in these isolates using multiplex PCR screening for genes encoding heat-labile (LT), and heat-stable toxins (STp, and STh) as previously described[34]. Primer sequences are listed in S1 Table.
To determine production of secreted virulence antigens by different ETEC strains, supernatants from overnight cultures were first precipitated with trichloroacetic acid (TCA) [19] and resuspended in sample buffer before polyacrylamide gel electrophoresis. Western blotting was then performed using polyclonal rabbit antisera against recombinant versions of either EatA[21], EtpA[19], or YghJ[27] that were pre-absorbed against an E. coli lysate column (Pierce) and affinity-purified using the antigen immobilized on nitrocellulose membranes as previously described [31,38], followed by detection with affinity-purified secondary goat anti-rabbit-(IgG)-HRP conjugate (Santa Cruz Biotechnology, SC2004).
To examine antigenic conservation of EatA among ETEC isolates for which genomic DNA sequences are currently available, BLASTP[39] was used to search GenBank https://www.ncbi.nlm.nih.gov/genbank/ using the full length sequence of the EatA protein from strain H10407 (https://www.ncbi.nlm.nih.gov/protein/AAO17297.1) as the query sequence. To construct alignments of EatA from positive strains, the 1042 residue passenger domain (corresponding to amino acids 57–1098 of EatA from H10407) was compared with EatA of ETEC isolates derived from different phylogenic lineages using a CLUSTAL Omega (release 1.2.0 AndreaGiacomo) [40] algorithm plugin for CLC Main Workbench v6.9.1. A similar approach was used to compare the amino-terminal sequence of EtpA (amino acids 1–600, GenBank accession number AAX13509.2).
Virulence protein expression data from the collection of 181 strains under study were included in the analysis. Heat maps were configured using R[41] version 3.1.0 (2014, http://www.R-project.org/) using gplots[42] and RColorBrewer[43] packages installed from http://CRAN.R-project.org using the heatmap2 function within gplots (see S2 Dataset).
The antigens used in these studies were produced as polyhistidine-tagged recombinant proteins and purified by immobilized metal ion affinity chromatography (IMAC) as previously described[27,29,44,45]. Additional polishing steps including size exclusion or ion exchange chromatography were performed as needed to produce highly purified antigens. Purity of each antigen was assessed by SDS-PAGE followed by sensitive Coomassie Blue staining. Purified recombinant antigens were stored at −80°C.
To quantitfy antibody concentrations directed at novel recombinant antigens, kinetic ELISA was performed on dilutions of plasma samples previously obtained from patients hospitalized at the International Centre for Diarrhoeal Disease Research in Dhaka, Bangladesh (icddr,b) with acute symptomatic ETEC infections. Plasma samples from non-infected adults and children obtained at icddr,b, or specimens obtained from children at Saint Louis Children’s Hospital were used as negative controls. Samples from human volunteer ETEC H10407 challenge studies were kindly provided by Dr. Robert Gormely and Dr. Stephen Savarino of National Naval Medical Center, Bethesda Maryland.
Use of these clinical materials was approved by the Institutional Review Boards of both icddr,b and Washington University School of Medicine. All plasma samples were maintained at 4°C in a humidified chamber prior to use in ELISA. Immune responses to purified recombinant proteins (rYghJ, rEaeH, rEtpA, rEatAp) were assessed by kinetic ELISA[46] as previously described [30,47]. Antigen binding to ELISA wells (Corning, Costar 2580) was first optimized to determine the optimal coating concentration and buffer system, using highly antigen-specific polyclonal rabbit antisera to detect binding by ELISA. Purified antigens were then diluted either in 50 mM carbonate buffer (pH 9.6) (rEtpA-myc-His6, 1 μg/ml; rEatAp, 10 μg/ml; rYghJ-myc-His6, 1 μg/ml); or in phosphate buffered saline (PBS, pH 7.4) (rEaeH-myc-His6, 1 μg/ml). ELISA plate wells were coated with 100 μl/well overnight at 4°C, washed with PBS containing 0.05% Tween-20 (PBS-T), and blocked for 1 h at 37°C with 1% BSA in PBS-T. All plasma samples were diluted at 1:4096 in blocking buffer. After incubation for 1 hour at 37°C, plates were washed with PBS-T, and secondary goat anti-human IgG(H+L)-HRP conjugated antibody (Pierce, 31410) was added at a final concentration of 1:10,000. After incubation for 30 minutes at 37°C, plates were washed and developed with TMB microwell peroxidase substrate [3,3’,5,5’-Tetramethylbenzidine] (KPL, 50-76-00). Kinetic absorbance measurements were determined at a wavelength of 650 nm, and acquired at 40 s intervals for 20 minutes using a microplate spectrophotometer (Eon, BioTek). All data were recorded and analyzed using Gen5 software (BioTek) and reported as the Vmax expressed as milliunits/min. Statistical calculations were performed using Prism v4.0c (GraphPad Software), using nonparametric Mann-Whitney (two-tailed) comparisons of data.
These studies were performed in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the National Institutes of Health, using an established protocol approved by the Washington University School of Medicine Animal Studies Committee.
Four groups of twelve CD-1 mice were immunized intranasally with either 1 μg of LT (adjuvant only controls), or 1 μg of LT + 15 μg of rEatAp(H134R), or 1 μg of LT + 15 μg of rEtpA, or 1 μg of LT + 15 μg of rEatA(H134R)+15 μg of rEtpA on days 0, 14, 28. On day 40, mice were treated with streptomycin [5 g per liter] in drinking water for 24 hours, followed by drinking water alone for 18 hours. After administration of famotidine to reduce gastric acidity, mice were challenged with 106 cfu of the kanamycin-resistant (lacZYA::KmR) strain jf876[48] by oral gavage as previously described[47]. Fecal samples (6 pellets/mouse) were collected on day 42 before oral gavage, re-suspended in buffer (10mM Tris, 100mM NaCl, 0.05% Tween 20, 5mM Sodium Azide, pH 7.4) overnight at 4°C, centrifuged to pellet insoluble material, and recover supernatant for fecal antibody testing (below). Twenty-four hours after infection, mice were sacrificed, sera were collected, and dilutions of saponin small-intestinal lysates were plated onto Luria agar plates containing kanamycin (50 μg/ml).
Murine immune responses to LT, EatA and EtpA were determined using previously described kinetic ELISA. Briefly, ELISA wells were coated with 1 μg/ml GM1, or 10 μg/ml of rEatAp(H134R), or 1 μg/ml rEtpA in carbonate buffer (15 mM Na2CO3, 35 mM NaHCO3, 0.2 g/L NaN3, pH8.6) overnight at 4°C. Wells were washed three times with phosphate-buffered saline containing 0.05% Tween 20 (PBS-T), blocked with 1% bovine serum albumin (BSA) in PBS-T for 1 h at 37°C, and 100 μl of fecal suspensions (undiluted) or sera (diluted 1:100 in PBS-T with 1% BSA) was added per ELISA well and incubated at 37°C for 1 h. Horseradish peroxidase-conjugated secondary antibodies were used and signal detected with TMB (3,3′,5,5′-tetramethylbenzidine)-peroxidase substrate (KPL) substrate.
All animal studies were performed under protocols approved by the Animal Studies Committee of Washington University School of Medicine (protocol number 20110246A1). All procedures complied with Public Health Service guidelines, and The Guide for the Care and Use of Laboratory Animals.
All human studies included were performed under a protocol approved by the Institutional Review Board of Washington University School of Medicine (IRB ID# 201110126). All of the human studies here report anonymous analysis of de-identified pre-existing sera previously stored from earlier studies for which no additional consent was obtained.
Two novel antigens, the EtpA adhesin, and the passenger domain of the EatA serine protease are encoded on the large 92 kilobase virulence plasmid of the prototypical ETEC strain H10407. Both of these secreted proteins[22,30] are required for H10407 to efficiently deliver heat-labile toxin to target epithelial cells. Furthermore, both of these antigens are immunogenic [25], and induce protective immune responses in a murine model of ETEC intestinal colonization[29,31]. To further assess their utility as potential vaccine antigens, we examined a large collection of ETEC strains that were well characterized with respect to associated clinical metadata pertaining to disease severity and which had not undergone repeated serial passage in the laboratory.
Altogether, we found that these antigens are relatively conserved in the ETEC pathovar, confirming the results of earlier studies that focused on strains from different phylogenies obtained in Guinea Bissau and Chile [37,49]. Of the 181 strains examined in the present study (Fig. 1), we found that more than half of all strains produced EtpA (102/181, 56%) and/or EatA (106/181, 59%) (S1 Dataset), and that more than three quarters of all strains produced at least one of these antigens. Both EtpA and EatA were identified more than twice as frequently as the most commonly identified CF (CS6), which was identified in 22% of strains in this collection (Table 1).
Importantly, although the genes encoding the etpBAC secretion system[19] and the EatA autotransporter[21] were initially discovered on the same large virulence plasmid of H10407, which also encodes the colonization factor (CF) CFA/I, we found that these loci were not restricted to strains expressing this particular CF, but were widely distributed among the different CFs, and were also present in strains for which no CF could be identified (Fig. 2A). Indeed, half of the strains for which no CF could be identified expressed either EtpA or EatA, suggesting that these antigens could complement existing vaccination strategies centered on CFs. As expected by the association with multiple CFs, we also found that EtpA and EatA were secreted by strains from multiple phylogenic lineages (Fig. 2B, C). Interestingly, however we found a negative association between the etpBAC locus and strains expressing CFA/IV antigens [50,51] including CS5 in that none of the 23 strains possessing CS5 fimbriae secreted the EtpA adhesin. Similarly, among strains expressing CS6, which is frequently co-expressed with CS5, only a minority secreted EtpA. These data are also consistent with our earlier observation that the prototype B7A strain, which also expresses CS6, lacks the etpBAC locus and does not secrete EtpA[19].
Interestingly, both the eatA and etpBAC loci were originally identified in ETEC strain H10407, originally isolated from an adult with severe, cholera-like illness in Bangladesh[52]. As has been noted previously, this strain also causes more severe illness in human clinical challenge studies relative to other strains like B7A that lack these loci[53]. Because we had clinical metadata pertaining to disease severity for all of the strains in our collection, we questioned whether the production of either of these antigens was associated with strains isolated from more severe forms of infection. However, we did not find any clear association between either of these putative virulence loci and clinical outcome (S1 Dataset).
We also examined the conservation of two chromosomally-encoded antigens which are not specific to the ETEC pathovar, but have recently been shown to play a role in virulence. The eaeH gene was originally identified on the chromosome of ETEC strain H10407 by subtractive hybridization with E. coli MG1655[54], is transcriptionally activated by cell contact [26], and under these conditions EaeH is produced by a diverse group of strains belonging to different phylogenies[28]. Using the EaeH peptide sequence from H10407 (GenBank accession AAZ57201), BLASTP searches of recently sequenced ETEC strains from Bangladesh and elsewhere (http://gscid.igs.umaryland.edu/wp.php?wp=comparative_genome_analysis_of_enterotoxigenic_e._coli_isolates_from_infections_of_different_clinical_severity) also revealed that the eaeH gene was present in 63 out of 91 distinct isolates (69%) (S1 Dataset). BLASTP searches of these data for another chromosomally encoded molecule, YghJ, a type II secretion system effector[55] recently shown to be involved in mucin degradation and toxin delivery[27] demonstrated that the yghJ gene was present on the chromosomes in 83 of 91 (91%) isolates. Similarly, we identified the YghJ protein in a majority (161/181, 89%) of ETEC culture supernatants (S1 Dataset). This antigen was produced across ETEC strains expressing multiple CF types including 31/36 strains that were CF-negative by monoclonal antibody screening.
Ideally, putative vaccine targets should be specific to the pathovar under study or restricted to pathogenic isolates, but not subject to significant antigenic variation. Therefore to further examine the potential utility of two ETEC pathovar specific antigens, EtpA and EatA, as vaccine candidates, we used recently obtained DNA sequence information from multiple ETEC genomes belonging to different phylogenies and from temporally and geographically disparate sources to compare the predicted amino acid sequences of these proteins.
For the prototype EatA molecule, first described in ETEC H10407[21], the 1042 residue region from amino acids 57–1098 is predicted for the secreted passenger domain that contains the serine protease catalytic triad[21] as well as protective epitopes[23]. We therefore compared this region of the molecule to those derived from the recently released genome sequences of multiple ETEC strains. Altogether, we found that the sequence of the EatA passenger domain (EatAp) was very highly conserved across strains, and exhibited between 95–100% identity to the prototype H10407 Eatp (Table 2). Likewise, the predicted serine protease catalytic motif formed by the histidine, aspartic acid and serine residues at positions 134, 162, and 267, respectively were universally conserved within the passenger domains of these proteins (S1 Fig.). Similarly, the predicted amino acid sequences of the secreted EtpA adhesin molecules from multiple strains exhibited between 94 and 100% identity to the H10407 prototype antigen (Table 3, S2 Fig.).
Despite the fact that the comparator strains included here spanned isolates collected over nearly 40 years, belonging to different phylogenies and that strains originated in diverse locations in Asia, Africa and the Americas, both proteins appear to exhibit remarkably little antigenic variation. Likewise, in analysis of the genomes of strains isolated recently within Bangladesh both proteins demonstrated similar degrees of sequence conservation (S1–S2 Fig.).
Earlier immunoproteomic studies suggested that a variety of conserved E. coli proteins as well as ETEC pathovar specific proteins are recognized during the course of experimental infections in mice, and these responses parallel those observed using pooled convalescent sera from ETEC patients[25]. To further characterize the immune response to novel antigens we focused on four proteins that have recently been shown to play a role in ETEC pathogenesis, including two plasmid-encoded secreted ETEC pathovar-specific antigens: EatA protease, and the EtpA adhesin, as well as the highly conserved chromosomally-encoded YghJ metalloprotease and the EaeH adhesin protein.
In comparing convalescent plasma from patients hospitalized at icddr,b to uninfected controls from Bangladesh, we found that patients in general exhibited significantly greater total antibody (IgG, IgM, IgA) responses to each of these antigens following diarrheal illness (Fig. 3) suggesting that these proteins are expressed during the course of infection. Similar results were obtained in comparing plasma from un-infected children from a non-endemic area in the United States (S3 Fig.).
We also examined the immune response to EtpA following infection by examining sera obtained before and after challenge of human volunteers with ETEC H10407. In sera obtained from two independent volunteer challenge studies, we also observed significant increases in immune responses to EtpA (S4 Fig.), strongly suggesting that this secreted protein is specifically recognized following infection by ETEC strains including H10407 that secrete this antigen.
Previous studies have demonstrated that individually, vaccination with either EtpA[31,56] or the passenger domain of the EatA serine protease[29] affords protection against intestinal colonization in mice. The data above suggest that collectively these antigens might significantly extend coverage presently offered by classical approaches to ETEC vaccine development. We therefore questioned whether these two antigens could be successfully combined in a subunit approach. Because we have previously demonstrated that the native secreted EatA passenger domain will degrade intestinal mucin[29] as well as the EtpA adhesin molecule[22], we elected to vaccinate animals with a modified recombinant version of the EatA passenger that lacks protease activity (rEatApH134R).
Co-vaccination with rEtpA and the mutant rEatApH134R molecule elicited robust serologic responses to both molecules that were comparable to vaccination with either antigen alone. As anticipated, each of the groups mounted strong serologic responses to the LT adjuvant (Fig. 4A), and both antigens retained their immunogenicity following co-immunization of EtpA with the rEatAH134R passenger domain (Fig. 4B,C) with responses that were at least comparable to those obtained following immunization with either antigen alone (Fig. 4C). Likewise, mice immunized with both antigens were significantly protected against colonization by ETEC (Fig. 4D), although co-vaccination with both antigens did not appear to be more effective than vaccination with either antigen alone. Collectively, however, these data suggest that co-immunization with these two antigens is feasible, and could be employed to expand present approaches to ETEC vaccine antigen selection.
Enterotoxigenic Escherichia coli remain one of the most common causes of infectious diarrhea worldwide, and severe disease caused by these pathogens persists as leading cause of death among young children in developing countries[1]. Despite recognition of these toxin producing E. coli as a cause of severe cholera-like diarrheal illness more than forty years ago[57], there remains no effective broadly protective vaccine for ETEC.
Most vaccinology efforts to date have focused almost exclusively on a subset of plasmid-encoded antigens, namely the colonization factors (CFs) and heat-labile toxin[9]. Vaccines based on this strategy have faced several impediments. First, the CFs are quite diverse with more than 26 distinct antigens described to date. In addition, a number of recent vaccine studies have suggested that simply engendering immune responses to CFs and/or heat-labile toxin may not be sufficient to provide sustained broad-based protection[14–16].
Recent studies of ETEC pathogenesis suggest that a number of virulence factors in addition to the CFs are involved in efficient delivery of toxins to their cognate receptors on the epithelial surface[2,22,29,30,48,58]. Similarly, the immune response to ETEC infection appears to involve many proteins[25,47] in addition to the classical antigens that are the present focus of most vaccines. Collectively, these findings suggest that there may be additional molecules that could be targeted to interdict toxin delivery by these pathogens, expand the list of potential protective antigens, and complement existing approaches to vaccine development for ETEC[59,60].
A major challenge to ETEC vaccine development in general is that the most highly conserved antigens of ETEC, typically encoded on core regions of the chromosome, are also shared with commensal E. coli[60]. Included among these chromosomally encoded conserved proteins are two antigens studied here, YghJ[27] and EaeH[28] that were recently shown to be important for ETEC virulence. While the present studies also demonstrate that these proteins are recognized during the course of ETEC infection, the degree to which these antigens can be safely targeted in vaccines without inadvertent disruption of the intestinal microflora remains to be studied.
The inherent plasticity of E. coli genomes contributes substantially to the difficulty in defining antigens unique to the ETEC pathovar that are widely conserved. No single antigen exclusive to these pathogens, but universally conserved in this pathovar, has been described to date. Some have suggested that this might be predicted based on the fact that the plasmid-encoded heat-labile and/or heat-stable toxins, which define the ETEC pathovar, could form a minimal complement of virulence genes in wide variety of E. coli host strains[61]. Nevertheless, earlier studies conducted on phlyogenicaly disparate strains from Guinea Bissau[49] and Chile[37] suggested that genes encoding two pathogen-specific antigens EatA and EtpA were present in a majority of strains.
In this context, we examined the gene conservation and the actual production of these proteins in a large collection of well-characterized strains from Bangladesh, complemented by strains from other locations that were associated with severe disease and for which there were available clinical metadata. Notably, two plasmid-encoded ETEC pathotype-specific antigens, the EatA serine protease and the secreted EtpA adhesin molecule were shared broadly among strains belonging to different CF groups with the exception of strains that produced CFA/IV antigens CS4, CS5, CS6 which only infrequently produced EtpA.
The studies reported here represent the largest screen for EtpA and EatA secretion in ETEC performed to date. Earlier studies reporting that genes encoding both proteins were highly conserved relied on either PCR[37] or screening of draft genomes[49] for the presence of the corresponding loci. In general, we found high degrees of concordance between the presence of these genes by PCR and production of the corresponding protein. We should point out however that draft genome assemblies typically fail to encompass the entire etpA gene as automated assembly algorithms cannot faithfully incorporate the multiple repeat regions comprising two thirds of etpA. This could impact interpretation of gene prevalence in ongoing large-scale ETEC genome sequencing projects. The prevalence of EtpA and EatA (56 and 59%, respectively) as determined by examination of protein expression in our study was slightly lower than previously reported in earlier studies that analyzed strains from Guinea Bissau, where both genes were present in 75% of strains [49]; or Chile, where etpA and eatA, were present in 71 and 75% of strains, respectively[37]. Nevertheless similar to these earlier studies, the strains that produced these antigens belonged to many different phylogenies suggesting that genes encoding these antigens have been widely dispersed.
The analyses of strains in these studies largely focused on isolates from Bangladesh. However, these data are potentially relevant for vaccine development for a number of reasons. First, Bangladesh is highly endemic for enterotoxigenic E. coli infections, and consequently remains an important site for vaccine field trials. In addition, ETEC has been under study in this region since the discovery of this pathotype, permitting us to compare sequence variation in candidate antigens over four decades. Understanding both current prevalence and sequence conservation of potential novel vaccine antigens in this population over time will be particularly important for making rational decisions about their inclusion in future iterations of ETEC vaccines. Finally, the geographic and temporal dispersal of genes encoding EtpA and EatA in multiple phylogenic backgrounds, further attests to importance of studying these molecules as potential vaccine targets as previously suggested by others[37,49].
The optimal formulation of an ETEC vaccine has yet to be defined, and many questions pertaining to the nature of protective immunity that develops following infections with these pathogens remain. Nevertheless, the data presented here do suggest that the novel pathovar-specific antigens could complement existing strategies for ETEC vaccine development by broadening the antigenic valency. Whether the expanded coverage afforded by inclusion of additional pathotype specific antigens would enhance vaccine efficacy beyond that presently achieved by targeting CFs and LT will need to be determined empirically.
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10.1371/journal.ppat.1002497 | Enhancement of Chemokine Function as an Immunomodulatory Strategy Employed by Human Herpesviruses | Herpes simplex virus (HSV) types 1 and 2 are highly prevalent human neurotropic pathogens that cause a variety of diseases, including lethal encephalitis. The relationship between HSV and the host immune system is one of the main determinants of the infection outcome. Chemokines play relevant roles in antiviral response and immunopathology, but the modulation of chemokine function by HSV is not well understood. We have addressed the modulation of chemokine function mediated by HSV. By using surface plasmon resonance and crosslinking assays we show that secreted glycoprotein G (SgG) from both HSV-1 and HSV-2 binds chemokines with high affinity. Chemokine binding activity was also observed in the supernatant of HSV-2 infected cells and in the plasma membrane of cells infected with HSV-1 wild type but not with a gG deficient HSV-1 mutant. Cell-binding and competition experiments indicate that the interaction takes place through the glycosaminoglycan-binding domain of the chemokine. The functional relevance of the interaction was determined both in vitro, by performing transwell assays, time-lapse microscopy, and signal transduction experiments; and in vivo, using the air pouch model of inflammation. Interestingly, and in contrast to what has been observed for previously described viral chemokine binding proteins, HSV SgGs do not inhibit chemokine function. On the contrary, HSV SgGs enhance chemotaxis both in vitro and in vivo through increasing directionality, potency and receptor signaling. This is the first report, to our knowledge, of a viral chemokine binding protein from a human pathogen that increases chemokine function and points towards a previously undescribed strategy of immune modulation mediated by viruses.
| Chemokines are chemotactic cytokines that direct the flux of leukocytes to the site of injury and infection, playing a relevant role in the antiviral response. An uncontrolled, unorganized chemokine response is beneath the onset and maintenance of several immunopathologies. During millions of years of evolution, viruses have developed strategies to modulate the host immune system. One of such strategies consists on the secretion of viral proteins that bind to and inhibit the function of chemokines. However, the modulation of the chemokine network mediated by the highly prevalent human pathogen herpes simplex virus (HSV) is unknown. We have addressed this issue and show that HSV-1, causing cold sores and encephalitis and HSV-2, causing urogenital tract infections, interact with chemokines. We determined that the viral protein responsible for such activity is glycoprotein G (gG). gG binds chemokines with high affinity and, in contrast to all viral chemokine binding proteins described to date that inhibit chemokine function, we found that HSV gG potentiates chemokine function in vitro and in vivo. The implications of such potentiation in HSV viral cycle, pathogenesis and chemokine function are discussed.
| Herpes simplex virus type 1 and 2 (HSV-1 and HSV-2, respectively) and varizella zoster virus (VZV) are the three human members of the Alphaherpesvirinae subfamily, which establish latency in the sensory ganglia of the peripheral nervous system. Both HSV-1 and -2 are highly prevalent viruses with values around 90% for HSV-1 and 12–20% for HSV-2 in adult populations of industrialized countries, reaching up to 80% for HSV-2 in developing countries [1], [2]. Infection by HSV can be either asymptomatic, show mild symptoms in localized tissues or cause severe diseases such as stromal keratitis or herpes simplex encephalitis (HSE), with high mortality and neurologic morbidity [3]. HSV infection of neonates can result in disseminated disease including infection of the central nervous system or involve several organs with mortality reaching 80% [4]. The causes of such different outcomes following HSV infection or reactivation are unknown but involve the interplay between the virus and the immune response.
Chemokines are essential elements of the antiviral response. They constitute a family of chemotactic cytokines that orchestrate leukocyte migration to sites of injury or infection [5]. Chemokines also play relevant roles in the developing and mature nervous system [6]. The chemokine network contains more than 45 chemokines and around 20 G-protein coupled receptors (GPCR). There are 4 subfamilies of chemokines classified on C, CC, CXC and CX3C. All chemokines are secreted. CXCL16 and CX3CL1 are also present as membrane-anchored forms. The chemokine network is complex, highly regulated and promiscuous, with some receptors interacting with more than one chemokine and some chemokines binding to more than one receptor. Alterations in the chemokine network are responsible for inflammatory, autoimmune diseases and the establishment of chronic pain [7], [8]. Binding of chemokine to glycosaminoglycans (GAGs) is relevant for chemokine function. GAGs promote chemokine oligomerization, mediate retention of chemokines onto the cell surface allowing chemokine recruitment in tissues, increase their local concentration in the microenvironment surrounding the GPCR, and modulate receptor recognition [9]. Interaction of the chemokine with the GPCR triggers a signal cascade that includes stimulation of mitogen activated protein kinases (MAPKs) such as Janus-N-terminal kinase 1 and 2 (JNK1-2), extracellular signal-regulated kinase 1-2 (ERK1/2) and p38 [10]. The proper function of chemokines is essential to trigger an appropriate and effective antiviral response. An exacerbated immune response, often triggered or maintained by chemokines, may lead to immunopathology. Patients suffering from HSE present higher level of chemokine expression in the cerebrospinal fluid than healthy individuals suggesting a relevant role for chemokines in the pathogenesis of HSE [11].
Both pox- and herpesviruses express proteins that interfere with chemokine function playing relevant roles in viral cycle, immune evasion and pathogenesis [12]. One of the strategies of chemokine interference involves the expression of secreted viral proteins that bind chemokines and inhibit chemokine function [13]. These proteins have been termed viral chemokine binding proteins (vCKBP). They lack amino acid sequence similarities among themselves or with host chemokine receptors, making difficult the detection of such proteins by sequence analysis.
We, and others, have previously shown that secreted glycoprotein G (gG) from some non-human alphaherpesviruses binds to chemokines and inhibits chemokine function. Examples of such viruses are bovine herpesvirus 5 (BHV-5), equine herpesvirus 1 and 3 (EHV-1 and EHV-3) [14], [15], pseudorabies virus (PRV) [16] and infectious laryngotracheitis virus [17]. Chemokine-binding activity was not observed when supernatants of cells infected with the human viruses VZV, HSV-1 and HSV-2 were tested using different radio-iodinated chemokines [14]. In the case of VZV, the gene encoding for gG is not present within its genome. However, both HSV-1 and HSV-2 contain the open reading frame us4 encoding gG. HSV-1 and HSV-2 gG (gG1 and gG2, respectively) are present on the viral particle and on the plasma membrane of infected cells [18]–[20]. gG2 is further processed and an N-terminal fragment is secreted to the medium of the infected cells [19], [20]. On the contrary, gG1 is not secreted, similarly to the rest of HSV glycoproteins. The functions of HSV-1 and HSV-2 gGs are not well understood. Two reports point to a role of the HSV gGs in the initial steps of entry. HSV-1 gG seems to be important for the infection of polarized epithelial cells [21]. The non-secreted portion of HSV-2 gG binds heparin and the cellular plasma membrane [22]. Deletion or disruption of us4 attenuates HSV-1 in vivo, indicating that gG is a virulence factor, although the mechanism(s) beneath such phenotype are unknown [23]–[25].
The main aim of this study was to investigate the modulation of the immune system by HSV. We focused initially on identifying the function of HSV gG and its possible interaction with chemokines. We show here that secreted, soluble HSV gG (SgG) binds both CC and CXC chemokines with high affinity through the GAG-binding domain of the chemokine. Moreover, we could detect chemokine-binding activity in the plasma membrane of HSV-1 infected cells and in the supernatant of HSV-2 infected cells. Further experiments indicate that HSV-1 full-length gG and secreted, soluble HSV gG (SgG) are responsible for this activity. In complete contrast to all previously described vCKBPs, HSV-1 and HSV-2 SgGs are not inhibitors of chemokine function. Instead, they increase chemokine-mediated cell migration both in vitro and in vivo through a mechanism that involves GPCR signaling and phosphorylation of MAPKs. HSV SgGs increase the potency of the chemokine, and the directionality of cell movement. This constitutes, to our knowledge, the first description of a chemokine binding protein expressed by a human pathogen that potentiates chemokine function. The data presented here suggest the existence of a novel viral mechanism of immune modulation and provide tools to investigate the pathways controlling chemotaxis. Given the relevant roles played by chemokines in both the immune and nervous systems, enhancement of chemokine function by HSV gG may be important for HSV-mediated immunopathogenesis.
To test whether HSV gGs bind chemokines, we expressed soluble, secreted forms of gG1 and gG2 (SgG1 and SgG2, respectively), lacking the transmembrane and cytoplasmic domains, in insect cells infected with recombinant baculovirus vectors (Figure 1A; Protocol S1; Text S1). Following infection, SgG1 and SgG2 were purified from the supernatant of Hi-5 insect cell cultures by affinity chromatography and the purity of the preparation was determined by Coomassie staining (Figure 1B). We routinely obtained two separate bands when SgG1 was expressed in insect cells, probably due to different levels of SgG1 glycosylation. A monoclonal antibody raised against gG1 [18] reacted with purified SgG1 but not SgG2 (Figure 1C, middle panel) whereas a monoclonal anti-SgG2 [26] recognized SgG2 only (Figure 1C, right panel). The anti-His antibody reacted with both proteins (Figure 1C, left panel).
Both purified SgG1 and SgG2 were covalently coupled to BIAcore CM5 chips and tested for chemokine binding by Surface Plasmon Resonance (SPR). A screening with 44 commercially available human (h) chemokines (Protocol S2) was performed by injecting each chemokine in a BIAcore X biosensor. Both SgG1 and SgG2 bound with high affinity hCCL18, hCCL25, hCCL26, hCCL28, hCXCL9, hCXCL10, hCXCL11, hCXCL12α, hCXCL12β, hCXCL13 and hCXCL14, and SgG2 also bound hCCL22 with high affinity (Figure 2A and Table 1). As negative controls for chemokine binding we used the cysteine-rich domain (CRD) of ectromelia virus cytokine response modifier B (CrmB), previously shown to lack chemokine-binding activity [27] (not shown). The affinity constants of the interactions between SgG1, SgG2 and the different chemokines were calculated using the SPR technology (Table 1). Both SgG1 and SgG2 interacted with chemokines with high affinity, in the nanomolar range. The interaction between HSV SgGs and chemokines was also observed by cross-linking assays (Protocol S3; Text S1) using radio-iodinated recombinant hCCL25, hCXCL10, and hCXCL12α (Figure 2B–D). As a negative control we employed CrmB-CRD (Figure 2C). Competition assays with [125I]-hCXCL12α and increasing concentrations of cold hCXCL12α showed the specificity of SgG2-chemokine interaction (Figure 2D).
We addressed whether chemokine-binding activity was present in the HSV-1 infected cells. To this end we infected BHK-21 cells (Protocol S4 and S5; Text S1) with HSV-1 wt and an HSV-1 virus where expression of gG had been disrupted by the insertion of the β-galactosidase gene [23] and determined binding of [125I]-hCXCL10 to the cells 14 to 16 hours post infection (h.p.i.). We could detect chemokine binding to HSV-1 wt-infected cells (Figure 3A; Protocol S5). Binding was not observed when the deletion mutant HSV-1ΔgG was used. We also obtained supernatants from mock- or HSV-2 infected Vero cells 36 h.p.i., and performed a crosslinking assay with [125I]-hCXCL12α. Two bands could be detected in the crosslinking assay (Figure 3B) that could correspond to the high mannose 72 kDa precursor and the 34 kDa secreted protein produced during gG2 expression and processing [19], [20]. Another possibility is that the higher molecular weight band observed corresponds to an SgG2 dimer complexed with chemokine.
To function properly, chemokines need to interact with both GAGs and GPCRs. We investigated the chemokine domain involved in the interaction with HSV SgGs using two experimental approaches.
First, to address whether HSV SgGs could affect chemokine-receptor interaction, we performed binding assays of [125I]-hCXCL12α and [125I]-hCCL25 with MOLT-4 cells (Protocol S4 and S6) expressing endogenous hCXCR4 (the receptor for hCXCL12) and hCCR9 (the receptor for hCCL25) in the presence of SgG-containing supernatant (not shown). We also performed binding assays of [125I]-hCXCL12α to MonoMac-1 cells expressing endogenous hCXCR4 (not shown). As a positive control, addition of supernatant containing BHV-5 SgG inhibited [125I]-hCXCL12α binding to MOLT-4 cells [14] (not shown). However, similar amounts of SgG1 or SgG2 did not decrease [125I]-hCXCL12α binding to MOLT-4 cells, MonoMac-1 cells or [125I]-hCCL25 binding to MOLT-4 (not shown) compared to the mock sample. Thus, SgGs do not inhibit binding of the chemokines to their receptors.
Second, to determine the implication of the GAG-binding domain of the chemokine in the interaction with HSV SgGs we utilized the SPR technology. The amount of chemokine binding to SgGs, covalently bound to a BIAcore chip, in the absence of heparin was considered 100% of binding (Figure 4). Competition experiments showed that increasing concentrations of heparin impaired chemokine binding to both SgG1 and SgG2 in a significant manner (Figure 4). As a control, each of the different heparin concentrations used were injected independently to confirm that no direct heparin binding to the chip occurred (not shown).
In summary, these results indicate that SgG1 and SgG2 interact preferentially with the GAG-binding domain of the chemokine and do not block the binding of chemokines to cell surface specific receptors.
We, and others, have previously shown that gG encoded by several non-human alphaherpesviruses inhibits chemotaxis [14]–[17], [28]. To examine the functional role of the interaction between HSV SgGs and chemokines we performed cell migration experiments. First we addressed whether the chemokine-binding activity observed in the supernatant of HSV-2 infected cells could have any effect on chemotaxis. We incubated CXCL12β with supernatant from mock- or HSV-2-infected cells and performed a chemotactic assay with MonoMac-1 cells (monocyte-like), a cell line that expresses hCXCR4, the receptor for hCXCL12. The supernatant from HSV-2-infected cells significantly enhanced chemokine function in a dose dependent manner when compared to the supernatant from mock-infected cells (Figure 5A). To address whether this effect could be due to SgG, we performed chemotactic experiments using several cell lines and recombinant protein. Incubation of SgG1 with hCXCL12β resulted in higher MOLT-4 migration (Figure 5B). A similar result was obtained with SgG2 whereas BHV-5 SgG inhibited hCXCL12β migration (not shown). We then incubated SgG1 and SgG2 with hCXCL13 and tested their effect on mouse B cells (m300-19) stably transfected with hCXCR5, the receptor for hCXCL13 (Figure 5C, Protocol S4). Inhibition of migration was observed with the vCKBP M3, as expected [29], [30] (Figure 5C). However, SgG1 and SgG2 required the presence of the chemokine and were not able to induce chemotaxis on their own (Figure 5C). The parental m300-19 cells, which do not express hCXCR5, did not respond to the hCXCL13 stimulus (not shown). To test whether binding to the chemokine was necessary for the enhancing effect, we performed chemotaxis experiments using MonoMac-1, a cell line expressing hCXCR4 and hCCR2, the receptor for hCCL2, a chemokine not bound by HSV SgGs (Figure 2 and Table 1). The enhancement in chemotaxis mediated by SgGs required SgG-chemokine interaction since SgG2 did not have any effect on the chemotactic properties of hCCL2 (Figure 5D), whereas it was able to potentiate hCXCL12β. A similar result was obtained with SgG1 (not shown). In all cases, the enhancement in chemotaxis was dose dependent and significant.
The effect of SgGs on chemotaxis was dependent on G protein activation since addition of pertussis toxin (PTX) inhibited both hCXCL12β-mediated cell migration and its enhancement mediated by SgGs (Figure 5E). Finally, we examined the effect of SgG1 and SgG2 on hCXCL12β-mediated cell migration utilizing increasing concentrations of hCXCL12β and a constant molar ratio (1∶100) between the chemokine and SgG (Figure 5F). The effect of hCXCL12β on in vitro cell migration had the characteristic bell-shaped curve (not shown). As a control we used PRV-SgG, which inhibited chemokine-mediated migration [16]. However, both SgG1 and SgG2 enhanced the potency of hCXCL12, displacing the chemotactic bell-shaped curve towards lower concentrations of the chemokine.
To analyze the impact of HSV SgG on different aspects of chemotaxis in real time we performed time-lapse video microscopy using freshly isolated human monocytes and hCXCL12β. The chemokine, alone or in combination with SgG2, was released from a micropipette with constant backpressure. Analysis of tracks recorded by time-lapse video microscopy from cell cultures stimulated with CXCL12β (Video S1) or CXCL12β-SgG2 (Video S2) clearly showed that chemotaxis in the presence of the viral protein was enhanced, compared to the migration towards the chemokine alone (Videos S1, S2 and Figure 6B). SgG2 was not able to trigger migration in the absence of the chemokine (Video S3). Consistent with our data from transwell assays (Figure 5), SgG2 greatly enhanced the number of human monocytes that moved towards a given concentration of the chemoattractant (Figure 6). The cells sensed the chemokine gradient from longer distance to the dispensing pipette than when chemokine was dispensed alone. Chemotactic parameters, i.e. velocity, FMI and distance traveled, were calculated during an initial 10-min period. The velocity of the cell movement and the Forward Migration Index (FMI), i.e. the ratio of the net distance the cell progressed in the forward direction to the total distance the cell traveled, were significantly increased when SgG2 was bound to CXCL12β (Figure 6C, D). Moreover, the cells travelled a longer distance when the chemokine and SgG2 were dispensed together than when the chemokine was dispensed alone (Figure 6E). Similar results were obtained when using CXCL12α (not shown). Transwell experiments performed in parallel with freshly isolated human monocytes confirmed the SgG2-mediated enhancement of CXCL12β chemotaxis observed by video microscopy (Figure 6F).
MAPKs are involved in several cellular processes including cell migration [31]. Binding of chemokine to its receptor activates a signaling cascade that involves phosphorylation and, thereby, activation of MAPKs. Incubation of MonoMac-1 cells with low doses of hCXCL12β resulted in low activation of MAPKs (Figure 7). Pre-incubation of different concentrations of hCXCL12β with a constant molar ratio (1∶200) of SgG1 enhanced the phosphorylation of ERK (Figure 7A and B). The SgG1-mediated increase in the phosphorylation of JNK1-2 was dose-dependent (Figure 7C and D). Similar results were obtained with SgG2 (not shown). Densitometer analysis of the blots shows a dose-dependent enhancement of MAPK activation in the range of 5 fold for both ERK and JNK at the highest chemokine concentration. These results showed, using a different biological assay, a similar enhancement of chemokine activity mediated by HSV SgGs. Activation of CXCR4 results in the dissociation of GDP from the Gαβγ heterotrimer followed by association of GTP to the Gα subunit. In order to measure the effect of HSV SgG on receptor occupancy we performed a [35S]-GTPγS binding assay. The results show that the incubation of CXCL12β with SgG results in higher levels of [35S]-GTPγS incorporation (Figure 7E).
We tested the functional relevance of SgG2-chemokine interaction in vivo using the mouse air pouch model, by performing injections of chemokine alone or in combination with SgG2. Injection of 0.2 µg of mCXCL12α or mCCL28 induced the migration of leukocytes into the air cavity (Figure 8). The presence of 2 µg SgG2 enhanced CXCL12α-mediated migration (Figure 8A) of total leukocytes (top panel, P<0.001), lymphocytes (middle panel, P<0.001) and granulocytes (bottom panel, P<0.05). As a control, we used 2 µg recombinant secreted gG from PRV (PRV-SgG), a vCKBP shown to inhibit chemotaxis [16]. PRV-SgG significantly inhibited CXCL12α-mediated chemotaxis of total leukocytes (top panel, P<0.001) and granulocytes (bottom panel, P<0.05). CCL28-mediated chemotaxis (Figure 8B) of total leukocytes (top panel) and lymphocytes (middle panel) was significantly increased by SgG2 (P<0.05), whereas the migration of granulocytes (bottom panel) was not affected by SgG2. This could be explained by the specificity of CCL28 in driving T cell chemotaxis. In contrast to the inhibition observed when CXCL12 was used, PRV-SgG did not significantly inhibit CCL28-mediated chemotaxis. This may be due to uncontrolled factors such as the stability of the PRV-SgG-CCL28 complex in vivo or the indirect activation of other chemoattractants that may also induce migration. Injection of SgG2 or PRV-SgG alone, in the absence of chemokine, did not result in differences in leukocyte chemotaxis when compared to PBS injection.
HSV glycoproteins play relevant roles in the viral cycle and pathogenesis, and constitute promising vaccine candidates [32], [33]. Among all HSV glycoproteins, gG is the least well characterized and its function has not been fully elucidated. A role for HSV gG on virus entry has been suggested. HSV-1 gG seems to be important for the infection of, but not initial binding to, polarized cells through the apical surface [21]. The non-secreted domain of HSV-2 gG could participate in initial interaction of the virion with the cell surface [21], [22]. A synthetic peptide encompassing amino acids 190–205 from the secreted domain of HSV-2 gG was found to have a proinflammatory role in vitro when bound to the formyl peptide receptor [34]. However, until present, no function has been attributed to the full-length secreted portion of HSV-2 gG. Here, we have investigated the function of secreted forms of gG from HSV-1 and HSV-2. We show for the first time a chemokine-binding activity both in HSV-1 infected cells and in the supernatant of HSV-2 infected cells. Disruption of the HSV-1 gG expression abrogated chemokine binding suggesting that HSV gG is the protein responsible for the interaction. We could indeed show that both HSV-1 and HSV-2 SgG bind with high affinity, in the nanomolar range, CC and CXC chemokines. This interaction was demonstrated by the use of two different experimental approaches: crosslinking assays and SPR. Finally, and more importantly, we describe the first vCKBP, to our knowledge, with the ability to increase chemotaxis both in vitro and in vivo by enhancing the potency of the chemokine and the directionality of cell migration. HSV SgGs enhancement of chemotaxis required the interaction with the chemokine through the chemokine GAG-binding domain and involved signaling through the GPCR and activation of MAPKs. We confirmed that supernatant containing gG secreted following HSV-2 infection enhances chemokine-mediated migration of leukocytes. Moreover, in preliminary experiments we have found that membrane-anchored gG expressed during HSV-1 replication in cell culture also enhances chemokine activity (N.M.-M. and A.V.-B., unpublished data).
During evolution, viruses have developed strategies to modulate the host immune response. Inhibition of chemokine function through the expression of vCKBP is a common strategy in members of the Poxviridae family [12], [35] indicating the importance of chemokines in antiviral defense. In the Herpesviridae family, however, there are only three examples of vCKBP reported to date, two of them expressed by animal viruses -gG from alphaherpesviruses and M3 from murine herpesvirus 68- and one expressed by a human pathogen, pUL21.5 encoded by human cytomegalovirus [14], [36]. In addition, interaction of HSV gB with a reduced number of chemokines has been reported [37]. However, this interaction was of low affinity, in the micromolar range [37] compared to the nanomolar range observed for all vCKBP [14], [16], [17], [29], [30], [36]. Moreover, gB did not seem to have an effect on chemotaxis [37]. Nearly all previously described vCKBP have been shown to inhibit chemotaxis either in vitro or in vivo. As a general rule, vCKBPs inhibit chemokine function through impairing chemokine-receptor interaction or chemokine presentation by GAGs [38]. For instance, gG from some animal alphaherpesviruses blocks chemokine interaction with its receptor [14], [28] and with GAGs [14] inhibiting chemotaxis [14], [16], [17]. To date, there are no reports of a vCKBP that potentiates chemokine function either in vitro or in vivo. HSV SgG is, therefore, the first vCKBP described, to our knowledge, which enhances chemokine function both in vitro and in vivo.
Our studies with SgG1 and SgG2 show that these viral proteins interact with the GAG-binding domain of the chemokines and enhance the chemokine activation of GPCRs. Chemokine-GAG interaction is required for correct chemokine function in vivo [9]. Several reports show that GAG-binding deficient chemokines are functionally impaired in vivo and when in vitro migration and invasion assays are performed [39], [40]. GAGs also modify chemokine quaternary structure and this seems to be required for chemokine function [39], [41]. We propose a model in which SgG1 and SgG2 act similarly to the GAGs, maybe by increasing the local chemokine concentration, modifying the chemokine quaternary structure or improving chemokine presentation to the receptor so that signaling is enhanced. This would cause the observed activation of chemokine signaling at lower doses of chemokine when gG is present. This contrasts with the related gGs encoded by non-human herpesviruses, which have been shown to inhibit chemokine-mediated signal transduction and cell migration [14]–[17]. It appears that HSV-1 and HSV-2 have evolved a vCKBP to enhance, rather than to inhibit, chemokine function, and this may represent an advantage to these human herpesviruses.
The functional relevance of chemokine enhancement in HSV life cycle and pathogenesis is unknown. The role of alphaherpesvirus gG in vivo is not fully understood. Results presented in several reports indicate that gG from animal alphaherpesviruses is relevant for pathogenesis and immune modulation [15], [17]. There are currently no data on the role of HSV-2 gG on pathogenesis. Three independent reports show that lack of gG expression in HSV-1 leads to different degrees of virus attenuation [23]–[25]. Thus, lower viral titers were detected in mouse tissues infected through scarification of the ear with an HSV-1 mutant lacking gG [23]. A double us3/us4 deletion mutant (with us3 encoding a kinase and us4 encoding gG) was attenuated following intracranial injection [24]. However, the relative contribution of either protein in that animal model could not be defined. Mutation of the us4 gene by the use of transposon Tn5 resulted in a HSV-1 mutant that was less pathogenic, was deficient in its ability to replicate in the mouse central nervous system and caused a delay in encephalitis induction [25]. The mechanisms of attenuation of HSV-1 gG mutant viruses are unknown, but the discovery that HSV-1 gG enhances chemokine function points to a role of HSV gG on deregulation of chemokine function that could explain the lower pathogenicity observed with the mutant viruses.
Although there are not yet systematic analyses on the expression of all known chemokines on the tissues relevant for HSV infection, the information obtained by several laboratories supports the relevance of chemokines on HSV infection and pathogenesis. The expression of some chemokines is upregulated upon HSV-1 and HSV-2 infection [42], [43] leading to leukocyte infiltration, which may be as pathogenic as viral infection [44]. In fact, chemokines are important in HSE pathogenesis in humans [11]. Deficiency in CXCR3 or CCR5 increases susceptibility to genital HSV-2 infection although through different mechanisms [43], [45]. Interestingly, the lack of CXCR3 does not result in lower leukocyte recruitment. On the contrary, CXCR3−/− mice show an increase in viral titers, infiltrating cells and neuropathology accompanied by a higher level of cytokine and chemokine expression in brain and spinal cord [46]. Differences were observed between CXCL10−/− and CXCR3−/− (the receptor for CXCL10) mice when challenged with ocular HSV-1 infection [47], [48]. However, CXCR3−/− responded like CXCL9−/− or CXCL10−/− in a genital model of HSV-2 infection [46]. There are also differences in susceptibility depending on the route of infection and the nature of the pathogen employed. The redundancy of the chemokine network may be beneath some of these differences and discrepancies.
The chemokines bound by SgG1 and SgG2 are expressed in tissues relevant for HSV infection, replication and spread. Among other cell types, mucosal epithelial cells express CCL25, CCL28 and CXCL13: (1) CCL25 expression is upregulated during oral wound healing [49]; (2) CCL28 is expressed in airway epithelial cells [50]; and (3) CXCL13 is required for the organization and function of the nasal-associated lymphoid tissue [51]. Human corneal keratinocytes express CXCL9, CXCL10 and CXCL11, expression that can be further induced by proinflammatory cytokines [52]. CXCL14 expression in taste-bud cells is remarkably high and secreted to the saliva [53]. Among other tissues, CXCL12 is expressed in nervous tissues where it has been suggested to play a role in leukocyte extravasation [54]. CXCL12 also induces migration of neural progenitors, is required for axonal elongation and pathfinding, is relevant for neurotoxicity and neurotransmission in the adult nervous system and contributes to chronic pain [6], [8]. Thus, modulation of the activity of chemokines mediated by gG1 and gG2 could occur in tissues infected by HSV and play a role in HSV biology.
Enhancement of chemokine function by HSV SgGs could impact at least four different scenarios relevant for HSV spread and pathogenesis. First, enhancement of GPCR signaling could aid in the early steps of infection and in viral replication. In fact, MAPK activation is required for efficient HSV replication [55]. In this scenario gG1, due to its presence in the viral particle and at the plasma membrane of the infected cells, may play a more relevant role than gG2, which is processed secreting its chemokine-binding domain. Second, increase in the level of infiltrating leukocytes, or differences in the composition of such infiltrate, could skew the immune response and favor viral replication. The fact that HSV SgGs only bind 11–12 out of 45 human chemokines with high affinity suggests the existence of a selectivity and specificity in the modulation of the immune response. Third, enhancement in the migration of a particular leukocyte population could recruit cells that may be subsequently infected by HSV, enhancing viral load. Fourth, modulation of chemokines present in the nervous system, such as CXCL12, could play a role in the initial infection of the ganglia, sites of HSV latency, and increase the ability of HSV to persist and cause disease. The impact of HSV gG-chemokine interaction on HSV biology requires further characterization.
In summary, this is the first report of a vCKBP that enhances chemokine function and suggests a novel mechanism of immune modulation mediated by a highly relevant and prevalent human pathogen. The findings reported here shall foster further investigations on the role of HSV gG on pathogenesis and immune modulation and will allow the design of novel immunomodulators, antiviral drugs and tools to study chemokine function.
All animal experiments were performed in compliance with Irish Department of Health and Children regulations and approved by the Trinity College Dublin's BioResources ethical review board. Human peripheral blood monocytes were prepared from buffy coats obtained from the local donor bank (“Servizio Trasfusione, Svizzera Italiana”, CH-6900 Lugano, Switzerland), with oral consent from the donors according to Swiss regulations. The use of buffy coats was approved by the institutional review board “Comitato Etico Cantonale, CH-6501 Bellinzona, Switzerland” and the experimental studies were approved by the “Dipartimento della Sanitá e della Socialitá”.
The interactions between chemokines and SgGs and their affinity constants were determined by SPR technology using a Biacore X biosensor (GE Healthcare) as previously described [16]. Both proteins were dialyzed against acetate buffer (pH 5.0 for SgG1 and pH 5.5 for SgG2) prior to amine-coupling of the recombinant proteins in CM5 chips. Chemokines that did not bind under kinetic conditions were considered negative and not taken into further consideration for the study. In competition experiments with heparin the chemokine was injected at 100 nM alone or with increasing concentrations of heparin in HBS-EP buffer (10 mM Hepes, 150 mM, NaCl, 3 mM EDTA, 0.005% (vol/vol) surfactant P20, pH 7.4) at a flow rate of 10 µl/min, and association and dissociation were monitored. All Biacore sensorgrams were analyzed with the software Biaevaluation 3.2. Bulk refractive index changes were removed by subtracting the reference flow cell responses, and the average response of a blank injection was subtracted from all analyte sensorgrams to remove systematic artifacts.
Competition experiments were carried out incubating 0.5 pmol of [125I]-hCCL25 or [125I]-hCXCL12α with or without different concentrations of SgGs (or baculovirus supernatants) at 4°C in binding medium (RPMI 1640 containing 1%FBS and 20 mM HEPES pH 7.4) during 1 h at 4°C. Then, 3×106 MOLT-4 or MonoMac cells were added to the mixture and incubated for further 2 h at 4°C with gentle agitation, subjected to phthalate oil centrifugation, washed twice with PBS, and cell-bound chemokine was determined using a gamma-counter.
Chemokines were placed in the lower compartment of 24-well transwell plates (Costar) or in 96-well ChemoTx System plates (Neuro Probe Inc., MD, USA) with or without recombinant gGs in RPMI 1640 containing 1% FBS. MOLT-4, MonoMac-1, m300-19 and m300-19-hCXCR5 cells were placed on the upper compartment (3×105 cells in the 24-well transwell plate and 1.25×105 cells in the 96-well ChemoTx System plate, with the exception of m300-19-hCXCR5 where 2.5×105 cells were used). To test the effect of supernatant from mock- or HSV-2-infected cells in chemotaxis, the cells were infected in the presence of Optimem (Gibco) and the supernatants were collected 36 h.p.i. These supernatants were inactivated with psoralen as previously described [56] and concentrated 10 times using a Vivaspin 500 (Sartorius) prior to use. Both chambers were separated by a 3 µm (for MOLT-4, MonoMac-1 cells and monocytes) or 5 µm (for m300-19 and m300-19-hCXCR5 cells) pore size filter. The plates were incubated at 37°C during 2–4 h and the number of cells in the lower chamber was determined using a flowcytometer (for 24-well transwell plates) or by staining the cells with 5 µl of CellTiter 96 aqueous one solution cell proliferation assay (Promega, WI, USA) during 2 h at 37°C and measuring absorbance at 492 nm, with the exception of monocytes and m300-19-hCXCR5 which were counted with a light microscopy. When the CellTiter 96 aqueous one solution cell proliferation assay was used, known amounts of cells were incubated with the CellTiter solution to quantify the number of migrated cells. When used, PTX was incubated with MonoMac-1 cells overnight at a concentration of 0.1 µg/ml, prior to the chemotaxis experiment.
Monocytes were isolated from blood of healthy donors by negative selection using Monocyte Isolation kit II (MACS Miltenyi Biotec). Peripheral blood mononuclear cells (PBMBs) were isolated from heparinized blood by Ficoll (Lymphoprep) gradient centrifugation. Cells were resuspended in MACs buffer and incubated with FcR blocking reagent at 4°C. Monocytes were purified by negative selection according to the manufacturer's protocol. Time-lapse video microscopy analysis of chemotaxis was performed immediately with a Leica DI6000 microscope stand connected to a SP5 scan head equipped with a temperature controlled chamber (Cube, LIS, Basel). Freshly isolated monocytes were placed in a humidified and CO2-controlled incubator, which was mounted on the microscope stage (Brick, LIS, Basel). Cells were resuspended in D-PBS containing calcium and magnesium (Invitrogen) supplemented with 1% FBS, Pen/Strep, 0.04 mM sodium pyruvate, 1 mg/ml fatty acid free BSA (Sigma), 1 mg/ml glucose (Fluka). Cells were plated on glass bottom petri-dishes (MatTek cultureware) which were coated previously with D-poly-lysine (5 µg/ml) and subsequently overlaid with 3 µg/ml VCAM-1 (BD Biosciences) at 4°C overnight. Before plating the cells, coated-dishes were treated with PBS containing FBS and BSA to block non-specific binding. Chemokine was dispensed with a micropipette (Femtotip II, Eppendorf) controlled by a micromanipulator (Eppendorf) at a constant backpressure of 30 hPa (Femtojet, Eppendorf).
Chemokine alone or in combination with SgGs was added to 106 MonoMac-1 cells and incubated during 1 min at 37°C. Cells were lysed in lysis buffer (20 mM triethanolamine pH 8.0, 300 mM NaCl, 2 mM EDTA, 20% glycerol, 1% digitonin and proteinase inhibitors). The lysate was analyzed by western blotting using anti-phospho-ERK, anti-phospho-P38 (Cell Signaling Technology) or anti-phospho-JNK1/2 polyclonal antibodies (Abcam). Blots were scanned and the densities of the bands were analyzed and compared with the Image J 1.43 software normalizing the densities obtained from each band from the MAPK blots to their respective loading controls.
Age-matched female C57BL/6 mice from Harlan (Bicester, U.K.) were housed in a specific pathogen-free facility in individually ventilated and filtered cages under positive pressure. All animal experiments were performed in compliance with Irish Department of Health and Children regulations and approved by the Trinity College Dublin's BioResources ethical review board.
Dorsal air pouches were induced in mice as described [57]. In brief, 5 ml of sterile-filtered air was injected subcutaneously into the dorsal skin of mice, with air pouches re-inflated with 3 ml of sterile air 3 days later. The dorsal air pouches of groups of 5–6 mice were injected 2 days later with 0.2 µg chemokine alone or in combination with 2 µg SgG. Mice were killed and air pouches were lavaged with PBS 3 h later. The air pouch aspirate was centrifuged and total leukocytes cells were counted.
Cells were stained with a panel of mAbs for surface markers for flow cytometric cell characterization as described [58]. mAbs used were from BD Biosciences; PerCP anti-CD4 (RM4-5), PerCP-Cy5.5 anti-CD19 (1D3), PerCP anti-CD8a (53-6.7), PerCP anti-CD11b (M1/70) and eBioscience: PE anti-Ly6G (RB6/8C5). Cells were defined as lymphocytes (CD4+CD8+CD19+) and Ly6GhiCD11b+ granulocytes (neutrophils). Data were collected on a CyAn (Beckman Coulter) and analyzed using FlowJo (Tree Star). Quadrants were drawn using appropriate isotype-controls and data plotted on logarithmic scale density- or dot-plots.
Statistical analyses of data were performed with the program GraphPad Prism. The significant value (P value) for the parameters measured in all assays was calculated using the student's t-test with the exception of the ones obtained in the air-pouch model experiments which was calculated using the one-way analysis of variance (ANOVA).
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10.1371/journal.pntd.0003046 | Efficacy and Safety of Praziquantel, Tribendimidine and Mebendazole in Patients with Co-infection of Clonorchis sinensis and Other Helminths | Both tribendimidine and mebendazole are broad-spectrum drugs for anti-intestinal nematodes. We aim to assess the efficacy and safety of tribendimidine and mebendazole in patients with co-infection of Clonorchis sinensis and other helminths.
We performed a randomized open-label trial in Qiyang, People's Republic of China. Eligible participants were randomly assigned to one of four groups: (i) a single dose of 400 mg tribendimidine, (ii) 200 mg tribendimidine twice daily, (iii) 75 mg/kg praziquantel divided in four doses within 2 days, and (iv) a single dose of 400 mg mebendazole. Cure rates and egg reduction rates were assessed, and adverse events were monitored after treatments. Uncured patients accepted the second treatment with the same drugs after the first treatment.
156 patients were eligible for the study. Results from the first treatment showed that the cure rates of single-dose tribendimidine and praziquantel against C. sinensis were 50% and 56.8%, respectively; the single-dose tribendimidine achieved the cure rate of 77.8% in the treatment for hookworm, which was significantly higher than that of praziquantel; Low cure rates were obtained in the treatment of single-dose tribendimidine against Ascaris lumbricoides and Trichuris trichiura (28.6% and 23.1%). Results of the second treatment illustrated the cure rates of tribendimidine and praziquantel against C. sinensis were 78.1% and 75%, respectively. Most adverse events were mild and transient. Adverse events caused by tribendimidine were significantly less than praziquantel.
Single-dose tribendimidine showed similar efficacy against C. sinensis as praziquantel with less adverse events, and achieved significantly higher cure rate in the treatment for hookworm than those of praziquantel and mebendazole. Low cure rates, which were still higher than other drugs, were obtained in the treatment of single-dose tribendimidine against Ascaris lumbricoides and Trichuris trichiura.
Controlled-Trials.com ISRCTN55086560
| Co-infection of Clonorchis sinensis and other helminths is common in places with poor settings. Preventive chemotherapy is commonly used to control the co-infection of helminths due to lack of effective vaccine. It is important to investigate the efficacy and safety of tribendimidine, a broad-spectrum anti-intestinal nematodes drug, against co-infection of C. sinensis and other helminths, in comparison with those of praziquantel and mebendazole. The cure rates of single-dose 400 mg tribendimidine against C. sinensis, hookworm, Ascaris lumbricoides and Trichuris trichiura in this study were 50%, 77.8%,28.6% and 23.1%, respectively. The single-dose tribendimidine achieved similar efficacy as the four-dose praziquantel in the treatment for C. sinensis with significantly less adverse events. Meanwhile, significantly higher cure rate of tribendimidine was found in the treatment for hookworm than other drugs. Most adverse events were mild and transient in this study. Tribendimidine seems a better drug choice for the patients co-infected with C. sinensis and other helminths than that of praziquantel.
| Clonorchiasis is one of the neglected food-borne trematodiasis caused by infection of Clonorchis sinensis (C. sinensis), which is mainly prevalent in East and Southeast Asia, especially in the People's Republic of China (P.R. China), the Republic of Korea, northern part of Vietnam, and the far eastern part of Russia [1]–[4]. An estimated 15 million people are globally infected with C. sinensis, more than 80% of whom (12.49 million) are Chinese [2], [5]–[8]. Because of social custom and unhealthy eating behaviors, more and more people were infected with C. sinensis, which significantly increased the burden of disease [9]–[11]. In some area, the prevalence of infection is even more than 65% [12]–[13]. Meanwhile, among those patients with C. sinensis infection, co-infection with other helminths, such as hookworm, Ascaris lumbricoides (A. lumbricoides) and Trichuris trichiura (T. trichiura), is common in some low-income areas with poor sanitation. As a subgroup of neglected tropical diseases, these soil-transmitting helminths infections affect nearly 1.4 billion people worldwide [14]. Owing to the absence of effective vaccine, preventive chemotherapy is commonly used to control the co-infection of helminths and reduce the morbidity.
Praziquantel exhibits satisfactory efficacy and becomes the first line drug for clonorchiasis. The recommended treatment regimen by WHO is 25 mg/kg thrice daily for two consecutive days [15], which can achieve the cure rates of 93.5–100% [16]–[17]. However, this treatment regimen is difficult to complete in the mass treatment because of multiple treatments and adverse events [18], whereas administration of single dose or reduction of treatment course results in less or unstable efficacy [19]–[21]. In addition, praziquantel also exhibits activity against hookworm [22]. A cure rate of 93% was reported when a single dose of 40 mg/kg praziquantel was administrated to patients with hookworm infection [23]. Imidazole drugs are recommended for treatment of soil-transmitting helminths by WHO. Among these, mebendazole is a broad-spectrum anthelmintic agent. Mebendazole was reported to be effective against C. sinensis in rats by a single dose of 150 mg/kg as the complete curative dose [24]. Similar results were also reported in Xiao's study [25]. Tribendimidine is an abroad-spectrum anti-intestinal nematodes drug that recently appeared in Chinese market. In updated reports, tribendimidine is proved to be effective to C. sinensis in rats and hamsters. Mean worm burden reductions of the single dose of 150 and 100 mg/kg tribendimidine in the rats and in hamsters were 98% and 100%, respectively [26]. Meanwhile, tribendimidine showed effective activity to juvenile C. sinensis in hamsters, 90.6% of the mean worm burden reduction were achieved by using the dose of 100 mg/kg tribendimidine [25]–[29]. In addition to these laboratory studies, tribendimidine also showed good therapeutic profiles against C. sinensis and Opisthorchis viverrini in clinical trials, and only mild and transient adverse events were reported [30]–[31]. Based on the aforementioned evidences, we aim to assess the efficacy and safety of praziquantel, tribendimidine and mebendazole in patients with co-infection of C. sinensis with other helminths in this randomized open-label trial.
The study was approved by the ethical review committee of the National Institute of Parasitic Diseases, Chinese Center for Diseases Control and Prevention (No. 201205). The trial was registered with Current Controlled Trials (ISRCTN55086560). Written informed consent was obtained from every participant or their guardian. We explained risk and benefits on the consent form. Participants were voluntary, and individuals could withdraw from the trial at any time.
The study was conducted in the Qiyang County, Hunan province, P.R. China, from June to September in 2012. A total of 867 habitants aged 15 to 65 in Dazhongqiao village and Sankoutang village were enrolled in the preliminary survey.
All residents of Dazhongqiao village and Sankoutang village aged 15 to 65 years old were invited to provide one stool sample to perform the Kato-Katz thick smears. Common intestinal worm eggs including C. sinensis, hookworm, A. lumbricoides and T. trichiura were checked and counted under the light microscopy. Eligible for inclusion were those who were infected with more than one species of helminth and provided the written informed consent in preliminary survey, and then submitted the second stool sample before the treatment.
Participants could be excluded from treatment if fulfilling any of the following exclusion criteria: who was pregnant (of the females), present of any abnormal medical disorder (i.e., fever and hepatomegaly), historical record of any acute or sever chronic disease, was psychiatric and neurological disorders, and gave anthelmintic treatment within the previous 4 weeks.
Sample size was based on a suggested sample size of 12 patients per group for proof-of-concept trials [32]. Taking into account of dropping out, we planned to enroll 40 participants per group.
Participants who met all study criteria were randomly assigned to one of the four treatment groups by a computer-generated randomization number. The random number sequence was generated with SAS software (version 9.1) according to the list of identification number of 156 patients. Participants and trial designer were not masked to treatment allocation, but the laboratory teams were masked throughout the study.
Tribendimidine (200 mg tablets) was purchased from Shandong Xinhua Pharmaceutical Corporation (Zibo, Shandong, P.R. China); praziquantel (200 mg tablets) was donated by Nanjing Pharmaceutical Corporation (Nanjing, Jiangsu, P.R.China); mebendazole (100 mg tablets) was the product of Guangxi Yingkang Pharmacy CO., LTD (Nanning, Guangxi, P.R.China).
First, a total of 867 residents were invited to participate in the preliminary survey within ten days. The individual information including name, age, sex, educational background, race and telephone were recorded. Participants received containers with unique identification numbers and were invited to bring a fresh stool sample in the following morning. Patients with a microscopically confirmed co-infection with helminths were asked for a second stool sample. Eligible participants were examined by clinicians before drug administration, and women aged 15–49 years old accepted urine samples test to exclude the pregnancy.
Second, the first treatment was given to all eligible participants. Drugs were swallowed with clean water and accompanied by a small food item to improve tolerability and increase bioavailability. Praziquantel was administrated orally according to regional policy in Hunan province: 75 mg/kg in four divided doses (twice daily spaced by 6 h for 2 days). Tribendimidine was given by two means: one is a single dose of 400 mg and the other is 200 mg twice daily spaced by 6 h. Mebendazole was given 400 mg as a single dose. In addition, parts of participants who were treated with tribendimidine accepted the tests for blood and urine samples (including blood and urine common biochemical indexes, hepatic and renal function indexes) and ECG examinations before and 24 h after the treatment. Third, the adverse events (AEs) were monitored and recorded.
Participants were asked to report any potential drug-related signs and symptoms at 3 h, 24 h and 48 h after the first administration. Solicited adverse events, including headache, vertigo, vomiting, nausea, asthenia, dizziness, anxiety, allergic reactions, abdominal pain and fever, were recorded. Intensity of AEs were recorded and graded as mild, moderate, severe and serious as judged by clinicians. Three weeks after treatment, participants were asked for two consecutive stool samples.
Finally, the second treatment was given to participants who were still C. sinensis egg-positive after the first treatment with the same doses of praziquantel and tribendimidine within 6 weeks after the first administration due to ethical reason. We adopted the same clinical trial practice process as the first treatment including performance of written informed consent. A total of 52 participants accepted the retreatment. At last, worm egg-positive participants who were enrolled in our study were treated with corresponding drugs.
Filled stool containers were taken to the laboratory at the Qiyang Center of Diseases Control and Prevention (Qiyang CDC). From each stool sample, three Kato-Katz thick smears were prepared and were quantitatively examined with light microscopy for worm eggs. Numbers of worm eggs were counted and recorded for each parasite species separately. 5% of slides were re-examined randomly for quality control by a senior microscope technician.
Blood and urine samples collected from some participants were taken to the local hospital with ice packs. Urine samples and parts of blood samples were tested within 30 min to examine the changes of common biochemical indexes. The other blood samples were stored at 4°C in the refrigerator for 4 h, and then were centrifugalized. The supernatants were tested for hepatic and renal function detection.
Primary outcomes were the cure rates (CRs) and egg reduction rates (ERRs) at 3 weeks after treatments as efficacy outcomes. The CR was defined as the percentage of participants excreting eggs before treatment who became negative after treatment. The ERR was defined as the group's reduction of geometric mean egg count after treatment divided by the geometric mean of the same patients before treatment, multiplied by 100. Secondary outcomes were the frequencies of AEs and the pathological changes of results of biochemical tests and ECG examination after treatments.
All data were double entered, and the per-protocol analysis was pursued. Statistical analyses were performed with SAS software (version 9.1, Statistical Analysis System, RTI, Cary, North Carolina, USA). The numbers of each kind of worm eggs recorded from 6 Kato-Katz slides before and after treatment were added to calculate the arithmetic mean of eggs per gram of stool (EPG) for every participant. The arithmetic means was used to determine the infection intensity, and the geometric EPG was calculated to assess the egg reduction rate among the treatment groups. Prevalence of C. sinensis was stratified, according to the classification of infection intensities, into three catalogues, e.g. light (1–999 EPG), moderate (1000–9999 EPG), and severe (>10000 EPG) infections [33]. Prevalence of hookworm was stratified into three catalogues, e.g. light (1–1999 EPG), moderate (2000–3999 EPG), and severe (>4000 EPG) infections, in accordance of the classification put forth by WHO [15]. Logistic regression model was used to examine cure rates of helminths in different treatment groups. Pearson's χ2 test was applied to compare the proportion of reported adverse events between the treatment groups. Negative binomial regression models were used to compare the numbers of adverse events in the treatment groups.
Among 160 patients invited for the treatment, 4 were excluded (Figure 1), because one had fever, two had hypertension and the other had lung cancer. Thus, a total of 156 patients were randomly assigned to 1 of 4 treatment arms, among them 22 patients (14.1%) were lost to follow up. Among 55 uncured patients after the first treatment, 3 were lost to follow up and 2 were identified as repeated infections. Then 50 persons accepted the second treatment. After the treatment, 2 patients dropped out for the study, and the complete data records for the final analysis were 48.
All baseline characteristics of treatment groups were similar in the first treatment. The mean age of the 156 patients was 53.5 years (Table 1). All the participants were infected with C. sinensis, and the intensity of C. sinensis infections was mainly moderate. The C. sinensis geometric mean egg counts ranged from 120 to 15779.7 EPG. The proportion of patients co-infected with hookworm ranged from 61.5% to 71.8%, and most of patients were lightly infected. The hookworm geometric egg counts ranged from 24 to 2839.8 EPG. The proportion of concurrent infections with A. lumbricoides and T. trichiura were between 30.8% and 41.0% with mild intensity of infection.
The two groups of treatment with praziquantel and tribendimidine were not equal in size in the second treatment. 16 patients accepted praziquantel treatment while 34 patients in the tribendimidine group. The mean age was 50.9 years (Table 2), and the intensity of infection in each group was mild. The geometric mean egg counts of C. sinensis ranged from 4 to 216.1 EPG. Prevalence of co-infection with hookworm ranged from 58.8% to 75%.
First, the CRs of four groups with three drugs against C. sinensis were observed. 56.8% of CR were obtained in the praziquantel group (Table 3), followed by the single-dose tribendimidine (50%) and 200 mg tribendimidine twice daily (33.3%) which were significantly lower than that of praziquantel (OR = 0.38, 95% CI 0.14–1.01, P = 0.05) (Table 4). But no significant difference was observed between praziquantel and single-dose tribendimidine (P>0.05). The ERRs of these groups were similar, with 98.0% of ERRs for praziquantel treatment, 98.3% of the single-dose tribendimidine, and 97.1% of tribendimidine 200 mg twice. No patients were cured in mebendazole group. Meanwhile, the CRs of these drugs administrated to patients with mild C. sinensis intensity were significantly higher than that with heavy infection (P = 0.024 and P = 0.045).
Second, CRs of the single dose and 200 mg twice daily of tribendimidine against hookworm were 63.6% and 47.8%, respectively (Table 3). Two doses of tribendimidine achieved significantly higher CRs than that of praziquantel in the treatment for hookworm (CR 18.2%, OR = 7.87, 95% CI 1.96–31.57, P = 0.01, and OR = 4.09, 95% CI 1.03–16.28, p = 0.046) (Table 4). However, no significant difference was found between two tribendimidine treatment groups. No patient was cured in mebendazole group. The highest ERR was achieved in the single dose of tribendimidine (77.8%), followed by tribendimidine 200 mg twice daily (65.4%), mebendazole (64.6%) and praziquantel (32.8%).
Third, low CRs were found in the treatments against A. lumbricoides by praziquantel, the single dose of tribendimidine, and mebendazole, of which CRs were 16.7%, 28.6% and 7.7%, respectively (Table 3). No significant difference was found among these groups. No patients were cured in tribendimidine 200 mg twice daily. Four treatment groups achieved moderate ERRs ranged from 66.9% to 79.5%, and no statistically difference was found among these treatment groups.
Fourth, the CRs of the single dose and 200 mg twice daily of tribendimidine against T. trichiura were 23.1% and 33.3%, respectively. The respective ERRs were 77.9% and 80.1% (Table 3). No patients were cured in praziquantel and mebendazole groups, but respective ERRs were 53.1% and 63.8%. No statistically significant difference was observed in the comparison of CRs or ERRs among four groups.
In total, tribendimidine achieved higher CRs against hookworm, A. lumbricoides and T. trichiura in comparison with that of other drugs (Figure 2), and a similar CR against C. sinensis as that of praziquantel.
First, high CRs against C. sinensis were achieved in praziquantel (75%) and tribendimidine (78.1%) groups (Table 5), with similar ERRs between respective two groups (75.8% and 74.2%). Second, the CRs of praziquantel and tribendimidine against hookworm were 16.7% and 55%, respectively. There is significantly difference between two groups (OR = 5.4, 95% CI 0.98–29.91, P = 0.05)(Table 6).
Adverse events were assessed at 3 h, 24 h and 48 h after each treatment. No symptom was reported before treatment. Most of AEs were mild and transient. In total, 45 (43.3%) mild, 3 (2.9%) moderate and 2 (1.9%) severe AEs were reported in the first treatment (Table 7), and 17 (35.4%) mild and2 (4.2%) moderate AEs were found in the second treatment. AEs of two tribendimidine groups were significantly less than that of praziquantel (p = 0.034 and p = 0.0002) (Table 8).
Most of reported AEs in the tribendimidine group were vertigo, headache, nausea, fatigue and anxiety. Severe vomiting and drug allergy events were found in the praziquantel and the single dose of tribendimidine groups, respectively. Among these AEs, vertigo was more common in the praziquantel group (35.1%), which was significant higher than that of tribendimidine (p = 0.03). Patients who had AEs were treated with antiemetics and an antiallergic agent to reduce the symptoms.
A total of 18 patients in the single dose of tribendimidine group and 20 patients treated with the other dose of tribendimidine accepted tests of the blood and urine samples, and ECG examination before and 24 h after treatment. No pathological changes were found from those results of biochemical and ECG examinations after tribendimidine treatments.
The efficacy outcomes of our study demonstrate that tribendimidine is as efficacious as praziquantel for treatment of C. sinensis infection. Similar results have been reported in the treatments of C. sinensis and O. viverrini with tribendimidine and praziquantel [30]–[31]. Meanwhile, higher CR can be achieved when patient who has mild intensity of C. sinensis infection were treated with tribendimidine in the first and the second treatments. High ERRs were obtained in the first treatment, which means tribendimidine can reduce the intensity of infection although can not eliminate the infection for those uncured patients. Taking into account of higher CR obtained in patients with mild infection, we believe that increasing the number of treatment time can enhance the CR of tribendimidine against C. sinensis. In addition, a significant higher CR was obtained in tribendimidine against hookworm compared to those of praziquantel and mebendazole. As to A. lumbricoides and T. trichiura, more than 70% of ERRs were achieved in single-dose tribendimidine group. Despite of low efficacy, the CRs of tribendimidine were still higher than those of praziquantel and mebendazole. Based on the above results, tribendimidine seems to show relative better efficacy against co-infection of helminths than that of praziquantel and mebendazole. Tribendimidine, first discovered and invented in China, is an amidantel derivative and has a broad spectrum of activity against infections of intestinal nematodes, e.g., hookworm and A. lumbricoides [29]. Tribendimidine has been proved to be an L-subtype nicotinic acetylcholine receptor agonist, similar to levamisole and pyrantel [34]. The p- (1-dimethylamino ethylimino) aniline and acetylated deacylated amidantel, as the metabolites of tribendimidine, are completely broken down and eliminated within 24 h, and no original compound of tribendimidine was found in plasma, urine, and feces of healthy volunteers administered orally with tribendimidine. The maximal plasma concentration after administration of 200 mg and 400 mg tribendimidine in healthy Chinese were 0.37 and 0.64 mg/L, and the half-life period was about 4 to 5 h [35]–[36]. The concentration of 0.1 ug/mL tribendimidine can kill adult worm in the vitro effect of tribendimidine against C. sinensis infection [37]. Therefore, the maximal plasma concentration after administration of 200 mg tribendimidine is more than the minimal concentration of tribendimidine to kill C. sinensis in the vitro. According to these facts, we designed two different doses of tribendimidine in the study. However, we did not get the satisfactory results since only 33.3% of CR was obtained when we used the dose of 200 mg twice daily to treat patients infected with C. sinensis. This reason may be that adult worms parasitize in the bile duct, and drug concentration in the bile is lower than that in the plasma.
As mentioned before, praziquantel is the first choice for C. sinensis and showed the activity against hookworm in some reports. For instance, about 80%–95% of CRs were reported in the treatment of praziquantel against C. sinensis infections [20], [38]. However, only 56.8% of CR was found in our trial, it is because following reasons. First, we used less doses of praziquantel than that recommended by WHO. This dose-choosing is based on the same treatment regimen as that used in the study area. Second, the higher intensity of C. sinensis infections for those patients who received treatment with praziquantel may be another reason. In order to compare the efficacy with same dose of tribendimidine, we also adopted the same single dose of 400 mg mebendazole instead of recommended dose by WHO (a single dose of 500 mg), no patients were cured in mebendazole groups. Taking into account of low absorption characteristic of mebendazole, reducing dose resulted in reduced efficacy.
Results from our study on AEs, both tribendimidine and praziquantel revealed to be well tolerated at the dosage of the trial, and most of AEs observed were mild and transient. However, the numbers of AEs caused by tribendimidine were significantly less than that of praziquantel. Patients treated with tribendimidine were less likely to experience vertigo than that with praziquantel treatment. No pathological changes were found in patients who accepted tribendimidine treatment. These outcomes illustrated tribendimidine is a safe drug for human use at the dosage of the trail. However, apart from the frequent reported adverse events, such as adverse reactions of nervous system and gastrointestinal system, caused by tribendimidine [39]–[41], we only observed a severe drug allergy reaction. Allergy symptoms appeared in 18 h after single-dose 400 mg tribendimidine treatment and disappeared in 7 days after its emergency.
In conclusion, one single dose of 400 mg tribendimidine shows similar therapeutic profiles as praziquantel against C. sinensis in this trial. It is benefit for preventive chemotherapy of C. sinensis infections in places with high prevalence. However, large-scale clinical study is warrant to perform in order to further verify the efficacy and appraise the safety. Meanwhile, taking into account of good efficacy of tribendimidine against hookworm, it has particularly noticed that tribendimidine is a better choice to cure patients with co-infection of C. sinensis and hookworm.
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10.1371/journal.pntd.0001894 | A New Strategy Based on Smrho Protein Loaded Chitosan Nanoparticles as a Candidate Oral Vaccine against Schistosomiasis | Schistosomiasis is one of the most important neglected tropical diseases and an effective control is unlikely in the absence of improved sanitation and vaccination. A new approach of oral vaccination with alginate coated chitosan nanoparticles appears interesting because their great stability and the ease of target accessibility, besides of chitosan and alginate immunostimulatory properties. Here we propose a candidate vaccine based on the combination of chitosan-based nanoparticles containing the antigen SmRho and coated with sodium alginate.
Our results showed an efficient performance of protein loading of nanoparticles before and after coating with alginate. Characterization of the resulting nanoparticles reported a size around 430 nm and a negative zeta potential. In vitro release studies of protein showed great stability of coated nanoparticles in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF). Further in vivo studies was performed with different formulations of chitosan nanoparticles and it showed that oral immunization was not able to induce high levels of antibodies, otherwise intramuscular immunization induced high levels of both subtypes IgG1 and IgG2a SmRho specific antibodies. Mice immunized with nanoparticles associated to CpG showed significant modulation of granuloma reaction. Mice from all groups immunized orally with nanoparticles presented significant levels of protection against infection challenge with S. mansoni worms, suggesting an important role of chitosan in inducing a protective immune response. Finally, mice immunized with nanoparticles associated with the antigen SmRho plus CpG had 38% of the granuloma area reduced and also presented 48% of protection against of S. mansoni infection.
Taken together, this results support this new strategy as an efficient delivery system and a potential vaccine against schistosomiasis.
| Schistosomiasis is one of the most important neglected tropical diseases and an effective control is unlikely in the absence of improved sanitation and vaccine. The selection of a suitable delivery system and an adjuvant to aid in the stimulation of the appropriate immune response is a critical step in the path to the development and employment of successful anti-schistosome vaccines. Here we propose a candidate vaccine based on chitosan nanoparticles associated with the antigen SmRho and coated with alginate, as an alternative strategy to induce protection against S. mansoni infection. This vaccination strategy offers many technical advantages, including the possibility of administration by oral route, which makes the vaccine safer than injectable vaccines and facilitates its use mainly in underdeveloped areas. Chitosan nanoparticles were prepared and characterized; the results showed that the formulation has features suitable to be delivery orally. Immunization studies suggest that the combination of chitosan nanoparticles associated to the antigen SmRho and CpG is an efficient vaccine candidate against schistosomiasis, which was able to modulate the granuloma area, that represents the major pathological response in schistosomiasis and also to induce protection against infection of S. mansoni.
| Schistosomiasis remains one of the most prevalent diseases in the world and so a significant public health problem, especially in developing countries [1]. This parasitic disease affects more than 240 million people worldwide, with a further 700 million individuals living at risk of infection [2] and it causes up to 250000 deaths per year [3].
Current schistosomiasis control strategies are mainly based on chemotherapy but, despite decades of mass treatment, the number of infected people remains constant [4]. Extensive endemic areas and constant reinfection of individuals, together with poor sanitary conditions in developing countries, make drug treatment alone inefficient [5]. Many consider that the best long-term strategy to control schistosomiasis is through immunization combined with drug treatment [6]. An anti-schistosomiasis vaccine that induces even a partial reduction in worm burdens could considerably reduce pathology and limit parasite transmission [7].
The current schistosoma vaccine candidates prove not to be the most effective, so it is important to identify new antigens and to explore alternative vaccination strategies, including new adjuvants to improve vaccine efficacy [8]. In schistosomiasis, there is evidence indicating the involvement of low molecular weight proteins that bind GTP (guanosine triphosphate) during the process of maturation and deposition of eggs by the females of S. mansoni [9]. Over expression in female worms may be attributed to the involvement of Rho-GTPase in female reproduction processes, especially on vitelline cell maturation and/or egg laying. Immunolocalisation of S. mansoni Rho1 on the parenchymal cells surrounding the vitellaria adds support to this suggestion [10]. This brings an interest in understanding the role of this protein in immunological processes resulting from schistosomiasis and on the evaluation of its potential as a vaccine candidate.
Considering that schistosome infection occurs predominantly in areas of rural poverty in sub-Saharan Africa, Southeast Asia and tropical regions of the Americas [11] a candidate vaccine that could be administered by oral route could offer an economical and effective solution to mass immunization. The main advantages presented by oral vaccine delivery are the target accessibility and enhanced patient compliance owing to the non-invasive delivery method. On the other hand, for effective oral immunization, antigens and plasmids must be protected from the acidic and proteolytic environment of the gastrointestinal tract and efficiently taken up by cells of the gut associated lymphoid tissue (GALT). With this in mind, several studies have been done and showed that the association of antigens with nanoparticles increases the internalization by M cells and prevents the degradation in the gastrointestinal (GI) tract [12]. Another important aspect is that these carrier systems can act as immunostimulants or adjuvants, enhancing the immunogenicity of weak antigens [13]. Biodegradable and mucoadhesive polymeric delivery systems seem to be the most promising candidates for mucosal vaccines. Several polymers of synthetic and natural origin, such as poly(lactic-co-glycolic acid) (PLGA), chitosan, alginate, gelatin, etc., have been exploited for efficient release of mucosal vaccines and significant results have been already obtained [14].
Chitosan is the deacetylated form of chitin and has many properties suitable for vaccine delivery. It is a mucoadhesive polymer, biodegradable and biocompatible. In particular, its ability to stimulate cells from the immune system has been shown in several studies [15], [16], [17], [18]. Nevetheless, the ability of chitosan in inducing a Th1, Th2 or mixed responses is still controversial as also the type of immune response induced by different administration routes [19], [20]. Additionally, chitosan is a cationic polymer, easily form complexes or nanoparticles in aqueous medium with the possibility to adsorb proteins, antigens and DNA [21] [22] that may protect them from degradation [23]. The oral administration of antigen adsorbed nanoparticles is demanding as processes like rapid antigen desorption from the particles or the attack of the antigens by enzymes or acidic substances from the GI fluids may occur. These obstacles may be overcome by coating those antigen loading particles with an acid resistant polymer, like sodium alginate [24].
Alginate coated chitosan nanoparticles was recently described [24] and it has the particular advantage of being constructed under very mild conditions (aqueous medium and mild agitation), which is a great benefit for the encapsulation of proteins, peptides and antigens. Moreover, Borges and co-workers [25] have demonstrated that these coated nanoparticles were able to be taken up by rat Peyer's patches which is one of the essential features to internalize, deliver and target the intact antigen to specialized immune cells from the gut associated lymphoid tissue (GALT) [26].
Herein, we proposed to evaluate in vitro characteristics of chitosan nanoparticles associated to protein, for which the antigen Rho1-GTPase of S. mansoni was chosen, to be used as a candidate oral vaccine against schistosomiasis. Once in vitro characterization showed favorable data, its in vivo role was evaluated through mouse immunization. Added to that, chitosan was evaluated not only for its performance as a delivery system but also for its contribution due to its adjuvant properties.
Additionally, since a mixed Th1/Th2 response seems to be optimal for a schistosomiasis vaccine, then bacterial CpG motifs (which induce the production of IL-12 by DCs and macrophages that express the appropriate TLR95) can be used as adjuvants to boost immunity and also with the aim of inducing a TH1-like immune response that can prevent the normal Th1 to Th2 transition. With this in mind we investigated the co-administration of synthetic unmethylated oligodeoxynucleotides containing immunostimulatory CpG motifs (CpG ODN), a TLR-9 ligand, with chitosan nanoparticles.
With this in mind, on this work we will report a new strategy of vaccination against schistosomiasis based on chitosan nanoparticles associated to SmRho antigen plus the adjuvant CpG, and coated with sodium alginate.
Chitosan (CH) (Chimarin DA 13%, apparent viscosity 8 mPa.s) was supplied by Medicarb, Sweden. CH was purified by filtration of an acidic chitosan solution and subsequent alkali precipitation (1 M NaOH). The purified polymer was characterized by gel permeation chromatography (GPC) and Fourier Transform-Infrared Spectroscopy (FT-IR). The average weight molecular weight of the material was found to be 1.2×105 (GPC in 0.5 M CH3COOH - 0.2 M CH3COONa, 25°C). The degree of acetylation determined by FT-IR according to Brugnerotto et al. [27] was found to be 16%. Endotoxin levels of the purified chitosan extracts were assessed according to Nakagawa et al. [28] using the Limulus amebocyte lysate (LAL) QCL- 1000 assay (Cambrex) and found to be lower than 0.1 EU/mL, respecting the US Department of Health and Human Services guidelines [29] for implantable devices. Imidazole modified chitosan (CHimi) was prepared as previously described [30] and it was used to prepare DNA chitosan particles, with the aim of obtaining higher rates of transfection, as reported by Moreira and co-workers [31] and also by our group [31]. Low viscosity pharmaceutical grade sodium alginate was kindly donated by ISP Technologies Inc., Surrey, UK). Class C, CpG ODN (2395) (5′-tcgtcgttttcggcgc:gcgccg-3′) was purchased from InvivoGen (San Diego, CA, USA). All the others reagents used were of analytical grade.
The SmRho cDNA sequence was amplified from an adult extract worm cDNA using specific oligonucleotides (Table 1) designed and used in a PCR reaction to amplify the complete open reading frame of SmRho (GenBank accession number AF140785). PCR was performed using Platinum Pfx enzyme (Invitrogen, Carlsbad, USA); the reaction was initiated with one cycle of 2 min at 94°C, followed by 25 cycles of 15 s at 94°C, 30 s at 56°C, and 1 min at 68°C and finalized with a step of 68°C of 3 min. PCR products were cloned by a BP recombination reaction into pDONR 221 cloning vector (Invitrogen, USA), according to manufacturer specifications. After producing the entry clone, a LR recombination reaction was performed with pDONR-rSmRho and pET-DEST42 to clone the full-length cDNA sequence of rSmRho into an expression vector (Invitrogen, USA). The resulting clone was then sequenced to confirm its identity.
To produce a recombinant (r) SmRho, the full-length DNA sequence was cloned into the expression vector pDEST42 (to produce a protein that contains a C-terminal hexahistidine tag). The resulting plasmid was transformed into Escherichia coli BL21 pRARE for protein expression. The recombinant protein was then purified using HiTrap Chelating HP according to the manufacturer instructions (Amersham Biosciences, Uppsala Sweden). The protein purity was assessed using sodium dodecyl sulfate polyacrylamide gel electrophoresis (SDS-PAGE).
After obtaining the purified protein SmRho, we wanted to confirm the ability of protein to be recognized by and to active the immune system after parasite infection. For this propose, sera of infected patient that had received drug treatment or not, compared to non-infected patient serum, were pooled and tested, by ELISA and Western Blotting. In this last assay, soluble extracts of schistosomula, eggs (SEA – soluble egg antigens) and adult worms (SWAP – soluble worm antigen preparation) were also evaluated, together with the protein SmRho, about their recognition against the sera tested.
The preparation of this delivery system contains three main steps, the manufacturing of the chitosan particles, their recombinant protein loading by adsorption and finally the coating with sodium alginate.
Nanoparticles were prepared by mixing, while vortexing, equal volumes of a 0.1% (w/v) of chitosan in 5 mM CH3COONa buffer,pH 5.5 and a solution containing 0.625% (w/v) of Na2SO4, both previously heated at 55°C for 10 min. Nanoparticles were allowed to form overnight under stirring. In the following day, the suspension was centrifuged at 3200 g for 20 min at 18°C. The particles were resuspended in about 1/8 of the volume with ultrapure water (Milli-Q, Millipore).
The loading was done as described by Borges et al [24], with some adaptations. Briefly, the solution of SmRho protein (1 µg/µL) was incubated with chitosan particles under mild agitation at 18°C. The loading efficacy of the uncoated particles were calculated by an indirect way, quantifying the protein that remained in solution. After 1 h of incubation, an aliquot of the particle suspension was centrifuged at 15700 g for 30 min and the protein in supernatant was quantified by BCA-protein assay (PIERCE, Rockford, USA) using a microplate reader with a 590 nm filter (Multiskan-EX 355). The absorbance reading value was corrected subtracting the average absorbance reading obtained in the BCA-protein assay from that one of the supernatants of unloaded nanoparticles prepared exactly in the same conditions. The corrected OD value was then used to calculate the concentration using the standard curve prepared at same time from individual protein standards.
The drug loading efficiency (LE) were calculated from the following equation:(1)
The coating was performed as described by Borges and colleagues [24]. While vortexing, equal volumes of protein-loaded particle suspension and 1% sodium alginate solution. The suspension of the particles was maintained under agitation with a magnetic stirrer for 20 min at 18°C. The suspension was then centrifuged for 30 min at 370 g and the supernatant was discarded. To chemically cross-link the alginate at the particle surface, the particles were re-suspended in 0.524 mM CaCl2 solution and kept under agitation for another 10 min.
The evaluation of the protein desorption during the coating procedure was performed during the incubation of the particles with sodium alginate. Aliquots of the particle suspension were collected, centrifuged at 15700 g for 30 min and the protein in the supernatant was assayed with a BCA protein assay as described previously.
SmRho+CpG ODN loaded alginate coated chitosan NPs were prepared by adding the CpG and protein at same time during the loading process and before coating the nanoparticles. The loading of CpG with chitosan particles was assessed by electrophoresis in 1% (w/v) agarose (Cambrex) gel, with 0.05 µg/mL of ethidium bromide (Q-BioGene) and also by measuring the OD of the nanoparticle supernatants at 260 nm and calculate CpG by the difference. To eliminate background interference, the supernatant of unloaded particles were treated of the same way.
The different formulations were administered orally with a gavage-feeding needle (groups I–V) and intramuscularly with an injection in the tibialis anterior muscle (group VI). The primary immunization was followed by two immunizations with an interval of two weeks. Seven days after the last immunization mice were challenged and then, after 50 days mice were sacrificed.
Blood samples were collected from tail veins from mice of each experimental group at two-week intervals and the sera were prepared by centrifugation and stored at −20°C until further analysis. Serum was collected from immunized and control mice to measure kinetics of SmRho specific antibodies.
A measurement of specific anti-SmRho antibodies was performed using an indirect enzyme-linked immunoabsorbent assay (ELISA) as described elsewhere [32]. Briefly, maxisorp 96-well microtiter plates (Nunc, Roskilde, Denmark) were coated with 10 µg/mL of SmRho in carbonate-bicarbonate buffer, pH 9.6, for 16 h at 4°C, followed by washes and then blocked for 1 h at 37°C with 200 µl/well of PBS-casein (phosphate buffer saline, pH 7.2 with 1.6% of casein). Next, 100 µl of serum sample from individual mice diluted 1∶100 in PBS-casein was added to each well and was incubated for 1 h at 37°C. Plate-bound antibodies were detected by peroxidase-conjugated anti-mouse IgG (SIGMA), IgG1 and IgG2a (Southern Biotechnology Associates, Inc., Birmingham, AL, USA) diluted 1∶5000 in PBS with 0.25% casein. The plates were revealed by the addition of 100 µl of detection solution (R&D systems, Minneapolis, USA) containing tetramethylbenzidine (Thermo Scientific Pierce) and H2O2 in each well; after 20 min reactions were stopped with the addition of 50 µl of 5% (v/v) of sulfuric acid per well. The absorbance was read at 450 nm in an ELISA plate reader (ELX 800 BIO-TEK Instruments Inc.).
Seven days after the last immunization, mice were challenged through percutaneous exposure of abdominal skin to water containing 25 cercariae (LE strain) for one hour. 50 days after challenge, mice were sacrificed and adult worms were perfused from their portal veins [33]. Protection was calculated by comparing the number of worms recovered from each vaccinated group compared with control group using the following equation:(2)where C represents the worms recovered from the saline control group and I represents the worms recovered from the experimental group.
Liver fragments from mice (5 mice per group) of control and experimental groups immunized and infected, were collected 50 days post-infection in order to evaluate the effect of immunization on granuloma formation. Livers fragments were fixed in 10% paraformaldehyde. Fragments processed for paraffin embedding and histopathological sections were cut using a microtome at 5 µm. Sections were stained on a slide with hematoxilin-eosin (HE). The areas of individual granulomas were obtained through the MacBiophotonics ImageJ software analyzer. Fifteen granulomas from each mouse with a single well-defined egg were randomly chosen using a microscope with the 4× objective lens; granulomas were then scanned using JVC TK-1270/RGB microcamera. Using a digital pad, the total area of each granuloma measured, and the results were expressed in square micrometers.
Statistical analysis was performed using the ANOVA test using the Graph Pad Prism 5 software package. The Bonferroni test was used to compare subgroups with the level of significance set at p<0.05.
The studies regarding sera samples taken from patients were approved by Research Ethics Committee (COEP) of UFMG, the protocol number 523/07, and a written informed consent was obtained from each patient before blood collection. Experiments with animals were performed in compliance with the guidelines of the Institutional Animal Care and Committee on Ethics of Animal Experimentation (“Comitê de Ética em Experimentação Animal” – CETEA, national guidelines, Law number 11.794, 8/10/2008) from Universidade Federal de Minas Gerais (UFMG); protocol number 204/2009 was approved on 24/03/2010.
The full-length sequence of the S. mansoni cDNA encoding SmRho was obtained from an adult worm cDNA library using PCR with specific oligonucleotides. The resulting full-length cDNA displayed an ORF of 579 bp, encoding a protein of 193 amino acids with a predicted molecular mass of approximately 21.8 kDa and an isoelectric point of 5.70. BlastP comparisons of the deduced protein sequence in GenBank exhibit a complete identity and similarity to S. mansoni Rho-GTPase protein (EMBL-Bank CDS: AAD31508.1)(data not shown).
The nucleotide sequence of this protein was cloned into a pET-DEST42 expression vector and the protein was expressed in E. coli BL21 pRARE strain. Protein extract of transformed bacteria showed a band at ∼26 kDa when induced with IPTG, since the plasmid add a C-terminal hexahistidine tag in the protein expressed. The bacteria were then lysed and the lysate separated into soluble and insoluble fractions. The inclusion bodies were shown to contain the majority of the recombinant protein (Figure 1A), which was mostly solubilized by extraction with 0.9% (w/v) N-Laurylsarcosine. The protein was bound to a nickel-charged column under denaturing conditions, and purified by affinity chromatography through an imidazole linear gradient. Eluted fractions containing rSmRho were pooled, and protein yield after purification was estimated to be around 3 mg/L (Figure 1B). After buffer exchange the protein was used in further experiments.
To confirm the immunogenicity of SmRho in S. mansoni infection, the recognition of the protein by sera of infected patients, who had received drug treatment or not, was evaluated. The results obtained in the ELISA test showed that SmRho was not recognized by sera of control patients, i.e. not infected with S. mansoni. On the contrary, sera of infected patients, in particular of drug treated ones, showed a specific reaction against SmRho (Figure 2A). Additionally, the immunogenicity of SmRho was evaluated by Western blot assay corroborating the above-mentioned result (Figure 2A). It was observed again the reactivity of the protein SmRho with sera of infected patients, drug treated and untreated, and no reaction in the control group. In the membrane incubated with sera of treated infected patient a protein band with a molecular weight close to SmRho band in the soluble extract of eggs (SEA) and adult worms (SWAP) was observed as well. This higher reactivity in treated group is probably due to a greater exposure of the protein after drug treatment which increases the recognition by immune system cells. Sera types incubated with each membrane are represented in Figure 2B.
The preparation of the delivery system was performed as described by Borges and colleagues [24] and it contains three main steps, the manufacturing of the chitosan particles, their protein loading by adsorption and finally the coating with sodium alginate. The efficiency of this process was assessed by the quantification of non-bound protein that remained in the supernatant after the loading and coating steps. In Table 3 one can observe that the encapsulation efficiency of the protein in the chitosan nanoparticles before the coating with alginate was around 95%. After the coating around 76% of the protein remained bound to the particles.
The association of CpG to alginate coated chitosan-SmRho nanoparticles was evaluated by two techniques using the supernatants of the particles. Both techniques indicated a CpGODN loading efficiency of about 100% since no DNA mobility in electrophoresis was verified as well as no absorbance at 260 nm was detected (data not shown).
The size of chitosan-SmRho particles, before and after coating, was determined by light scattering technique. One of the major currently described drawbacks of this methodology of particle preparation is the high polydispersity of the obtained nanoparticles [35] that results from particle aggregation during its formation. The average size presented by particles was approximately 750 nm scale before coating and after that a reduction of particles size was observed (Table 4). The unpredictable result of coated nanoparticles had a smaller size than those uncoated was most probably related with the aggregation phenomena of the particles. Therefore, average size values had the contribution of aggregates size, presented on particles suspension.
These particles were also characterized in terms of zeta potential (Table 4). In the first step, zeta potential determinations have shown that an excess of polymer allowed the assembly of particles with a positive global net charge. During the coating procedure an inversion of the surface charge of the particles to negative values was observed due to the negative charge of sodium alginate. This zeta potential inversion is a strong indication of the presence of an alginate coating on the surface of the particles.
Release studies were performed to evaluate the stability of the coated nanoparticles and the profiles of SmRho protein desorption from these nanoparticles in simulated gastric fluid (SGF) and simulated intestinal fluid (SIF), at 37°C. It can be observed in Figure 3 that coated nanoparticles presented a great stability in SGF, with less than 40% of the protein released after 2 hours and it was even better in SIF, where less than 15% of the protein was released after 20 hours of assay.
Further in vivo studies were performed to investigate the potential utility of rSmRho loaded chitosan-based nanoparticles in eliciting the production of antibodies or modulating the immune response following intramuscular or oral administration of the suspension of the particles. To evaluate the levels of SmRho-specific IgG antibodies serum samples from vaccinated animals from each group were tested by ELISA. The measures of IgG antibodies showed that nanoparticles were able to induce the production of specific SmRho antibodies mainly in the experimental group vaccinated with coated SmRho-chitosan nanoparticles by intramuscular route, which showed high levels of IgG that appeared at the day 45 (5 weeks after the first immunization) and presented the highest level at the day 75, compared to the control group (Figure 4). To determine the type of immune response induced after vaccination, the subclasses IgG1 and IgG2a were also analyzed. For this same experimental group, the levels of specific anti-SmRho IgG1 antibodies were increased since day 30 until day 90, when the animals were sacrificed, and a peak of IgG1 antibodies was observed at the day 60, after performed the three rounds of immunization. The levels of IgG2a were also increased after the three rounds of immunization, with a peak at the day 60, which remained elevated until the day 90. The high levels of IgG1 and IgG2a showed a mixed Th1/Th2 profile. In relation to the remaining groups, there was no significant production of specific SmRho antibodies during the period evaluated.
To investigate the protective activity induced by vaccination with different formulations of chitosan nanoparticles in murine model of S. mansoni infection, immunized mice were challenged with 25 cercariae. The difference in the number of adult worms recovered in the experimental groups compared to control group was calculated 50 days post-challenge (Table 5). Groups immunized with coated SmRho nanoparticles, associated or not to CpG also presented the highest level of protection protection of 48% and 55%, respectively. Interesting, the group of animals immunized with CH nanoparticles without SmRho protein and challenged with cercariae showed a significant reduction of 47% in adult worm burden. This result suggests that chitosan has an important role in inducing nonspecific immunity against S. mansoni infection, and that the adjuvant CpG did not have a considerable contribution in reducing worm burden in this experiment.
To evaluate the effect of the proposed vaccine on reducing granuloma reactions, histological analysis was performed by digital morphometry. Seven days after the third immunization, mice were challenged with 25 cercariae. After 50 days of challenge infection, mice were sacrificed and liver samples were taken for histological analysis. Hematoxilin and eosin stained liver sections were then used to measure the size of individual granulomas. Vaccination with coated SmRho-CpG-chitosan nanoparticles by oral route reduced liver granuloma area by 38.4% (Table 5), compared with mice that were immunized with PBS. The remaining groups also presented a considerably granuloma area reduction, however, these granulomas were not as small as those observed in groups above mentioned. These findings suggest that the antigen SmRho associated with CpG in nanoparticles was important to induce this anti-pathological effect.
Cytokine profile evaluation was performed using splenocyte cultures from individual mice immunized with chitosan-based nanoparticles. The production of IFN-γ, IL-10 and TGF-β was measured in the supernatants of spleen cells cultured only with complete RPMI medium or in the presence of SmRho, SEA (soluble egg antigens) or SWAP (soluble worm antigen preparation). The highest levels of the immunomodulatory cytokine IL-10 were produced by SEA-stimulated splenocytes from mice immunized with coated SmRho-CpG-chitosan nanoparticles, compared with the control-stimulated splenocytes. In the others experimental groups significant levels of IL-10 were also observed, although their levels were not so high compared with those above described (Figure 5A). Significant levels of IFN-γ, a cytokine typical of Th1-type immune response, were produced by SmRho-stimulated splenocytes from groups immunized with coated SmRho-chitosan and coated SmRho-CpG-chitosan (Figure 5B). Taken together, these results show that groups which presented the highest levels of the immunomodulatory cytokine IL-10, also were able to achieve a significant protective response and a reduced liver pathology, which is probably related to the prevention of an excessive Th1 and/or Th2 response by IL-10.
Schistosomiasis is one of the most important neglected tropical diseases and an effective control is unlikely in the absence of improved sanitation and vaccine. The antigens tested, so far as vaccine candidates, prove not to be so effective as desirable, consequently, it is important to continue identifying new target antigens [36].
The selection of a suitable delivery system and an adjuvant to aid in the stimulation of the appropriate immune response is a critical step in the path to the development and employment of successful anti-schistosome vaccines, and a number of approaches are being tested, with some success [8].
Here we proposed a candidate vaccine formulation based on SmRho antigen loaded chitosan nanoparticles, coated with alginate, as an alternative strategy to induce protection against S. mansoni infection. This vaccination strategy offers many technical advantages, including the possibility of administration by oral route, which makes the vaccine safer than injectable vaccines and facilitates its use mainly in underdeveloped areas [37].
The recombinant expression of SmRho was optimized using an E. coli pRARE lineage, which co-expresses rare tRNAs required for the synthesis of some eukaryotic proteins. The expression and purification of the protein of interest was obtained with high yield and showed a protein with approximately 26 kDa of molecular mass. The immunogenicity of SmRho protein was confirmed through the reaction with sera of patient infected with S. mansoni. Besides that, the presence of SmRho in soluble egg and adult worms extracts was verified, which is supported by Vermeire and co-workers reports [10].
The methodology for the preparation of coated chitosan nanoparticles was successfully adapted from Borges and co-workers [24] as demonstrated by particle characterization results. The protein loading of the nanoparticles was done by adsorption process based on electrostatic interaction [38] and this process was favored considering that SmRho has an isoeletric point of 6.5. Consequently, in a physiological solution that has a pH of 7.4, SmRho is negatively charged and can easily interact with the positively charged chitosan nanoparticles. In the present work, SmRho loading efficiency of uncoated particles was 95%, which are better than those results found in literature [39] [24]. After coating, the loading efficiency showed a significant decrease to 76%. Nevertheless, the result obtained here was still better than those found by Borges and co-workers [24] and Li and co-workers [39], which was 60% and 66%, respectively, for ovalbumin. A disturb of the protein adsorption equilibrium can occur and a new equilibrium can be established having alginate as a direct competitor for positive charges on chitosan surface which explain our results.
In vitro release studies were performed in SGF and SIF medium at 37°C in order to evaluate the release profiles of protein SmRho and also to assess the stability of the designed delivery system when submitted to medium with different pH, ionic strength at physiological temperature. In SGF, there was a gradual release of the protein, and after 2 h, about 30% of the protein had been released. After this period, it is believed that the particles have passed through the stomach therefore, it can be deducted that nanoparticles are quite resistant to the influence of acid environment. Despite using a similar system, Borges et co-workers (2006) [25] obtained a protein release rate of over than 90% in SGF medium after the same period of the release test. The protein release profile in SIF had an even better result, since after 20 h of assay, only about 15% of the protein had been released. This result was also better than that obtained by Borges and colleagues [25] and Li and colleagues [39]. Again, the system under study remained stable in SIF medium conditions, what is desirable for an efficient antigen delivery. After characterization and based on the results which showed that the nanoparticles have suitable features to be delivery orally, the immunization was realized to investigate the effect of coated chitosan nanoparticles loaded with antigen on mice immune system and their potential to prevent infection of S. mansoni. Our results demonstrated that an anti-pathological or a protective response induced against infection with cercarie are not necessary correlated with high levels of specific antibodies. This can be presumed because only the group intramuscularly immunized with coated chitosan nanoparticles presented high levels of SmRho specific IgG1 and IgG2a antibodies, and it did not show any significant reduction in granuloma area or in worm burden. On the other hand, SmRho-CpG-chitosan nanoparticles administered by oral route reduced liver granuloma area by 38.4% and were not able to induce systemic specific antibodies. This result confirm our previous work, recently published [31] where the immunization with chitosan-DNA nanoparticles did not induce antibodies, however was able to reduced liver pathology. Nevertheless, this group (CH-Rho-Alg i.m.) showed an important role of alginate coated chitosan nanoparticles as adjuvant, considering that any other adjuvant or immunopotentiator was used and high levels of antibodies were produced.
It is a consensus that the development of new and safe adjuvants is necessary not only for parenteral vaccination but also for the more challenging mucosal routes of administration in order to maximize the effectiveness of new antigens as well as those already available [40]. Within this perspective, and due to their unique and interesting properties recently reviewed in several scientific journals [41], [42], [43], chitosan-based nanoparticles have been used associated with various antigens as carrier system for vaccination and administered by different mucosal routes [12], [44], [45]. However, its potential as an adjuvant to parenteral vaccination has been less studied. In a previous study [16] a solution of chitosan was explored as an adjuvant for mice subcutaneous immunization with a model antigen. It was shown that chitosan was able to increase more than five times antibody titers and the proliferation of specific CD4+T cells more than six times.
With respect to sodium alginate, some studies show that alginate microparticles are internalized by M cells of the mucosa [46] and were able to transport antigens to mucosa associated lymphoid tissues, inducing systemic and mucosal immune response in a variety of animal species following oral administration [47]. Moreover, alginate microparticles can be used not only as a carrier system, but also as an adjuvant, as it was shown to induce the production of cytokines such as TNF-α, IL-6 and IL-1 [48] and it increases the antibodies production similarly to other adjuvants, such as incomplete Freund's adjuvant and aluminum hydroxide [47]. Mata and colleagues also showed that immunization studies in Balb/c mice by intradermal route using alginate as adjuvant elicited higher humoral and cellular immune responses leading to more balanced Th1/Th2 profile [49].
Pathology resulting from granuloma formation around the eggs in murine schistosomiasis is characterized by Th2-type of immune response and the granuloma size can be reduced by neutralization of IL-4 [50]. Thus, morbidity and mortality in murine schistosomiasis were hypothesized to be developed as a direct consequence of the egg-induced Th2 type of immune response. Herein, we suggest that the addition of CpG to nanoparticles formulation was important to induce, even in a small proportion, a shift from Th2 to Th1 immune response and also due a regulatory role for IL-10 on CpG–ODN-induced Th1 immune response, as recently reported by Jarnicki et al [51]. They found that TLR ligands, including CpG–ODNs promoted IL-12 and IL-10 production from dendritic cells. This resulted, with both components, Th1 immune response and induction of the IL-10-secreting T regulatory cells (Treg) could help to prevent an exacerbated granuomatous reaction. The role of CpG as an immune modulator was also described by Slütter and Jiskoot [52] and the mechanism by which CpG induces a reduced granuloma formation is probably the same as observed when CpG is used in therapy for asthma/allergy as has been widely reported [53]. In the present study the group immunized with formulations CH-Rho-CpG-Alg, which showed a reduced granuloma area of 38%, also showed higher levels of the immunomodulatory cytokine IL-10 when splenocytes were stimulated with SEA. This probably contributed to reduce inflammation and liver pathology observed in this group. It has been reported that IL-10 plays a key regulatory role in preventing the development of severe pathology due to excessive Th1 and/or Th2 responses [54]. Additionally to these balance of cytokines produced in groups immunized with coated chitosan nanoparticles containing CpG, the presence of the antigen SmRho is also supposed to have an important role in the modulation of immunopathological responses of S. mansoni infection. This is likely due to the induction of INF- γ production that can prevent the normal Th1 to Th2 transition that occurs in infected hosts after the onset of egg production by the parasites, and therefore acts preventing the development of severe chronic morbidity [55]. These observations revealed an anti-pathological role of SmRho but more studies are required to obtain a better understanding of the involvement of this protein in the process of infection of S. mansoni.
To determine whether coated chitosan nanoparticles conferred protection against S. mansoni infection, immunized mice were challenged with cercariae and worm burdens were assessed. All groups immunized with coated chitosan nanoparticles by oral route showed a significant reduction in worm burden and even the group immunized with chitosan nanoparticles without protein showed a 47% of protection. It suggests that chitosan has an important role in inducing a protective immune response against schistosomiasis that is likely due to its immunostimmulatory properties. Nevertheless, the mechanism behind the ability of chitosan in inducing protection is still unknown. Its role is more likely related to trigger an innate immune response, since chitin, which is a progenitor to chitosan, has been demonstrated to function as a PAMP and it is linked with the accumulation of innate cells including alternatively activated macrophages, eosinophils and basophils [56] [57]. At sites of infection with chitin-containing agents anti-infectious immune responses and local chitinases are believed to induce chitin fragmentation. The ability of chitin to induce an acute inflammatory response is already described, but it seems to act by different pathways depending of the size of the fragment and the time point of assessment. Da Silva and co-workers demonstrated that chitin fragments induced a macrophage- and neutrophil-rich inflammatory response with only a modest degree of eosinophil infiltration, while Reege et al. showed mainly eosinophil-based response [17], [58]. When viewed in combination, these studies suggest that chitin induces an inflammatory response that is initially neutrophilic and becomes eosinophilic over time. In the early times, macrophages were shown to be stimulated by chitin particles in vitro and in vivo, by a TLR-2 dependent mechanism that utilizes a MyD88-dependent pathway to induce IL-17 elaboration and enhance the expression of the IL-17AR. These studies also demonstrated that this novel innate immune pathway plays an essential role in the regulation of macrophage cytokine production and the induction of acute inflammation [58]. Additionally, it is well described that IL-4/IL-13-activated alternative macrophages are essential for surviving acute schistosomiasis [59]. These facts cooperate with the convincing evidence that immune elimination of challenge parasites occurs in the lungs and macrophages is expected to mediate the protective response [60]. At later times Yasuda et al., suggested that the effects of IL-33 for the expansion and the activation of eosinophils might aid to expel infected worms from the lungs [18]. These reports support an important theory by which chitosan induce a protective immune response against S. mansoni infection, however it needs to be further investigated.
Mice immunized with coated chitosan nanoparticles associated or not with CpG also showed high percentage of protection 48% and 55%, respectively. The antigen and CpG seems not to have a high contribution in conferring protection against worm infection, nevertheless, they demonstrated important roles in granuloma down-modulation, as discussed before.
As a final conclusion of this work, we believe that the combination of chitosan nanoparticles associated to the antigen SmRho plus CpG is an efficient vaccine formulation candidate against schistosomiasis in light of the data obtained from murine studies, which was able to modulate the granuloma area, that represents the major pathological response in schistosomiasis and also to induce protection against infection of S. mansoni. It is important to highlight that these results were obtained with oral administration of the formulation and can be compared to those obtained from conventional routes of administration. Comparing with our previous work, in which DNA-chitosan nanoparticles were explored, this system achieved better results to be used as a vaccine because induced both protection as well as reduced the granulomatous reaction, while the first presented just an anti-pathological effect with granuloma modulation. Chitosan-based nanoparticles were also found to play an important role as adjuvant and this characteristic should be more explored with other antigens. Furthermore, the role of chitosan in inducing a protective immune response against schistosomiasis deserves special attention and requires more studies to confirm and understand this feature. Taken together, these results support this new strategy to find a safe and efficacious vaccine against schistosomiasis.
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10.1371/journal.ppat.1006147 | Evidence for Integrin – Venus Kinase Receptor 1 Alliance in the Ovary of Schistosoma mansoni Females Controlling Cell Survival | In metazoan integrin signaling is an important process of mediating extracellular and intracellular communication processes. This can be achieved by cooperation of integrins with growth factor receptors (GFRs). Schistosoma mansoni is a helminth parasite inducing schistosomiasis, an infectious disease of worldwide significance for humans and animals. First studies on schistosome integrins revealed their role in reproductive processes, being involved in spermatogenesis and oogenesis. With respect to the roles of eggs for maintaining the parasite´s life cycle and for inducing the pathology of schistosomiasis, elucidating reproductive processes is of high importance. Here we studied the interaction of the integrin receptor Smβ-Int1 with the venus kinase receptor SmVKR1 in S. mansoni. To this end we cloned and characterized SmILK, SmPINCH, and SmNck2, three putative bridging molecules for their role in mediating Smβ-Int1/SmVKR1 cooperation. Phylogenetic analyses showed that these molecules form clusters that are specific for parasitic platyhelminths as it was shown for integrins before. Transcripts of all genes colocalized in the ovary. In Xenopus oocytes germinal vesicle breakdown (GVBD) was only induced if all members were simultaneously expressed. Coimmunoprecipitation results suggest that a Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex can be formed leading to the phosphorylation and activation of SmVKR1. These results indicate that SmVKR1 can be activated in a ligand-independent manner by receptor-complex interaction. RNAi and inhibitor studies to knock-down SmILK as a representative complex member concurrently revealed effects on the extracellular matrix surrounding the ovary and oocyte localization within the ovary, oocyte survival, and egg production. By TUNEL assays, confocal laser scanning microscopy (CLSM), Caspase-3 assay, and transcript profiling of the pro-apoptotic BCL-2 family members BAK/BAX we obtained first evidence for roles of this signaling complex in mediating cell death in immature and primary oocytes. These results suggest that the Smβ-Int1/SmVKR1 signaling complex is important for differentiation and survival in oocytes of paired schistosomes.
| Parasites of the genus Schistosoma cause schistosomiasis, a life-threatening infectious disease for humans and animals worldwide. Among the remarkable biological features of schistosomes is the differentiation of the female gonads which is controlled by pairing with the male and a prerequisite for egg production. Eggs, however, are not only important for the maintenance of the life-cycle; they also cause the pathological consequences of schistosomiasis. Part of the eggs gets trapped in host tissues such as liver and spleen and trigger inflammatory processes, finally leading to liver cirrhosis. Research activities of the last decade have indicated that different families of cellular and receptor-type kinases but also integrins contribute to the control of mitogenic activity and differentiation the female goands. In this context an unusual class of receptor tyrosine kinases (RTKs) has been identified, the venus kinase receptors (SmVKRs). By biochemical and molecular approaches we demonstrate that SmVKR1 activation can be achieved by cooperation with a signaling complex consisting of the beta integrin receptor Smβ-Int1 and the bridging molecules SmILK, SmPINCH, SmNck2. Besides unravelling a novel way of SmVKR1 activation, we provide evidence that this complex controls the differentiation status of oocytes by regulating cell death-associated processes.
| Communication of cells with their environment is an essential requirement to regulate fundamental biological processes such as cell growth and differentiation. Different types of membrane-linked receptors mediate these communication processes, sometimes in a solitary, single receptor-mediated way, sometimes in a cooperative, multiple receptors-mediated way. The latter leads to the integration of different signaling cascades to execute one or more complex operations [1–4].
Schistosomes are parasitic platyhelminths causing schistosomiasis, one of the most threating infectious diseases worldwide after malaria [5–7]. As the only members of the trematodes, schistosomes have evolved separate sexes. The pathology of the disease is caused by eggs which are produced by paired schistosome females in the final host. Egg production is a complex process that involves not only the participation of different cell types, oocytes and vitellocytes. It also comprises the participation of different organs, ovary and vitellarium, whose development in the female depends on a close and permanent pairing contact with the male [8–11]. Although this nearly unique way of regulating sexual development in the animal kingdom is long known [12] and fundamental for the reproductive biology of schistosomes as well as for the pathogenic consequences of schistosomiasis, understanding the underlying molecular principles is still in its infancy.
A number of signaling cascades have been uncovered that are involved in the control of gonad differentiation in paired schistosome females [13, 14]. In S. mansoni, a kinase complex of three different cellular tyrosine kinases (CTKs) was postulated, whose members were able to interact with different receptors such as β integrin (Smβ-Int1) and venus kinase receptors (SmVKRs) [15–18). The latter represent an unusual type of receptor tyrosine kinases (RTKs) consisting of an intracellular tyrosine kinase (TK) domain with homology to that of insulin receptors (IR) and an extracellular venus-flytrap (VFT) module, whose structure is similar to the ligand binding domain of G protein-coupled receptors (GPCRs) of the C class [19, 20]. RNAi-mediated knockdown of Smβ-Int1 and SmVKRs exhibited their roles in oogenesis and egg formation of S. mansoni females [17, 21]. As potential ligands, L-Arginine (L-Arg) and calcium ions were discovered, which activated SmVKR1 and SmVKR2, respectively, when they were expressed in Xenopus oocytes [22]. Studies in Aedes aegypti have substantiated roles of VKRs for reproduction. AaeVKR expression was found in the ovaries of blood-fed adult females and its activation by the neuroparsin, ovary ecdysteroidogenic hormone, was demonstrated [23]. Since neuroparsins are neuropeptides specific for arthropods [24] it still remains elusive whether and which other molecules except ions and amino acids may be able to activate schistosome VKRs [25].
Physical associations were documented between integrins and GFRs [26]. The latter include RTKs, whose activities can be likewise influenced by integrins [27, 28]. As shown in skin fibroblasts, interactions with integrins support the activation of the GFRs even in the absence of a ligand [29]. Among other functions the αvβ3 integrin was found to directly associate with the insulin-like IGF1 receptor in vascular cells [30]. Such integrin-GFR interactions are mediated by bridging molecules such as ILK (integrin-linked kinase), PINCH (particularly interesting new cysteine-histidine-rich protein) and Nck2 (non-catalytic region of tyrosine kinase adaptor protein). They are central parts of an integrin-actin hub mediating many protein interactions that regulate processes such as pericellular matrix deposition, cell morphology, motility and apoptosis [31–33].
Aims of our study were to investigate whether Smβ-Int1 and SmVKR1, which colocalize in the ovary of S. mansoni females and whose RNAi-mediated knock-downs led to similar phenotypes [17, 21], may interact to govern differentiation processes in this organ. Our findings provide first evidence for this cooperation and for a Smβ-Int1-induced activation of SmVKR1, which is independent from an extracellular VKR ligand. Furthermore, our data suggest that Smβ-Int1/SmVKR1 cooperatively control the differentiation status of oocytes by regulating cell death-associated processes.
In eukaryotic systems integrin-GFR cooperation can be accomplished by ILK, PINCH, and Nck2. As cytoplasmic molecules they bind to the intracellular parts of integrin (ILK) or GFR (Nck2), or simultaneously to both receptors with PINCH as bridging molecule connecting ILK and Nck2 [32, 33]. To investigate the possibility of such an interaction in S. mansoni, we first searched for orthologs in the schistosome database [34, 35]. Based on comparisons to orthologs from human, potential candidate genes were identified and analyzed in silico. Deletion clones, including those potentially originating from alternative splicing events, were excluded from further analyses. Finally, full-length cDNAs of the longest variants of SmILK (Smp_079760), SmPINCH (Smp_020540.2), and SmNck2 (Smp_014850) were amplified by RT-PCR, cloned, and sequenced.
Detailed sequence analyses showed the cloned cDNAs of SmILK, SmPINCH, and SmNck2 isolated from the Liberian strain of S. mansoni [36] were 100% identical to those of the Puerto Rican strain used for genome sequencing [34, 35, 37]. BLAST analyses showed 97% and 89% identity at the cDNA level to ILK orthologs of S. haematobium (XM_012945977.1) and S. japonicum (AY810458.1), respectively. Furthermore, SmILK exhibited all typical domains for this class of enzymes such as three N-terminal ankyrin repeat domains as well as one C-terminal kinase-like domain (S1A Fig). The latter is considered as a catalytically inactive domain, which makes ILK a potential pseudokinase without catalytic but with structural importance [38, 39].
As zinc-finger adaptor protein, PINCH contains five Lim (similar to Lin11, Isl-1 and Mec-3 proteins) domains including eight zinc-binding residues [40]. SmPINCH follows this characteristic structural organization (S2A Fig). At the cDNA level SmPINCH showed 91% and 81% identity to PINCH orthologs of S. haematobium (XP_012797616.1) and S. japonicum (AAX26687.2), respectively.
Nck2, finally, represents another adaptor protein consisting of three SH3-domains and one C-terminal SH2-domain. The latter is important for binding to GFRs whereas one or more of the SH3-domains can support GFR binding or mediate interactions to downstream partners such as PINCH [41]. The occurrence of all these domains at comparable positions (S3A Fig) indicated that SmNck2 is an ortholog of Nck2 proteins. BLAST analyses showed 94% and 85% identity of SmNck2 at the cDNA level to Nck2 orthologs of S. haematobium (XM_012936558.1) / S. japonicum (AY809191.1), respectively.
Phylogenetic analyses of the three molecules with orthologs of vertebrates and invertebrates demonstrated that the schistosome ILK, and Nck2 formed separate clusters together with other parasitic platyhelminths, and schistosome Nck2 was part of a trematode cluster separate from the cestodes and other invertebrates (S1B–S3B Figs). This observation coincides with previous findings made for the schistosome α and β integrins, which according to phylogenetic analyses constitute parasite-specific clades separate from free-living flatworms and further metazoan integrins [17].
In situ-hybridization localized the transcripts of SmILK, SmPINCH and SmNck2 in the ovary and the vitellarium of the female as well as in the testis of the male (Fig 1). Ovary and testis transcription were independently confirmed by gonad RNA-specific RT-PCRs [42] showing amplification products of the expected sizes (S4 Fig). In each case the in situ-hybridization signals appeared to be stronger in the large part of the bulb-like ovary which contains mature primary oocytes. Furthermore, SmILK and SmPINCH transcripts were found in the parenchyma of both genders and in the subtegumental area, within the gastrodermis of males, and around the ootype, although not as dominant as in the gonads. Sense transcripts of all three genes as controls showed varying degrees of week signals (very low in case of SmNck2). This indicates antisense regulation, a finding made for different schistosome genes before including integrins and further molecules involved in reproduction [17, 43, 44].
To elucidate the roles of SmILK, SmPINCH, and SmNck2 in complex formation with Smβ-Int1 and SmVKR1, we started a series of biochemical experiments in Xenopus oocytes. Previous studies had demonstrated the efficiency of expression of schistosome genes in this system and, furthermore, the possibility to study kinase activities by their capacities to induce resumption of meiosis and germinal vesicle breakdown (GVBD) [15, 45]. Activation by L-Arg of the SmVKR1 kinase led to GVBD, which failed when a dead-kinase mutant of SmVKR1 was used [22].
No GVBD was observed in Xenopus oocytes when a wildtype form of SmILK was expressed (Table 1). This is in agreement with the present view that SmILK may represent a pseudokinase ortholog of eukaryote ILKs lacking catalytic activity [38, 39]. Also PINCH and Smβ-Int1 failed to induce oocyte maturation. According to previously published data [22], the wildtype form of SmVKR1 induced 90% GVBD only in the presence of its activating ligand L-Arg. The constitutively active SmVKR1 mutant induced GVBD, whereas a dead kinase mutant did not.
When Smβ-Int1, SmILK, and SmPINCH were coexpressed, no GVBD was observed. However, when these three proteins were co-expressed with SmVKR1, GVBD was obtained independently of the addition of L-Arg (Table 1, Inj 11). This suggested that SmVKR1 kinase activation could be induced by its participation to the complex with Smβ-Int1, SmILK and SmPINCH. However, in this injection (no. 11) GVBD was activated in the absence of SmNck2. This finding led to the questions whether S. mansoni Nck2 is dispensable for complex formation, or whether Xenopus Nck2 may have rescued complex formation in this case? When deletion mutants of SmILK (SmILKΔAnk1, missing the first ankyrin repeat necessary for interaction with PINCH; [32, 33]) or SmPINCH (SmPINCHΔLIM4, missing the fourth Lim domain necessary for interaction with Nck2/GFR; [32, 33]) or SmNck2 (SmNck2ΔSH3, missing the SH3 domain necessary for interaction with PINCH; [33]) were used, activation of SmVKR1 was no more observed. The result with the deletion mutant of SmNck2 (Table 1, Inj 15) indirectly indicated the presence of Xenopus Nck2 in the complex and a competitive situation between Xenopus Nck2 and SmNck2ΔSH3 when the latter protein was present. Furthermore, adding the ILK-inhibitor QLT-0267 (1 μM) also prevented GVBD in oocytes expressing the wildtype forms of Smβ-Int1, SmILK, SmPINCH, SmNck2, and SmVKR1. These data suggest a direct interaction of these proteins, and also that Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex formation is able to induce GVBD in Xenopus oocytes in the absence of a ligand for SmVKR1. This interaction appeared to be specific for SmVKR1, since other RTKs such as SmVKR2 [22], SER (S. mansoni EGF Receptor; [45, 46] or the insulin receptor orthologs SmIR 1 and SmIR2 [47] were not activated by this complex. Furthermore, since GVBD was supposed to be dependent on the kinase activation of SmVKR1, we checked the autophosphorylation status of SmVKR1 by Western blot analysis. GVBD occurred only when SmVKR1 was phosphorylated (see below).
To confirm the existence and the function of this complex, the HA-tagged intracellular part of Smβ-Int1 [17] was co-expressed in Xenopus oocytes together with V5-tagged variants of SmILK (wildtype and SmILKΔAnk1), SmVKR1 (dead kinase and constitutively active mutants), or Flag-tagged SmPINCH (wildtype and SmPINCHΔLIM4) and SmNck2 (SmNck2ΔSH3) for co-immunoprecipitation. In this series of experiments, L-Arg was not used for stimulating SmVKR1 activity (Table 2). Besides investigating the GVBD-inducing activity of appropriate combinations of complex members in their wildtype or mutated forms, oocyte lysates were immunoprecipitated with an α-HA antibody, and Western blot analyses were performed with α-HA or α-V5 to investigate the presence of members complexed with Smβ-Int1. The results showed V5-tagged SmILK, SmPINCH, and SmVKR1 in HA-tagged precipitates only when the wildtype forms were used (Fig 2, lane 11). A replication of the experiment demonstrated complex formation also when the constitutively active SmVKR1 variant was used (Fig 3, lane 14). As expected, no V5-tagged precipitates were detected, when the deletion variants SmILKΔAnk1 or SmPINCHΔLIM4 or the dead kinase variant of SmVKR1 were used. These results corresponded to the GVBD results obtained, confirming the formation of a complex of these four proteins. However, complex formation was possible due to the presence of Xenopus Nck2 (Fig 3), which was detected by Western blot analysis. This confirmed the previous interpretation of the GVBD experiment (Table 1).
To investigate SmVKR1 activation upon complex formation, the phosphorylation status of this receptor was investigated. After confirming that SmNck2 is also part of the immunoprecipitated protein complex (Fig 4A), Western blot analyses showed that SmVKR1 phosphorylation (without adding L-Arg) occurred only when it was coexpressed together with the wildtype forms of Smβ-Int1, SmILK, SmPINCH, and SmNck2 (Fig 4B). When deletion mutants of individual complex partners or the ILK inhibitor QLT-0267 were used, no SmVKR1 phosphorylation was detected. This is in perfect agreement with the GVBD results obtained with the same combinations of molecules (Table 1).
In a previous study it was shown that upon SmVKR1 stimulation with L-Arg signaling pathways known to be involved in RTK signaling were activated in Xenopus oocytes. Among these were ERK, JNK, and Akt pathways [21]. To find out whether Smβ-Int1/SmVKR1 complex formation without L-Arg induction activates the same signaling cascades in Xenopus oocytes, we performed cotransfection experiments and subsequent phosphorylation assays. Indeed, the obtained results showed that SmVKR1 in cooperation with all complex partners induced the phosphorylation of ERK, JNK, and AKT in a ligand-independent manner (Fig 5). In analogy to the previous results, no phosphorylation of these signaling molecules was observed when one of the complex members was used in its mutated form or when the ILK-inhibitor QLT-0267 was applied. The effect of Smβ-Int1/SmVKR1 complex formation on the phosphorylation of ERK, JNK, and AKT resembled the activation of Xenopus oocyte receptors by insulin or the natural ligand progesterone.
Because SmILK is one of the decisive complex partners mediating Smβ-Int1 cooperation with SmVKR1 we functionally analyzed this molecule in more detail. RNAi-mediated SmILK knock-down experiments were performed with S. mansoni couples in vitro, and the knock-down value determined by qPCR to be nearly 90% (S5 Fig). Following treatment with SmILK-dsRNA, pairing stability was not affected, and the amount of couples was similar to the untreated control group. However, egg production per (remaining) couple of the treated group significantly decreased during the observation period from 48 h post treatment on compared to the control (Fig 6).
Inhibiting ILK was also achieved by QLT-0267, and following treatment with different concentrations (50–200 μM) a negative effect on pairing stability was observed. Furthermore, also egg production per remaining couple decreased in a concentration-dependent manner from 48 h post treatment on (Fig 7). Morphologically, CLSM analysis showed effects of QLT-0267 on oogenesis in paired females. This inhibitor caused not only a reduction of oocyte number and the mislocalization of oocytes of various stages of differentiation in the different parts of the ovary but also oocyte degeneration (Fig 8). The intensity of the phenotype increased with QLT-0276 concentration. A similar oocyte-related observation was made by RNAi in ILK-dsRNA treated paired females (Fig 8), although the strength of the observed phenotype (less oocytes, mislocalization, degeneration) was weaker compared to inhibitor treatment.
Previous studies in cancer cells provided evidence that among other functions ILK is involved in cytoskeletal reorganization and cell survival, and its deregulation can contribute to errors in cell division and genomic instability [48]. Microtubule disruption was shown to induce cytoskeleton as well as cell adhesion changes. This led to focal adhesion kinase hydrolysis and the onset of apoptosis, a phenotype that was rescued by ILK overexpression [49]. Because there is evidence that apoptosis has a biological function for the maintenance of the maturation state of the reproductive organs of paired females [50], we investigated whether SmILK may be involved in this processes in S. mansoni. To this end we compared paired females treated with QLT-0267 and DMSO as control and performed immunolocalization with a β-tubulin antibody (S6 Fig). Under inhibitor influence the number of immature and primary oocytes was reduced in inhibitor-treated females. Compared to the control, primary oocytes clustered closer together, they appeared more compact, and some appeared as rounded up (Fig 9).
In a previous study first hints were obtained that laminins as extracellular matrix proteins may interact with Smβ-Int1 [17]. To investigate whether there is also an influence on components of the extracellular matrix we immunolocalized laminin in paired females treated with QLT-0267 or DMSO (S6 Fig). Indeed, a concentration-dependent decrease of laminin staining was observed within the epithelium surrounding the ovary of treated females (Fig 10). TUNEL assays finally confirmed apoptotic processes in ovaries of females treated with QLT-0267. TUNEL-positive cells occurred mainly within the smaller part of the ovary containing immature oocytes (Fig 11). To get further support for apoptotic processes induced by QLT-0267 in females, caspase-3 activity was determined in inhibitor-treated females. Following treatment the level of caspase-3 activity increased significantly (Fig 12). Next we investigated whether the expression of genes involved in early steps of apoptosis is affected. To induce the mitochondrial apoptosis pathway, a number of pro-apoptotic BCL-2 (B cell lymphoma 2) proteins collaborate with the outer mitochondrial membrane to permeabilize it. BAK (BCL-2 Antagonist Killer 1) and BAX (BCL-2 Associated X protein) are pro-apoptotic BCL-2 family members which are essential for the permebilization of the mitochondrial outer membrane [51]. We selected these genes because presumptive orthologs exist in the genome of S. mansoni (BAK, Smp_095190; BAX, Smp_072180). Therefore, we investigated the transcript profiles of Smp_095190 and Smp_072180 in schistosome females after treatment of couples for 72 h with 50 μM QLT. Compared to a DMSO control, we detected an upregulation of BAX after treatment, whereas BAK transcription remained constant. Upon RNAi both schistosome orthologs BAK and BAX were transcribed at higher levels (S7 Fig).
Although much research has been performed on integrins and integrin signaling in different organisms, there is not much known about their roles in platyhelminths. Here we report on an integrin-signaling complex in S. mansoni consisting of Smβ-Int1, SmILK, SmPINCH, SmNck2, and SmVKR1. According to phylogenetic analyses, SmILK, SmPINCH, SmNck2 form clusters that are specific for parasitic platyhelminths as it was shown for integrins before [17]. Together with the exclusive role of VKRs [25], it appears likely that parasites have modified the function of insulin-like signaling as well as integrins and their interacting partners for specific signaling purposes. Among these, at least one deals with the reproductive biology of platyhelminths. In this context schistosomes exhibit remarkable features because of the pairing-dependent development and maintenance of the differentiation status of female gonads. The involvement of schistosome VKRs and integrins for this physiological process has already been demonstrated [17, 21], and studies on the VKR ortholog AaeVKR of A. aegypti support the assumption of a specific role of VKRs for oogenesis and/or egg formation [23].
Our study provides first evidence for cooperation between integrin and VKR signaling in S. mansoni. This interaction is mediated by SmILK, SmPINCH, and SmNck2, cytoplasmic molecules with bridging function. Their colocalization with Smβ-Int1 and SmVKR1, especially in the ovary, indicated potential functions for the reproductive biology of schistosomes. In all three cases the intensities of localization signals in the ovary were higher in its posterior part which contains mature primary oocytes. Smβ-Int1 and Smα-Int1 were localized in the ovary—also dominating in its posterior part -, the vitellarium, the testes, the ootype-surrounding area, the subtegument, and within the parenchyma [17]. SmVKR1 expression was localized mainly in the female ovary, especially in mature, primary oocytes in the posterior part. In addition, SmVKR1 was also localized around the ootype and in the parenchyma of males [21]. Thus SmILK, SmPINCH, SmNck2 colocalized widely with Smβ-Int1 and SmVKR1 including their preferential occurrence in mature, primary oocytes, a prerequisite for potential interactions.
Different experiments with Xenopus oocytes expressing the complex members alone or in defined combinations of wildtype or mutated forms finally confirmed also by co-immunoprecipitation that a Smβ-Int1-SmILK-SmPINCH-SmNck2-SmVKR1 complex can be formed. GVBD assays demonstrated the biochemical function of this complex and the potentiality of SmVKR1 to be activated inside of the complex and to induce—in the absence of its ligand—processes leading to GVBD, which was confirmed by the results obtained. This suggests a new mode of SmVKR1 activation, which is achieved in a ligand-independent fashion by indirect cooperation with a β-integrin receptor. As mediators, GFR-specific and β integrin-specific adapter molecules operate, in this case SmILK, SmPINCH, and SmNck2. The participation of SmNck2 was shown indirectly by the use of a deletion mutant that negatively influenced GVBD, by Western blot analysis confirming the presence of Xenopus Nck2 in complexes without SmNck2, and finally by co-immunoprecipitation of SmNck2 after its addition.
In Xenopus oocytes, ligand-activated RTKs as IRs trigger the activation of Erk MAPK and PI3K/Akt/mTOR and JNK pathways resulting in meiotic maturation [52]. As shown for L-Arg-activated SmVKR1 [21], in our actual study the phosphorylation of Erk1/2, Akt, and JNK in Xenopus oocytes was achieved also by Smβ-Int1/SmVKR1 complex formation without ligand activation. Thus similar to IR activation, complex-activated SmVKR1 induced signaling processes involved in protein synthesis and cellular growth associated with Xenopus oocyte maturation, which substantiates the IR-like function of SmVKR1 but also its conjunction with oogenesis.
Functional analyses of SmILK in Xenopus oocytes or as a member of the complex by RNAi and inhibitor studies finally indicated that this molecule represents a pseudokinase being involved in different processes in schistosomes. Among these is inside-out signaling in the ovary because the extracellular matrix as part of the epithelium surrounding the ovary was changed upon inhibiting SmILK as shown by laminin immunolocalization. Furthermore, SmILK appeared to control oocyte localization within the ovary, and oocyte survival. Inhibiting SmILK activity led to the reduction of the amount of immature oocytes and the degeneration of mature, primary oocytes. This is in part explained by apoptotic processes, for which evidence was obtained by TUNEL assays in case of immature oocytes, by determining caspase-3 activity which increased following inhibitor treatment, and by transcriptional analysis of the schistosome orthologs of BAK and BAX, two pro-apoptotic genes [51]. A recently conducted RNA-seq study revealed that both genes were expressed in schistosome females and within the ovary. Interestingly, the profiling of transcript abundance revealed that both genes were more abundantly transcribed in the ovaries of unpaired, immature females. After pairing, transcript abundance of both genes decreased in the ovary ([53]; S7 Fig). This supports the conclusion that apoptosis plays a role in oocyte differentiation, and that males exert a regulatory influence on this—suppressing apoptosis in the gonads of their female partners during a constant pairing contact (Fig 13). Degenerated primary oocytes were also detected that did not respond to TUNEL staining. Thus it seems feasible that further, apoptosis-independent processes leading to cell death contribute to oocyte degeneration.
In summary, the results presented here strongly suggest that the Smβ-Int1/SmVKR1 complex in the ovary of paired schistosomes is important for the maintenance of the differentiation status of oocytes and their survival. Against the background of the unusual reproductive biology of schistosomes this conclusion supports findings of a previous, independent study showing that apoptosis is used to control vitelline cell survival in a pairing-dependent manner in S. mansoni [50]. In this context our results match to a scenario of cell death processes controlling gonad maintenance in schistosome females and thus contribute to the understanding of biological processes controlling reproductive biology in this exceptional parasite. It has been hypothesized before that SmVKR1, possibly activated by L-Arg delivered with the male seminal fluid [21], is responsible for meiosis resumption and/or oocyte migration in schistosome females. In view of the new results it appears feasible that integrin-signaling contributes to this process providing a SmVKR ligand-independent alternative for activation (Fig 13). This could be achieved by mechanosensory forces. Indeed, integrins have been shown to sense, sort, and transduce mechanical forces into cellular responses. This form of integrin-based mechanotransduction contributes among others to cell growth, cell migration, gene expression including the activation of kinases, but also to apoptosis [54–57].
Animal experiments using Syrian hamsters (Mesocricetus auratus) as model hosts were performed in accordance with the European Convention for the Protection of Vertebrate Animals used for experimental and other scientific purposes (ETS No 123; revised Appendix A) and were approved by the Regional Council (Regierungspraesidium) Giessen (V54-19 c 20/15 c GI 18/10).
S. mansoni was maintained in Biomphalaria glabrata as the intermediate host, with Syrian hamsters (Mesocricetus auratus) as the definitive hosts [36]. Adult worms were obtained by hepatoportal perfusion at day 46 or day 67 (in case of single sex infection; [58]) post-infection, respectively, and kept in M199 medium (Gibco) supplemented with 10% newborn calf serum and 1% ABAM-solution (10,000 units penicillin, 10 mg streptomycin and 25 mg amphotericin B per ml) at 37°C and 5% CO2 for 24h until the experiments started.
Couples were cultivated in 6-well plates in groups of eight per well (n = 3) and 3 ml supplemented M199 medium for RNA interference (RNAi) experiments (see below) or inhibitor studies. The latter were performed with the integrin-linked kinase (ILK) inhibitor QLT-0267 (Dermira, Inc., USA; [59]) which was added at final concentrations and period of times as indicated. It targets the ATP-binding site of ILK and was shown to be as effective as siRNA-mediated depletion of ILK [60]. Since ILK exerts no catalytic function, the inhibitory effect of QLT-0267 was explained by an impairment of the stability of ILK [61]. Equivalent volumes of dissolvent DMSO was used as control. Worms were monitored by bright-field microscopy (CX21, Olympus; Labovert FS, Leitz) over periods of 24 h– 96 h to analyze pairing stability, egg production, gut peristalsis and movement.
For cloning of the full-length cDNAs of SmILK (Smp_079760), SmPINCH (Smp_020540.2), and SmNck2 (Smp_014850), total RNA was isolated from adult schistosomes using Trizol reagent (Invitrogen). Residual DNA was removed by DNase digestion (RNAeasy kit, Qiagen) following the manufacturer’s instruction. RNA quality was checked by Bioanalyzer microfluidic electrophoresis (Agilent Technologies). Starting RT-PCR the synthesis of cDNA was performed with 1 μg RNA using QuantiTect Reverse Transcription Kit (Qiagen). PCR reactions were performed in a final volume of 25 μl using primer end concentrations of 800 nM, denaturation at 95°C for 30 sec, annealing at 54°–64°C depending on the primer combinations (S1 Table), and elongation at 72°C for up to 2 min, and using FirePol-Taq (Solis biodyne). As vectors for cloning, pACT2 (Clontech), pcDNA3.1 (Invitrogen), or pBridge (Clontech) were used for directional cloning via restriction enzyme sites. Full-length SmILK cDNA was cloned via NotI and XbaI into pcDNA3.1, full-length SmPINCH cDNA via EcoRI/PstI into pBridge, and full-length SmNck2 via BamHI and XbaI into pcDNA3.1 (S1 Table). Primers designed for RT-PCRs to generate these cDNAs contained appropriate restriction sites for cloning. The sequence integrities of all cloned cDNAs were verified by sequencing (LGC Genomics, Berlin).
Ovaries of female worms were isolated using the combined detergents/enzyme-based organ isolation protocol [42]. In short, isolated adult females (about 50 each) were transferred into 2 ml-reaction vessels and washed twice with 2 ml of non-supplemented M199-medium at room temperature. The medium was removed, and 500 μl of tegument solubilisation (TS)-solution was added (0.1% of each following compounds in DEPC (diethylpyrocarbonate)/PBS (phosphate-buffered saline): Brij 35 (Roth), Nonidet P40-Substrate (Fluka), Tween80 (Sigma) and TritonX-405 (Sigma), pH 7.2–7.4) followed by incubation in a thermal shaker (TS-100, Biosan) for 5 min at 1,200 rpm at 37°C. Shaking was repeated twice, and the solution was replaced after each cycle. Then the worms were rinsed three times with M199 and subsequently treated with elastase (300 μl elastase solution: 5 U/ml in M199; Sigma) at 37°C and 650 rpm in the thermal shaker to release the ovaries. Digestion was monitored by bright-field microcopy (Leica) and stopped when the gonads were released from the disrupted and digested worm carcasses. Finally, the gonads were manually collected by pipetting and transferred into supplemented M199 medium.
Sample preparation of S. mansoni adults was conducted as described previously [62]. In short, schistosome pairs were fixed in Bouin's solution (picric acid/acetic acid/formaldehyde; 15/1/5) followed by embedding in paraplast (Paraplast plus, Sigma). Sections of 5 μm thickness were incubated in xylol and after rehydration, the sections were treated with proteinase K (1 μg/ml) and dehydrated. As probe, in vitro-generated transcripts were synthesized and labeled with digoxigenin as suggested by the manufacturer (Roche). The correct sizes of labeled sense and antisense transcripts were checked by gel electrophoresis, and the RNA quality was tested by blotting and detection of digoxigenin using alkaline phosphatase-conjugated anti-digoxigenin antibodies, naphtol-AS-phosphatase, and Fast Red TR (Sigma). In situ-hybridization was performed at 57°C for 16 h. Afterwards, the sections were washed up to 0.5 × SSC (75 mM NaCl, 7.5 mM sodium citrate, pH 7.0), and detection of alkaline phosphatase was performed as mentioned above.
The intracellular part of Smβ-Int1 containing the C-terminus with an HA-tag at its N-terminus was subcloned into pcDNA 3.1 (Invitrogen) as described earlier [17]. Capped messenger RNA (cRNA) encoding Smβ-Int1 C-term was synthesized in vitro (T7 mMessage machine Kit, Ambion, USA) following a previously established protocol [45]. Furthermore, V5-tagged SmILK and SmPINCH and Flag-tagged SmNck2 were cloned the same way into pcDNA 3.1, and their sequence identities confirmed by commercial sequencing. Also cRNAs were prepared from these clones as well as from V5-tagged SmVKR1 variants (wildtype SmVKRwt, dead kinase mutant SmVKRdk [= KO], and a constitutively active mutant SmVKR1YYRE [= XE]) cloned in pcDNA 3.1 as reported in a previous study [22]. Interaction studies between these proteins (see results) were done by co-injecting different cRNA combinations into Xenopus oocytes as reported before [17, 45]. Expressed proteins were detected by immunoprecipitation and Western blot analyses. Following the standard procedure [45], 30 oocytes were lysed in 300 μl of buffer (50 mM HEPES, pH 7.4, 500 mM NaCl, 5 mM MgCl2, 1 mg/ml bovine serum albumin, 10 μg/ml leupeptin, 10 μg/ml aprotinin, 10 μg/ml soybean trypsin inhibitor, 10 μg/ml benzamidine, 1 mM PMSF, 1 mM sodium vanadate) after 5 h or 15 h of expression. Following centrifugation at 4°C for 15 min and 10,000 g, the resulting supernatants were incubated with anti-HA (1:100; Invitrogen) or anti-V5 (1:100; Invitrogen) then added to protein A-Sepharose beads (5 mg, Amersham Biosciences) for 1 h at 4°C. After washing three times, immune complexes were eluted from the beads in Laemmli buffer and analyzed by SDS-PAGE (7.5%–15% polyacrylamide gels).
Western blot analyses were performed using anti-V5 (1: 50,000), anti HA (1: 50,000), anti-Flag (1: 1,000), anti-human nck2 (1: 1,000, nck2(8.8): sc-20020, Santa Cruz Biotechnology), or PY20 (1: 10,000; anti-phosphotyrosine, BD Biosciences) antibodies. The following primary antibodies were applied to confirm the presence of total or phosphorylated ERK2, JNK and Akt kinases: anti-ERK2 (1: 10,000; Santa Cruz Biotechnology), anti-phospho p44/p42 MAPK (ERK1/2; Thr 202/Tyr 204; 1: 10,000; Cell Signalling Technology), anti-c-jun N-terminal kinase JNK (1: 10,000; Sigma), anti-active JNK polyclonal antibody (1: 8,000; Promega), anti-Akt1 (C-20; 1: 5,000; Santa Cruz Biotechnology), anti-phospho Akt (Thr308; 1: 5,000; Upstate Biotechnology) and anti-phospho Akt (Ser 473; 1: 5,000; Upstate Biotechnology). Mouse, rabbit or goat Trueblot secondary antibodies (eBioscience) were used as secondary antibodies and chemoluminescence was detected using the advanced ECL detection system (Amersham Biosciences).
Following standard protocols for RNAi in adult schistosomes [15, 63], double-stranded RNA of approximately 500 bp was synthesized (nucleotide position 559–1016) using the MEGAscript RNAi kit (Life Technologies). Gel electrophoresis in 1.2% agarose-MOPS was conducted to prove for single RNA bands of the correct size. Schistosome couples in groups of eight pairs (n = 3) were electroporated in the presence of 25 μg dsRNA and subsequently soaked in vitro for 96 h. Every 24 h the treated worms were inspected and different parameters evaluated such as pairing stability, egg production, gut peristalsis and movement.
Schistosome samples were collected and transferred into PeqGOLD TriFast (Peqlab). After storage at -80°C or immediately after transfer, RNA isolation was done following the manufacturer’s instructions (Peqlab). About 500 ng total RNA was used for cDNA synthesis using the Quantitect Reverse Transcription Kit (Qiagen). For PCR, 1 μl of a 1:20 dilution of cDNA was tested using exon-spanning PDI (protein dilsufide isomerase) 5’/3’ primers; forward: 5´-AAATGATGCCCCGACTTACC-3´ and reverse: 5´- TCATCCCAAACTGGAGCAAG-3`[62, 64]) to confirm that the genomic DNA was properly removed. For quantitative RT PCR (qRT-PCR), a RotorGene-Q PCR cycler (Qiagen) was used and all reactions were set up in triplicates. Each reaction had a final volume of 25 μl; 5 μl of a 1:20 cDNA served as template and 125 nM (final concentration) of each primer were added to 12.5 μl of 2x PerfeCTa SYBR Green super mix (Quanta). No template controls (NTC) were included in each run. RNAi-mediated knockdown of gene expression was analysed by absolute quantification. Therefore, a standard curve on diluted gel eluate was included in each run [65]. For qPCR analyses to study transcript profiles of SmBAK (Smp_095190), SmBAX (Smp_072180), SmmTor (Smp_122910), and SmSod (Smp_056440) several genes were tested for their suitability as reference. Based on transcriptome data [53] the gene Smp_008900 (annotated as eukaryotic translation initiation factor 4 gamma) fulfilled this criterion and was further tested under various condition using an absolute quantification approach. To this end, the Smp_008900 amplicon was cloned into pDrive (Qiagen) and served as template in dilution series. Different cDNAs of electroporated and inhibitor-treated S. mansoni couples confirmed constant numbers of Smp_008900 transcripts (primers, see S1 Table).
Sliced specimens of 4 μm thickness on slides were deparaffinized, dehydrated and then equilibrated in proteinase-K buffer (100 mM Tris/Cl, 50 mM EDTA pH 8.0) for 5 min. Subsequently, treatment with proteinase K (1 μg/ml) was performed for 20 min at 37°C. Afterwards, slides were rinsed once with 1x PBS and immersed twice with 500 μl washing buffer provided by the fluorometric DNA-fragmentation detection kit III (F-dUTP; Promokine) for 5 min at ambient temperature. The staining solution containing FITC-labeled dUTP was prepared according to the manufacturer’s instruction (Promokine). A control solution was prepared without TdT enzyme. Subsequently, specimens were immersed with 100–200 μl of the staining solution and kept in the dark. Following incubation for 60 min at 37°C, the slides were rinsed twice with 500 μl rinse buffer (Promokine) for 5 min at ambient temperature and counterstained with 200 μl propidium iodide/RNase A solution (Promokine) for 15 min. Slides were finally mounted with FluoroMount (Roth) and analyzed within 3 hours after staining by fluorescence microscopy (Ex/Em = 488/520 nm for FITC, and 488/623 nm for PI).
Following perfusion, S. mansoni couples were taken in culture and treated with DMSO or with QLT-0267 (100 μM) for 72 h. Following treatment the couples were carefully separated with featherweight forceps, and females and males transferred separately into 1.5 ml tubes with 500 μl 1x PBS for washing. After sedimentation, the supernatant was replaced by 50 μl cold (4°C) cell lysis buffer of the caspase-3 colorimetric assay kit (Promokine) and the samples kept on ice. After 10 min incubation, worms were homogenized with sterile pestils and kept on ice for further 10 min. Debris were subsequently sedimented by 10.000 x g centrifugation for 2 min at 4°C and afterwards placed back on ice. Subsequently, 20 μl of the supernatant were mixed with 30 μl of pre-transferred cell-lysis buffer in 96-well plates, and 50 μl of two-fold reaction buffer complemented with 10 μM DTT was added. Two hours incubation at 37°C allowed cleaving the p-nitroanilide-labeled substrate DEVD (Promokine) that was added at a final concentration of 200 μM. Samples were read at 405 nm with a Varioscan plate reader (Thermo Fisher Scientific). Samples were normalized to the protein concentration that was determined using the BCA assay kit (Pierce) promptly after the caspase-3 assay was set up. The results relied on the analysis of three biological replicates obtained from independent perfusions. In contrast to previously published protocols [50] caspase-3 activity was not detected when the homogenization step was omitted.
Adult worms were stained with carmine red for a general morphological analysis by CLSM according to previously published protocols [14, 66]. For microscopy (CLSM; Leica TSC SP2 microscope) and documentation the probes were excited with a 488 nm He/Ne laser and emission was captured with a 470 nm long-pass filter in reflection mode as described before [14].
Fluorescence microscopy of antibody-stained worm sections (5 μM) was done using an Olympus IX 81 inverted microscope. Anti-laminin (antibodies-online.com; LN, ABIN268409) and anti-β-tubulin (antibodies-online.com; anti-TUBB, ABIN269949) antibodies were used in concentrations of 1: 5,000 each, as recommended by the manufacturer (Novus Biologicals). As secondary antibody, a fluorescence-labeled goat anti-rabbit antibody was used (LI-COR Bioscience, IRDye 680LT; 1: 5,000). These antibodies were tested on lysates of adult schistosomes by Western blot analyses as described before [42] using 15 μg protein each, which had been size-separated by SDS-PAGE using 7.5%–15% polyacrylamide gels depending on the size of the protein to be detected.
The following public domain tools were used: BLASTx (http://www.ncbi.nlm.nih.gov/BLAST), SchistoDB (http://schistodb.net/schisto/; [67]), and WormBase ParaSite (release 6, April 2016; http://parasite.wormbase.org/; [37]). The online-tool SMART (http://smart.embl-heidelberg.de/) [68] was used to predict protein domains. Primer3Plus was used for primer design (http://www.bioinformatics.nl/cgi-bin/primer3plus/primer3plus.cgi) and Oligo Calc for analyzing primer properties (http://www.basic.northwestern.edu/biotools/oligocalc.html).
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10.1371/journal.ppat.1003902 | Reengineering Redox Sensitive GFP to Measure Mycothiol Redox Potential of Mycobacterium tuberculosis during Infection | Mycobacterium tuberculosis (Mtb) survives under oxidatively hostile environments encountered inside host phagocytes. To protect itself from oxidative stress, Mtb produces millimolar concentrations of mycothiol (MSH), which functions as a major cytoplasmic redox buffer. Here, we introduce a novel system for real-time imaging of mycothiol redox potential (EMSH) within Mtb cells during infection. We demonstrate that coupling of Mtb MSH-dependent oxidoreductase (mycoredoxin-1; Mrx1) to redox-sensitive GFP (roGFP2; Mrx1-roGFP2) allowed measurement of dynamic changes in intramycobacterial EMSH with unprecedented sensitivity and specificity. Using Mrx1-roGFP2, we report the first quantitative measurements of EMSH in diverse mycobacterial species, genetic mutants, and drug-resistant patient isolates. These cellular studies reveal, for the first time, that the environment inside macrophages and sub-vacuolar compartments induces heterogeneity in EMSH of the Mtb population. Further application of this new biosensor demonstrates that treatment of Mtb infected macrophage with anti-tuberculosis (TB) drugs induces oxidative shift in EMSH, suggesting that the intramacrophage milieu and antibiotics cooperatively disrupt the MSH homeostasis to exert efficient Mtb killing. Lastly, we analyze the membrane integrity of Mtb cells with varied EMSH during infection and show that subpopulation with higher EMSH are susceptible to clinically relevant antibiotics, whereas lower EMSH promotes antibiotic tolerance. Together, these data suggest the importance of MSH redox signaling in modulating mycobacterial survival following treatment with anti-TB drugs. We anticipate that Mrx1-roGFP2 will be a major contributor to our understanding of redox biology of Mtb and will lead to novel strategies to target redox metabolism for controlling Mtb persistence.
| Approximately 30% of the global population is infected with Mycobacterium tuberculosis (Mtb). Persistence of Mtb in host phagocytes depends on its ability to resist oxidant-mediated antibacterial responses. Mycothiol (MSH) is the main antioxidant that provides an abundant source of reducing equivalent, which protects Mtb from oxidative stress encountered during infection. The majority of research into redox signaling in Mtb has relied on chemical analysis of MSH in whole cell extract, which creates oxidation artifacts and prohibits dynamic imaging of MSH redox state during infection. We have successfully developed a novel and noninvasive tool based on genetically encoded redox sensitive fluorescent probes to perform real-time measurement of mycothiol redox potential (EMSH) in Mtb during infection. For the first time we reveal the EMSH of virulent and avirulent mycobacterial strains, including drug-resistant clinical isolates. We used this technology and came to the surprising conclusion that within a single infected macrophage there is heterogeneity in the redox signature of individual Mtb bacilli. Importantly, we show that anti-TB drugs accelerate oxidative stress in Mtb within infected macrophages and redox heterogeneity can contribute to emergence of drug tolerant population. These findings have implications for mycobacterial persistence following treatment with anti-TB drugs.
| It is estimated that nearly 2 billion people currently suffer from latent Mycobacterium tuberculosis (Mtb) infection and ∼1.4 million people succumb to tuberculosis (TB) annually [1] and [www.who.int/tb]. The ability of Mtb to adapt and resist killing by the immune system facilitates its survival, replication, and persistence. Following aerosol exposure, Mtb is engulfed by macrophages, and exposed to antimicrobial redox stresses including reactive oxygen and nitrogen species (ROS and RNS) [2]. Mycobacterial killing by ROS and RNS is important for host resistance as demonstrated by the increased susceptibility of NADPH oxidase (NOX2) and nitric oxide synthase (iNOS) deficient mice following Mtb challenge [3], [4]. Moreover, children with defective NOX2 suffer from chronic granulomatous disease, are susceptible to TB and even develop severe complications after vaccination with BCG [5]. Consistent with these observations, a recent study elegantly demonstrated the requirement of NADPH oxidase in exerting neutrophil-mediated killing of mycobacteria during infection [6].
In response to oxido-reductive stress, Mtb upregulates multiple redox sensing pathways, including SigH/RshA, DosR/S/T, MosR, and the WhiB family, to maintain redox homeostasis [7]–[12]. Furthermore, modified cell wall lipids [13], antioxidant enzymes, and cellular redox buffers, such as MSH, thioredoxins (TRXs), and ergothionine (ERG), assist Mtb in mitigating redox stress [14]. Collectively, these studies indicate that dynamic reprogramming of intrabacterial redox metabolism in response to host environment is vital for the persistence of Mtb. Despite its recognized importance, tools for monitoring changes in redox state of Mtb during infection do not exist. Customary approaches involving NAD+/NADH and MSH/MSSM measurements require cell disruption, which precludes real-time analyses, and are plagued by oxidation artifacts. Alternatively, redox-sensitive dyes which are commonly used to detect ROS generation in cells, suffer from non-specificity, irreversibility, and these dyes cannot deliver information regarding the redox potential of a specific redox couple [15], [16]. Therefore, development of a specific, sensitive, and non-invasive technology to study defined redox changes in Mtb would contribute significantly to delineating novel redox pathways involved in persistence, drug tolerance, as well as pathogenesis of Mtb, and may also have utility in high throughput screens to identify small-molecule modulators of intrabacterial redox homeostasis.
Genetically encoded reduction-oxidation sensitive GFP indicators (roGFPs) have been developed to measure the intracellular glutathione (GSH) redox potential (EGSH) through interaction with glutaredoxins (Grxs) in many organisms [17]. However, the preference of roGFPs for GSH limits their use in non-GSH producers like gram positive bacteria (e.g. Bacillus species, Staphylococcus aureus, Deinococcus radiodurans) and actinomycetes (e.g. Mycobacteria, Corynebacteria, Streptomyces, Nocardia), which respectively contain bacillithiol (BSH) and MSH as their major redox buffers [18], [19].
Because MSH is functionally analogous to GSH and plays a prominent role in maintaining the reduced state of the mycobacterial cytoplasm [20], [21], we engineered roGFP2 to generate a MSH-specific intracellular probe, Mrx1-roGFP2. Importantly, Mrx1-roGFP2 allows imaging of EMSH in diverse mycobacterial species and strains, including drug-resistant clinical isolates during infection. Lastly, we examine the potential of antibiotics in inducing intramycobacterial oxidative stress in the physiological context of infection and demonstrate the functional importance of MSH redox signaling in intramacrophage survival and sensitivity to anti-TB drugs. Our study provides an elegant tool to probe redox biology of Mtb under diverse environmental conditions including in vivo experimental models of TB.
We selected roGFP2 as a fluorescent partner in the biosensor construct because it exhibits the largest dynamic range, it is brighter, pH insensitive, and is resistant to photoswitching [22]. The oxidation of two cysteines on either side of roGFP2 chromophore (S147C and Q204C) [22] generates a disulfide bond and increases the fluorescence intensity at ∼400 nm with concomitant decrease at ∼490 nm, while reduction reverses the spectrum. The 400/490 nm ratio thus reports the redox state of the cell or compartment in which it is expressed [22]. Because the sensor is ratiometric it eliminates errors due to variations in roGFP2 concentrations during different growth phases of an organism.
Conventional roGFP2 predominantly equilibrates with the cytosolic glutathione redox buffer through interaction with endogenous glutaredoxins [23]. However, the response kinetics of roGFP2 was slow and absolute specificity towards GSH/GSSG redox couple cannot be guaranteed [17], [24]. To resolve this, roGFP2 bioprobe was recently fused to human glutaredoxin 1 (Grx1; Grx1-roGFP2), which ensured complete specificity and rapid equilibration with intracellular GSH/GSSG couple [24]. On this basis, we explored the concept of covalently coupling roGFP2 to a mycothiol-specific oxidoreductase such as mycoredoxin (Mrx1) to generate a biosensor (Mrx1-roGFP2) that exclusively responds to perturbations in mycothiol redox potential (EMSH). The mechanistic basis of coupling Mrx1 to roGFP2 for measuring EMSH is depicted in Figure 1. Recently, a glutaredoxin (Grx1) homologue (mycoredoxin-1; Mrx1) which exclusively interacts with the mycothiol redox system has been reported in a non-pathogenic saprophytic mycobacteria, Mycobacterium smegmatis (Msm) [25]. We performed homology based analysis and identified three putative Mrx1 like proteins in Mtb H37Rv. Out of the three proteins (Rv3053c, Rv0508, and Rv3198A), Rv3198A demonstrate highest similarity with Mrx1 of Msm (72% identity). Based on this, we selected Rv3198A ORF as a putative mycoredoxin-encoding gene and named its product as Mtb Mrx1. The Mtb Mrx1 contains an active site (CGYC) similar to Msm Mrx1, supposedly required for thiol-disulfide exchange activity [25]. We independently replaced the two cysteine (Cys) residues present in the catalytic site of Mrx1 by alanine to generate Mrx1(CGYA) and Mrx1(AGYC). The wt Mrx1 along with its Cys variants were then separately fused to the N-terminus of roGFP2 via a 30-amino acid linker, (Gly-Gly-Ser-Gly-Gly)6. The resulting chimeras were affinity purified as His-tagged proteins and analyzed by spectrofluorometry. We observed that Mrx1-roGFP2 exhibits two distinct excitation peaks (390 nm and 490 nm) at a fixed emission wavelength of 510 nm. Therefore, in all subsequent experiments using spectrofluorometer the excitation ratio from these two wavelengths (390 and 490 nm) was measured to determine the extent of biosensor oxidation. Our analysis demonstrated that the intrinsic ratiometric changes exhibited by roGFP2 upon oxidation or reduction were recapitulated in Mrx1-roGFP2 (Figure S1A). In addition, we monitored the response of Mrx1-roGFP2 over a physiologically relevant pH range (pH 5.5–8.5) and found that the fluorescence excitation ratio exhibited by Mrx1-roGFP2 was insensitive to pH variations (Figure S1B). We also verified that the reported midpoint potential (−280 mV) and dynamic range (−320 mV to −240 mV) of roGFP2 were not influenced by the fusion (Figure S1C).
Since redox stress leads to an increase in oxidized mycothiol (MSSM) concentrations [26], we anticipated that the Mrx1 fusion would enable roGFP2 to selectively monitor this transformation. To test this proposal, we reduced uncoupled roGFP2, Mrx1-roGFP2, Mrx1(CGYA)-roGFP2, and Mrx1(AGYC)-roGFP2 and examined their oxidation by MSSM. Only Mrx1-roGFP2 (Lane 2, Figure 2A) and Mrx1(CGYA)-roGFP2 (Lane 4, Figure 2A) ratios increased upon MSSM addition, whereas uncoupled roGFP2 (Lane 1, Figure 2A) and Mrx1(AGYC)-roGFP2 (Lane 3, Figure 2A) remained non-responsive. This suggests that the Mrx1-roGFP2 reaction mechanism is similar to monothiol mechanism of glutaredoxins, wherein nucleophilic N-terminus cysteine interacts with GSSG to form mixed protein-glutathione disulfide intermediate followed by protein oxidation [24]. Importantly, Mrx1-roGFP2 did not respond to other disulfide-based compounds such as cystine (Cys2), GSSG, or 2-hydroxyethyl disulfide (HED), thus confirming the specificity of this biosensor towards MSSM (Figure 2B). Next, we examined the response of Mrx1-roGFP2 to reduced mycothiol (MSH). To continuously maintain a reduced state of MSH in our assays, we used MSH disulfide reductase (Mtr) enzyme. Mtr is known to catalyze the NADPH-dependent reduction of MSSM to MSH in the mycobacterial cells [27]. We first confirmed the activity of purified Mtr by monitoring NADPH oxidation in the presence of MSSM. A time-dependent decrease in 340 nm absorption due to NADPH consumption confirmed cycling of electrons from NADPH to MSSM by Mtr (Figure S1D). Next, oxidized Mrx1-roGFP2 was added as substrate for the MSH/Mtr/NADPH electron transfer assay as depicted in Figure 2C. By monitoring the decrease in the 390/490 excitation ratio, we examined the real-time response of oxidized Mrx1-roGFP2 to MSH generated via NADPH-dependent reduction of MSSM by Mtr. As shown in Figure 2D, a time-dependent decrease in 390/490 ratio confirms reduction of Mrx1-roGFP2 by MSH. The slow response of this biosensor towards MSH is consistent with the low catalytic turnover rate of Mtr [27]. No response was observed if either MSH was omitted from the Mtr/NADPH/Mrx1-roGFP2 mixture (Figure 2D) or catalytically inactive Mrx1(AGYC)-roGFP2 (Lane 3, Figure 2E) or uncoupled roGFP2 (Lane 1, Figure 2E) were used as substrates, whereas a response was readily detected in the case of Mrx1(CGYA)-roGFP2 (Lane 4, Figure 2E). Finally, to measure the sensitivity of the biosensor, we incubated pre-reduced Mrx1-roGFP2 with various ratios of MSH/MSSM at a physiological concentration (1 mM total) under anaerobic conditions and the ratiometric response was monitored. We found that a small increase in MSSM led to a significant increase in biosensor oxidation. For example, an increase in the amount of mycothiol oxidation (OxDMSH, see Materials and Methods for a mathematical explanation) from 0.00001 to 0.0001 (i. e an ∼100 nM increase in absolute MSSM) led to a larger increase in the biosensor oxidation (i. e from ∼40% to 90%) (Figure 2F). These results confirm that Mrx1-roGFP2 is capable of rapidly sensing nanomolar changes in MSSM against the backdrop of a highly reduced MSH pool (1 mM). Lastly, uncoupled roGFP2 remained completely non-responsive to changes in MSH/MSSM ratios (Figure 2F). Together, our results show that Mrx1-roGFP2 is exceptionally sensitive to measure physiological and dynamic changes in MSH/MSSM redox state.
To investigate the redox responsiveness of Mrx1-roGFP2 in mycobacteria, we stably expressed Mrx1-roGFP2 in Msm. Next, we confirmed that the biosensor responds ratiometrically upon exposure of Msm to diamide or dithiothreitol (DTT) (Figure 2G). To examine if Mrx1-roGFP2 senses EMSH in vivo, we pharmacologically and genetically perturbed MSH levels in Msm. We first analyzed the response of Mrx1-roGFP2 upon depletion of the cytosolic MSH pool in Msm using dequalinium [an established small-molecule inhibitor of MSH ligase (MshC) [28]]. For comparison, we also tested inhibitors of TRX reductase (cisplatin) [29] and dihydrolipoamide dehydrogenase (5-methoxyindole-2-carboxylic acid) [30], both of which do not affect the cellular MSH pool. As expected, only dequalinium treatment led to a substantial increase in the fluorescence excitation ratio of Mrx1-roGFP2 (Figure 2H).
We further validated the MSH-specific response of Mrx1-roGFP2 by expressing it in MSH-negative (MsmΔmshA) and MSH-depleted (MsmΔmshD) strains of Msm [31], [32]. In addition to MSH, mycobacteria express the NADPH-dependent TRX system to efficiently counter oxidative stress [33]. Importantly, multiple components of the TRX system were down-regulated in mycobacterial strains lacking extracytoplasmic sigma factor, SigH [33], [34]. Therefore, to rule out the interaction of Mrx1-roGFP2 with the TRX system inside mycobacteria, we analyzed the biosensor response in SigH-deleted strain of Msm (MsmΔsigH). Oxidation of the biosensor was nearly quantitative (∼95%±3 oxidized) in MsmΔmshA as compared to ∼20%±2 in both wt Msm and MsmΔsigH, and ∼50%±4 in MsmΔmshD (Figure 2I). Since MsmΔmshD contains only 1% to 3% of total cellular MSH but produces two related thiols (Suc-mycothiol and formyl-mycothiol) [35], our data suggest that Mrx-1 can facilitate roGFP2 reduction with Suc-MSH and/or formyl-MSH, albeit suboptimally. A previous study reported a marked accumulation of ERG in MsmΔmshA [36]. Therefore, we also examined if Mrx1-roGFP2 can function as a sensor of ERG in mycobacteria. However, Mrx1-roGFP2 did not respond to ERG in vitro, further supporting mycothiol-specific response of Mrx1-roGFP2 (Figure S2A). Taken together, data generated from several independent techniques demonstrate that Mrx1-roGFP2 responds to the MSH redox buffer in mycobacteria.
Specific equilibration of Mrx1-roGFP2 with the MSH redox buffer enables precise measurement of the EMSH in various strains of Msm using the Nernst equation as described in SI Materials and Methods. Our studies reveal EMSH in wt Msm, MsmΔmshA, MsmΔmshD, and MsmΔsigH to be −300±2 mV, −239±7 mV, −275±7 mV, and −300±3 mV, respectively. Notably, the oxidizing EMSH values observed for MsmΔmshA and MsmΔmshD are consistent with our earlier findings indicating that the thiol-disulfide redox switch in Mrx1-roGFP2 is a specific substrate for the MSH reductive pathway.
We next investigated whether coupling of roGFP2 with Mrx1 enhanced the sensitivity of the new biosensor towards transient changes in intracellular EMSH. Initial studies confirmed that H2O2 alone (0.5–5 mM) does not directly oxidize the Mrx1-roGFP2 protein in vitro (Figure S2B). By contrast, application of H2O2 to cells led to rapid (∼2 min) and substantial oxidation of the biosensor within Msm (Figure 3A). These results suggest that H2O2–mediated oxidation of MSH to MSSM is necessary for biosensor oxidation in Msm. Also, Mrx1-roGFP2 ratio rapidly increased upon exposure of Msm to diverse oxidants such as menadione, aldrithiol, and diamide (Figure S2C).
The sensitivity of Mrx1-roGFP2 in Msm was further investigated upon exposure to lower concentrations of H2O2. Addition of 100 µM, 500 μM and 1 mM of H2O2 resulted in rapid, but short-lived (∼5 min) increases in Mrx1-roGFP2 ratio, suggesting efficient mobilization of anti-oxidant response mechanisms in Msm (Figure 3B). In contrast, Msm expressing either uncoupled roGFP2 (Figure 3C) or Mrx1(AGYC)-roGFP2 (Figure 3D) responded slowly to lower concentrations of H2O2 and did not display an anti-oxidative response. The poor response shown by uncoupled roGFP2 and Mrx1(AGYC)-roGFP2 could either be due to their non-specific interaction with other mycobacterial redox systems (e.g. TRX, ERG etc) or suboptimal equilibration with the MSH/MSSM couple through mediation by endogenous Msm Mrx1. Finally, Mrx1(CGYA)-roGFP2 retained transient responses similar to Mrx1-roGFP2 (Figure 3D), further substantiating the crucial role of N-terminal Cys residue of Mrx1 in promoting a rapid and reversible equilibration of biosensor with the intracellular MSH/MSSM redox buffer. To decisively show that Mrx1-roGFP2 is capable of detecting dynamic changes in EMSH, we exploited another Msm mutant lacking MSH disulfide reductase (Mtr) activity (MsmΔmtr). The Mtr enzyme maintains intramycobacterial MSSM/MSH ratios by reducing MSSM to MSH upon oxidative stress, consequently its absence results in the depletion of MSH [37]. Exposure to H2O2 led to a ∼2-fold increase in the ratio of oxidized Mrx1-roGFP2 in MsmΔmtr as compared to wt Msm (Figure 3E). Importantly, the short-lived oxidative deflections of EMSH in response to limiting amounts of H2O2 were significantly extended in the MsmΔmtr mutant, thereby implicating Mtr in orchestrating an efficient anti-oxidative response in Msm (Figure 3F). On this basis, we propose a biochemical mechanism of sensing mycobacterial EMSH using Mrx1-roGFP2 bioprobe (Figure 3G).
It can be argued that the overexpression of a redox-based enzyme in our biosensor can influence cytoplasmic MSH/MSSM redox state to compromise redox measurements. However, we found that the absolute concentration of Mrx1-roGFP2 inside Msm cells is 1000–10000 fold lower (1 µM/cell; Figure S2D) as compared to the high millimolar concentrations of mycothiol (1–10 mM) present in mycobacteria. This shows that reducing equivalents (thiols) introduced by the expression of biosensor are significantly less than the combined pool of MSH and other thiols present in mycobacteria. Furthermore, ambient EMSH of Msm cells overexpressing either Mrx1-roGFP2 or its catalytically inactive derivative [Mrx1(AGYC)-roGFP2] was found to be comparable i. e −300±2 mV and −298±3 mV, respectively, indicating that Mrx1 activity does not perturb steady state EMSH. Lastly, Mrx1-roGFP2 harboring Msm, MsmΔmshA, and MsmΔmtr strains showed similar survival profile upon exposure to H2O2 as compared to control strains, suggesting no adverse influence of intracellular levels of biosensor on resistance to oxidative stress (Figure S2E). Taken together, we demonstrate that by integrating Mrx1 with roGFP2, the biosensor becomes catalytically self-sufficient in establishing a rapid and specific equilibration with the MSH redox buffer.
To obtain information about the basal redox potential differences between various species and strains of mycobacteria, we expressed Mrx1-roGFP2 in vaccine strain (M. bovis BCG), virulent laboratory strain (Mtb H37Rv), and several Indian clinical isolates of Mtb including single-drug resistant (BND 320), multi-drug resistant (MDR - Jal 2261, 1934, Jal 2287), and extensively-drug resistant (XDR - MYC 431). First, we confirmed that overexpression of Mrx1-roGFP2 does not affect metabolic activity and growth of Mtb using metabolic indicator dye, Alamar blue and by measuring culture absorbance (Figure S3A and S3B). Any downstream imaging analysis of the BSL3 class pathogens requires their chemical fixation by paraformaldehyde (PFA), which we found to oxidize the biosensor (Figure S3C). To circumvent PFA-mediated oxidation artifacts, we alkylated the thiols of Mrx1-roGFP2 using the cell permeable fast-acting thiol-modifier, N-ethyl maleimide (NEM). Control experiments clearly show that NEM treatment efficiently prevents oxidation during PFA-fixation of Mtb cells (Figure S3C). A similar chemical-fixation strategy was successfully exploited to measure the redox potential of glutathione (EGSH) in HeLa cells [24], and in the sub-cellular compartments and tissues of Drosophila using Grx1-roGFP2 [38]. With this system in hand, we confirmed that Mrx1-roGFP2 responds ratiometrically to oxidant (cumene hydroperoxide; CHP) and reductant (DTT) in Mtb H37Rv (Figure S4A). A concentration and time-dependent oxidation of Mrx1-roGFP2 upon H2O2 exposure was also detected in Mtb H37Rv (Figure S4B and S4C). Of note, induction of anti-oxidative response upon exposure to H2O2 was significantly delayed in Mtb (∼120 min) as compared to rapid response observed earlier in Msm (∼5 min) (Figure S4C), suggesting important variations in sensing and responding to redox stress between the two species.
Next, we evaluated the redox potential of various slow growing lab-adapted and clinical mycobacterial strains. The resulting data indicates that there is relatively little variation in the redox state within and between drug-resistant clinical (MDR/XDR) and drug-sensitive lab (Mtb H37Rv, M bovis BCG) strains, as exemplified by EMSH values around −273 mV to −280 mV (Table S1). This finding suggests that the steady-state EMSH is relatively unaffected by either genotypic or phenotypic variations within Mtb strains under laboratory growth conditions. However, EMSH in slow growing mycobacterial strains is notably oxidizing compared to Msm (−300±2 mV), which is consistent with an earlier report showing higher MSH/MSSM ratio in Msm (200∶1) as compared to BCG (50∶1) [39]. Lastly, to rule out any contribution of the chemical fixation procedure to the observed variation in EMSH between Mtb and Msm, we treated Msm expressing Mrx1-roGFP2 with NEM-PFA and confirmed that Msm maintains EMSH of −300±2 mV.
We next determined whether Mrx1-roGFP2 could be used to quantify redox changes that occur in the natural context of infection. To investigate this issue, we infected THP-1 macrophages with Mtb H37Rv expressing Mrx1-roGFP2 at a multiplicity of infection (moi) of 10 and monitored intramycobacterial EMSH. To do this, we performed NEM-PFA based fixation technique followed by ratiometric fluorescence analysis by flow cytometry (see SI Materials and Methods). Since the flow cytometric based measurements are dependent on fixed wavelength lasers, we excited Mrx1-roGFP2 biosensor with the canonical 405 and 488 nm laser wavelengths at a fixed emission wavelength of 510 nm (see SI Materials and Methods). We first confirmed that intramycobacterial Mrx1-roGFP2 responds ratiometrically to oxidant; CHP and reductant; DTT inside macrophages (Figure 4A, 4B, 4C, and 4D). To measure changes in EMSH during infection, an in vitro redox calibration curve was generated by treating Mtb H37Rv with buffers of known redox potentials. By fitting Mrx1-roGFP2 ratio to the redox calibration curve, we precisely calculated the EMSH of Mtb inside macrophages (Figure S5A, see SI Materials and Methods). Intriguingly, flow cytometric analyses of ∼30,000 infected macrophages demonstrated the presence of cells with a gradient of intramycobacterial EMSH. For the purpose of measurements, infected macrophages were gated into three subpopulations on the basis of their corresponding intramycobacterial EMSH (Figure 4E and 4F). An EMSH-basal population with an intermediate EMSH of −275±5 mV, and two deflected populations were observed (Figure 4E and 4F). Deflected cells with a mean EMSH of −240±3 mV represent an EMSH-oxidized subpopulation, based on the observation that CHP treatment of infected macrophages results in a significant fraction of these gated cells (∼98%) (Figure 4E and 4F). The population with an average EMSH of −300±6 mV represents an EMSH-reduced subpopulation, as treatment of infected macrophages with the DTT results in ∼96% of the cells gating into this subpopulation (Figure 4E and 4F). Mtb cells present in media alone and analyzed in parallel did not show redox heterogeneity (Figure 4G and 4H), suggesting that the intramacrophage environment perturbs redox homeostasis to induce redox variability in Mtb. Furthermore, we infected macrophages with BCG, and measured intramycobacterial EMSH with and without NEM-PFA treatment. Both conditions induce similar degree of heterogeneity in intramycobacterial EMSH (Figure 4I, 4J, and 4K), demonstrating that redox heterogeneity has a biological basis and is not due to aberrant quenching of fluorescent signals during NEM-PFA treatment.
With the flow cytometry workflow in hand, we measured time-resolved changes in intramycobacterial EMSH during infection of THP-1 macrophages. Our results indicated that the initial period (0–24 h post-infection [p.i.]) of infection was associated with a gradual increase in cells with reduced EMSH (60±7%) followed by an oxidative shift (25±5%) at 48 h p.i. and then a significant recovery from oxidative stress, as revealed by a decrease in the population with oxidized EMSH (7±3%) at 72 h p.i. (Figure 5A). The observed redox heterogeneity and oscillatory patterns were confirmed by repeating experiments at least six times in quadruplicate and data from biologically independent experiments were combined and presented as mean ± standard deviation. We also verified the presence of both time-dependent heterogeneity and oscillations in intramycobacterial EMSH upon infection of THP-1 macrophages at a low moi of 1 (Figure S5B). However, we noticed that infection with lower moi induced higher proportion of bacteria with oxidized EMSH at each time point examined as compared to cells infected at a moi of 10 (Figure S5B and S5C).
Next, we investigated if the macrophage environment induces strain-specific variations in redox heterogeneity among various slow growing mycobacteria including BCG and Indian clinical MDR/XDR isolates. For this, we infected THP-1 macrophages with various strains of Mtb at a moi of 10 and time-resolved changes in intramycobacterial EMSH were measured as described in the earlier section. Interestingly, while heterogeneity in EMSH for BND 320 largely followed the reductive-oxidative-reductive oscillatory pattern of H37Rv (Figure 5B), distinct redox deviations were displayed by other strains. For example, Jal 2287, Jal 2261 and 1934 displayed overrepresentation of the EMSH-oxidized subpopulation at 48 h p.i. followed by a poor recovery at 48–72 h p.i., as compared to H37Rv (Figure 5C–5E). Noticeably, intramacrophage growth of MYC 431 displayed a loss of redox oscillatory pattern and showed a steady increase in EMSH-oxidized subpopulation over 48 h p.i. (Figure 5F). Intriguingly, intramacrophage profile of BCG displayed temporal changes in EMSH comparable to Jal 2261, 1934, and MYC 431. As shown in Figure 5G, infection with BCG showed a continuous decrease in EMSH-reduced subpopulation with a concomitant increase in EMSH-oxidized subpopulation over time (Figure 5G). Together, these findings for the first time revealed that macrophage environment triggers heterogeneity in EMSH of Mtb and uncovered redox variance among clinical field isolates.
In order to examine the biosensor response upon stimulation of oxidant-mediated antimycobacterial stresses, we performed additional experiments in immunologically activated murine macrophages (RAW 264.7). Activated murine macrophages are known to control mycobacterial proliferation by producing ROS and RNS [2]. RAW 264.7 were activated with IFN-γ and LPS prior to infection with Mtb H37Rv [11]. Ratiometric flow cytomteric analysis showed a significant and sustained oxidative shift in EMSH of Mtb H37Rv inside activated macrophages at each time point investigated, whereas intramycobacterial EMSH inside naïve macrophages showed redox oscillations similar to THP-1 cells (Figure 5H and 5I). These results indicate that Mtb cells were able to recover from mild oxidative stress conditions inside naïve macrophages, whereas recovery was compromised in IFN-γ/LPS primed macrophages. Since nitric oxide (NO) generated via iNOS is considered to be one of the major contributors of redox stress in Mtb inside immune-activated murine macrophages [40], we treated IFN-γ/LPS activated RAW 264.7 macrophages with a well established iNOS inhibitor NG-methyl-L-arginine (NMLA;[41]) and monitored intramycobacterial EMSH. Strikingly, a substantial reduction in subpopulation with oxidized EMSH was observed upon addition of NMLA (Figure S6A). These results indicate that Mtb responds to host derived environmental cues by modulating EMSH, and further illustrates the utility of Mrx1-roGFP2 in dissecting redox signaling during infection.
Intracellular Mtb exists in different vacuolar compartments, which may contribute to significant heterogeneity in mycobacterial gene expression, metabolic state and survival [42]. On this basis, we next asked whether trafficking into distinct vacuolar compartments could promote redox heterogeneity in the Mtb population using ratiometric confocal microscopy. First, we performed confocal imaging of Mtb cells inside THP-1 macrophages at 24 h p.i. Conforming to our flow cytometric findings, confocal analyses revealed that the intrabacterial EMSH varied markedly at a single-cell level. Similar to flow cytometry, this gradient in redox heterogeneity can be classified into EMSH-basal (−277±5 mV, 26%), EMSH-oxidized (−242±6 mV, 23%), and EMSH-reduced (−304±10 mV, 51%) sub-populations (Figure 6A and 6B). Mtb grown in media indicated an overrepresentation of the cells with uniform EMSH (Figure S6B), validating that both flow cytometry and confocal imaging revealed very similar ratiometric changes upon infection.
Next, we measured intrabacterial EMSH within early endosomes, lysosomes, and autophagosomes by visualizing the co-localization of Mtb H37Rv expressing Mrx1-roGFP2 with compartment specific fluorescent markers at 24 h p.i. using confocal microscopy (see SI Materials and Methods). For marking early endosomes, cells were stained with anti- early endosome autoantigen (EEA1) and anti-Rab5 antibodies. To study lysosomes, we used acidotropic dye Lysotracker and anti-cathepsin D antibody, whereas autophagosomes were labeled with anti-LC3 antibody (see SI Materials and Methods). The resulting data show that the majority of bacilli within early endosomes were likely to exhibit reduced (∼54%) as compared to oxidized (∼22%) or basal (∼24%) EMSH (Figure 6C and Figure S7A). Interestingly, in lysosomes, deflected subpopulations with EMSH-oxidized were clearly higher in proportion (58%), whereas EMSH-reduced (12%) and EMSH-basal (30%) subpopulations were underrepresented (Figure 6D and Figure S7B). The percent distribution of subpopulations with EMSH-reduced and EMSH-oxidized were significantly different between early endosomes and lysosomes (p<0.001), while EMSH-basal subpopulation remained comparable within these compartments. Furthermore, 100% of the Mtb population inside autophagosomes displayed a maximal oxidative shift in EMSH (Figure 6E). We also measured changes in EMSH during intramacrophage residence of drug-resistant strains Jal 2287 and MYC 431. While Jal 2287 displayed redox deviations similar to Mtb H37Rv (Figure 7A–7D), MYC 431 showed over-representation of subpopulations with EMSH-oxidized within the macrophage and sub-vacuolar compartments at 24 h p.i. (Figure 7E–7H). Taken together, our results suggest that distinct sub-vacuolar environments lead to the generation of Mtb subpopulations with a gradient of redox potentials.
Several studies have shown that MSH-deficient mycobacteria and other actinomycetes are sensitive to antibiotics [21], [43], [44]. On the other hand, MSH also contributes to susceptibility to INH and ETH [45], and mutations in mycothiol biosynthesis genes were identified in drug-resistant clinical isolates of Mtb [46]. While these constitute an important foundation linking antibiotic action with mycothiol redox homeostasis in vitro, they provide little insight into how antibiotics modulate intramycobacterial EMSH in the physiological setting of infection. To examine this, we characterized the effect of anti-TB drugs on redox heterogeneity in Mtb cells during intramacrophage residence. To this end, infected macrophages were exposed to anti-TB drugs (5-fold the in vitro MIC) with different modes of action (e.g. isoniazid [INH; mycolic acid inhibition], ethambutol [EMB; arabinogalactan inhibition], rifampicin [RIF; inhibition of transcription], and clofazimine [CFZ; redox cycling and ROS production; [47]) and the redox response was measured by flow cytometry. Treatment with all antibiotics induced variable levels of oxidative shift in EMSH of Mtb subpopulations at 12, 24, and 48 h p.i. (Figure 8A). The skew towards oxidizing EMSH was activated at an early time point (12 h p.i.) and increased significantly at 48 h p.i. (Figure 8A). Next, we examined if enhanced oxidizing EMSH correlated with the killing potential of anti-TB drugs during infection. At 12 h post-antibiotic treatment, the Mtb survival rate was comparable to the untreated control, as determined by colony forming unit (CFU) assay (Figure 8B). However, a modest (∼1.5-fold) to a significant reduction (∼5-fold) in intramacrophage bacillary load as compared to untreated control was detected at 24 and 48 h p.i., respectively (Figure 8B). These findings show that bactericidal antibiotics with different mechanisms of action induce oxidative changes in intramycobacterial EMSH during infection. To further validate these results, we infected macrophages with INH resistant clinical strains (BND 320 and Jal 2287) and measured intramycobacterial EMSH in response to INH at 48 h p.i. Since these strains are sensitive to CFZ, we used CFZ as a positive control in this experiment. Figure 8C clearly shows that the EMSH of strains genetically resistant to INH remained uninfluenced in response to INH. On the other hand, CFZ exposure generated considerable oxidative shift in EMSH of these strains (Figure 8C).
Lastly, to investigate if anti-TB drugs directly induce oxidative stress in vitro, we exposed Mtb cells grown in 7H9 medium supplemented with albumin, dextrose and sodium chloride (7H9-ADS) to INH, CFZ, RIF, and ETH (5× MIC) and tracked EMSH at 1, 2, 6, and 24 h post-treatment. This study revealed that only CFZ induces significant oxidative shift in EMSH of Mtb (Figure 8D). Other anti-TB drugs such as INH induce low levels of EMSH-oxidized at 24 h, post-treatment, while RIF and ETH do not influence the EMSH of Mtb (Figure 8D). Our results suggest that antibiotics do not perturb EMSH of Mtb per se, but co-opt host cellular responses to stimulate excessive oxidative stress during infection. These findings underscore the importance of studying redox-based mechanisms of drugs action under physiologically relevant microenvironmental conditions such as those encountered during TB infection in macrophage.
It has been suggested that long term anti-TB therapy is required because the mycobacterial population is functionally heterogeneous and harbors cells that are differentially sensitive to antibiotics [48]. However, the physiological determinants of phenotypic heterogeneity in Mtb population and its relation with antibiotic tolerance remains poorly characterized. Because macrophage environment quickly creates variability in mycobacterial cells to generate drug tolerant subpopulations [49], we hypothesize that heterogeneity in intrabacterial EMSH may be one of the factors that underlies emergence of Mtb populations with differential antibiotic susceptibility. We therefore sought to determine the susceptibility of Mtb cells with basal, oxidized, and reduced EMSH to antibiotics during infection of THP-1 cells. To do this, we analyzed the membrane integrity of Mtb cells expressing biosensor by assessing their capacity to exclude fluorescent nucleic-acid binding dye, propidium iodide (Pi), upon treatment with antibiotics during infection. The state of the bacterial membrane is a crucial physiological indicator, as Pi+ cells are considered damaged or dying [50]. Importantly, we found that NEM treatment of infected macrophages fixed the redox state of intracellular Mtb such that bacterial cells released from macrophages retained redox variations comparable to bacteria within macrophages (Figure S8). This allowed us to quantify bacterial viability by Pi staining of Mtb cells released from infected macrophages at various time points post antibiotic exposure. Infected THP-1 cells were exposed to anti-TB drugs (5× MIC) and intracellular bacteria were fixed with NEM at 12, 24, and 48 h p.i. Infected macrophages were lysed; released bacteria were stained with Pi, and ∼30,000 bacilli were analyzed by multi-parameter flow cytometry to simultaneously profile EMSH and viability status. As shown earlier, all antibiotics induce significant oxidative shift in intramycobacterial EMSH during infection. Furthermore, bacilli with oxidized EMSH were more sensitive to killing as evident by a time-dependent increase in Pi staining across all antibiotic treatments within this subpopulation as compared to other two subpopulations (Figure 9A, p<0.05). At 48 h p.i., 35–40% of EMSH-oxidized bacilli were Pi+. The EMSH-basal subpopulation demonstrates a modest increase in Pi+ staining (∼5–10%) at 24 and 48 h p.i. (Figure 9A). Surprisingly, EMSH-reduced subpopulation remained completely unaffected by antibiotics as shown by the absence of Pi+ cells within this group (Figure 9A). These results show that bacteria with lower EMSH are capable of excluding Pi and therefore maintain membrane integrity post-antibiotic treatment.
Thus, we find that redox heterogeneous bacteria vary in their susceptibility to antibiotics, consistent with the model that macrophage induced heterogeneity in intrabacterial EMSH creates physiologically distinct subpopulations of cells. Since environment inside autophagosomes induces substantial oxidative shift in intrabacterial EMSH, we hypothesized that treatment of infected macrophages with a well established autophagy inducer (rapamycin) would increase the localization of Mtb to autophagosomes and shift redox heterogeneity towards EMSH-oxidized. This would allow us to examine the influence of host antibacterial mechanisms (i. e autophagy) on intrabacterial EMSH and drug tolerance during infection. To examine this, infected macrophages were treated with a low non-lethal concentration of rapamycin (200 nM) and antibiotic-mediated redox stress and killing was monitored by assessing EMSH and Pi status. As shown in figure 9B, treatment of infected macrophages with rapamycin significantly increases fraction of Mtb cells exhibiting oxidizing EMSH as compared to untreated control (P<0.05). Furthermore exposure of infected macrophages to both rapamycin and antibiotics (INH or CFZ) induces oxidative shift in EMSH which supersedes that produced by either INH or CFZ or rapamycin alone (Figure 9B, p<0.05). Consistent with this, treatment of infected macrophages with rapamycin-INH or rapamycin-CFZ substantially increases the fraction of Pi+ Mtb cells, indicating augmented bacterial death by these combinations (Figure 9C, p<0.05).
Lastly, to show that EMSH-reduced subpopulation contributes to drug tolerance, we measured the sensitivity of Mtb towards anti-TB drugs in the presence of reducing agent DTT in vitro. First, we confirmed that exogenous addition of 5 mM DTT maintained a reductive EMSH (−320±2.9 mV) equivalent to the EMSH of reducing subpopulation and is non-deleterious for Mtb. In the absence of DTT treatment, antibiotics exposure led to a significant reduction (6–12-fold) in the survival of Mtb (Figure 9D). By contrast, ∼80% of Mtb survived an exposure to INH or ETH and ∼50% in the case of CFZ in the presence of DTT (Figure 9D). Collectively, our data suggest that oxidized EMSH potentiates antibiotic action, whereas EMSH-reduced promotes tolerance to anti-TB drugs and suggest that host-induced cell-to-cell variation in EMSH may be a novel mechanism by which Mtb resists antimicrobial treatment during infection.
Basic research on persistence and drug-tolerance in Mtb is hampered by the lack of tools to study bacterial physiology during infection. Here, we developed a new biosensor, Mrx1-roGFP2, to image dynamic changes in the EMSH of Mtb during infection. Using confocal and flow cytometry, we quantified EMSH and unraveled mycothiol-linked redox heterogeneity in Mtb at the single-cell level during infection and highlighted the utility of bioprobes in exploring new mechanisms of drug action in Mtb bacilli. In Mrx1-roGFP2, the genetic coupling of roGFP2 with the mycothiol-specific oxidoreductase (Mrx1) ensures that roGFP2 functions as a physiological substrate for Mrx1 and therefore dynamically oxidizes and reduces in response to EMSH. Since cytoplasmic levels of chromosomally encoded Mrx1 may differ between mycobacterial species/strains or under diverse environmental conditions, one of the main advantages of coupling Mrx1 with roGFP2 is to facilitate rapid and continuous equilibration of biosensor with MSH/MSSM redox couple independent of endogenous Mrx1 pool. Our results agree with a recent study which showed that the homologue of Mtb Mrx1 in Msm specifically interacts with the mycothiol redox system in vitro [25]. Related non-disruptive approaches have been employed to develop GSH-specific and H2O2-specific in vivo bioprobes [24], [51].
The utility of Mrx1-roGFP2 in investigating mycobacterial physiology during infection comes from our flow cytometric and confocal data showing that the environment inside macrophage induces redox heterogeneity and oscillations in intrabacterial EMSH. The reductive-oxidative-reductive oscillations in EMSH during intramacrophage growth corresponds to the early induction of genes linked to reductive stress (e.g., whiB3, whiB7, and dosR) [10], [42], followed by extensive bacterial killing during intermediate phase of relative increase in subpopulation with oxidized EMSH at 48 h p.i as compared to 24 h p.i., and progressive increase in replication upon anti-oxidative shifts in EMSH at later stages of infection [42]. Interestingly, we observed some variations between confocal microscopy and flow cytometry based measurements of intrabacterial EMSH. For example, a higher percentage of subpopulation with oxidized EMSH was detected using confocal microscopy as compared to flow cytometry at 24 h p.i. (Figure 5A and 6A). However, although flow cytometry allows monitoring of large number of cells at multiple time points with high statistical power, the read out is derived from averaging Mtb cells inside infected macrophages. Therefore, confocal microscopy is a much more accurate indicator of intrabacterial EMSH at the level of individual bacteria inside macrophages. In agreement to this, the moi dependent variations in the distribution of subpopulations with different EMSH could also be due to averaging relatively higher number of bacteria present inside macrophages infected at a moi of 10 than 1 by flow cytometry. Nonetheless, both techniques were in reasonable agreement and complement one another to generate a highly resolved view of intrabacterial EMSH during infection. We also discovered that sub-vacuolar compartments such as endosomes, lysosomes and autophagosomes are the source of redox variability in Mtb populations. While lysosomes and autophagosomes enrich EMSH-oxidized bacteria, early endosomes induce a reductive shift in EMSH of Mtb. Similarly, a greater fraction of bacteria displayed oxidized EMSH at a moi of 1 than 10, which is consistent with the reported increase in phagosomal maturation and intracellular trafficking of Mtb to lysosomes at low moi [52]. Finally, our results showing a substantial oxidative shift in EMSH inside activated macrophages and its reversal upon treatment with iNOS inhibitor (NMLA) are in agreement with studies implicating the role of vacuolar NOX2 and iNOS systems in creating overwhelming oxidative stress within lysosomes and autolysosomes [2]. It is well known that Mtb actively remodels endosomal/phagosomal pathways during infection [53] to induce variability among phagosomes during infection [54]. This suggests that Mtb-induced alterations within sub-vacuolar compartments can generate a range of environmental conditions for the evolution of redox deviations in Mtb population.
Intriguingly, we found that redox heterogeneity is uniformly present in H37Rv and MDR/XDR strains inside macrophages. However, distinct strain-dependent variations in redox heterogeneity were also evident. Because macrophage compartments distinctly manipulate EMSH of Mtb, varied redox oscillatory behavior could simply be a consequence of differences in co-localization kinetics of Mtb strains within sub-vacuolar compartments over time. Alternatively, phenotypic variations such as uptake rate and intramacrophage growth kinetics may influence the EMSH of these strains. While the molecular mechanisms behind these redox variations and their influence on evolution of drug-resistance and fitness await further investigation, several studies have documented strain-specific differences in bacterial and host gene expression during infection [55], [56], and resistance to redox stress [57], [58].
Numerous studies using oxidant-sensitive dyes have demonstrated that bactericidal antibiotics, regardless of their target, exert toxicity by stimulating Fenton-catalyzed ROS production [59]–[61]. However, two recent studies demonstrate that measurements using dyes may be inconclusive owing to their non-specificity and that the bactericidal potential of antibiotics does not correlate with ROS generation in vitro [62], [63]. Furthermore, to the best of our knowledge, the contribution of antibiotics-stimulated oxidative stress in the physiological context of infection has not been evaluated. In this context, Mrx1-roGFP2 allowed us to investigate the redox-basis of drug action in Mtb. We show that Mtb maintains EMSH in response to antibiotic exposure (except in case of CFZ) during in vitro growth, whereas a significant oxidative shift was induced by all drugs during intramacrophage growth. This data along with the antibiotic sensitivity displayed by the MSH-deficient strains [21] suggest that MSH biosynthesis or recycling efficiently buffers toxicity associated with drugs during normal growing conditions. In this regard, mycothiol-S-conjugate detoxification system (Mca) maintains cytoplasmic MSH levels upon antibiotic exposure by rapidly converting MSH-antibiotic adducts to mercapturic acids and excreting them into the culture media [44]. Another Mtb antioxidant, ERG, was found to be over-expressed in MSH-mutants, however, it does not provide protection against antibiotics [64].
Importantly, we show that the antibiotic-mediated increase in EMSH-oxidized precedes bacterial death inside infected macrophages. Oxidative shift in EMSH was substantial at time points earlier than those at which drug antimycobacterial activities were achieved. Moreover, INH-resistant clinical strains remained uninfluenced by INH-mediated oxidative changes in EMSH during infection, thus supporting our conclusions. Our results suggest that an active cooperation between host factors and antibiotics could disrupt intramycobacterial EMSH during chemotherapy. This is in agreement with a recent study demonstrating the role of anti-TB drugs in inducing host ROS production and potentiating mycobactericidal activity by delivering Mtb into lysosomal and autophagosomal compartments [65]. Since lysosomes and autophagosomes predominantly contain Mtb cells with an oxidized EMSH, our findings present a novel insight into the mechanism of antibiotic action during infection. Together, our data suggest that direct effects of antibiotics on Mtb physiology and on host innate immune mechanisms such as autophagy and phagosomal maturation, jointly eliminate Mtb bacilli by stimulating overwhelming intramycobacterial oxidative stress in vivo. In line with this hypothesis, we show that treatment with autophagy inducer (rapamycin) significantly augments mycobactericidal activities of antibiotics during infection. While MSH has been shown to be dispensable for growth of Mtb in mice [66], future experiments should target MSH levels during antibiotic treatment to understand its function in modulating bacterial killing during antimicrobial therapy in vivo, where the heterogeneity in EMSH may be one of the critical determinants of persistence and drug tolerance. These exciting hypotheses can now be investigated by integrating Mrx1-roGFP2 technology with high-resolution live cell profiling of intramycobacterial EMSH at the single-cell level during infection.
How do these findings relate to human TB? Within human host, Mtb persists in a state of drug unresponsiveness in oxygen-depleted and lipid-rich granulomas [67]. The center of TB granuloma is hypoxic (3 mM Hg) and contains lipid-laden foamy macrophages and free fatty acids released from necrotic macrophages [67], [68]. Recent studies suggest that β-oxidation of host fatty acids by Mtb as the primary carbon source in an O2-deficient environment leads to massive accumulation of NADH/NADPH, which generates intrabacterial reductive stress in persisting cells during infection [10], [69]. Work presented here demonstrates that drug tolerance observed during persistence may be mediated by increased reductive capacity. Further strengthening this connection are recent findings demonstrating elimination of Mtb persisters by drugs which become active under reductive stress (e.g., metranidazole) or generate overwhelming ROS (e.g., CFZ) [70], [71]. Another common clinical observation is that Mtb genetically resistant to only a subset of anti-TB drugs resists clearance from all other antibiotics and thus, survive combination drug therapy [72]. One interesting possibility revealed by this work is that redox heterogeneity within genetically drug-resistant clinical isolates (MDR/XDR) provides a subpopulation that tolerates antibiotics against which bacteria are genetically susceptible. These understandings shed new light on drug resistant mechanisms and suggest that novel approaches that disrupt redox metabolism in Mtb may significantly impact eradication of both genetic and phenotypic drug resistant populations.
In summary, we have developed a genetically encoded, fluorescent reporter capable of monitoring intrabacterial EMSH within macrophages. We anticipate that Mrx1-roGFP2 will play an important role in high content screening of small-molecule inhibitors of intrabacterial redox homeostasis. Based on this work, one can readily imagine development of new biosensors to measure the redox state of specific and unusual redox thiols, such as BSH, trypanothione, ovothiol A found in a variety of pathogenic organisms. Finally, macrophage-induced redox heterogeneity and its connection to drug sensitivity may be relevant to other intracellular pathogens. For example, the macrophage environment induces expression of genes responsible for antioxidant production and drug-tolerance in Legionella pneumophilla [73]. Thus, our findings may have relevance to several intracellular pathogens causing chronic and relapsing infections where persistence and tolerance pose challenges for treatment.
The roGFP2 containing vector was obtained from Tobias P. Dick [24]. The roGFP2 open reading frame (ORF) was released using NcoI and HindIII and was cloned downstream of hsp60 promoter into similarly digested E. coli-mycobacterial shuttle vector, pMV762 [74] to generate pMV762-roGFP2. Mrx1-roGFP2 biosensor construct was generated by fusing the coding sequence of Mtb Mrx1 (Rv3198A) with roGFP2 having a 30-amino acid linker (GGSGG)6 between the two genes. Mrx1 coding sequence was amplified from Mtb genome (Forward primer: 5′ ATGCCCATGGTGATCACCGCTGCG 3′. Reverse primer: 5′ ATGCACTAGTACCCGCGATCTTTAC 3′). Cysteine mutations in Mrx1 coding sequence were introduced by site-directed mutagenesis as described [11]. Primers used were: Mrx1 (AGYC) forward primer: 5′ CTATACGACATCATGGGCTGGCTATTGCCTTCGAC 3′, reverse primer: 5′ GTCGAAGGCAATAGCCAGCCCATGATGTCGTATAG 3′, Mrx1 (CGYA) forward primer: 5′ CATCATGGTGTGGCTATGCCCTTCGACTCAAAACAG 3′, reverse primer: 5′ CTGTTTTGAGTCGAAGGGCATAGCCACACCATGATG 3′. All the fusion constructs were sub cloned into the expression vector pET28b (Novagen), expressed in the E. coli strain BL21 DE3 (Stratagene), and fusion proteins were purified via hexahistidine affinity chromatography as described [11]. Aerobically purified Mrx1-roGFP2 and ro-GFP2 were found to be in the fully oxidized state. To study the effect of various oxidants in vitro, the roGFP2 fusion proteins (1 µM) were first reduced with 10 mM DTT for 30 min on ice and desalted with Zeba Desalt spin columns (Pierce Biotechnology). In vitro measurements using various roGFP2 variants were performed on SpectraMax M3 microplate reader.
Mtb mtr ORF (Rv3198A) was cloned into pET28b (Novagen) and expressed in the E. coli strain BL21 DE3. Purification of Mtr protein was performed as described in the earlier section. Mycothiol is purchased from JEMA Biosciences, San Diego, CA, USA. To perform Mtr electron transfer assay, a mixture of 2.5 µM purified Mtr, 250 µM MSSM and 500 µM NADPH was prepared in 50 mM HEPES pH 8.0 in a 96-well plate. In the control reaction, Mtr was absent. The mixture was incubated at 37°C and consumption of NADPH was monitored at 340 nm for 60 min. To check the specificity of roGFP2 fusion proteins towards MSH, Mtr assay mixture was prepared as described above. After incubation at 37°C for 30 min, oxidized roGFP2 fusion proteins (1 µM) were added to the mix. Ratiometric sensor response was monitored for 200 min. A control reaction without MSSM was included.
Pre-reduced uncoupled roGFP2 and Mrx1-roGFP2 (1 µM) were incubated with mycothiol solutions (1 mM total) containing increasing fractions of MSSM. The total concentration of MSH (MSH total) refers to MSH equivalents i. e MSHtotal = [MSH]+2[MSSM]. OxDMSH is the fraction of MSH total that exists as [MSSM] and can be conveniently calculated using the following formula:Reduced from of mycothiol (MSH) was obtained by reducing MSSM with immobilized TCEP disulfide reducing gel (Thermo Scientific) under anaerobic conditions as per manufacturer's instructions.
Detailed Materials and Methods are provided in the supporting information.
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10.1371/journal.pntd.0005185 | First Identification and Description of Rickettsioses and Q Fever as Causes of Acute Febrile Illness in Nicaragua | Rickettsial infections and Q fever present similarly to other acute febrile illnesses, but are infrequently diagnosed because of limited diagnostic tools. Despite sporadic reports, rickettsial infections and Q fever have not been prospectively studied in Central America.
We enrolled consecutive patients presenting with undifferentiated fever in western Nicaragua and collected epidemiologic and clinical data and acute and convalescent sera. We used ELISA for screening and paired sera to confirm acute (≥4-fold rise in titer) spotted fever and typhus group rickettsial infections and Q fever as well as past (stable titer) infections. Characteristics associated with both acute and past infection were assessed.
We enrolled 825 patients and identified acute rickettsial infections and acute Q fever in 0.9% and 1.3%, respectively. Clinical features were non-specific and neither rickettsial infections nor Q fever were considered or treated. Further study is warranted to define the burden of these infections in Central America.
| Rickettsial infections and Q fever cause illness characterized by fever and non-specific symptoms and signs. Not only are these infections difficult to recognize, they are also difficult to diagnose because of limitations in existing tests for them. Despite sporadic reports, rickettsial infections and Q fever have not been prospectively studied in Central America. We enrolled consecutive patients presenting with undifferentiated fever in western Nicaragua and collected data regarding potential risk factors as well as symptoms and signs associated with the illnesses. Additionally, we collected blood samples at the initial visit and 2 to 4 weeks thereafter. We used serologic assays to differentiate new (rising antibody titers) vs. old (stable antibody titers) infections. Characteristics associated with both acute and past infection were assessed. We enrolled 825 patients and identified acute (new) rickettsial infections and acute Q fever in 0.9% and 1.3%, respectively. Clinical features were non-specific and neither rickettsial infections nor Q fever were considered nor treated. Further study is warranted to define the burden of these infections in Central America.
| Rickettsioses, including spotted fever group (SFGR) and typhus group (TGR), and Q fever (caused by Coxiella burnetii) are increasingly recognized worldwide [1]. Both rickettsioses and Q fever often manifest as undifferentiated fever and cannot easily be distinguished clinically from other causes of acute febrile illness (AFI). Furthermore, both are difficult to confirm in the laboratory, since convalescent sera, specific diagnostic reagents, and expertise are required. These infections are especially underappreciated in low resource settings where lack of laboratory capacity limits both individual diagnosis and validation of clinical acumen. Under-recognition may lead to unnecessary morbidity and even mortality, since empirical regimens for AFI usually do not treat rickettsioses or Q fever.
In 1971 a large serosurvey documented the presence of rickettsiae and Q fever in humans in Central America [2]. However, these agents have not been prospectively studied in humans in Central America nor have there been cases reported of acute infection with these agents in Nicaragua.
To identify, quantify, and characterize potentially treatable rickettsioses and Q fever among AFI in Nicaragua, we studied a cohort of children and adults presenting with fever at a large hospital.
Written informed consent was obtained from patients or their guardians (for patients <18 years of age) and written assent was obtained from patients aged 12–17 years. The institutional review boards of Johns Hopkins University and Duke University Medical Center (USA) as well as Universidad Nacional Autónoma de Nicaragua, León (Nicaragua) approved the study.
We recruited patients in the emergency department and adult and pediatric wards of Hospital Escuela Oscar Danilo Rosales Arguello (HEODRA), the 400-bed primary public teaching hospital of Universidad Nacional Autónoma de Nicaragua in León, Nicaragua, which serves rural areas around León as well as the city itself. Between August 2008 and May 2009, we enrolled consecutive febrile (≥38°C, tympanic) patients ≥1 month old without prior (within 1 week) trauma or hospitalization who presented during the day or early evening hours Monday through Saturday. Dedicated study doctors verified eligibility and willingness to return for follow-up and obtained written informed consent from patients (≥18 years) or parents (<18 years), and assent if 12–17 years. Study personnel recorded structured epidemiological and clinical data, including the duration of illness and clinical provider’s presumptive (leading clinical) diagnosis, on a standardized form at enrollment and then obtained peripheral blood specimens in EDTA and in a serum-separator tube for on-site clinician-requested testing and off-site research-related testing. Patients returned for clinical and serologic follow-up 2 to 4 weeks later, or were visited at home if they did not return and could be located. Blood in the serum separator tube was centrifuged and sera and EDTA blood frozen on site at -80°C.
We used stringent criteria to define a confirmed case [6].
Confirmed acute rickettsial infection. ≥4 fold rise in IgG titer for SFGR and/or TGR;
Confirmed past rickettsial infection. IgG titer by IFA of ≥160 for SFGR and/or TGR in the absence of a ≥4-fold rise in titer.
Confirmed acute Q fever. ≥4 fold rise in C. burnetii Phase 2 IgG titer by IFA.
Confirmed past Q fever. C. burnetii Phase 2 IgG titer of ≥160 by IFA in the absence of a ≥ 4-fold rise in titer.
Probable acute rickettsial infection or Q fever. Seroconversion without a ≥4 fold rise in titer (e.g., acute-phase sera negative at 1:80 and convalescent sample positive at 1:80.
Probable past rickettsial infection or Q fever. IgG titer of 80 in the absence of seroconversion or 4-fold rise in titer. Those with equal SFGR and TGR titers were SFGR/TGR group-indeterminate.
Possible acute rickettsial infection or Q fever. 2-fold rise in titer (e.g. acute sample positive at 1:80 and convalescent sample positive at 1:160)
SFGR vs. TGR infection. A ≥2-fold difference in titer defined SFGR vs. TGR; if titers were equal, the rickettsial infection was categorized as group indeterminate
Acute phase EDTA-anticoagulated blood samples from patients with possible, probable, and confirmed acute rickettsial and/or Q fever infections (IFA-confirmed seroconversion with convalescent titer ≥ 80 and/or a ≥2-fold rise in titer) as well as past infections were used to prepare DNA for PCR analyses. For this, 1 mL of EDTA-anticoagulated blood was subjected to automated DNA preparation using a QIAsymphony SP instrument and the DSP DNA mini Kit; the final elution volume was 200 μL. DNA samples were initially tested using a multiplex 5’ nuclease assay targeting conserved regions in SFGR ompA, TGR 17 kDa genus-common antigen gene (RURANT17KB), and human ACTB [7]. In addition, acute phase blood DNA samples from patients with a convalescent-phase SFGR and/or TGR IFA titer of ≥ 160 and a ≥ 2 fold increase in titer were tested separately for SFGR hypothetical protein gene RC0338 [8], TGR hypothetical protein gene Rpr 274P [9], and C. burnetii IS1111 spacer PCR (courtesy D. Raoult, Unite des Rickettsies, Marseille, France) [10].
We correlated epidemiologic and clinical findings with serologic results. Proportions were compared by the Chi-square test or Fisher’s exact test and continuous variables by Student’s t-test or analysis of variance if normally distributed and Wilcoxon-Mann-Whitney or Kruskal-Wallis test if not normally distributed. Analyses were done with Stata IC 11.0 (StataCorp, College Station, TX).
Serologic testing for rickettsial infections and Q fever was completed for 800 (97.0%) of 825 consecutively enrolled patients. Of these 800, 748 (90.7%) had paired sera available, since 52 patients did not return and could not be located for follow-up. The likelihood of a subject returning for convalescent serum sampling and clinical follow-up did not differ by age (p = 0.90), sex (p = 0.93), or self-reported urban vs. rural residence (p = 0.53). The reported median distance from residence to hospital was 2 km for those who followed up versus 3 km for those who did not (p = 0.08). Among the 748 patients with paired sera, the median age was 9 years (IQR 3–29). Slightly more were male (52.5%), and males were younger than females (median age 9 vs. 11, p = 0.007). The median reported duration of fever and of illness at presentation was 2 days (IQR 1–4) and 3 days (IQR 1–5), respectively. Many (30.0%) reported taking an antibiotic before presentation. The median interval between acute and convalescent follow-up was 15 days (IQR 14–28). A total of 5 patients were treated with doxycycline, none of whom had acute rickettsial infection or acute Q fever.
Overall, 14 (2%) of 747 patients had evidence of Q fever infection. Patients with definitive serologic evidence of Q fever infection were older than those without (median 36 vs. 9 years, p = 0.0003); 3.9% of adults age 18 years or older were seropositive for Q fever compared with 0.7% of children (p = 0.002). Acute Q fever occurred throughout the study period (1 each in October to March except for 3 in April and 2 in November) without seasonality or specific association with rainfall or temperature. Otherwise, despite extensive investigation, no demographic characteristics or environmental exposures were associated with Q fever. However, patients with Q fever infection were more likely than others to have definitive serologic evidence of rickettsial infection (21% vs 4%, p = 0.002).
Using stringent serodiagnostic criteria and achieving 90% follow-up, we document and describe Rickettsia spp. and C. burnetii as causes of acute febrile illness in Nicaragua. We required a 4-fold change in IgG titer to define a laboratory-confirmed case of acute rickettsial infection, which is in keeping with the latest (2010) case definition for acute spotted fever infection from the Centers for Disease and Prevention [6]. We found that SFGR and C. burnetii caused undifferentiated febrile illness predominantly in adults, especially male adults for SFGR. These infections mimicked other acute febrile illness and were both unsuspected and untreated, as we found in a similar cohort study in Sri Lanka [11]. Improved awareness and diagnostic tests may decrease morbidity and mortality by enhancing case detection and prompt provision of appropriate therapy.
It is plausible that rickettsioses, both SFGR and TGR, would cause acute febrile illness in Nicaragua. R. rickettsii, the cause of Rocky Mountain spotted fever (RMSF) is distributed broadly throughout the Western Hemisphere, and confirmed cases have been documented elsewhere in Central America (specifically Mexico, Costa Rica, and Panama) [12–18]. Although not found in this study, fatal infections with R. rickettsii have been reported in Mexico, Costa Rica and Panama [15–17,19], and fatal SFGR, presumably R. rickettsii, in Guatemala [14]. In Mexico, RMSF is directly linked to R. rickettsii-infected Rhipicephalus sanguineus ticks harbored by the large population of peridomestic stray dogs [20]; notably, in the U.S., this same vector-host dynamic has been associated with 4-times higher RMSF case-fatality among American Indians compared with other ethnic groups [21–24]. Moreover, other important vectors of R. rickettsii in Central and South America (Amblyomma mixtum and A. sculptum, respectively) are present in Nicaragua, which heightens the likelihood that the serologic responses we observed are the result of R. rickettsii infection [25]. R. parkeri, another SFGR, is not yet implicated in human SFGR infection in Central America, but A. maculatum is present in the region and R. parkeri-like illness was reported in a traveler returning from Honduras [25,26]. In Brazil, Amblyomma ovale is the vector of Rickettsia sp. strain Atlantic rainforest, a R. parkeri-like agent [19]. Although serologic cross-reactions occur among species of SFG rickettsiae and serologic testing alone cannot directly identify a causative agent, higher titers in most cases to R. rickettsii than to R. parkeri suggest that R. rickettsii or a novel agent more closely related to R. rickettsii may have caused the confirmed SFG rickettsial infections. The relatively mild clinical illness we observed suggests the latter.
Although C. burnetii is technically not a member of the Rickettsiales, the disease it causes, namely Q fever, is plausible as a cause of acute febrile illness in Nicaragua and is found worldwide when sought. Q fever was first identified in Central America (Panama) in the 1940s [27,28] and more recently a serosurvey of Q fever in livestock workers confirmed a seroprevalence as high as 10% [29]. A 1971 serosurvey of rickettsioses and Q fever in humans in Central America identified SFGR and Q fever antibodies by complement fixation and microagglutination and found an SFGR seroprevalence of 0.3% (1/312) and Q fever seroprevalence of 0.6%-1.0% [2].
We found that patients with acute rickettsial infections had illnesses and findings that closely resembled those of other patients with acute febrile illness. Patients with rickettsioses, however, were relatively older, reported a longer duration of fever, and more frequently had joint and muscle pain than did others; no patient had a rash. Although fever, headache, and rash constitute the classic clinical triad for rickettsioses, headache is frequent with other illnesses and rash is often absent when patients present early in illness, as in our cohort, or are individuals with dark skin [30]. Our group and others have also found that clinical characteristics are often not helpful in identifying rickettsial infections, with the exception possibly of older age [11,31–35]. In our study, acute rickettsial infections (almost all SFGR) were associated with self-reported rural residence, contact with livestock, and drinking well or river versus tap water. Tick-borne rickettsial infections are increasingly recognized as zoonotic infections that are more common in rural areas with complex tick-animal reservoir relationships that merit One Health approaches for control [11,36,37]. Patients with rickettsioses were also more likely to report exposure to ticks, fleas, and lice but not mosquitos. These findings are also plausible, since ticks and fleas harbor SFGR, and fleas and lice TGR. Patients with Q fever were also relatively older than other patients, but perhaps surprisingly did not report rural residence or exposure to livestock. We suspect this reflects small sample size, since individuals with confirmed acute Q fever did have acute Q fever-compatible illnesses, including hepatomegaly and prolonged illness associated with fever and cough.
We found that both acute rickettsial infections and Q fever were unsuspected and untreated, despite the availability of doxycycline and its use in a few patients without rickettsioses. In addition to non-specific findings, rickettsioses and Q fever pose a diagnostic and therapeutic challenge because paired serology, the reference standard, is intrinsically retrospective and current PCR protocols are insensitive. Our ability to examine and identify distinguishing clinical features if present was greatly improved by the >90% follow-up albeit limited by sample size. Although many SFGR infections and Q fever cases are self-limited, unconfirmed and potentially fatal rickettsial infections could have occurred in the 10% lost to follow-up, the unknown number of individuals too sick or poor to reach the hospital, and among the 5 who reported doxycycline treatment (since treatment can dampen the IgG immune response) [38]. Severe disease and deaths due to confirmed and presumed RMSF have been reported in Central America [15–18], [12–16] with case fatality up to 20% [39]. However, the diversity, relative frequency, and clinical spectrum of SFG rickettsioses is not known in Central America, nor are the determinants of disease severity among SFG rickettsioses
Conservatively, 5% (34/748) of patients with AFI had definitive evidence of rickettsial infection and 2% (14/748) Q fever. Our estimate is conservative because our algorithm of confirming ELISA positives with reference standard IFA assured specificity but not sensitivity, the latter of which would have required performing IFA in all patients. We required an IFA titer of 160 to be positive because we preferred to underreport rather than falsely report Rickettsia spp. and C. burnetii as newly identified causes of acute febrile illness in Nicaragua. However, it is likely that these etiologic agents caused much more than 6% of acute febrile illnesses and that the seroprevalence of rickettsioses and Q fever together was at least 10%. Because IFA is inherently subjective and readings can vary by one dilution even among experts [30],we conservatively required a convalescent titer of 160 to define confirmed infection as we have done previously [11], including in the setting of seroconversion. Confirmed acute rickettsial infections required demonstration of a 4-fold rise in IgG by IFA on paired sera, which is in consistent with the US Centers for Disease Control and Prevention’s decision to no longer accept a single high IFA titer or a positive ELISA as confirmatory for R. rickettsii infection [34]. We ascribed infections to SFGR rather than TGR if the former had 2-fold higher titers as we have done previously [11]. Although cross-reactions occur between groups and especially species within groups, titers would be expected to be higher in the homologous group. In addition to our stringent case definitions, it is likely that cases were missed because of lack of access to care and possibly also due to early treatment or mortality.
A major strength of our study is use of SFG and TG ELISA to identify probable rickettsial infections followed by SFG and TG IFA on paired specimens to confirm acute vs. past infections. In contrast with existing seroepidemiological studies of rickettsial infections that solely utilize ELISA performed on a single serum sample, we opted to use a two-tiered approach that leverages the strengths of ELISA methods (high throughput and objective evaluation) but retains IFA (gold standard, but somewhat cumbersome and subjective) to strengthen certainty of our data. Our approach is in keeping with the US Centers for Disease Control and Prevention’s latest case definition [6] that classifies ELISA-positive cases as probable, and requires a 4-fold rise in titer by IFA to confirm SFG infections. We additionally found that ELISA screening should include both SFG and TG antigen, since two of 6 patients diagnosed with acute spotted fever rickettsiosis would have been missed if simultaneous IFA screening for both typhus and spotted fever group rickettsiae was not conducted. It is possible that we missed additional acute rickettsial infections that would have been identified had we performed IFA on the full cohort; hence, because of ELISA’s imperfect sensitivity in addition to specificity, we would emphasize the importance of IFA and empiric treatment for management of patients with suspected rickettsial infections. Given the limitations of serology, we also sought to confirm cases by PCR; however, PCR on whole blood is intrinsically insensitive for rickettsioses, due to low levels of bacteremia and the intracellular location of these bacteria within endothelial cells [40,41]. PCR for Coxiella is also insensitive, especially among seropositive patients [42,43]. Therefore, it is not surprising that paired IFA-confirmed infections were not corroborated by PCR.
In summary, we provide definitive evidence and a conservative estimate of unsuspected and untreated rickettsial infections and Q fever among patients with AFI in Nicaragua. A population-based longitudinal study with speciation of SFGR will be required to define the full clinical spectrum of R. rickettsii vs. other possible SFGR species and the case fatality rate of specific SFGR, TGR, and Q fever in Nicaragua. Better diagnostic tests, evaluated relative to gold standard paired serology such as we achieved here, and further epidemiologic study will be necessary to understand the biology of human rickettsioses and Q fever in Central America, to identify vector-host relationships, and to guide treatment and preventive measures.
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10.1371/journal.pcbi.1006430 | Network mechanisms underlying the role of oscillations in cognitive tasks | Oscillatory activity robustly correlates with task demands during many cognitive tasks. However, not only are the network mechanisms underlying the generation of these rhythms poorly understood, but it is also still unknown to what extent they may play a functional role, as opposed to being a mere epiphenomenon. Here we study the mechanisms underlying the influence of oscillatory drive on network dynamics related to cognitive processing in simple working memory (WM), and memory recall tasks. Specifically, we investigate how the frequency of oscillatory input interacts with the intrinsic dynamics in networks of recurrently coupled spiking neurons to cause changes of state: the neuronal correlates of the corresponding cognitive process. We find that slow oscillations, in the delta and theta band, are effective in activating network states associated with memory recall. On the other hand, faster oscillations, in the beta range, can serve to clear memory states by resonantly driving transient bouts of spike synchrony which destabilize the activity. We leverage a recently derived set of exact mean-field equations for networks of quadratic integrate-and-fire neurons to systematically study the bifurcation structure in the periodically forced spiking network. Interestingly, we find that the oscillatory signals which are most effective in allowing flexible switching between network states are not smooth, pure sinusoids, but rather burst-like, with a sharp onset. We show that such periodic bursts themselves readily arise spontaneously in networks of excitatory and inhibitory neurons, and that the burst frequency can be tuned via changes in tonic drive. Finally, we show that oscillations in the gamma range can actually stabilize WM states which otherwise would not persist.
| Oscillations are ubiquitous in the brain and often correlate with distinct cognitive tasks. Nonetheless their role in shaping network dynamics, and hence in driving behavior during such tasks is poorly understood. Here we provide a comprehensive study of the effect of periodic drive on neuronal networks exhibiting multistability, which has been invoked as a possible circuit mechanism underlying the storage of memory states. We find that oscillatory drive in low frequency bands leads to robust switching between stored patterns in a Hopfield-like model, while oscillations in the beta band suppress sustained activity altogether. Furthermore, inputs in the gamma band can lead to the creation of working-memory states, which otherwise do not exist in the absence of oscillatory drive.
| Oscillations are ubiquitous in neuronal systems and span temporal scales over several orders of magnitude [1]. Some prominent rhythms, such as occipital alpha waves during eye-closure [2] or slow-oscillations during non-REM sleep [3] are indicative of a particular behavioral state. Other rhythms have been specifically shown to correlate with memory demands during working memory tasks, including theta (4–8Hz) [4–7], alpha/beta (8–30Hz) [8–10] and gamma (20–100Hz) [11–13]. Understanding the physiological origin and functional role of such oscillations is an area of active research.
Here we study how oscillatory signals in distinct frequency bands can serve to robustly and flexibly switch between different dynamical states in cortical circuit models of working memory and memory storage and recall. In doing so we characterize the dynamical mechanisms responsible for some of the computational findings in an earlier study [14]; we go beyond that work to include new results on oscillatory control of network states. Specifically, we consider the response of multistable networks of recurrently coupled spiking neurons to external oscillatory drive. We make use of recent theoretical advances in mean-field theory to reduce the spiking networks to a low-dimensional macroscopic description in terms of mean firing rate and membrane potential, which is exact in the limit of large networks [15]. This allows us to perform a systematic and detailed exploration of network states analytically or with numerical bifurcation analysis, which informs us about suitable parameter sets for numerical simulations. The latter serve to give representative examples of the dynamical phenomena investigated here. As a result, we can completely characterize the dynamics of the forced system.
Specifically, we consider networks which exhibit multistability in the absence of forcing. Such attracting network states have been proposed as the neural correlate of memory recall [16, 17], and as a possible mechanism for sustaining neuronal activity during working memory tasks [18–20]. We find that an external oscillatory drive interacts with such multistable networks in highly nontrivial ways. Low-frequency oscillations are effective in switching on states of elevated activity in simple bistable networks, while in higher dimensional multistable networks they allow for robust switching between stored memory states. Higher frequencies, in the beta range, destabilize WM states through a resonant interaction which recruits spike synchrony. Such oscillatory signals can therefore be used to clear memory buffers. Finally, when networks operate outside the region of multistability, e.g. due to reduced excitability, an oscillatory signal in the gamma range can be used to recover robust memory recall.
Networks of recurrently coupled excitatory neurons can exhibit bistability given sufficiently strong synaptic weights. Such networks act as binary switches: a transient input can cause a transition from a baseline state to a state of elevated activity, or vice-versa. We asked to what extent an oscillatory signal alone could also drive transitions between states in such a network. In particular we were interested in knowing if the directionality of the transition, and hence the final state of the system, could be controlled via the frequency of the oscillatory drive.
To investigate this we simulated a network of recurrently coupled excitatory quadratic integrate-and-fire neurons, see Methods for details. Fig 1 shows an illustration of the network dynamics as a function of the stimulus frequency and initial state of the network. In particular, at low frequencies, the oscillations push the system from the state of low activity into the state of high activity, which persists under such forcing, see Fig 1A. As the frequency is increased past a critical value, it is no longer effective in driving a transition, and the network remains in its initial state, see Fig 1B. A further increase then shows the opposite effect: The state of high activity becomes unstable under the forcing, whereas the state of low activity persists, Fig 1C. At large enough frequencies we then observe again that no transitions occur and the initial network state persists, Fig 1D.
The results from Fig 1 show that the frequency of an external oscillatory drive can be used to selectively destabilize a given network state. For the parameter values used here, oscillation frequencies in the delta range result in a WM state while frequencies in the beta range force the system to the “ground” state, essentially clearing the WM state, a result seen also in [14]. Oscillations outside these ranges are ineffective in driving transitions. We seek to understand the mechanisms underlying these transitions, and additionally to determine to what extent the precise frequency ranges are influenced by the network parameters. To do this we will take advantage of recent work in which the authors derived a set of simple equations for the mean firing rate and mean membrane potential in a network of recurrently coupled quadratic integrate-and-fire (QIF) neurons [15]. In the large-system limit these equations are exact and fluctuations can be neglected. The exact correspondence between the low-dimensional mean-field equations and the original network allows us to use standard dynamical systems techniques to fully characterize the range of dynamical states in the network.
The dynamics in networks of recurrently coupled QIF neurons can be described exactly under the assumptions of all-to-all coupling and quenched neuronal variability, i.e. static distributions in cellular or network properties. For the case of a single network of excitatory cells in which the input currents to individual neurons are distributed, the resulting mean-field equations are [15]:
τ 2 r ˙ = Δ π + 2 τ v r , τ v ˙ = v 2 + J τ r + η + I ( t ) - π 2 τ 2 r 2 . (1)
Here, r is the network average of the firing rate and v is the network average of the membrane potential, J is the strength of synaptic weights. In the derivation of the mean-field equations each synaptic weight is scaled as 1/N, where N is the system size, leading to an order one contribution to the mean input in the thermodynamic limit, whereas fluctuations vanish. η and Δ are, respectively, the center and width of the static distribution of inputs, which is considered to be Lorentzian. External, time-variant forcing is represented here by I(t). The time constant τ is the membrane time constant of the individual neurons and is set to 20ms throughout.
This macroscopic model permits a straightforward investigation of the stationary states in the full network. For sufficiently strong synaptic coupling two stable fixed points co-exist over a range of mean external inputs, see Fig 2A. Linear stability analysis further reveals that the stable high-activity fixed point is a focus for sufficiently high rates, whereas the stable low-activity fixed point is a node [15]. The network therefore shows a damped oscillatory response to external perturbations in the high-activity state. This response reflects transient spike synchrony which decays over time due to the heterogeneity; the characteristic time scale of the desynchronization is in fact proportional to the width of the distribution of input currents Δ [21]. This type of spike synchrony is seen ubiquitously in networks of both heterogeneous and noise-driven spiking neurons operating in the mean-driven regime, in which neurons fire as oscillators [15, 22, 23], and is captured in Eq 1 by the interplay between the mean sub-threshold membrane potential and mean firing rate [24].
We use this macroscopic description to systematically investigate the network response to periodic forcing with amplitude A and frequency f, see Eq 8. Fig 2B shows a phase diagram of the network dynamics as a function of these two parameters. As in Fig 1 we keep track of the final state of the network as a function of the initial state. For sufficiently slow frequencies and over a range of amplitudes the network is always driven to the high-activity state (green). This region therefore corresponds to recall of the memory state, see Fig 2C (left). For an intermediate range of frequencies a sufficiently strong forcing always drives the network to the low-activity state (red), which corresponds to clearance, Fig 2C (right). The frequency band for clearance is essentially set by the frequency of intrinsic oscillations of the high-activity state, i.e. it is a resonant effect, see Fig 3. Weak forcing and forcing at very high frequencies fail to drive any transitions, while strong forcing at low enough frequencies can enslave the network dynamics entirely (orange). For the parameter values used here recall occurs for frequencies below about 2Hz and clearance in the range between 10-30Hz.
In order to characterize the role of spike synchrony in determining the network response, we derive a reduced firing rate equation with the identical fixed-point structure as in the original, exact mean-field equations Eq 1, but without the subthreshold dynamics. Specifically, the fixed-point value of the firing rate in Eq 1 can be written as
r 0 = Φ ( J τ r 0 + η ) , (2)
where Φ is the steady-state f-I curve, which in the case of Eq (1) is
Φ ( x ) = 1 2 π x + x 2 + Δ 2 . (3)
We use the steady-state f-I curve to construct a heuristic firing rate model given by
τ r ˙ = - r + Φ ( J τ r + η + I ( t ) ) , (4)
and investigate its response to periodic forcing I(t). Eq 4 is similar in form to the classic Wilson-Cowan firing rate model for a single population [25]. In this case the high-activity branch of the firing rate is a node, i.e. it no longer shows damped oscillations in response to perturbations, see Fig 2D. Furthermore, the region of “clearance” has completely vanished in the phase diagram in Fig 2E, confirming that in the original network it was due to a resonance reflecting an underlying spike synchrony mechanism.
Given the simplicity of the mean-field equations Eq 1 we can calculate the linear response of the system analytically, without the need for extensive numerical simulations. The response of the focus to weak sinusoidal inputs (linear response) already shows a clear resonance for the high-activity state (Fig 3A), where the resonant frequency is
f r e s = 1 2 π 2 r 0 ( 2 π 2 r 0 - J τ ) , (5)
see Methods. Furthermore, additional, sub-harmonic resonance peaks occur when the forcing is sharply peaked, leading to a broadening of the resonance spectrum (Fig 3A, right); this effect is due to the presence of many sub-harmonics of the linear resonance in the forcing term itself. Conversely, the node does not show such a resonance, indicating a qualitative difference in the response of the two stable fixed points.
However, the switching behaviors seen in Figs 1 and 2 and the corresponding destabilization of network states cannot be attributed to this linear resonance alone—nonlinear effects have to be taken into account. This can be seen by plotting the bifurcation diagram for the response of the network to the forcing for several values of the forcing amplitude. For relatively weak, but finite forcing, the network response consists of a periodic orbit in the vicinity of the corresponding unforced fixed-point, Fig 3B (top). As the forcing amplitude is increased, the resonance peak of the focus moves towards slower frequencies, akin to a softening spring. Then, a pair saddle-node bifurcations leads to a range of frequencies in which three periodic orbits coexist, see Fig 3B middle-right.
At large enough amplitudes for the sharply-peaked, non-sinusoidal forcing two additional bifurcations occur which are responsible for the “recall” and “clearance” behaviors respectively, see Fig 3B (bottom-right) and Fig 3C. Specifically, for sufficiently large frequencies, the stable periodic orbit due to the low-activity node (blue line) coexists with the unstable one due to the saddle-point (green line), and with a third state, emanating from the focus (red line). When the forcing frequency is sufficiently small, only the latter solution persists. This can be understood as quasi-stationary response of the system due to the slow forcing, see the Methods section for details. In other words, the forcing here is slow and large enough to push the system beyond the bistable regime into the basin of attraction of the focus. Therefore, at low frequencies the only solution is the periodic orbit in the vicinity of the high-activity focus, which explains why low frequencies are effective in switching on the high-activity state, i.e. for “recall”.
On the other hand, in the range of frequencies over which the network response is resonant, period-doubling bifurcations of the focus lead to a frequency band in which all periodic orbits around the focus are unstable. This is due to the rapid occurrence of further period-doubling bifurcations, leading to the emergence of chaotic responses to the forcing. As we show in the Methods section, there exist narrow frequency bands in which these chaotic responses are stable, but a numerical investigation of these shows that they quickly become unstable as the frequency of the forcing is changed. Therefore, the periodic orbit in the vicinity of the low-activity node is the only stable solution. Frequencies in this range are therefore effective in switching off the high-activity state, i.e. for “clearance”. As we show in Fig 3D, the loci of bifurcations that periodic orbits around the focus undergo, explain well the parameter range in the (A, f)-plane in which clearance is observed, i.e. the red area in Fig 2B.
A single bistable network of neurons serves as a canonical illustration of a memory circuit. However, such a network can only store a single bit of information; actual memory circuits must be capable of storing more information. In terms of neuronal architecture this can be achieved by having a network which is comprised of several or many neuronal clusters [16, 17, 26]. We asked to what extent the frequency-selective switching behavior seen in a single bistable network could also be found in a clustered network. We look first at a simple, two-cluster network and then the more general case of a higher-dimensional multi-clustered network.
Thus far we have treated oscillations as an extrinsic effect, i.e. we are agnostic as to their origin. To be effective for flexible control of memory states, the oscillatory forcing we have considered here must fulfill two requirements: First, it must have a broad range of possible frequencies, and secondly, it must have a burst-like shape. Here we show that a simple circuit comprised of interacting excitatory and inhibitory populations can satisfy both these requirements.
Specifically, we construct a network of QIF neurons consisting of an E-I circuit which spontaneously oscillates, and drives a downstream population of E cells, which itself is bistable, see Fig 6. Using the corresponding mean-field equations for the E-I circuit, we found a broad region of oscillatory states of the E-I network as a function of the mean external drive to the E and I populations, ηe and ηi respectively, see the phase diagram Fig 6B. By adjusting the external drive to the E and I populations alone we can tune the output frequency over an order of magnitude. This allows us to selectively switch the downstream network on and off, as shown in Fig 6C.
Outside the region of bistability (or multistability in the case of clustered networks), neuronal networks will relax to a single stationary state in the response to a transient input. Here we show that this need not be the case if the network activity is subjected to ongoing oscillatory modulation.
As an illustration we take a single population of excitatory neurons with strong recurrent excitation, but insufficient tonic drive to place it in the region of bistability. As a result, the response of the network to a transient excitatory stimulus decays to baseline, as seen in Fig 7A (top). However, in the presence of an oscillatory input in the gamma range, which itself only very weakly modulates the network activity (Fig 7A middle), the transient input now switches the network to an activated state with prominent gamma modulation Fig 7A (bottom). Once the oscillations cease (green arrow) the activated state vanishes.
This phenomenon depends crucially on the presence of the spike-synchrony mechanism underlying the damped oscillatory response of the high-activity focus discussed earlier. Specifically, for the parameter values used in Fig 7 the only fixed-point solution which exists is the low-activity node. Nonetheless, oscillatory forcing at sufficiently high firing rates can still recruit and resonate with the damped oscillatory interaction between the mean firing rate and mean membrane potential in the network. The resulting resonant frequency can no longer be associated with the linear response of the focus as it is a fully nonlinear network property.
The phase diagram Fig 7B shows the regions of bistability given an oscillatory forcing, for different forcing amplitudes. For zero amplitude the curve corresponds to the saddle-node (SN) bifurcation of the unforced system (horizontal black line). Note that only sufficiently high frequencies allow for bistability given tonic inputs which place the network below the SN. Furthermore, there is a clear resonance in the range of 60–90Hz for these parameter values. As the forcing frequency f → ∞ the curves converge to the SN line of the unforced system. This is because the forcing we use has zero-mean and hence, given the low-pass filter property of neuronal networks, has no effect on the network dynamics at high frequencies.
In this article we have studied the role of oscillations in switching or maintaining specific brain states. Specifically, we identified distinct frequency bands: delta, beta, and gamma with specific functional roles. This finding is especially intriguing given that the networks we study are relatively simple. Connectivity is all-to-all and neurons are exclusively excitatory. For the multi-population networks, interactions between populations are assumed to be mediated by fast inhibition, leading to a winner-take-all behavior. Furthermore, synaptic transmission is considered to be instantaneous, with the only relevant time scale being the membrane time constant (τ = 20ms). The susceptibility of the networks to forcing of distinct frequencies therefore does not depend on the presence of multiple time scales associated with intrinsic currents, synaptic kinetics or sub-classes of inhibitory cells. Rather, the key dynamic factors are: bistability or multistability due to recurrent excitatory reverberation, and transient spike synchrony in response to external drive. Given this, we expect to see the same phenomenology in more biophysically realistic networks as long as there is bistability and external noise sources are not too strong. Additionally, none of the mechanisms we study depend crucially on the specific choice of neuron model, at least for type I spiking models. The “switching-on” at low frequencies depends only on the presence of a saddle-node bifurcation, which is ubiquitous in networks of spiking neurons in the bistable regime. Similarly, the “switching-off” or “clearance” depends only on recruiting spike synchrony, which occurs readily in both integrate-and-fire models as well as conductance-based spiking models [24]. In fact, in the mean-driven regime spiking networks in general robustly exhibit a resonance to oscillatory inputs, which reflects the underlying synchrony mechanism [20, 23, 33].
In the region of bistability, low frequencies are effective in pushing the network into a high-activity state; for not too large amplitudes the network remains in the activated state on the downswing of the input. The cut-off frequency for this “recall” signal is determined by the escape time of the network from the vicinity of the saddle-node bifurcation in the low-activity state, and here is a few Hertz, see Fig 2A. In multi-stable networks, this same mechanism allows for robust switching between distinct memory states. On the other hand, frequencies in the beta range are effective in switching off the high-activity state by resonantly driving bouts of spike synchrony. The precise frequency range depends on network parameters, see Fig 8. In both cases the relevant frequency ranges scale with the membrane time constant of the neurons. Therefore, e.g. choosing a time constant τ = 10ms will simply stretch the x-axis of the phase diagram in Fig 2B by a factor of two. Finally, we showed that forcing in the gamma range can allow for robust working memory states which otherwise do not exist, i.e. the system sits outside the region of bistability with oscillatory forcing. This mechanism once again depends on resonantly recruiting spike synchrony.
We find that non-sinusoidal, burst-like drive is most effective in switching the network state, see Fig 3A and 3B. In fact, this is precisely the type of oscillation which readily emerges in a simple E-I network. Furthermore, the oscillation frequency can be modulated over a wide range through changes in the tonic drive to the E-I circuit alone, see Fig 6. This means that the state of downstream memory networks can be flexibly controlled via an E-I circuit through global changes in excitability alone.
While here we have considered networks in which intrinsic oscillatory activity is due to transient spike synchronization, spiking networks can also generate oscillatory activity due to E-I and I-I loops, which can occur in the absence of strong spike synchrony. For example, networks of coupled excitatory (E) and inhibitory (I) spiking neurons readily generate oscillations via a Hopf bifurcation when excitation is sufficiently strong and fast [34, 35]. The E-I loop, and in particular the ratio of E to I time constants, largely sets the frequency of these oscillations, which tend to lie in the gamma range (30–100Hz). On the other hand, the I-I loop itself can underlie the generation of fast oscillations (>100Hz), the frequency of which is set by the inhibitory synaptic delay [35, 36]. Both the E-I and I-I loops contribute to the population frequency in E-I networks, with the E-I loop dominating when recurrent excitation is strong. Resonant responses to periodic stimuli due to the E-I loop in neuronal circuits have been studied in firing rate models [37–39] as well as in networks of LIF neurons [33].
Damped oscillatory activity due to the E-I loop can also arise in the high-activity state of the bistable regime of E-I networks [40]. In this scenario external periodic drive could also be used for “clearance” of the activated state by resonating with the E-I loop. While the phenomenology of this resonance would be similar to the resonance we have considered in this manuscript, the mechanism is nonetheless distinct as it does not involve spike synchrony. On the other hand, spike synchrony does robustly lead to resonances in E-I networks, as measured for example by the linear response [23]. In principle both resonances could be present in the bistable regime of E-I networks, allowing for an even more complex response to oscillatory input than we have studied here.
Current non-invasive brain-stimulation techniques, such as repetitive transcranial magnetic stimulation [41], or transcranial alternating current stimulation [42], apply transient oscillatory signals to large parts of the brain. Our study may be useful to investigate the impact of such signals on the dynamics of neuronal mass models and the psychological and behavioral effects of neuromodulation. Our results could also be of relevance for investigating the use of deep-brain stimulation to treat Parkinson’s disease [43] and (pharmacologically) treatment-resistant depression [44]. Although the model used here describes networks of spiking neurons with instantaneous synapses, future studies could also incorporate synaptic dynamics with appropriate time scales for excitatory and inhibitory transmission [24]. These time scales can be influenced by drugs, or (pathological) changes in neurotransmitters. The framework developed here may therefore serve as a tool to study the cause of functional deficiencies in synapse-related conditions, so-called “synaptopathies” [45, 46].
Neural mass and neural field models are an important tool for understanding macroscopic neuronal dynamics. Classical models include the Wilson-Cowan model [25, 47] or the Amari model [48, 49]. However, such macroscopic models of brain activity often pose a stark simplification of the actual dynamics, and often miss important features from the spiking dynamics, such as spike synchronization. Recently, there have been advances in linking the microscopic and macroscopic dynamics of networks of spiking neurons [15, 23, 50–57].
We consider a neural mass model that was recently derived from networks of all-to-all coupled quadratic integrate-and-fire neurons in the thermodynamic limit [15], see Eq (1). To simplify the mathematical treatment, we divide t by τ which represents the case of time being measured in units of τ, thus eliminating τ from the equations:
r ˙ = Δ π + 2 v r , v ˙ = v 2 + J r + η + I ( t ) - π 2 r 2 . (6)
Here, r represents the ensemble average of the firing rate of neurons, and v represents the ensemble average of the membrane potential. The parameters η and Δ represent the center and witdh of the Lorentzian distribution of time-invariant input currents into the neuronal ensemble, and J is the coupling constant between neurons. Time-varying external inputs are given by I(t). The original model (1) can then be recovered by t → τt, r → r/τ. As we set τ = 20ms, r = 1 here corresponds to a firing rate of r = 50Hz in the full model.
Here, we consider I(t) to be T-periodic, i.e. I(t + T) = I(t). We distinguish between two types of input: sinusoidal input,
I ( t ) = A sin ( 2 π f t ) , (7)
and non-sinusoidal input,
I ( t ) = A ( γ sin ( π f t ) n - 1 ) , (8)
where we take n = 20 for the simulations presented in this paper. The parameter A represents the amplitude of the forcing. The constant γ is chosen such that ∫ 0 T I ( t ) d t = 0. We choose this type of zero-mean forcing to avoid any changes in network excitability which a tonic DC-offset might cause. In other words, the input models a reorganization of afferent spikes into periodic volleys without adding any additional spikes. In the non-sinusoidal case the spikes are more synchronized than in the sinusoidal case.
We compare the full model equations with its equivalent heuristic firing rate equation, which preserves the fixed point structure but reduces the dynamical behavior. This is done by considering stationary solutions given by
0 = Δ π + 2 v r , 0 = v 2 + J r + η - π 2 r 2 . (9)
Solving these equations for r is equivalent to solving Eq 2. Thus, the reduced heuristic firing rate equations can be expressed by
r ˙ = - r + Φ ( J r + η ) , (10)
where the f-I function Φ(Jr + η) is given by Eq 3.
Ignoring transient dynamics, the response of the model equations to the external input I(t) is T-periodic as well, at least in the limit of small amplitudes A (an exception are period-doubled solutions, which are a nonlinear phenomenon only relevant at larger A). In this case the corresponding Fourier spectra of the firing rate r(t) and of the membrane potential v(t) are discrete:
r ( t ) = r 0 + ( r 1 e i ω t + r 2 e 2 i ω t + … + c . c . ) , v ( t ) = v 0 + ( v 1 e i ω t + v 2 e 2 i ω t + … + c . c . ) (11)
For brevity of exposition we use here the angular frequency ω = 2πf instead of the ordinary frequency f. This approach describes the projection of solutions of r and v from a continuous space R onto a discrete function space V, with orthogonal basis functions einωt, n ∈ Z. The same Fourier decomposition applies to the input current I(t):
I ( t ) = I 0 + ( I 1 e i ω t + I 2 e 2 i ω t + ⋯ + c . c . ) (12)
To determine the linear response of the model equations [58], we first carry out a Fourier decomposition of the system linearized around the fixed points given by r0 and v0:
i n ω r n = 2 v 0 r n + 2 r 0 v n , i n ω v n = J r n + I n + 2 v 0 v n - 2 π 2 r 0 r n . (13)
Solving this set of linear equations, we obtain
r n = 2 r 0 I n Ω n - 1 , v n = ( i n ω - 2 v 0 ) I n Ω n - 1 , (14)
with
Ω n = ( 2 v 0 - i n ω ) 2 + ω 0 2 , (15)
where ω0 is the (angular) resonant frequency:
ω 0 2 = - 2 r 0 ( J - 2 π 2 r 0 ) . (16)
The resonant frequency is state-dependent and changes with model parameters. Reintroducing the time scale τ, perturbations of the upper branch solution resonate at a frequency
ω r e s = 2 r 0 ( 2 π 2 r 0 - J τ ) , (17)
where r0 is the value of the steady-state firing rate. This is true as long as the argument of the square root is positive. Therefore as the firing rate decreases along the upper branch, for decreasing external input, the frequency decreases to zero at which point the focus becomes a node. This point occurs before the saddle-node is reached unless Δ = 0 in which case it exactly coincides with the saddle-node.
Fig 8 shows how the linear resonant frequency of the stable focus in the bistable regime of a network of excitatory QIF neurons varies as a function of the mean external input η and the strength of synaptic coupling J. Recall is not possible to the left of the red curve given the nonlinear forcing used here. This line is determined by setting Amin = Amax, see Eqs (26) and (27) further below.
The time-dependent linear response of the firing rate and the membrane potential is now given by
r ( t ) = ∑ n = 1 ∞ 2 r 0 I n Ω n - 1 e i n ω t + c . c . , v ( t ) = ∑ n = 1 ∞ ( i n ω - 2 v 0 ) I n Ω n - 1 e i n ω t + c . c . (18)
From this, we can derive the amplitude of the linear response of the firing rate,
r l i n ( ω ) = ( max t r ( t ) - min t r ( t ) ) / 2 , (19)
and analogously of the membrane potential. Alternatively, one can derive the time-averaged linear response (“power”) of the system:
R 2 ( ω ) = 1 T ∫ 0 T r ( t , ω ) 2 d t = 8 r 0 2 ∑ n = 1 ∞ | I n | 2 | Ω n | - 2 , (20) V 2 ( ω ) = 1 T ∫ 0 T v ( t , ω ) 2 d t = 2 ∑ n = 1 ∞ ( n 2 ω 2 + 4 v 0 2 ) | I n | 2 | Ω n | - 2 . (21)
Here we have made use of the orthogonality of the basis functions, and the fact that T = 2π/ω.
In order to exhaustively and accurately trace the bifurcations that occur in the model equations, we make use of AUTO 07p [59]. Since this software is designed to deal with autonomous systems, we recast the (non-autonomous) model Eq (1) into a set of autonomous equations:
r ˙ = Δ π + 2 v r , v ˙ = v 2 + J r + η + A I ( x ( t ) ) - π 2 r 2 , x ˙ = x + ω y - ( x 2 + y 2 ) x , y ˙ = y - ω x - ( x 2 + y 2 ) y . (22)
The last two equations create the periodic stimulus x(t) = sin(ωt) in the model equations. We distinguish the sinusoidal case,
I ( x ( t ) ) = x ( t ) , (23)
and the non-sinusoidal case
I ( x ( t ) ) = γ x ( t ) 20 - 1 . (24)
Continuation of the forced system is performed by starting from a known fixed point (r0, v0) at A = 0, and continuing solutions by increasing A up to the desired value. We use the L2-norm as a scalar measure to represent periodic solutions:
L 2 ( r ) = 1 T ∫ 0 T r ( t ) 2 d t . (25)
Where we perform this one-parameter continuation, we represent solution branches by plotting the L2-norm against the parameter that is being varied. Where we perform two-parameter continuation, we plot the loci of bifurcations against the two parameters being varied.
Here we illustrate in greater detail the mechanisms underlying the “switching on” of activated network states (or simple switching between attractors in the case of a multi-stable network) at low frequencies, and the “switching off” of activated states at frequencies in the beta range.
A natural extension of the single-population model is to consider a network of neural masses:
r ˙ n = Δ π + 2 v n r n , v ˙ n = v n 2 + J ∑ m = 1 N A n m r m + η + I ( t ) + σ ξ n ( t ) - π 2 r n 2 , (28)
where the adjacency matrix A with entries Anm determines the connectivity structure between neural masses. The term σξn(t) describes an additional noise input, where σ is the noise amplitude, and ξn(t) is the random variable.
In this paper we consider two scenarios, the first of which is two neural populations with recurrent excitation and mutual inhibition. The adjacency matrix of such a network is given by
A = ( J e J i J i J e ) , (29)
where Ji < 0 < Je.
In the second scenario, we examine the dynamics within a Hopfield network [16]. Rather than creating the network through learning algorithms, we build the network as follows. First, we choose the patterns that the network should encode and write them into an array U. Each column of this array represents one pattern, where we put 1 for populations that are active in this pattern, and 0 otherwise. As a result, the array U has the size N × Npat, where Npat is the number of patterns encoded, and N is the network size. Each pattern consists of Np active populations. The adjacency matrix of a network that encodes these patterns can then be constructed as follows [17],
A = ( U - p ) × ( U - p ) T - Q , (30)
with p = Np/N. The entries of A are capped at a maximum value of (1 − p)2 − Q to account for saturation effects in synaptic plasticity. Otherwise, the strength of connections in the network would steadily increase as patterns are added. The offset Q introduces global inhibition that stabilizes the encoded patterns. We set Q = 0.2.
Each population is subjected to an independent Ornstein-Uhlenbeck process ξn(t) to break the symmetry of the networks. The Ornstein-Uhlenbeck process is implemented as Langevin equation:
τ ξ ˙ n = - ξ n + ζ n ( t ) , (31)
where ζn(t) are independent Gaussian white noise sources, 〈ζn(t)ζm(t − s)〉 = δ(s)δmn, and τ is the characteristic time scale, which we set to τ = 20ms.
To create a network that generates oscillations, we consider a network of an excitatory population interacting with an inhibitory one:
r ˙ e = Δ π + 2 v e r e , v ˙ e = v e 2 + J e r e + J i r i + η e - π 2 r e 2 , r ˙ i = Δ π + 2 v i r i , v ˙ i = v i 2 + J e r e + J i r i + η i - π 2 r i 2 . (32)
For simplicity, we choose Je = −Ji = J. The two populations differ in terms of the means of their tonic input currents, ηe and ηi. We vary these two parameters to identify the regime where stable oscillations exists, and to change the frequency of these oscillations.
In the network model, the high-activity branch of solutions in the bistable regime exhibits damped oscillations. Periodic external drive can resonate with these intrinsic oscillations, leading to destabilizing period-doubling bifurcations as seen in the previous section. Here we show that this mechanism is present in the simplest possible model exhibiting a saddle-node bifurcation and for which the upper branch becomes a focus:
x ˙ = y , (33) y ˙ = μ - x 2 - a x y + I ( t ) . (34)
This model is a particular unfolding of the so-called Takens-Bogdanov normal form [60], for which there is no Hopf bifurcation, which is the relevant case for our network model. It is easily shown that a saddle-node bifurcation occurs in these equations at μ = 0 and that the fixed point solutions are x 0 = ± μ and y0 = 0 for μ > 0, see Fig 11A.
Furthermore, the solution x 0 = - μ is a saddle, and x 0 = μ is a stable focus for which the frequency goes to zero smoothly as μ → 0. Fig 11B shows that in the forced system there is a range of frequencies for which there is no stable solution; in the normal form equation the solution diverges while in the network model the system settles to a periodic orbit in the vicinity of the low-activity state. The instability is due to a series of period-doubling bifurcations as in the full system. Furthermore, comparison of the phase diagram of the normal form equation with that of the full system shows they are qualitative similar, see Fig 11C. This indicates that the nonlinear resonance seen in the network of QIF neurons is a generic feature of any system with a stable focus in the vicinity of a saddle-node bifurcation.
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10.1371/journal.pgen.1004310 | dGTP Starvation in Escherichia coli Provides New Insights into the Thymineless-Death Phenomenon | Starvation of cells for the DNA building block dTTP is strikingly lethal (thymineless death, TLD), and this effect is observed in all organisms. The phenomenon, discovered some 60 years ago, is widely used to kill cells in anticancer therapies, but many questions regarding the precise underlying mechanisms have remained. Here, we show for the first time that starvation for the DNA precursor dGTP can kill E. coli cells in a manner sharing many features with TLD. dGTP starvation is accomplished by combining up-regulation of a cellular dGTPase with a deficiency of the guanine salvage enzyme guanine-(hypoxanthine)-phosphoribosyltransferase. These cells, when grown in medium without an exogenous purine source like hypoxanthine or adenine, display a specific collapse of the dGTP pool, slow-down of chromosomal replication, the generation of multi-branched nucleoids, induction of the SOS system, and cell death. We conclude that starvation for a single DNA building block is sufficient to bring about cell death.
| Starvation of cells for DNA precursor dTTP is strikingly lethal in many organisms, like bacteria, yeast, and human cells. This type of death is unusual in that starvation for other nutritional requirements generally results in growth arrest, but not in death. The phenomenon is called thymineless death (TLD), because it was first observed some 60 years ago when a thymine-requiring (thyA) E. coli strain was exposed to growth medium lacking thymine. The TLD phenomenon is of significant interest as it is the basis for several chemotherapeutic (anticancer) treatments in which rapidly growing cells are selectively killed by depletion of the cellular dTTP pool. The precise mechanisms by which cells succumb to dTTP depletion are of significant interest, but have remained elusive for a long time. In the present work, we demonstrate for the first time that the effect is not specific for dTTP starvation. We show that an E. coli strain starved for the DNA precursor dGTP dies in a manner similar to dTTP-starved cells. The effect, which we have termed dGTP starvation, might be exploited - like TLD - therapeutically.
| Starvation of cells for the DNA precursor dTTP can cause rapid cell death in all domains of life [1]. This phenomenon, called thymineless death (TLD), was first discovered in 1954 in E. coli upon exposing thymine-requiring (thyA) strains to medium lacking thymine [2]. As TLD can be promoted in cells from bacteria to man, it has been widely employed for therapeutic purposes. Methotrexate and trimethoprim, both antifolates, and 5-fluorouracil are antitumor and antibacterial agents based on their ability to block thymidylate (dTMP) synthesis [3], [4] leading to low dTTP levels that kill or prevent proliferation of actively-dividing cells. However, despite decades of interest, our understanding of the TLD phenomenon is still incomplete, particularly with regard to the primary initiating events that cause cell death. Recent progress has revealed a complexity of participating and contributing events, and has led to models centered on the impairment of DNA replication and resulting stalling of replication forks [5]–[9]. Such stalled forks give rise to DNA breakage if not repaired by homologous recombination. Importantly, despite the stalling of existing replication forks, initiation of new replication forks at the oriC chromosomal origin can continue [9], [10], causing increased complexity of the chromosome, which becomes a major determinant of cell death [7], [9]. Recombinational processes play an important role throughout TLD in at least a dual fashion: they can rescue starving cells from early stages of TLD, but ultimately contribute actively to death at later stages by creating unresolvable or unrepairable intermediates and DNA breaks [5], [8]. Notably, significant breakage and disappearance of origin-containing DNA is observed, consistent with the importance of ongoing DNA initiation in TLD [6], [9]. TLD is also accompanied by persistent SOS induction, which contributes to cell death by initiating lethal filamentation [5].
However, a critical unanswered question is whether the phenomenon is truly thymine-specific or can be, likewise, imposed by starvation for other DNA precursors. Obviously, models based on stalled DNA replication would apply equally if stalling were mediated by starvation for any other dNTP. However, selective manipulation of the concentration of individual dNTPs is experimentally difficult, because their joint de novo synthesis is regulated by feedback on the enzyme ribonucleotide reductase [11]. Until now, dTTP was the only nucleotide for which the phenomenon could be demonstrated, as its pool can be manipulated separately through the thymine salvage pathway [12].
Nevertheless, long-sought conditions by which cells can be starved specifically for a dNTP other than dTTP were found, serendipitously, in our studies of the optA1 allele of the E. coli dgt gene. The dgt gene encodes a dGTPase with an unusual activity, hydrolyzing dGTP into deoxyguanosine and triphosphate (PPPi) [13], [14]. Its deletion was found to result in a spontaneous mutator effect [15], which was attributed to possible changes in the cellular dNTP levels, particularly dGTP. Indeed, an approximately 2-fold increase in the dGTP level of a dgt mutant had been described [14]. The optA1 allele of dgt is a promoter-up mutation, which increases gene expression by as much as 50-fold [16]. Consistent with this up-regulation a decrease in dGTP level was reported [17], although modest in view of the 50-fold gene overexpression. This presumably reflects the ability of cells to adjust their dNTP levels through feedback regulation on the ribonucleotide reductase. In the present study we report that a more dramatic and specific dGTP decrease can be achieved by combining the optA1 allele with a defect in the gpt gene. The gpt gene functions in purine salvage by converting guanine into guanosine monophosphate (GMP). In the present study, we show that an optA1 gpt strain grown in minimal medium with casamino acids (CAA) in the absence of an external purine source, like hypoxanthine (Hx) or adenine, dies in a manner sharing many of the features associated with TLD. Certain differences with TLD are also noted, which we argue reflect different kinetic manifestations of the same intrinsic mechanism. We propose to term this phenomenon dGTP starvation.
The initial observation that triggered our interest was that the optA1 allele of dgt caused impaired growth when the strain also contained the large (120-kb) Δ(pro-lac)X111 chromosomal deletion [18]. When stationary cultures of such a strain were diluted by at least 5,000-fold in minimal glucose medium enriched with CAA (1%), the cultures failed to grow beyond OD630 nm = 0.1. Complementation of the deletion by an F'prolac covering the deleted region reverted the cells to normal growth. Upon reconstruction of this defect in the widely-used MG1655 strain background, we found that the growth impairment was attributable to the combination of optA1 with the lack of gpt, a gene located inside the boundaries of the Δ(pro-lac)X111 deletion. The gpt gene encodes Guanine Phosphoribosyltransferase, a purine salvage enzyme responsible for salvaging guanine, hypoxanthine, and xanthine via their conversion to the corresponding NMP, with guanine being the preferred substrate due to the lowest Km [19]. The growth curves displayed in Fig. 1A show the growth defect of the optA1 gpt strain. While the single optA1 or gpt strains show normal growth in the minimal medium with casamino acids, the optA1 gpt double mutant fails to reach beyond OD630 nm = 0.1 for at least 10 hrs. In contrast, the double mutant strain grows normally in the presence of the added purine sources hypoxanthine (Hx) or adenine (Ade), as shown in Fig. 1B. Addition of guanine (Gua) as purine source has no such effect; in fact, it exacerbates the growth defect.
The deleteriousness of the optA1 gpt combination may be understood based on the activities of the corresponding Dgt and Gpt enzymes within the salvage and de novo purine biosynthesis pathways. Diagram 1C shows how enhanced dGTPase activity resulting from optA1 leads to increased breakdown of dGTP, yielding deoxyguanosine (G-dRib) and deoxyribose-1-phosphate (dRib-1-P) and, subsequently, guanine (Gua) upon further metabolism by the DeoD purine nucleoside phosphorylase. In the gpt background, this guanine cannot be readily returned back, via GMP, to the guanine nucleotide pool; therefore, the expected result is a limitation for purine nucleotides, presumably most acutely for dGTP. Consistent with this model are the observed alleviation of the growth inhibition by addition to the growth medium of exogenous purines like hypoxantine (Hx) or adenine (Ade), which do not require gpt action (Fig. 1C), but not guanine (Gua), which is, in fact, inhibitory (see Fig. 1B). Any accumulated guanine is expected to contribute further to the starvation, as it is a corepressor for the PurR repressor [20], [21] controlling de novo purine biosynthesis (Fig. 1C). Indeed, we found that one alternative way of circumventing the gpt block (see Fig. 1C) is the stimulation of de novo IMP production by inactivation of the purR repressor [21], as shown in Fig. 1A.
Cell and nucleoid morphology of optA1 gpt strains were followed by microscopy, as shown in Fig. 2. The starved cells developed progressively extensive filamentation with swollen regions (bulges) (Fig. 2D and E) in the middle of the cells. At the 7 hr time point, DAPI staining revealed disturbed nucleoid shapes within the filaments, which became fused and compacted (Fig. 2D). The enlarged nucleoids coincided with the filament bulges, thus accounting for the distortion of the cell envelopes. Use of Live/Dead staining indicated extensive death of the filaments (Fig. 2F).
Subsequent experiments were designed to measure more carefully the physiology of the starving cells. It soon became clear that the growth defect of the optA1 gpt strain was dependent on the cell density: only strongly diluted cultures (for example 1/5,000 from overnight cultures) showed the growth restriction. This suggested to us that maintenance of an active growth phase was required; reduced growth experienced at higher biomass values would allow the cells to escape. On that basis, we developed a standardized protocol in which inoculates from overnight cultures were first grown for a sufficient number of generations in the presence of hypoxanthine to assure exponential growth, and then cells were transferred to medium without hypoxanthine. When necessary, subsequent dilutions were made in fresh medium to keep the OD630 nm at or below 0.2. Performed in this manner, the experiment of Fig. 3A shows that non-starved cells are able to continue in exponential growth indefinitely, as expected. However, cells growing without hypoxanthine display a slowdown in biomass growth (OD) rate compared to the control and an eventual complete growth arrest at 5–6 hrs. More importantly, the viable count of the starved culture increased initially only by about 4-fold overall and then declined by 40- to 50-fold, indicating extensive death of the cells.
Interestingly, after the 6 hr time point the culture appeared to recover (Fig. 3A). As discussed later in more detail, this recovery reflects the accumulation of suppressor mutants that have become resistant to the starvation condition. However, we will first report on the properties of the starving and dying optA1 gpt cells prior to the appearance of the suppressors. The Supplementary Fig. S1 shows the importance of the 50-fold dilution at the 150 min time point; lower dilutions are not sufficient.
In addition to turbidity and viable cell count, we also monitored the extent of DNA synthesis in the starving cells. The results in Fig. 3B show that the starved cells synthesized DNA at a reduced rate. Much of this reduction may reflect a slow down of ongoing replication forks due to the shortage of dGTP, as further explored in the following three experiments.
In the experiment of Fig. 3C we followed the status of the bacterial chromosomal DNA by determining the ratio of chromosomal origins to termini (ori/ter) by Q-PCR. The ratio was around 2.6 in the non-starved cells, but it increased progressively to a value above seven during hypoxanthine starvation. Such increase is consistent with a slowed-down progression of the replication forks (increase in chromosomal replication time or C-time) along with a continued production of new forks at the oriC origin. This is also one of the hallmarks of thymine starvation, at least in the early stages of the process [7], [9], [10]. Informatively, the ori/ter ratio can be used to predict the branching (complexity) of the nucleoid [22] under these conditions. As shown in Fig. 3C (right side), the chromosomes of the starved cells are predicted to become increasingly complex.
We also investigated the progress of existing replication forks in the absence of new initiations. For this, we used the antibiotic rifampicin, which permits, in principle, continuation of DNA synthesis from existing forks but prevents new initiations [23]. The results in Fig. 3D show that the non-starved cultures display the saturation kinetics of DNA synthesis typically observed in this kind of experiment (replication fork run-out). In contrast, the hypoxanthine-starved cultures show a dramatic loss of DNA synthesis capacity (to roughly only 15% of the control), consistent with the predicted sudden limitation in dGTP upon hypoxanthine removal. It appears that, in the absence of transcription, even existing forks cannot be completed. The simplest explanation would be a near complete loss of dGTP upon hypoxanthine deprivation under these conditions of the inhibited transcription.
To more directly investigate the rates of DNA synthesis in starved vs. non-starved cells, we conducted pulse-labeling experiments using [methyl-3H]-thymidine. The results shown in Fig. 3E indicate that the hypoxanthine-starved culture suffers from an immediate about 20-fold reduction in the DNA synthesis rate. Interestingly, within the next 40 minutes, DNA synthesis capacity recovers slowly to approximately 50% of the control value, presumably due to transcriptional adaptation that may recover dGTP levels, at least in part. The near-complete loss of DNA synthesis capacity at the earliest time point is fully consistent with the inability of the cells to complete their ongoing forks in the presence of rifampicin (see Fig. 3D). These results are also supported by the changes in dGTP level as described in a subsequent section.
The Fig. 3E also shows the appearance of a spike of thymidine incorporation about 50 minutes after the start of starvation. This spike does not represent an occasional fluctuation within the measurements, as it was observed reproducibly in at least four repeated experiments. The simplest explanation for this spike may be an initiation event occurring at this time point. If correct, the interesting question arises as to how the present circumstances can lead to a coordinated culture-wide event. Following the spike, DNA synthesis capacity quickly drops to the pre-spike level. This inability to sustain the new higher rate of [methyl-3H]-thymidine incorporation indicates that, regardless of the number of forks, the total amount of DNA synthesis is severely constrained, likely due to the amount of available dGTP (see section below).
To investigate the effects of the starvation on the dNTP DNA precursors, we analyzed the intracellular dNTP pools after 2-hr and 4-hr incubations in the purineless medium. A clear ∼6-fold reduction, was seen in the dGTP concentration at 2 hrs (Fig. 4A, Table S1), while the concentration of the other dNTPs was not significantly affected (except for a possible small increase in the dCTP and dTTP pools). At times later than 2 h, dGTP could no longer be detected, although this was due in part to the appearance of another, as yet unidentified peak in the HPLC profile nearby the dGTP peak (See Fig. 4C). No changes in dGTP level were noted in the single optA1 or gpt mutants. In contrast, only a slight reduction in the rGTP pool was noted (2-fold or less, see Fig. 4B, Table S1).
To investigate the time course of dGTP depletion upon Hx removal we withdrew samples at a series of earlier time points and analyzed them by HPLC by a slightly different protocol designed to improve the resolution of the dGTP peak. During the first 30 minutes after hypoxanthine withdrawal, the dGTP pool was dramatically reduced (Fig. 4D); in fact, no dGTP could be detected above the (0.001) detection limit, indicating that the dGTP level was decreased by 10-fold or more. Interestingly, the dGTP level temporarily recovered at the 45-min time point but declined afterwards. We note that the kinetics of dGTP reduction of Fig. 4D are fully consistent with the DNA synthesis rate changes (Fig. 3E). Thus, DNA synthesis in the optA1 gpt mutant appears strictly governed by dGTP availability.
One of the hallmarks of TLD is the occurrence of significant DNA damage and induction of the SOS response [5], [8]. To investigate whether cell death during dGTP starvation is also accompanied by SOS induction, we assayed the expression level of an umuC::lacZ reporter construct, which can be used as a diagnostic for induction of the damage-inducible SOS response [24]. The results revealed SOS induction in the optA1 gpt strain starting at 4 hours after purine removal (Fig. 5A). This time coincides with the culture reaching its maximum value of nucleoid complexity (see Fig. 3C) and the start of the decline in culture viability (Fig. 3A).
We also studied the effect of recA and sulA deficiencies on the survival of the optA1 gpt strain. These mutations produced opposite effects (Fig. 5B): the recA defect dramatically sensitized the cells, leading to an immediate viability loss after Hx removal, very similar to the rapid early death of recA strains during TLD [5], [8]. In contrast, the sulA defect alleviated lethality (Fig. 5B), indicating that SOS-mediated lethal filamentation is a contributing factor to cell death [5].
Another informative aspect of dGTP starvation was revealed from plating efficiency determinations on solid media. In these experiments, the optA1 gpt strain was first grown to saturation in medium containing hypoxanthine, followed by plating on medium lacking hypoxanthine. It was observed that the optA1 gpt strain was able to form colonies on these plates with normal efficiency, at least when the plates were placed at 37° (Fig. 6A). In contrast, a strongly reduced plating efficiency (10−4 or less) was observed when the plates were placed at 42° (Fig. 6C). This loss of plating efficiency required the presence of casamino acids (CAA) (Fig. 6D). The more sensitive optA1 gpt recA triple mutant strain was not able to produce colonies, even at 37° (Fig. 6B). Overall, these results are consistent with the requirement for maintenance of an active growth status for the optA1 gpt strains. Presumably, in developing colonies on the plate the growth rate can be sufficiently slowed down, perhaps due to nutrient limitation in the colony environment, to permit survival. Such escape from death is apparently not possible at 42°C, probably due to increased origin firings at this higher temperature [25]. No escape is possible at any temperature for the ΔrecA strain. The surviving fraction of cells (10−5 to 10−4) observed on these plates also reflects the appearance of suppressor mutants, as described below.
The starvation experiments in the liquid media show an apparent recovery of the optA1 gpt and optA1 gpt recA cultures after some 6–8 hrs (Figs. 3A and 5B). In fact, fully-grown cultures can be obtained in many cases after overnight growth. When these fully-grown cultures were diluted and subjected to a repeat starvation procedure, the cells proved resistant. We concluded that they had incurred suppressor mutations rendering the cells resistant. We also investigated the colonies that appeared on the 42°C glucose+CAA solid media plates of Fig. 6. When testing several of these colonies, it was found that they, too, were resistant to subsequent starvation, indicating that they had acquired mutations that allowed them to escape death.
The plating experiment of Fig. 6C proved a convenient avenue for obtaining a large number of resistant clones for further analysis. A total 108 colonies (from 10 independently grown cultures) were picked from the restrictive plates, purified and subjected to a repeat growth and plating cycle. Out of the 108 clones, 106 proved fully resistant. One obvious way by which resistance could be acquired is loss of the OptA1 phenotype through inactivation of the dgt gene. We tested for the possible loss of the OptA1 phenotype using the bacteriophage T4 assay (see Materials and Methods). This revealed that 20 out of the 106 clones (∼20%) had lost the OptA1 phenotype, likely due to loss of dgt function. The remaining 80% presumably represent mutants that lost some other functions involved in nucleotide metabolism (for example purR, see Fig. 1A) or in aspects of DNA replication and/or recombination. Further study of these suppressors may prove informative regarding the underlying mechanisms.
One additional experiment (Fig. 7) provided useful insight into the emergence of suppressors along with confirming the critical importance of cell or biomass density (biomass density is a more accurate term here because cells progressively filament during starvation). In this experiment, a non-starved stationary culture was diluted to different extents - over a range of three orders of magnitude - and inoculated into media with or without hypoxanthine for an overnight growth attempt. The results showed that all cultures were able to reach full-grown density after overnight growth. However, when analyzed for the presence of suppressors, the cultures differed dramatically depending on their starting dilution. For cultures started at low to modest dilutions (100- to 800-fold) the majority of cells in the final culture had remained sensitive to dGTP starvation. On the other hand, dilutions of 3,000-fold or larger yielded cultures composed entirely of resistant clones (Fig. 7). Thus, relatively high densities permit survival without mutation, likely by reduction in growth rate, whereas low densities produce apparent survival only by mutation.
In this work, we have characterized a novel type of replication stress caused by specific starvation for the DNA precursor dGTP, which leads to growth impairment and cell death. This phenomenon, which we have called dGTP starvation, shares features with the previously described phenomenon of thymine-less death (TLD), which has been investigated for many decades. In TLD, specific starvation is for the DNA precursor dTTP, and current models of TLD have focused on the impairment of DNA synthesis by this lack of dTTP. If correct, starvation for any single dNTP is predicted to have the same or similar consequences. Our present study addresses this critical point, which could not be addressed previously due to the lack of means to specifically starve cells for any dNTP other than dTTP. In addition, our present results can be used to take issue with certain other models for TLD focused on the uniqueness of thymine nucleotides, perhaps related to the synthesis of cell wall components or other essential cell compounds [26]. Overall, we note similarities and dissimilarities with TLD, which we will summarize below.
Our investigation of the hypoxanthine-starved optA1 gpt strains can be summarized by the following findings:
Each of the findings (a-h) above can find their counterpoint in the phenomenology of TLD, although quantitative differences are certainly noted. This similarity of observations supports our contention that TLD and dGTP starvation are, in fact, two manifestations of the same underlying phenomenon, in which cells deprived of a single DNA precursor suffer from chromosomal distress that ultimately kills them.
We conclude that in dGTP-starved cells, like in TLD, ongoing replication forks are slowed down and even stall (Fig. 3E) due to the deprivation of one of the DNA precursors. The deprivation of dGTP is seen in dramatic fashion immediately upon hypoxanthine withdrawal, where both dGTP level and DNA synthesis rate are reduced to near zero (Figs. 3E, 4D), but also later when, despite a modest recovery, dGTP levels remain reduced and continue to decline. At the same time, the nutritional status of the cells remains high, leading to continued high levels of RNA and protein synthesis, as is also clear from the about 100-fold increase in biomass during the first few hours of starvation (Fig. 3A). Continued biomass growth permits continued initiations of new forks at the chromosomal origin (oriC). The slowdown of DNA synthesis along with continued new initiations is supported by the observed increase in the ori/ter ratio (Fig. 3C). The resulting build-up of chromosomal complexity (Fig. 3C) then leads to a series of secondary consequences, such as replication forks collisions, DNA bulges reflecting unresolved complex chromosomes (Fig. 2D and E), double-stranded breaks, SOS induction (Fig. 5A), and lethal filamentation (Fig. 2), as also occurring in TLD. The occurrence of double-strand breaks and the need for their repair is clearly suggested by the exquisite sensitivity of the recA-deficient optA1 gpt strain to dGTP starvation (Figs. 5B, 6B). Survival of the cells during the early cessation of DNA synthesis (Figs. 3E, 4D) appears to be critically dependent on recombinational repair, indicating that double-strand breaks are occurring at this stage [27]. This fully resembles the sensitization of recA mutants to the early stages of the TLD process [8], [9]. While in rec+ cells the damaged forks can be repaired, their repair does not solve the stalling of the forks as long as the dGTP concentration stays limiting. It is likely that this early stalling is an important contributor to the build-up of chromosome complexity (ori/ter ratio) that takes place over the next several hours, culminating in SOS induction, filamentation, and cell death at the later time (4–6 h).
At the same time, differences between dGTP starvation and TLD can be noted. Quantitatively, TLD appears to be a more destructive phenomenon, causing a more immediate loss of colony-forming ability (three orders of magnitude during three hours [2], [5], [8], [9]. During dGTP starvation, we observed only an ∼1.5 order of magnitude decline after the initial phase of continued cell divisions (Fig. 3A). Thus the effect of dGTP starvation is milder than that of TLD. The important distinction is likely that in TLD the necessary precursor thymine is experimentally totally absent [26], [28], while the cells have no avenue to synthesize dTTP by alternative means. In contrast in dGTP starvation, dGTP can still be produced, albeit at limited levels.
It might be suggested that dGTP starvation is more comparable to the phenomenon of thymine limitation, where thyA cells are grown at low, rate-limiting thymine concentrations while displaying an increased ori/ter ratio [29]. However, dGTP starvation differs from this type of limitation in one critical aspect: thymine-limited strains grow indefinitely in a steady state [30], i.e., the optical density, DNA concentration, and colony forming units increase exponentially at the same rate provided that the external thymine concentration is above the minimal required according to strain's background. In the case of dGTP starvation, cells are definitely not in steady state. For example, the increase in biomass until the arrest (about 100-fold) is not matched by that of the viable cell count (about 4-fold) (Fig. 3A). This discrepancy between biomass and cell count is consistent with the observed filamentation of the dGTP-starved cells (Fig. 2). Also, during thymine limitation no cell death is observed. Kinetically, the initial rapid disappearance of dGTP (Fig. 4D) along with the associated cessation of replication fork movement (Fig. 3E) places dGTP starvation phenomenologically closer to dTTP starvation than to thymine limitation.
dGTP starvation differs from TLD in that our experiments show a recovery of dGTP concentration at later time points. This recovery is likely a transcriptional response to the hypoxanthine withdrawal, which has no equivalent for dTTP production in TLD. Nevertheless, the recovery of dGTP is insufficient and dGTP levels continue to decline from that point on. Also note that the reduced DNA synthesis rate measured at later times is to be distributed over an increased number of forks. Thus, the rate of progression for each individual fork will be reduced accordingly. A rough estimate suggests that the rate per fork may be down at least one order of magnitude. At such reduced rates, lethal chromosomal complexity may not be avoidable.
A third difference between dGTP starvation and TLD is that we did not find evidence for extensive origin destruction. Origin destruction has been discovered as one of the later aspects of TLD, purportedly by RecA-mediated ‘repair’ of multi-forked oriC [6], [9]. Presumably, in the presence of low dGTP concentrations, enough DNA synthesis can take place to avoid this step of origin destruction, although it does not prevent cell death. Interestingly, during dGTP starvation, ΔrecA strains die at an accelerated rate that is very similar to the death rate of ΔrecA strain during TLD (∼5% survival after 2 h of starvation) (Fig. 5B and [8], [9]). In both examples, no origin destruction occurs [9]. In this case, efficient killing occurs by events away from the origin, likely by lack of repair of stalled and broken replication forks.
Nucleoids with increased complexity occur normally in bacteria under conditions of fast growth. Under these conditions, the generation time (τ) is shorter (faster) than the C-time (time needed to complete a round of chromosomal synthesis), which is achieved by the firing of newly replicated origins prior to completion of the previous rounds, resulting in more complex chromosomes [31]. On the other hand, it appears that in wild-type cells the C-time never is never greater than twice the lowest achievable doubling time (C≤2τ), so that the number of ongoing replication rounds (or ‘fork positions’, n = C/τ [32]), a quantitative measure of nucleoid complexity, rarely exceeds 2. Conditions of n>2 have been obtained experimentally upon severe thymine limitation of thyA strains at very low extracellular thymine concentrations [29], [33]. Under such conditions, cells continue to increase their size, culminating in distorted, monstrous shapes [34], resembling the ones observed in our study (Fig. 2). Due to the deviation from steady-state growth, a physiological characterization of such distorted cells is difficult, and the same reason prevents a precise calculation of C and τ during dGTP starvation. However, based on our measured ori/ter ratios it is possible to estimate n using the equation [22]. During dGTP starvation, the ori/ter ratio reaches a value of near 8, indicating a value of n near 3.0 (Fig. 3C). This would indicate the presence of a total of 14 (2+4+8) active forks per chromosome (see Fig. 3C). Extreme chromosome complexity due to overinitiation in a dnaA overexpressor strain has been shown to lead to collapse of replication forks, collisions between adjacent forks, and lethal chromosomal damage [35]. The existence of a limit to the extent of nucleoid complexity was proposed by Zaritsky [36] in his Eclipse model (this term was adopted from the Nordström studies on plasmid R1 replication [37]), which states that, due to structural constraints, scheduled initiations normally fire only if the previous fork has moved away some minimal distance from the origin. If this critical condition is not met, the scheduled initiation is postponed to avoid collisions of replication forks that may endanger the integrity of DNA [36]. One might speculate that under the starvation conditions discussed here this minimal distance limit is breached.
Another, informative distinction between dGTP starvation and TLD is the recovery of the dGTP-starved liquid cultures after about 7 h (Fig. 3A). As described in the Results, recovery in this experiment is due to growth of mutants that have become resistant to the starvation condition. No production of resistant mutants occurs during TLD. This distinction between the two starvation procedures undoubtedly results from the fact that in TLD there is a zero provided supply of dTTP precursors, combined with the fact that the thyA cells do not have access to any alternative pathway by which dTTP might be synthesized, while during dGTP starvation there is a restricted, but not necessarily zero dGTP supply. Investigation of the suppressors of dGTP starvation may provide new insights into the various metabolic processes that can affect the ability of the cells to survive the dGTP-restricted conditions. We already noted that one category of suppressors is deduced to map in the dgt gene (∼20%), which is expected to restore dGTP levels. The majority of suppressors however reside at other loci, and their nature remains to be determined. It is an interesting question whether the suppressors represent preexisting mutants present in the population prior to initiation of the starvation conditions, or whether they are generated during the limited growth permitted during the starvation conditions. In view of the dNTP pool imbalances generated due to the dGTP drop, DNA replication during these conditions is likely to have reduced fidelity [38], [39]. Possibly such reduced fidelity may account for the relatively high frequency (10−4 to 10−5) of suppressors found in the plating experiments of Fig. 6.
As an alternative to genetic mutation, cells can survive dGTP starvation by entering a slower-growth phase in which origin firing is reduced to be compatible with the newly established slow DNA synthesis rate (Figs. 6, S1). This is clearly evidenced by the dilution experiments (Fig. S1), where increased cell densities slow down growth and permit survival. A further example of this type of survival is provided by the plating-efficiency of the optA1 gpt strain on solid media lacking hypoxanthine (Fig. 6). When plated at 37°C, the cells are able to develop into colonies, but at 42°C this is not the case; instead, suppressor colonies appear, at a frequency of 10−5 to 10−4. Inside developing colonies, growth may be slowed down sufficiently to allow cells to reach an adaptive phase, and this may be the case at 37°C, but not at 42°C, where increased metabolic activity, such as increased origin firings [25] may push the cells over the edge of sustainability. Conversely, reduction in origin firings by omitting the casamino acid supplement (CAA) from the plates allows cells to survive even at 42°C (Fig. 6D).
These growth-dependent effects described above are not unique to dGTP starvation. For example, even in TLD, growth-dependent effects have been described. While in TLD no growth on plates lacking thymine occurs (as thymine is an essential compound and thyA strains do not posses any alternative ways for dTTP synthesis), the extent of TLD can, nevertheless, be moderated by changes in growth conditions. For example, immunity to TLD can be provided by inhibition of protein or RNA synthesis [40], [41] or by the silencing of new initiations in dnaA(Ts) strains [42]. In fact, nutritional shift-up of certain thyA strains can promote death even in the presence of thymine, presumably due to a newly created imbalance between the rates of origin firing and DNA synthesis [43].
The discovery and analysis of the phenomenon of dGTP starvation solves one of the outstanding questions regarding TLD since it was discovered some six decades ago: whether the phenomenon is thymine-specific or whether it can be provoked by starvation for other DNA building blocks. Indeed, even though the proposed mechanisms for the inactivation of thyA strains during TLD have become increasingly substantiated, it is still important to clearly separate the killing process from the thymine specificity. The phenomenon of dGTP starvation solves this basic issue: starvation for other DNA precursors (like dGTP) should be equally lethal.
The finding that a critical starvation for the DNA precursor dGTP can cause cell death may lead to additional avenues for therapeutic applications. The dGTP model as described here may present a particularly realistic model system for cell death in such applications. In such cases the affected nucleotide may become critically restricted as presented here but not completely absent as in the TLD model system. dGTP may also be an attractive target as it has typically the lowest concentration among the four DNA precursors [44]. Interestingly, human cells have been found to contain a novel dGTPase, termed SAMHD1, that has properties similar to the bacterial Dgt enzyme: it likewise hydrolyzes dGTP to yield deoxyguanosine and triphosphate [45], [46]. SAMHD1 activity has been shown to act like a viral restriction factor in cells where it is expressed at elevated levels by lowering the dNTP concentrations sufficiently so that viral entities like HIV-1 cannot replicate [45], [46]. SAMHD1 also protects the cells against autoimmune responses, such as the Aicardi-Goutieres syndrome [45]. While in those cases SAMHD1 acts like a restriction factor by inhibiting viral replication and creating conditions that lead to the breakdown of a variety of RNA and DNA substrates, it is imaginable that this activity under certain physiological conditions in actively growing cells could also be directed towards the cellular DNA.
All shown experiments used E. coli strain MG1655 and its derivatives. Genetic deficiencies were introduced into MG1655 by P1 transduction using P1virA. The optA1 allele of dgt was introduced linked to transposon zad-220::Tn10 as described [43]. The gpt::kan allele was obtained from the National BioResource Project (NIG) of Japan (http://www.shigen.nig.ac.jp/ecoli/strain/top/top.jsp). The purR::cat, recA::cat and sulA::cat mutants were generated by the method of Datsenko and Wanner [47] using primers described in Table 1. For testing SOS induction, the relevant strains were transformed with plasmid pSK1002, which contains the lacZ reporter gene fused to the umuDC promoter [24].
For strain construction, maintenance, and determination of viable counts, LB medium was used with supplementation of the following antibiotics, where appropriate: tetracycline (15 µg/ml) for optA1 linked with zad-22::Tn10, kanamycin (50 µg/ml) for gpt::kan, chloramphenicol (25 µg/ml) for the purR::cat, recA::cat and sulA::cat alleles, and ampicilin (100 µg/ml) for pSK1002 transformants. For experiments relating to starvation, cells were grown at 37°C in minimal medium containing Vogel-Bonner salts [48] containing glucose (0.4%), casamino acids (1%) (Becton-Dickinson), D-pantothenic acid (5 µM), and hypoxanthine (50 µg/ml). To assay the differential responses in media with or without purine source, two aliquots were filtered through a 25, 47 or 90-mm diameter polycarbonate membrane filter (0.4 µm pore size; Millipore) and diluted up to 10-fold in the identical medium with or without hypoxanthine (50 µg/ml). Aliquots for different assays were withdrawn at densities not exceeding 0.2 OD630 nm.
Culture aliquots (300–350 ml) harvested at OD630 nm = 0.2 were filtered through a 90-mm diameter polycarbonate membrane filter (0.4 µm pore size; Millipore). The filter was transferred to a Petri dish lid containing 10 ml of 60% aqueous methanol at −20°C. After 2 h at −20°C the filter was removed and the liquid suspension boiled for 5 min, followed by centrifugation for 15 min at 17,000× g and lyophilization of the supernatant. The residue was dissolved in 1 ml of sterile water, filtered through syringe filter (Millipore, 0.22 µm pore size) and lyophilized again. The final residue was dissolved 50 µl sterile water. HPLC analysis of the extracted dNTPs was performed by reversed-phase chromatography on an Agilent 1100 high-pressure liquid chromatography instrument with UV detection at 254 nm. Nucleotides were separated on a Zorbax Eclipse XDBC18 3.5 µM (150 by 4.6 mm) column equipped with a Zorbax Eclipse XDBC18 guard column, adapting a prior method used for the separation of nucleotides [38]. At a flow rate of 0.8 ml/min, a linear gradient of 70∶30 buffer A to buffer B was run to 40∶60 over 30 min. The gradient was then changed over 60 min from 40∶60∶0 to 0∶87.5∶12.5 for buffer A - buffer B - buffer C. To wash the column between samples the gradient was first changed from 0∶87.5∶12.5 to 0∶70∶30 over 10 minutes with a final stepwise change to 70∶30∶0 for an additional 20 min.
In a later set of experiments aimed at quantifying specifically dGTP during an extended starvation time course (Fig. 4D), a modified protocol was used, as follows. At a flow rate of 1 ml/min, a linear gradient of 75∶25 buffer A to buffer B was changed to 52∶48 over 23 min. The gradient was then changed over 12 min from 52∶48 to 49∶51 and for an additional 10 min from 49∶51 to 40∶60. To wash the column between samples the gradient was first changed from 40∶60∶0 to 0∶77.5∶22.5 for buffer A - buffer B - buffer C over 15 minutes and for an additional 10 min from 0∶77.5∶22.5 to 0∶70∶30 with a final stepwise change to 70∶30∶0 for an additional 10 min.
Buffer A consisted of 5 mM tetrabutyl ammonium phosphate (PicA Reagent; Waters), 10 mM KH2PO4, and 0.25% methanol adjusted to pH 6.9. Buffer B consisted of 5 mM tetrabutyl ammonium phosphate, 50 mM KH2PO4, and 30% methanol (pH 7.0). Buffer C was acetonitrile. Nucleotide standards were obtained from Sigma.
For chromosomal DNA extraction, 7- or 13-ml culture aliquots were harvested at various time points into the same volume of ice-cold PBS solution containing 20 mM NaN3. DNA extraction was performed with the Easy-DNA kit (Invitrogen) and quantitated by staining with Picogreen (Invitrogen). For determination of run-out DNA synthesis (Fig. 3D), rifampicin was added at time zero at a concentration 300 µg/ml. For ori/ter determination, the DNA was digested with EcoRI and subjected to Quantitative PCR (Stratagene Mx 3000) with SIBR Green detection using primers (Table 2) specific to the origin and the terminus regions of the E. coli chromosome [49].
For determination of the DNA synthesis rate by pulse labeling, optA1 gpt cultures were grown with hypoxanthine to OD630 nm 0.1, filtered, resuspended in the identical media with or without hypoxanthine, and brought to the same turbidity. Every 10–15 minutes 0.5 ml samples were withdrawn and pulse-labeled with 1 µCi of [methyl-3H]-thymidine at specific activity 0.5 µCi/nmole for 3 minutes. Samples were quenched with 0.5 ml of cold trichloroacetic acid (TCA 10%) containing 500 µg/ml of unlabeled thymidine to a final concentration 5% TCA and 250 µg/ml of unlabeled thymidine and kept on ice bath at least for 30 minutes. The entire samples were then collected on pre-wet 25-mm glass microfibre filters (Whatman), and washed with cold TCA (5% with 250 µg/ml of unlabeled thymidine) and 100% ethanol. The radioactivity on the filters was determined in a LS6500 liquid scintillation counter (Beckman) with Ecolume liquid scintillation cocktail (MP Biomedicals).
Aliquots of growing cultures were fixed with 0.25% formaldehyde and stained with DAPI. The cells were visualized by Nomarsky and DAPI fluorescence microscopy (NIKON eclipse E600) and photographed using a Micropublisher CCD color Camera (QImaging). Live/Dead stain (Invitrogen) was used to test viability of the cells.
5.0-ml samples were removed, and the cells were pelleted in a microcentrifuge. β-Galactosidase assays were performed essentially as described by Miller [18]. Cell pellets were resuspended in 1 ml of Z-buffer (60 mM Na2HPO4, 40 mM NaH2PO4, 10 mM KCl, 1 mM MgSO4, 10 mM dithiothreitol). 80 µl of chloroform and 40 µl of 0.1% sodium dodecyl sulfate (SDS) were added to the cell suspension, which was then vortexed vigorously for 10 s. To start the reactions, 200 µl of ONPG (4 mM) was added, and the reaction mixtures were incubated at 30°C for 4.5 min. The reactions were stopped with 0.5 ml of 1 M sodium bicarbonate, and the cellular debris was pelleted. The optical density was recorded with a BECKMAN DU 640 spectrophotometer with a 405-nm filter. Miller units were calculated as follows: units = 1,000[(OD405/(t×v×OD630 nm)], where OD405 nm denotes the optical density at 405 nm. OD630 nm reflects the cell density at 630 nm, t is the reaction time in minutes, and v is the volume of culture used in the assay.
To test whether suppression of the sensitivity of the optA1 gpt strains to hypoxanthine deprivation resulted from loss of the optA1 allele, a large number of clones that survived on the starvation plates at 42°C (see Fig. 6C) were tested for their ability to support growth of the bacteriophage T4 tsL141 mutant [50], as described [43]. Clones that restrict growth of this phage at 30°C are optA1 [51]. Wild-type phage T4D was used as a positive control. The T4 phages were obtained from Dr J.W. Drake, NIEHS.
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10.1371/journal.pntd.0004936 | Sero-Molecular Epidemiology of Japanese Encephalitis in Zhejiang, an Eastern Province of China | Sporadic Japanese encephalitis (JE) cases still have been reported in Zhejiang Province in recent years, and concerns about vaccine cross-protection and population-level immunity have been raised off and on within the public health sphere. Genotype I (GI) has replaced GIII as the dominant genotype in Asian countries during the past few decades, which caused considerable concerns about the potential change of epidemiology characteristics and the vaccine effectiveness. The aim of this study was to investigate the prevalence of JE neutralizing antibody and its waning antibody trend after live attenuated JE vaccine immunization. Additionally, this study analyzed the molecular characteristics of the E gene of Zhejiang Japanese encephalitis virus (JEV) strains, and established genetic relationships with other JEV strains.
A total of 570 serum specimens were sampled from community population aged from 0 to 92 years old in Xianju county of Zhejiang Province in 2013–2014. Microseroneutralization test results were analyzed to estimate the population immunity and to observe antibody dynamics in vaccinated children. E genes of 28 JEV strains isolated in Zhejiang Province were sequenced for phylogenetic tree construction and molecular characteristics analysis with other selected strains. Positive JE neutralizing antibody rates were higher in residents ≥35 years old (81%~98%) and lower in residents <35 years old (0~57%). 7 or 8 years after the 2nd live attenuated vaccine dose, the antibodies against for 4 different strains with microseroneutralization test were decreased by 55%~73% on seropositive rates and by 25%~38% on GMTs respectively. JEV strains isolated in recent years were all grouped into GI, while those isolated in the 1980s belonged to GIII. On important amino acid sites related to antigenicity, there was no divergence between the Zhejiang JE virus strains and the vaccine strain (SA14-14-2).
JE neutralizing antibody positive rates increase in age ≥10 years old population, likely reflecting natural infection or natural boosting of immunity through exposure to wild virus. JE seropositivity rates were quite low in <35 years old age groups in Zhejiang Province. Waning of neutralizing antibody after live attenuated vaccine immunization was observed, but the clinical significance should be further investigated. Both the peripheral antibody response and genetic characterization indicate that current live attenuated JE vaccine conferred equal neutralizing potency against GI or GIII of wild strains. GI has replaced GIII as the dominant genotype in Zhejiang in the past few decades. Although the chance of exposure to wild JE virus has reduced, the virus still circulates in nature; therefore, it is necessary to implement immunization program for children continually and to conduct surveillance activity periodically.
| Japanese encephalitis (JE) remains one of the most significant public health problems in Asia and the Western Pacific region. A JE viral infection can cause death and severe sequelae. Vaccination is the most effective method for preventing JE currently. After decades of routine vaccination, the number of JE cases declined considerably in Zhejiang Province, China. However, emergence of genotype I of JE as the most common genotype in China in recent decades has become a major public health problem. As all the currently available vaccines are derived from genotype III strains, the circulations of another genotype have caused considerable concerns about vaccine effectiveness. In this study, we found that population immunity against JE was quite low in children and adolescents. Waning of JE neutralizing antibody after JE immunization was observed. Therefore, issues about duration of protection and booster dose necessity need further research. On the bright side, evidence shows that the JE vaccine currently used is effective for both genotype I and III of wild viruses. Although clinical JE cases have reduced, the virus is still spreading in nature; therefore, we encourage children and other high-risk groups to adhere to the immunization program continuously.
| Japanese encephalitis (JE) is a common mosquito-borne viral encephalitis disease and it is prevalent in Asia, the Western Pacific, and northern Australia. It is estimated that approximately 67,900 JE cases occur worldwide annually, with a fatality rate range from 20% to 30%. Though reported cases have decreased dramatically due to immunization programs, improved living conditions and avoiding animal hosts, as an enzootic cycle disease, JE will remain a prominent public health problem in the Asian-Pacific region [1,2].
JE is caused by the Japanese encephalitis virus (JEV). The 1500-nt envelope (E) protein gene was suggested to provide reliable information reflecting the broad geographical and temporal relationships of JEV [3,4]. Based on the E gene, JEV can be divided into five genotypes [5] and the different genotypes have certain regional distribution features [6]. Genotype I (GI) and III (GIII) are mostly associated with epidemic diseases in temperate regions of Asia [7]. Three JEV genotypes have been isolated in China so far. The dominant genotypes were GI and GIII, only one strain of genotype V was reported to have been isolated in Tibet in 2009 [5,8]. As all the currently available vaccines are derived from GIII strains, circulation of other genotypes has caused theoretical concern about the vaccine effectiveness [9].
Zhejiang is an eastern coastal province in China that situated in the subtropical climate area. It has an area of 101,800 km2 and a population of 54.43 million (2010 census data). According to the detailed morbidity data since 1952, JE was epidemic in the 1960s and 1970s in Zhejiang Province. The epidemic peaked in 1967, with 14,597 cases of JE reported, representing an annual incidence rate of 47.5/100,000. Through a combination of vaccination, improved housing and socioeconomic conditions, environmental management, and vector-control efforts [10], the morbidity rate has decreased sharply after 1970s. Since 1990, the incidence rate was less than 1/100,000, and it further dropped to less than 0.5/100,000 in the past 5 years with only dozen sporadic cases reported annually (Fig 1). Universal vaccination with an inactivated JE vaccine (P3 strain) on children under 10 years old was launched in Zhejiang Province in 1970s. JE vaccine was introduced in routine immunization program in 1986 and a live attenuated vaccine (SA14-14-2 strain) replaced the inactivated vaccine quickly in Zhejiang Province since 1999. Currently, two doses of the live attenuated vaccine are required in national immunization program, the first at 8 months and the second as a booster dose at 2 years old [10].
Based on the pathogenic surveillance activity, dozen of JEV strains have been isolated in Zhejiang Province in 1980s and between 2007–2014. Additionally, a serosurvey was conducted in community population in 2013–2014 in Zhejiang Province. Although several population-based seroprevalence surveys of JE had been reported in other regions, most of them only used a single viral strain in neutralizing antibody test and lacked a refined division of age groups among children. To our knowledge, an exhaustive seromolecular epidemiology of JE study in Zhejiang Province has not been reported. In this study, we collected serum samples from 570 subjects in 2013–2014 serosurvey to investigate the prevalence of JE neutralizing antibody and to elucidate the waning antibody trend after live attenuated JE vaccine. Additionally, we analyzed the molecular characteristics of the E gene of 28 JEV strains isolated in Zhejiang Province and established their genetic relationships with other JEV strains.
This work was approved by the ethics committee of the Zhejiang Provincial Center for Disease Control and Prevention and was conducted in accordance with Good Clinical Practice guidelines. Written informed consents were obtained from all participants or guardians (for children ≤18 years) prior to enrollment in the study. The subjects’ names were not disclosed to the authors.
Fig 2 shows the geographical position of Zhejiang Province and the samples collection sites (county) in the present study.
JE cases have been a legally reportable communicable disease in China since 1951[5]. When physicians reported a suspected case, the local county’s Center for Disease Control and Prevention staff would carry out the case investigation and collect serum or cerebrospinal fluid specimens for diagnosis verification in recent years. For calculation of yearly average incidence rates, the onset day between 2010 and 2014 of JE cases were exported from the National Notifiable Diseases Reporting System (NNDRS), an internet-based real-time case reporting system, which was established in 2004. Non-Zhejiang Province citizen cases were excluded in calculating the yearly average incidence rates between 2010–2014.
Microseroneutralization tests were conducted under biosafety level 2 conditions in the JE laboratory of the Zhejiang Provincial Center for Disease Control and Prevention, a member of the National Reference JE Laboratory. The procedure has been described elsewhere in detail [11,12]. Briefly, serum samples were heated for 30 min at 56°C and titrated with 6 dilutions (1:10; 1:20; 1:40; 1:80; 1:160; 1:320). An equal volume of JEV (50% tissue culture infective dose [TCID 50] in 100 μL) was added to all specimen sera, and the plates were incubated for 1.5 h at 37°C. Four strains of JEV, i.e. P3 (GIII), ZJ83-8 (GIII), ZJ10-7 (GI) and ZJ13-3 (GI), were neutralized by each serum sample. The P3 strain was obtained from the National Institute for Food and Drug Control, China, and the other strains were isolated from mosquito samples in Zhejiang Province in 1983, 2010, and 2013, respectively. The mixture was added to a monolayer of the cell line BHK-21 (Baby Hamster Kidney) in a 96-well plate, and the plate was then incubated in a 5% CO2 humidity chamber at 37°C. Observation of cytopathic effects (CPE) began at 48 h later, and neutralization titers were determined 7 days later.
A serum sample with JE-neutralizing antibody ≥1:10 was considered as seropositive [14]. The chi-square test was used to assess the significance of seroprevalence between different strains or different age groups. The chi-square test for the linear trend was used to assess the association between seroprevalence with the elapsed years post-booster dose. Geometric mean titers (GMTs) of neutralizing antibody were calculated using a log transformation and were reported as back transformed titers. The results were presented in reciprocal form. Values below the detection threshold (1:10) were assigned half of the threshold value (1:5) in calculation. One-way analysis of variance was used to test the differences between strains, and multiple comparison tests were conducted by Bonferroni correction. Statistical analyses were performed by SPSS (Version 21.0, Chicago, USA) and two-sided P-values <0.05 were considered significant.
The overall GMTs of JE neutralizing antibody against 4 strains in participants were 8.4~11.3 with the seropositive rates 31.9%~42.8% (Table 3). Both of the GMTs (F = 12.47, P<0.001) and seropositive rates (χ2 = 15.91, P = 0.001) were significantly different between different strains. In multiple comparisons of GMTs among the 4 strains, the antibody against P3 strain was significantly higher than any other 3 wild virus strains, while there was no significant difference between ZJ83-8, ZJ10-7, and ZJ13-3 strains.
The seropositive rates of JE neutralizing antibody against 4 different strains had similar profiles (W-like curve) on age-group distributions (Fig 3). Starting from 24.5%~42.9% in <3 months old infants, the seropositive rates decreased to the lowest (0–10.5%) in 3 months-1 year old age group. The rates then increased moderately in the age group above 2 years old (the initial age of JE vaccine booster dose) followed by a dip in 10 years old age group. At last a dramatic rise could be seen in adults. In 35 years old age group, the rates increased to more than 80%, while in 60 years old age group all the rates ascended above 95%. The seropositive rates were statistically significant between <35 years old and ≥35 years old age groups (χ2 = 119.1, 165.5, 1624.4 and 119.3 for P3, ZJ83-8, ZJ10-7, ZJ13-3 strain, respectively, all P<0.001). To compare the yearly average incidence rates in Zhejiang Province between 2010 and 2014, 2 distinct age group peaks, i.e. 6 months and 5 years old age groups, were drawn out. The incidence rates dipped in the 7-year-old age group and older residents. The age-specific seropositive rates curve displayed quite contrary compared to the average incidence rates curve.
Waning of JE neutralizing antibody after the 2nd live attenuated vaccine dose was discovered in 185 child recipients. Generally, the longer of the booster dose elapsed, the lower of the GMTs and seropositive rates of JE-neutralizing antibody were (Fig 4). For example, the seropositive rate of neutralizing antibody against P3 strain declined from 53.3% to 23.8% 7–8 years after the year on booster dose, and the GMT declined from 10.93 (95%CI: 7.01~17.05) to 7.77 (95%CI: 5.75~10.48) as well. Except for the ZJ83-8 strain (χ2 for linear trend = 1.95, P = 0.163), the seropositive rates of the other 3 strains dropped with statistical significance (χ2 for linear trend = 6.67, 7.42, 10.31 and P = 0.010, 0.006, 0.001 for P3, ZJ10-7, ZJ13-3 strain, respectively). No significant differences (χ2 = 2.42, P = 0.298) on average seropositive rates between 3 wild strains (ZJ83-8, ZJ10-7 and ZJ13-3).
The 28 nucleotide sequences of E gene derived from Zhejiang Province (S2 Supplementary Data) were compared with selected strains from different isolation sites, times, and genotypes. In the phylogenetic tree, the Zhejiang strains were grouped into two genotypes (Fig 5), i.e. GI and GIII. The strains isolated in 2007–2014 were all clustered into GI, while those in 1982–1983 were all clustered into GIII. Zhejiang GI strains had a closer relationship with the China mainland JEV strains like SC04-27 (Sichuan, 2004), SH17M-07 (Shanghai, 2007), 3XG123 (Guangdong, 2011), and Taiwan strain TPC0806c (2008).
E gene sequences of the Zhejiang JEV strains showed substantial genetic stability. The minimal sequence similarities were 88.0% and 98.8% at the nucleotide and amino acid sequence levels in 9 selected Zhejiang JEV strains. The nucleotide sequence divergences within the genotype were only 0.2%~1.5% (GI) and 0.9% (GIII). The nucleotide sequence divergence between the two genotypes was 11.7%~12.0%. Compared with the live attenuated vaccine strain (SA14-14-2), the nucleotide and amino acid sequence similarities were 87.7%~97.9% and 97.2%~98.0%, respectively (Table 4).
Eight amino acid residues of E protein (E107, E138, E176, E177, E264, E279, E315 and E439) were discovered as critical amino acid mutations in relation to virulence and virus attenuation [15]. To analyze these key amino acids, we compared the E protein of different genotypes of Zhejiang strains (ZJ82-2 and ZJ14-10) with the vaccine strain (SA14-14-2) and the known virulent strain (Beijing-1). No differences were found on the eight key amino acid residues among the two Zhejiang strains and Beijing-1 strain. On the contrary, these strains differed completely with SA14-14-2 strain on the eight residues. Therefore, it is deemed that both the GI and GIII Zhejiang strains possess typical characteristics of wild virulence (Table 5).
Our seroepidemiological findings indicate that JE vaccine immunization, waning of neutralizing antibody, and natural infection may influence the seroprevalence pattern in population. JE neutralizing antibody positive rates were higher in ≥35 years old age groups (81%~98%) in Zhejiang Province and lower in <35 years old age groups (0~57%). Seropositive rates fluctuated during the childhood. Beginning with the decay of maternally transferred antibody at birth, the rates reached the lowest in 3 months to 1 year old infants (0~10%). The rates increased to some extent after the booster dose (for 2 years old children) and then decreased gradually during the adolescent. The curves rose up dramatically in adults and in the aged, most of them should experience one or more natural infections in life. Our findings demonstrated quite the contrary between the age-specific seropositive rates and the average incidence rates. By the way, use of different JEV strains may have considerable influence on neutralizing antibody levels, so it is important to standardize the protocol for better comparability among different studies.
Heterogeneous seropositive rates were reported in different regions in recent years. The rate was 44% for 0–2 years old children in Jiangsu, a province adjacent to Zhejiang, China, where rates were above 70% for ≥3 years old children, 87% for 15–19, and 94% for ≥20 years old age groups [16]. In Shanxi Province, China, the positive rate was 95% for ≥10 years old age groups in high JE incidence rate prefectures, while the positive rate was only 22% in low incidence rate regions [17]. In two cities in Henan Province, China, the positive rates were 55% and 45% for 0–14 years old age groups, 98% and 49% for ≥15 years old age groups, respectively [18]. Based on a serum survey in Taiwan, researchers found that the cohort born between 1963 and 1975 exhibited the lowest positive rate (54%). The highest seropositive rate (86%) was observed in the cohort born before 1952 [19]. Investigation on the prevalence of neutralizing antibodies in high-risk age groups in South Korea, researchers found a very high seropositive rate (98%) in ≥30 years old residents [20]. Difference in rates between regions might owe to circulation intensity of JEV in local places, JE vaccination programs, or various neutralizing antibody test methods (including selection on JEV strains), etc. However, a similar feature was JEV seropositive rates increased with an increase of age group in high-risk areas.
The present work indicates that children immunized with 2 doses of live attenuated JE vaccine had equal immunogenicity against GI or GIII wild JEV strains, but the neutralizing potency was higher against P3 strain. Epidemiology surveillance data and animal experiments [21,22] also demonstrated that the JE vaccine could confer cross-protection, and our study may add valuable information on serum neutralizing test results. The result is also supported by surveillance data in Zhejiang Province (Fig 1). The genotype replacement events seem not bring about the irruption of new outbreaks. Waning of neutralizing antibody after live attenuated vaccine immunization was also observed in this work. The seropositive rates and GMTs decreased by 55%~73% and 25%~38%, respectively, for antibodies against the 4 different strains 7–8 years after the 2nd dose. However, several studies pointed out that the protection correlated better with cellular immune responses than neutralizing antibody responses following live attenuated vaccine immunization [22,23]. Vaccine field trials also indicated that the duration of protection was quite long. A case-control study conducted in Nepal provided evidence that a single dose of the SA14-14-2 vaccine maintained a high protection (96%) for 5 years [24]. So, the clinical significance of waning antibody should be further investigated.
JEV E protein is closely related to virulence. Several existing studies have shown that virulence may be altered by mutations on critical residues. Researchers observed that a single amino acid substitution at E138 (Glu to Lys) was causally linked to attenuation [25]. As far as the eight amino acid residues of E protein critically related to the virulence, NO differences were found among GI, GIII of Zhejiang strains and the highly virulent strain (Beijing-1). Zhejiang JEV strains isolated in different periods all possessed typical characteristics of high virulence. Additionally, E protein is the dominant antigen in eliciting neutralizing antibody and protective immune responses. When comparing the important amino acid sites in relation to antibody-mediated virus neutralization [25–27], there was no divergence between Zhejiang JEV strains and SA14-14-2 strain. Therefore, genetic evidences supported the point of view that present live attenuated vaccine is still effective.
GIII strains had resulted in numerous JE epidemics throughout history in Asia until 1990s. The earliest available strain of GI was collected in Cambodia in 1967, and GI remained undetected for 10 years until another strain was identified in southwest China (Yunnan Province) in 1977 [28]. JEV strains were almost grouped into GIII before 2000 in China, but thereafter, the proportion of GI strains increased and became the dominant one in recent decades [29]. In the past few years, multiple reports have indicated a similar phenomenon in a number of Asian countries, such as Thailand, Korea, Japan, Malaysia, Vietnam, and India [28]. The mechanism of the genotype replacement had remained unknown until now. However, a viral multiplication experiment indicated a selective advantage of GI viruses for increased multiplicative ability in mosquito cells [28]. There is no firm evidence that different JEV genotypes circulating differ in their virulence [7].
In eastern China, the earliest strain of GI was collected from mosquito specimens in Shanghai in 2001. Subsequently, GI JEV strains had been identified in most other provinces. Genotype distribution data revealed that GI was gradually replacing GIII as the dominant genotype in the region (Table 6). The present study also indicated that Zhejiang JEV strains had changed from GIII to GI during the past decades, though the exact year was unknown due to a gap in surveillance activities for 24 years. According to a phylogeographic reconstruction study by Gao X et al, dispersal of GI lineage into Zhejiang was estimated to be in 2000 [30]. Similarly, several studies estimated the most recent common ancestor age of JEV and indicated that GI, as the youngest genotype, began to replace GIII approximately 20 years ago[9,31].
The present work has some limitations. First, the sero study was confined to small local areas which might not reflect the provincial population immunity level. The conclusion of neutralizing antibody dynamic was not based on a follow-up design. Second, we did not adopt any PCR methods in phylogenetic analysis. It is likely that some pools of mosquitoes could negative for virus isolation but yielded amplified nucleic acids for analysis. Last, in the absence of 24 years interim, it is hard to come to a conclusion about a particular year when the genotype replacement event occurred in Zhejiang Province.
In conclusion, this study discovered that JE neutralizing antibody positive rate increases with age over 10 years, likely reflecting natural infection (in the unvaccinated) and natural boosting of immunity through exposure to wild virus (in the vaccinated). JE seropositivity rates were quite low in <35 years old age groups in Zhejiang Province. Waning of neutralizing antibody after live attenuated vaccine immunization was observed, but the clinical significance of waning antibody should be further investigated. Selection of different JEV strains in the neutralizing assay had considerable influence on antibody titer. JEV strains isolated in the recent years were all grouped into GI in Zhejiang Province, while those isolated in the 1980s belonged to GIII. On important amino acid sites related to antigenicity, there was no divergence between the Zhejiang JEV strains and SA14-14-2 strain. Both the peripheral antibody response and genetic characterization indicate that current live attenuated JE vaccine conferred equal neutralizing potency against GI or GIII wild strains. Although the chance of exposure to wild JEV has reduced, the virus is still regularly being isolated in nature. Therefore, it is necessary to implement immunization programs for children continually and to conduct surveillance activity periodically.
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10.1371/journal.pgen.1006213 | Exploiting Single-Cell Quantitative Data to Map Genetic Variants Having Probabilistic Effects | Despite the recent progress in sequencing technologies, genome-wide association studies (GWAS) remain limited by a statistical-power issue: many polymorphisms contribute little to common trait variation and therefore escape detection. The small contribution sometimes corresponds to incomplete penetrance, which may result from probabilistic effects on molecular regulations. In such cases, genetic mapping may benefit from the wealth of data produced by single-cell technologies. We present here the development of a novel genetic mapping method that allows to scan genomes for single-cell Probabilistic Trait Loci that modify the statistical properties of cellular-level quantitative traits. Phenotypic values are acquired on thousands of individual cells, and genetic association is obtained from a multivariate analysis of a matrix of Kantorovich distances. No prior assumption is required on the mode of action of the genetic loci involved and, by exploiting all single-cell values, the method can reveal non-deterministic effects. Using both simulations and yeast experimental datasets, we show that it can detect linkages that are missed by classical genetic mapping. A probabilistic effect of a single SNP on cell shape was detected and validated. The method also detected a novel locus associated with elevated gene expression noise of the yeast galactose regulon. Our results illustrate how single-cell technologies can be exploited to improve the genetic dissection of certain common traits. The method is available as an open source R package called ptlmapper.
| Genetic association studies are usually conducted on phenotypes measured at the scale of whole tissues or individuals, and not at the scale of individual cells. However, some common traits, such as cancer, can result from a minority of cells that adopted a special behavior. From one individual to another, DNA variants can modify the frequency of such cellular behaviors. The body of one of the individuals then harbours more misbehaving cells and is therefore predisposed to a macroscopic phenotypic change, such as disease. Such genetic effects are probabilistic, they contribute little to trait variation at the macroscopic level and therefore largely escape detection in classical studies. We have developed a novel statistical method that uses single-cell measurements to detect variants of the genome that have non-deterministic effects on cellular traits. The approach is based on a comparison of distributions of single-cell traits. We applied it to colonies of yeast cells and showed that it can detect mutations that change cellular morphology or molecular regulations in a probabilistic manner. This opens the way to study multicellular organisms from a novel angle, by exploiting single-cell technologies to detect genetic variants that predispose to certain diseases or common traits.
| Modern genetics aims to identify DNA variants contributing to common trait variation between individuals. A high motivation to map such variants is shared worldwide because many heritable traits relate to social and economical preoccupations, such as human health or agronomical and industrial yields. In addition to the molecular knowledge they provide, these variants fuel the development of personalized and predictive medicine as well as the improvement of economically-relevant plants, animal breeds or biotechnology materials. However, this high ambition is accompanied by a major challenge: common traits are under the control of numerous variants that each contribute little to phenotypic variation [1], and this modest contribution of each variant hampers the statistical power to detect them. Power is further limited by the multiplicity of linkage tests when scanning whole genomes. The consequence of this has been debated under the term "missing heritability": most of the genetic variants of interest remain to be identified. Currently, this issue is handled by modelling the effect of known or hidden factors, and by scaling up sample size up to tens of thousands of individuals [2–4]. Practically, however, cohort size cannot be infinitely increased, and relevant factors are difficult to choose. Studies would therefore greatly benefit from a better detection of small genetic effects, and from a reduction of the number of genomic loci to test.
Small-effect variants are typically associated with predisposition (or incomplete penetrance): carriers of a mutation display a phenotype at increased frequency, but not all of them do. In this probabilistic context, the statistical properties of cellular traits may sometimes become informative: a tissue may break because cells have an increased probability to detach, a tumor may emerge because a cell type has an increased probability of somatic mutations, a chemotherapy may fail if cancer cells have an increased probability to be in a persistent state. In other words, molecular events in one or few cells can have devastating consequences at the multicellular level. As discussed previously [5], cellular-scale probabilities are likely related to the genotype and this relation may sometimes underlie genetic predisposition [6]. Striking examples are genetic factors affecting the mutation rate of somatic divisions and thereby modifying cancer predisposition. These loci have a probabilistic effect on a cellular trait: the amount of de novo mutations in the cell's daughter. Other loci may modulate the heterogeneity between isogenic cancer cells that underlies tumour progression [7,8] and resistance to chemotherapy [9–11]. They would then change the fraction of problematic cells between individuals and thereby disease progression or treatment outcome.
Fortunately, the experimental throughput of single-cell measurements has recently exploded. Technological developments in high-throughput flow cytometry [12], multiplexed mass-cytometry [13], image content analysis [14–16] and droplet-based single-cell transcriptome profiling [17,18] now offer the possibility to estimate empirically the statistical distribution of numerous molecular and cellular single-cell quantitative traits. We therefore propose to scan genomes for variants that modify single-cell traits in a probabilistic manner, which we call single-cell Probabilistic Trait Loci (scPTL). This requires to monitor not only the macroscopic trait of many individuals but also a relevant cellular trait in many cells of these individuals. After scPTL are found, they can constitute a set of candidate loci to be directly tested for a possible small effect on the macroscopic trait of interest.
Methods are needed to detect scPTL. With its fast generation time, high recombination rate and reduced genome size, the unicellular yeast Saccharomyces cerevisiae offers a powerful experimental framework for developing such methods. Using this model organism, scPTL were discovered by treating one statistical property of the single-cell trait, such as its variance in the population of cells, as a quantitative trait and by applying Quantitative Trait Locus (QTL) mapping to it [19,20]. However, this approach is limited because it is difficult to anticipate a priori which summary statistics must be used.
We present here the development of a genome-scan method that exploits all single-cell values with no prior simplification of the cell population phenotype. Using simulations and existing single-cell data from yeast, we show that it can detect genetic effects that were missed by conventional linkage analysis. When applied to a novel experimental dataset, the method detected a locus of the yeast genome where natural polymorphism modifies cell-to-cell variability of the activation of the GAL regulon. This work shows how single-cell quantitative data can be exploited to detect probabilistic effects of DNA variants. Our approach is conceptually and methodologically novel in quantitative genetics. Although we validated it using a unicellular organism, it opens alternative ways to apprehend the genetic predisposition of multicellular organisms to certain complex traits.
We specify here the concepts and definitions that are used in the present study. Let X be a quantitative trait that can be measured at the level of individual cells. X is affected by the genotype of the cells and by their environmental context. However, even for isogenic cells sharing a common, supposedly homogeneous environment, X may differ between the cells. To describe the values of X among cells sharing a common genotype and environment, we define a single-cell quantitative trait density function f [5] as the function underlying the probability that a cell expresses X at a given level (Fig 1A). Statistically speaking, f represents the probability density function of the random variable X. In the present study, this function f(X) constitutes the 'phenotype' of the individual from whom the cells are studied. As for any macroscopic phenotype, it can depend on the environmental context of the individual (diet, age, disease…) as well as on its genotype. Single-cell trait density functions also obviously depend on the properties of the cells that are studied, such as their differentiation state or proliferation rate.
We focus here on the effect of the genotype. Conceptually, cells from one individual may follow a density function of X that is different from the one followed by cells of another individual, because of genotypic differences between the two individuals (Fig 1B). The important concept is that the genetic difference has probabilistic consequences: it changes the probability that a cell expresses X at a given level, but it does not necessarily change X in most of the cells. Depending on the nature of trait X and how the two functions differ, such a genetic effect can have implications on macroscopic traits and predisposition to disease [5]. The term single-cell Probabilistic Trait Locus will refer here to a genetic locus modifying any characteristics of f (that is, changing allele A in allele B at the locus changes the density function f of X, i.e. fB ≠ fA).
A quantitative trait locus (QTL) linked to X is a location on a chromosome where a genetic variant changes the mean or the median of X in the cell population. Similarly, a varQTL is a genetic locus changing the variance of X and a cvQTL is a genetic locus changing the coefficient of variation (standard deviation divided by the mean, abbreviated CV) of X in the cell population. All three types of loci (QTL, varQTL and cvQTL) assume a change in f and they are therefore special cases of scPTL. However, not all scPTL are QTL: many properties of f may change while preserving its mean, median, variance or CV. The purpose of the present study was to develop an approach that could identify scPTL without knowing a priori how it might change f.
An important question before investing efforts in scPTL mapping is whether genotypes can modify f without affecting its expected value (the mean of X). If not, then QTL mapping will capture the genetic modifiers of f and searching for more complex scPTL is not justified. In contrast, if other-than-mean genotypic changes of f are frequent, then scPTL can considerably complement QTL to control single-cell traits. In this case, scPTL mapping becomes important.
In multicellular organisms, cell types and intermediate differentiation states constitute the predominant source of cellular trait variation. Studying their single-cell statistical characteristics requires accounting for the developmental status of the cells. This constitutes a major challenge that can be avoided by studying unicellular organisms. The yeast S. cerevisiae provides the opportunity to study individual cells that all belong to a single cell type, in the context of a powerful genetic experimental system. By analysing specific gene expression traits in this organism, we and others identified loci that meet the definition of scPTL but not of QTL [20,21]. This illustrated that, for some traits, scPTL mapping could complement classic quantitative genetics to identify the genetic sources of cellular trait variation.
To estimate if non-QTL scPTL are frequent, we re-analysed an experimental dataset corresponding to the genetic segregation of many single-cell traits in a yeast cross (Fig 2A). After a round of meiosis involving two unrelated natural backgrounds of S. cerevisiae, individual segregants had been amplified by mitotic (clonal) divisions and traits of cellular morphology were acquired by semi-automated fluorescent microscopy and image analysis [22]. This way, for each of 59 segregants, 220 single-cell traits were measured in about 200 isogenic cells, which enabled QTL mapping of these traits. We reasoned that if all scPTL of a trait are also QTL, then a high genetic heritability of any property of f should coincide with a high genetic heritability of the expected value of f. In particular, the coefficient of variation (CV) of a single-cell trait should then display high heritability only if the mean value of the trait also does. To see if this was the case, we computed for each trait the broad-sense genetic heritabilities of both the mean and CV of the trait. Note that the genetic heritability computed here is not the same as the mitotic heritability of cellular traits transmitted from mother to daughter cells. Here, a value (mean or CV) is computed on a population of cells, and its heritability corresponds to the proportion of its variation that can be attributed to genetic differences between the cell populations (see methods). Overall, heritability of mean was higher than heritability of CV, and the two types of heritabilities were correlated (Fig 2B). We also observed that several traits had high heritability of CV and low heritability of their mean value, or vice versa. This indicates that, for some traits, genetic factors exist that modify the trait CV but not the trait mean. This observation is in agreement with the complex CV-vs-mean dependency previously reported in this type of data [23,24]. We therefore sought to develop a method that can detect scPTL that do not necessarily correspond to QTL.
One way to identify scPTL from experimental measures is to compute a summary statistic of the trait distribution, such as one of its moments, and then scan for QTL controlling this quantity. This approach is particularly appropriate when searching for specific genetic effects on f, such as a change in the level of cell-to-cell variability, and a few previous studies successfully used it to map varQTL and cvQTL [19,20,22,25,26]. However, it is less adapted when nothing is known on the way f may depend on genetic factors.
Scanning for scPTL considers the entire distribution of single-cell trait values as the phenotype of interest and searches the genome for a statistical association with any change in the distribution. We assume that for a set of genotypic categories (individuals for multicellular organisms, or populations of cells for unicellular ones), a cellular trait has been quantified in many individual cells of the same type. This way, the observed distribution of the trait constitutes the phenotypic measure of individuals. We also assume that a genetic map is available and the individuals have been genotyped at marker positions on the map. The method we propose is based on three steps. First, a distance is computed for all pairs of individuals in order to quantify how much their phenotype differs. We chose the Kantorovich metric (also known as the Wasserstein distance or the earth-mover's distance) to measure this distance because, unlike the Kullback-Leibler divergence, it satisfies the conditions of non-negativity, symmetry and triangle inequality and, unlike the Hellinger distance, it does not converge to a finite upper limit when the overlap between distributions diminishes [27]. The Kantorovich metric can be viewed as the minimum energy required to redistribute one heap of earth (one f-function) into another heap (a second f-function). It has enabled developments in various fields, ranging from mathematics [28] to economy (the minimal transportation problem) [29,30] to the detection of states from molecular dynamics data [27]. The next two steps are inspired from methods used in ecology, where spatial distinctions between groups are often searched after determining distances between individuals [31,32]. In step 2 of our method, individuals are placed in a vectorial space while preserving as best as possible the distance between them (Fig 3A). This is achieved by multi-dimensional scaling, a dimension-reduction algorithm [33]. The third step is the genetic linkage test itself. At every genetic marker available, a linear discriminant analysis is performed to interrogate if individuals of different genotypic classes occupy distinct sectors of the phenotypic space (Fig 3B and 3C). The optimal choice of dimensionality is determined dynamically and a permutation test assesses statistical significance in the context of the corresponding degrees of freedom. Note that if the dimensions have been reduced to a single one, then canonical analysis is not needed: the phenotypic value of each individual has become a scalar and linkage can be performed by standard QTL mapping. Finally, scPTL linkage is scored using the Wilks' lambda statistics. Statistical inference is made using empirical p-values produced by permutations where the identities of individuals are re-sampled. The full procedure is described in details in the methods section.
We first evaluated if our method could detect scPTL from simulated datasets. To do this, we considered a probabilistic single-cell trait governed by a positive feedback of molecular regulations. This is representative of the expression level of a gene with positive autoregulation. As depicted in Fig 4A, the employed model is based on three parameters. For each individual, a set of parameter values was chosen and single-cell values of expression were generated by stochastic simulations. We chose to simulate a scPTL that modified the expected values of the parameters so that the skewness of cellular trait distribution is affected. To do so, we considered a panel of individuals and their genotype at 200 markers evenly spaced every 5cM. Parameter values of each individual were drawn from Gaussians and the mean of these Gaussians depended on the genotype at the central marker. This defined two sets of phenotypes that are depicted by blue and red histograms in Fig 4B. A universal noise term η was added to introduce intra-genotype inter-individual variation which, in real datasets, could originate from limited precision of measurements or from non-genetic biological differences between individuals. For each of five increasing values of η, about 130 individuals were simulated.
We first scanned the generated dataset by QTL mapping, treating either the mean trait or its variance as the phenotype of interest. This way, the central scPTL locus was detected only when intra-genotype noise was null or very low (Fig 4C). This was anticipated because the mean and variance of the simulated trait values slightly differed between the two sets of individuals. In contrast, our new method allowed to robustly detect the scPTL locus even in the presence of high (up to 20%) intra-genotype noise (Fig 4D and 4E).
The results described above using a simulated dataset suggest that the method can complement usual QTL mapping strategies. To explore if this was also the case when using real experimental data, we applied scPTL scans to the dataset of Nogami et al. [22] mentioned above (Fig 2A) where 220 single-cell traits were measured in about 200 cells from segregrants of a yeast cross. We applied three genome x phenome scans, each one at FDR = 10%. Two consisted of QTL interval mapping and were done by considering either the mean cellular trait value of the population of cells or the coefficient of variation of the cellular trait as the population-level quantitative trait to be mapped. The third scan was done using the novel method described here to map scPTL. Significant linkages obtained from this scan are available in S1 Table. As shown in Fig 5, the three methods produced complementary results. We detected more linkages with the scPTL method than with the 2 QTL scans combined (71 vs. 61 traits mapped). This illustrates the efficiency of using the full data (whole distribution) of the cell population rather than using a summary statistic (mean or CV). In addition, we expected that a fraction of scPTL would match QTL, because QTL controlling the mean or CV of cellular traits are specific types of scPTL. This was indeed the case, with 67% of scPTL corresponding to loci that were detected by at least one of the two QTL scans. For 11 cellular traits, a locus was found by QTL or cvQTL mapping but it was missed by the scPTL scan. This illustrates that the methods have different power and sensitivity. Importantly, 22 cellular traits were associated to scPTL that were not detected by the QTL search, suggesting that some probabilistic effects may affect poorly the trait's mean or CV. Altogether, these observations highlight the complementarity of the different approaches and show that scPTL mapping can improve the detection of genetic variants governing the statistical properties of single-cell quantitative traits.
Examples of scPTL of yeast cellular morphology are shown in Fig 6. One of the cellular traits measured was the distance between the center of the mother cell and the brightest point of DNA staining (Fig 6A). No QTL was found when searching genetic modifiers of the mean or CV of this trait, but a significant scPTL was mapped on chromosome II. When displaying trait distributions, it was apparent that segregants carrying the BY genotype at the locus had reduced cell-cell variability of the trait as compared to segregants having the RM genotype (Fig 6A, right panel). Consistently, a small cvQTL peak was seen on chromosome II, although this peak did not reach genome-wide statistical significance. This trait, which relates to the statistical properties of DNA migration during the early phase of cell division, provided a biological example where scPTL scan identified a genetic modulator of cell-to-cell variability that was missed by the QTL approach.
Three other traits were of particular interest because they mapped to a position on chromosome VIII where a functional SNP was previously characterized in this cross. This SNP corresponds to a non-synonymous I->S mutation at position 469 in the Gα protein Gpa1p. It targets a domain that is essential for physical interaction with pheromone receptors Ste2p and Ste3p [34,35]. In the presence of pheromone, Gpa1p is released from the receptor and triggers a signalling cascade of molecular response that causes cell-cycle arrest and cell elongation (a process called 'shmooing'). In the absence of pheromone, improper binding of Gpa1pI469S to the receptor causes residual activation of the pathway in the BY strain, as seen by transcriptomic profiling [36], which explains why BY cells are more elongated [24] and proliferate slower [37] than RM cells. Here we saw that this locus is a scPTL, but not a QTL, of the degree to which cells are elliptical (Fig 6B). Displaying the distributions of this trait in each segregant revealed a remarkable amount of variability between the segregants, and that the BY allele at the locus corresponded to a modest reduction of the trait value as compared to the RM allele (sharper mode at slightly lower value). To see if this was due to the GPA1I469S mutation, we examined the data from a BY strain where this mutation was cured [22]. Remarkably, the single amino-acid substitution caused a mild but statistically significant redistribution of the trait values (Fig 6B). This change was comparable to the difference seen among the segregants, demonstrating the causality of the GPA1I469S SNP. Another trait, corresponding to the distance between the bud tip and the short axis of the mother cell, also mapped to this locus, with the RM allele associated to greater cell-cell variability, and data from the GPA1I469S allele-replaced strain validated this SNP as the causal polymorphism (Fig 6C). These observations suggest that either the residual activation of the pathway in absence of pheromone is not uniform among BY cells, or the proper inactivation of the pathway is not complete in all RM cells. This, and the fact that the mutation does not prevent BY cells from proliferating (as compared to pheromone-arrested cells), indicate that the detachment of Gpa1pI469S from the receptor is a rare event that has probabilistic effects on the cellular phenotype. Further investigations based on biochemistry, dynamic recording of individual cells and stochastic modelling are needed to understand how variation in binding affinity accounts for this effect. The results described here illustrate that scPTL scans can identify individual SNPs that modify single-cell trait distributions without necessarily affecting the trait mean.
Finally, another trait corresponding to the angle of bud site position mapped to two scPTL loci and no QTL. One of these loci contained the GPA1 gene on chromosome VIII. Although the phenotype of bud site selection is not related to 'shmooing', we examined if the GPA1I469S SNP was involved and found that it was not: the allele-replaced strain did not show a different trait distribution than its control (Fig 6D). Thus, other genetic polymorphisms at the locus should participate to the statistical properties of cellular morphology, by affecting the position of budding sites.
We then explored if scPTL scanning could provide new results when applied to a molecular system that had been extensively characterized by classical genetics. The system we chose was the yeast GAL regulon which, in addition to be one of the best described regulatory network, presented several advantages. Natural strains of S. cerevisiae are known to display differences in its regulation [38,39] and the transcriptional response of cell populations can be tracked by flow cytometry. This provides data from large numbers of cells and therefore a good statistical power to compare single-cell trait distributions. In addition, acquisitions on many genotypes are possible using 96-well plates. We reasoned that if features of the cell population response segregate in the BY x RM cross (described above for morphology), then scPTL scanning might identify genetic variants having non-deterministic effects on the regulation of GAL genes.
We first compared the dynamics of transcriptional activation of the network in the two strains BY and RM. This was done by integrating a PGal1-GFP reporter system in the genome of the strains, stimulating them by addition of galactose in the medium, and recording the response by flow cytometry. As shown in Fig 7, both strains responded and full activation of the cell population was reached after ~2 hours of induction. Interestingly, remarkable differences were observed between the two strains regarding the distribution of the cellular response. The BY strain showed a gradual increase of expression through time that was relatively homogeneous among the cells (unimodal distribution with relatively low variance), whereas the RM strain showed elevated cell-cell heterogeneity at intermediate activation time points (higher variance, with fraction of non-induced cells). This suggested that genetic polymorphisms between the strains might control the level of heterogeneity of the cellular response at these intermediate time points.
We sought to map one or more of these genetic factors. To do so, we acquired the response of 60 meiotic segregants of the BY x RM cross. Using the data collected at each time point, we scanned the genome for scPTL of the reporter gene expression level using the novel genome-scan method described above. The procedure identified a locus on chromosome V position 350,744 that was highly significant (genome-wide p-value < 0.001) at 30 minutes post induction, the time at which heterogeneity markedly differed between the BY and RM strains (Fig 7B and 7C). The locus was also significant at times 20 min (p < 0.005) and 40 min (p < 0.005) post induction.
Visualizing the distributions of single-cell expression levels at 30 minutes revealed that the RM and BY genotypes at this locus corresponded to high and low cell-cell heterogeneity, respectively (Fig 7D and 7E). Thus, this locus explains, at least in part, the different levels of heterogeneity observed between the parental strains. It should therefore also be detected as a varQTL or cvQTL. This was indeed the case: the LOD score linking the locus to the variance of expression was 4.5 and reached statistical significance (P = 0.005). Importantly, the scPTL was not a QTL: the locus genotype did not correlate with the mean level of expression of the population of cells (LOD score < 2.8).
When surveying the genomic annotations of the locus [40], we realized that it contained no obvious candidate gene that would explain an effect on the heterogeneity of the response (such as genes known to participate to the transcriptional response). One potentially causal gene was DOT6, which encodes a poorly characterized transcription factor that was shown to shuttle periodically between the cytoplasm and nucleus of the cells in standard growth conditions [41]. Given that i) the shuttling frequency of such factors can sometimes drive the response to environmental changes and ii) numerous non-synonymous BY/RM genetic polymorphisms were present in the gene, we constructed an allele-replacement strain for DOT6 and tested if the gene was responsible for the scPTL linkage. This was not the case. Strains BY and BY-DOT6RM (isogenic to BY except for the DOT6 gene which was replaced by the RM allele) displayed very similar transcriptional responses at intermediate times of induction (S1 Fig). Fine-mapping of the locus and a systematic gene-by-gene analysis are now needed to precisely identify the polymorphisms involved. By highlighting a novel genetic locus modulating cell-cell variability of the transcriptional response to galactose, our results show that scPTL scanning can provide new knowledge on the fine structure of a well-studied system.
We have developed a novel method to scan genomes for genetic variants affecting the probabilistic properties of single-cell traits. We validated the method using data from colonies of a unicellular organism, which constitutes a first step before transferring the method to multicellular organisms. Our approach extends the usual genetic analysis of quantitative traits both conceptually and methodologically: by incorporating large samples of phenotypic values at the cellular scale, variants that have probabilistic effects can be detected and their possible contribution to trait heritability at the macroscopic (multicellular) scale can be investigated.
When considering macroscopic phenotypes, it is important to distinguish the situations where scPTL mapping is biologically relevant from those where it is not. The determinants of human height, for example, act via countless cells, of multiple types, and over a very long period of time (~ 16 years). In such cases, the macroscopic trait results from multiple effects that are cumulated and considering the probabilistic individual contribution of specific cells is inappropriate. Similarly, many tissular traits heavily rely on communications between cells and probabilistic changes in a few may not affect the collective output of the cell population. In contrast, a number of macroscopic traits can be affected by particular events happening in rare cells or at a very precise time (see below). In these cases, studying the probabilities of a biological outcome in the relevant cells or of a molecular event within the critical time interval can provide invaluable information on the emergence of the macroscopic phenotype, and scPTL mapping then becomes relevant.
A striking example of such traits is cancer. Genetic predisposition is conferred by variants affecting somatic mutation rates and these loci are special cases of scPTL: the cellular trait they modify is the amount of de novo mutations in the cell's daughter. These variants have classically been identified by genetic linkage of the macroscopic trait (disease frequency in families and cohorts), and their role on the maintenance of DNA integrity was deduced afterwards by molecular characterizations. For a review on the genetics of cancer syndrome predisposition, see [42,43]. scPTL mapping is also relevant to the non-genetic heterogeneity of cancer cells which was shown to be associated with tumour progression [7,8] and treatment efficiency [9–11]. Genetic loci changing the fraction of problematic cells are likely modulators of the prognosis. If the functional properties (expression level, phosphorylation status, subcellular localization) of a key molecular player, such as a critical tumor-suppressor gene, can be monitored in numerous individual cells, then scPTL mapping, as presented here, may help identify genetic factors that modulate the activity of this gene in a probabilistic manner. Once identified, the association of these loci with the macroscopic phenotype can then be tested directly, avoiding at least partly the statistical challenges of whole-genome scans.
To illustrate this, we considered an idealized case where three scales are bridged: at the molecular level, a scPTL affects the expression of a protein X (same regulation as in Fig 4); at the cellular level, cells have higher probability to divide if their level of X is low (Fig 8A); and at the whole-organism level, disease appears if too many cells are present. Using a stochastic model of this scenario, we simulated a cohort of individuals and recorded the state and number of cells in every individual over time (Fig 8B, see methods for details). Disease appeared in all individuals, between age 22 and 29. Using the data at age 23, we compared the power of GWAS and scPTL mapping. For GWAS, the trait of individuals was whether they had declared the disease or not. For scPTL mapping, the trait was the expression level of X in 10,000 of their cells. As expected by the moderate effect on disease frequency, GWAS failed to detect the locus (Fig 8C). In contrast, scPTL detection was highly significant from the same cohort of individuals (Fig 8D). Importantly, although not significant genome-wide, the GWAS score at the locus had a nominal p-value lower than 0.01 (Fig 8C). The locus would therefore be considered significant if it had been the only one tested. This illustrates the added value of scPTL mapping: while keeping cohort size constant, it can highlight candidate loci of the genome that can then be tested individually for association to the disease. This power clearly results from i) additional traits (cellular ones) that are included before scanning the genome and ii) relaxation of multiple-testing correction when testing association to disease. Note that other system genetics methods, such as expression QTL (eQTL) mapping, improve power in a similar way: they highlight relevant candidates via the addition of intra-individual traits (molecular ones) [44]. Note also that recruiting large cohorts remains important: Methods detecting scPTL and eQTL can improve genetic mapping but their detection power remain strictly dependent on the number of individuals available in the study.
In real studies, external knowledge is needed on the link between the cellular trait and the disease: what single-cell trait should be measured? Can it be measured in a sufficiently large number of cells? If a reporter system of de novo mutations, for example based on the intracellular distribution of a fluorescently tagged repair protein [45,46], can be introduced in a relevant and large population of cells, then the high number of cell measurements may allow to detect loci that modify even slightly the mutation rate. For non-genetic features of problematic cells, choice of the trait can be driven by investigations at the molecular level, such as stochastic profiling [47], and at the cellular level, such as recording the response of cell populations to treatment or differentiation signals [9,10]. For example, the distribution of the biomarker JARID1B (a histone demethylase) in populations of melanoma cells is indicative of an intra-clonal heterogeneity that is important for tumour progression [7], biomarkers CD24 and CD133 can distinguish rare cells that persist anti-cancer drug treatments [10] and multiplexed markers of signalling response can reveal patterns of population heterogeneity that predict drug sensitivity [48]. When relevant markers are not known, a possibility is to screen for them using stochastic profiling [47]. This method interrogates the transcriptomic variability between pools of few cells in order to identify transcripts displaying elevated cell-to-cell variability in specific biological contexts. It allowed the discovery of two molecular states of extracellular matrix-attached cells that can be distinguished by the jun D proto-oncogene and markers of TGF-β signalling [8]. Such markers of isogenic cellular subtypes may allow the development of scPTL mapping in humans.
An important statistical requirement to identify scPTL is the abundance of cells on which the probabilistic trait is quantified. For human studies, peripheral blood offers access to many cells but, unfortunately, many internal organs do not. This requirement also implies using technologies where the throughput of quantitative acquisitions is high. This is the case for flow-cytometry and, although at higher costs, for high-content image analysis [14,15] and digital microfluidics [17,18]. For these practical reasons, it is possible that mouse immunological studies will help making progress in mammalian scPTL mapping. For example, the work initiated by Prince et al. [49] describing pre- and post-infection flow-cytometry profiles of F2 offsprings from different mouse strains may provide an interesting pilot framework.
The interest of scPTL mapping is not restricted to cancer biology. Developmental processes and cellular differentiation are also vulnerable to mis-regulations happening in few cells or during short time intervals. Their macroscopic outcome can therefore be affected by probabilistic events at the cellular scale. For example, stochastic variation in the expression of the stem cell marker Sca-1 is associated with different cellular fates in mouse hematopoietic lineages [50], suggesting that genetic factors changing this stochastic variation may impact blood composition. Similarly, embryonic stem cells co-exist in at least two distinct molecular states that are sensitive to epigenetic and reprogramming factors [51]. Genetic variants modulating these factors may change the statistical partitioning of these states. Two observations made on flies remarkably support the existence of natural genetic factors altering developmental processes in a probabilistic manner. The first one is the fact that high levels of fluctuating asymmetry can be fixed in a wild population of D. melanogaster under artificial selection [52]. The second one comes from a comparative study of Drosophila species [53]. Embryos of D. santomea and D. yakuba display high inter-individual variability of expression of the signal transducer pMad at the onset of gastrulation, as compared to D. melanogaster embryos. This increased variability was attributed to a reduced activity of the homeobox gene zerknüllt thirty minutes before this stage. Very interestingly, it is accompanied by phenotypic variability (inter-individual variance of the number of amnioserosa cells) in D. santomea but not in D. yakuba. These and other examples [54] illustrate how developmental variability and phenotypic noise can evolve in natural populations. Applying scPTL mapping may allow to dissect the genetic factors responsible for this evolution.
Our new method based on the Kantorovich distance is not the only one by which scPTL can be identified. Applying classical QTL mapping to summary statistics of the cellular traits can also be efficient. We emphasize that the two approaches are complementary. For example, our method missed to detect linkage for 9 yeast morphological traits for which cvQTL scans were successful, but it detected several significant scPTL that were missed by the QTL-based approach (Figs 5 and 6). Second, we observed that scPTL detection was often efficient when the mean value of cellular trait differed among genotypic categories. As shown on Fig 5B, traits successfully mapped tended to display high heritability of the mean. Thus, after a scPTL is detected, it is necessary to examine the effect on the trait distributions and to determine if it is a QTL or not. Third, alternative ways of mapping scPTL are open and may prove more appropriate in some contexts. For example, if a cellular trait becomes preoccupying when it exceeds a certain threshold value, then the fraction of cells above this threshold can be used as a macro-trait to be mapped by QTL analysis. This way, the focus is made on the relevant aspect of the cellular trait, avoiding variation in other parts of the distribution. We therefore recommend conducting Kantorovich-based scPTL mapping in addition to classical methods and not as a replacement strategy.
While the principle of genotype-phenotype genetic linkage dates back to several decades ago, the statistical methods that test for linkage are still being improved, especially regarding multi-loci interactions or population structure corrections [55,56]. The present study provides a priming of a generic scPTL mapping approach (exploiting thousands of single-cell trait values) and demonstrates its feasibility and potential (new loci were detected). Since it is new, we anticipate that it will also evolve in the future. It is currently based on three steps: (i) computing pairwise distances between individuals by using the Kantorovich metric, (ii) using the resulting distance matrix to construct a relevant phenotypic space and (iii) testing for genetic linkage by LDA. A number of methodological considerations can be made in anticipation to future developments and applications.
Estimating the proportion of variance explained by scPTL is not straightforward: the 'captured variance' as quantified by the eigenvalues of the LDA is not the same as the 'explained variance' which must be re-computed by regression; and if linearity of the data is questionable, the method remains a useful tool if it detects scPTL but interpreting variance proportions need justifications.
A phenotypic space can be constructed by alternative ways that do not require the Kantorovich metric. For example, we considered representing individuals in a "space of moments", where the coordinate of every individual on the i-th axis was the i-th moment of the cellular trait distribution associated to this individual. We applied this to the yeast morphological data and we searched for genetic linkages by linear discriminant analysis as described above. This approach detected many significant scPTL but we encountered a difficulty that was avoided by our Kantorovich-metric based method. When searching for significant linear discrimination, the dimensionality of the phenotypic space is important. At high dimensionality, discriminant axes are more likely to be found. This improves detection in the actual data but at the expense of increasing the degrees of freedom and therefore the false positive hits estimated from the permuted data. In a "space of moments", the properties of the single-cell trait distributions are very important because they define which axis (moments) are relevant to separate individuals. Keeping the 4-th axis may be crucial even if all individuals have very similar first, second or third moments. Choosing the appropriate dimension for LDA is then arbitrary and it becomes difficult to keep a good detection power while still controlling the FDR. In fact, applying QTL mapping on the 3rd and 4th moments of all traits was fruitless because the FDR could not be controlled at the genome-by-phenome scale. This issue is avoided in the case of Kantorovich distances because multi-dimensional scaling can be applied without normalization and the axes of the phenotypic space are ranked by descending order of their contribution to the inter-individual differences. The 4-th axis, for example, contributes less than the first three axes to the separation of individuals in the space. If keeping the 4-th dimension prior to LDA is beneficial for linkage, then keeping the first three axes is also highly relevant, and this is true regardless of the properties of the single-cell trait distributions. We found this very useful: our algorithm adds dimensions one by one and evaluates the benefit of each increase (see methods).
There are at least three lines along which our method may be further improved. First, LDA is only appropriate if genotypic categories can be distinguished along linear axis. If individuals in the phenotypic space are separated in non-linear patterns, other methods such as those based on kernel functions [57] may be more appropriate. Second, we propose to compute confidence intervals of scPTL position by bootstrap, following a method sometimes applied to QTL positions [58]. As expected, resampling not only affected scPTL position but also the optimal dimensionality retained (S2A Fig). A deeper investigation of the simultaneous variation of these two outputs could help improve the precision of mapping. And third, single-cell data acquisitions often generate multiple trait values for each individual cells. This is the case for morphological profiling as in the dataset we used here, but also for gene expression [59] or parameters describing the micro-environment of the cells [60]. It would therefore be interesting to search for scPTL affecting multiple cellular traits simultaneously instead of treating cellular traits one by one. A multidimensional analysis could be performed in order to extract a set of informative meta-traits, such as principal components or representative medoids and scPTL of these meta-traits could be searched using our method. This dimension-reduction approach would benefit from the redundant information available from correlated traits (e.g. the perimeter of a cell and its area are two measurements of its size), but the biological interpretation of a probabilistic effect on a meta-trait may not be straightforward. Alternatively, one might want to identify scPTL affecting the joint probability distribution of multiple cellular traits. In this case, a natural extension of our method would be to compute Kantorovich distances between multivariate distributions. However, the Kantorovich metric cannot be easily computed for more than two marginals (i.e. cellular traits in our case). In fact, its existence as a unique solution to the multi-dimensional transportation problem was itself a subject of research [61]. A possible alternative could be to compute a Euclidean distance in the "space of moments" mentioned above and then apply multi-dimensional scaling.
Furthermore, although our study was focused on probability density functions, steps (ii), constructing the phenotypic space, and (iii), testing for genetic linkage, could in principle be applied to other types of functions, provided that a relevant metric estimating the dissimilarity between such functions exists. This could be interesting in the case of function-valued traits, such as speech sound or other time-series functions. The evolution of these functions is being studied using phylogenetic methods that present challenging statistical issues [62,63]. Extending our approach to such functions may open the possibility to study them from a (complementary) quantitative genetics angle.
Finally, we can anticipate that gene-gene and gene-environment interactions also shape the probability density function of cellular traits. Our results on the activation of the yeast galactose network remarkably illustrate this: the effect of the scPTL on chromosome V is apparent only transiently, and in response to a change of environmental conditions. It is tempting to extrapolate that signaling pathways in plants and animals may be affected by scPTL that act at various times and steps along molecular cascades.
In conclusion, our study provides a novel method that can detect genetic loci with probabilistic effects on single-cell phenotypes, with no prior assumption on their mode of action. By exploiting the power of single-cell technologies, this approach has the potential to detect small-effect genetic variants that may underlie incomplete trait penetrance at the multicellular scale.
Single-cell gene activity was modeled by a stochastic variable X that represented the number of proteins in one cell at a given time. Under the model, the dynamics of X is controlled by two processes: (1) protein production with rate α and (2) protein degradation with rate β. We assume that the gene is positively auto-regulated by a 4-mer complex, meaning that α is an increasing function of X with a typical Hill-like shape
α = α0+α1(XK)41+(XK)4
with α0 the leaky production rate in absence of X 4-mers at the promoter, α1 the production rate in presence of 4-mers, and K the dissociation constant of the 4-mer. We set β = 1, which corresponds to scaling time units. The dynamics of the mean value of X in a population of isogenic cells follows the equation shown in Fig 4A. To obtain the probability distribution of X, we performed exact stochastic simulations of the chemical system defined by the two reactions rate α and β, using the Stochastic Simulation Algorithm [64].
To generate two groups of individuals, we assumed that the set of parameters (α0, α1, K) was controlled by one locus that could exist in two alleles (A and B) with mean values (μ0A/B, μ1A/B, μKA/B) and, for simplicity, that the individuals were haploids. To account for sources of inter-individual variability within genotypic groups, the values of the parameters for one individual were drawn from normal distributions of mean values μ0A/B, μ1A/B and μKA/B and of standard deviations ημ0A/B, ημ1A/B and ημKA/B where η represented the strength of inter-individual variability. η was assumed to be the same for A and B alleles. Values were: μ0A = 6.3, μ0B = 0.1, μ1A = 12, μ1B = 10, μKA = 10 and μKB = 1.6.
All statistical analysis were done using R (version 3.1.2) [65]. The data from Nogami et al. [22] consisted of 220 traits, acquired on >200 cells per sample. Note that most traits are related to one of three division stages. Each trait was therefore measured on a subset of cells of the sample (less than 200). There were nine samples of the BY strain, nine of the RM strain, and three of each of 59 segregants of the BY x RM cross. For each trait, we computed the genetic heritabilities of the mean and CV as follows. The mean and CV of the cellular trait in each sample were computed, leading to two scalar values per sample that we call macro-traits hereafter (to distinguish them from the single-cell values). The broad-sense genetic heritability of each macro-trait was H2 = (varT − varE) / varT, with varT and varE being the total and environmental variance, respectively. For Fig 2B, we estimated varT by randomly choosing one of the three replicate sample of each segregant and computing the variance across these 59 values. This was repeated 100 times and the estimates were averaged. Our estimate of varE was the pooled variance of varBY, varRM, varSeg1, varSeg2, …, varSeg59 which were the between-replicates variance of each strain. Confidence intervals on H2 values were computed by bootstrapping the strains. For the filtering step prior to linkage, H2 was computed slightly differently in order to be consistent across mapping methods (see below).
We first normalized the distributions as densities (division of all bin counts by half the total number of cells). Following [27], we then computed the Kantorovich distance between two distributions f1 and f2 as the area under the absolute value of the cumulative sum of the difference between the two distributions:
KD(f1,f2) = ∫−∞+∞|∫−∞xf1(t)−f2(t) dt|dx
Multi-dimensional scaling of the resulting distance matrix was then performed using the R function cmdscale() from the stats package. The number of dimensions retained (ndim) was the number of eigenvalues exceeding the expected value under the hypothesis of no structure in the data (i.e. mean of all eigenvalues, Kaiser criterion). We computed the heritability of each yeast morphological trait in this multidimensional space. This was done as above for one dimension, by computing the total variance of the data, and estimating the environmental variance from the replicated experiments made on the parental strains. For 147 traits, heritability was greater than 0.5 and scPTL were searched. Details on how these steps were implemented in R are described in S1 Methods, and the code is available in the open source ptlmapper R package (https://github.com/fchuffar/ptlmapper).
The yeast genotypes we used were from Smith and Kruglyak [66]. For the morphological traits, we pooled triplicates together in order to increase the number of cells per sample. The data then corresponded to 220 traits, measured on >600 cells per sample, with 3 BY samples, 3 RM samples, and 1 sample per segregant. We scanned the genetic map with two methods. First, we considered the coordinates of each segregant on the first axis of the multi-dimensional scaling, and we considered this coordinate as a quantitative trait that we used for interval mapping using R/qtl [67]. Secondly, we applied a linear discrimination analysis (LDA) on the phenotypes data, using the genotype at every marker as the discriminating factor. An important issue in this step is the multidimensionality of the data: axis 2, 3 and more may contain useful information to discriminate genotypic groups, but if too many dimensions are retained, a highly-discriminant axis may be found by chance only. To deal with this issue, we evaluated the output of LDA at all dimensions d ranging from 2 to ndim. For each value of d, we applied LDA at every marker position and we recorded the Wilks' lambda statistics:
Λ = ∏j = 1d 11+λj
where λj was the j-th eigenvalue of the discriminant analysis. Low values of this statistics allow to reject the null hypothesis of no discrimination by the factor of interest [68] which, in our case, is the genotype. We defined a linkage score (W score) as:
W = −Log10(P)
where P is the p-value of the Wilk's test (deviation of Λ from the F-statistics with relevant degrees of freedom). Note that P is not interpreted directly as a significance value for linkage (see the permutation test below).
We then quantified how much the best marker position was distinguished from the rest of the genome by computing a Z-score:
Z = Wbest − Wσw
where Wbest, <W> and σW were the highest, the mean and the standard deviation of all W scores found on the genome, respectively. Finally, we chose the dimension that maximized this Z-score (i.e. dimension where the linkage peak had highest contrast). Very importantly, the same degrees of freedom (exploration of the results at various dimensionalities) were allowed when applying the permutation test of significance (see below). The distribution of the dimensionalities retained for the morphological traits is shown in S2B Fig. Additional details are provided in S1 Methods and the code is available in the open source ptlmapper R package (https://github.com/fchuffar/ptlmapper).
QTL-based mapping was performed as follows. A quantitative trait was considered at the cell-population level. This macro-trait was either the mean (for QTL), the coefficient of variation (standard deviation divided by mean, for cvQTL) or the variance (for varQTL) of the cellular trait in the population of cells. For the yeast morphology data, we selected the traits with H2 > 0.5 prior to linkage. To do so, we re-computed H2 values in a way that was consistent with the heritability calculation of the phenotypic space prior to scPTL mapping, where replicates of segregants were pooled together before analysis of inter-strain variation (see above). In this case, only 3 replicates of BY and 3 replicates of RM are then available to estimate the environmental variance. Therefore, we estimated varT as the variance of the 59 macro-trait values of the segregants and varE as (varBY + varRM) / 2, with varBY (resp. varRM) being the variance of the three macro-trait values of the BY (resp. RM) strain. We then scanned the genome using the scanone function from r/qtl [67] with a single QTL model and the multiple imputation method [69]. Our code implementing the calls to r/qtl is available in the open source ptlmapper R package (https://github.com/fchuffar/ptlmapper).
We first explain the case where a single trait is studied. When the trait was mapped using R/qtl, significance was assessed by the permutation test implemented in function scanone() of the package [67]. For scPTL, we implemented our own permutation test as follows. The significance of an scPTL is the type one error when rejecting the following null hypothesis: "there is no marker at which the genotype of individuals discriminates their location in the phenotypic space", where one 'individual' refers to one population of isogenic cells, and where the 'phenotypic space' is the multi-dimensional space built above by computing Kantorovich distances and applying multi-dimensional scaling. The relevant permutation is therefore to randomly re-assign the phenotypic positions to the individuals before scanning genetic markers for discrimination. We did this 1,000 times. Each time, LDA was applied at dimensions 2 to ndim, the dimension showing the best contrast (high Z score) was retained, and the highest W score obtained at this dimension was recorded. The empirical threshold corresponding to genome-wide error rates of 0.1%, 1% and 5% were the 99.9th, 99th and 95th percentiles of the 1,000 values produced by the permutations, respectively. These thresholds are typically those employed in whole-genome scans for a single trait.
We now explain the case of the morphological study, where multiple traits (220) were considered. This case is similar to system genetics studies, where the FDR must be controlled. Keeping it below 10% ensures that 9 out of 10 results are true positives, which is often considered as acceptable. Four different methods were used. For three of them, single-cell trait values were resumed to a scalar macro-trait and QTL was searched. The three methods differed by the choice of this macro-trait, which was either the mean or the coefficient of variation of single-cell traits, or the coordinate of individuals on the first axis of the phenotypic space. For each of the three methods, morphological traits with less than 50% genetic heritability (see above) were not considered further, and QTL was searched for the remaining Ntraits traits only. For each of these traits, LOD scores were computed on the genome by interval mapping using the macro-trait value as the quantitative phenotype of interest. Significance was assessed by random re-assignment of the macro-trait values to the individuals (yeast segregants). We did 1,000 such permutations. For each one, the genome was scanned as above and the highest LOD score on the genome was retained. This generated a 1,000 x Ntraits matrix Mperm of the hits expected by chance. At a LOD threshold L, the FDR was computed as:
FDR = NFalseL / NActualL
where NActualL was the number of linkages obtained from the actual dataset at LOD > L, and NFalseL was the expected number of false positives at LOD > L, which was estimated by the fraction of elements of Mperm exceeding L.
The fourth method considered all coordinates of the individuals in the phenotypic space. At this step, for each morphological trait, a phenotypic space of ndim dimensions had been built as explained above by computing Kantorovich distances and applying multi-dimensional scaling. Let P1, P2 and PS be the phenotypic matrices of parent 1 (strain BY), parent 2 (strain RM) and segregants, respectively, with rows being the samples (replicates for P1 and P2, and segregants for PS) and columns being the ndim coordinates of each sample in the phenotypic space. These matrices had dimensions 3 x ndim for P1 and P2 and 59 x ndim for PS. Genetic heritability was computed as H2 = (varT—varE) / varT, where the total variance varT was the variance of the samples in PS, and where the environmental variance varE was estimated as (varP1 + varP2) / 2, with varP1 (resp. varP2) being the variance of the samples in P1 (resp. P2). Morphological traits showing H2 < 0.5 were discarded, and scPTL mapping was applied to the remaining Ntraits traits as described above (choice of dimensionality with highest contrast and recording of the best W score obtained on the genome at this dimensionality). Significance of W scores was assessed as described above for the LOD scores, by performing 1,000 permutations and determining the FDR associated to various thresholds of W scores.
For each cell, the probability to divide depended on the concentration of gene product X according to the following Hill-like function (Fig 8A):
P(X) = β01+(Xθ)n+ β∞
with β0 = 0.2, ϑ = 2.5, β∞ = 0.05 and n = 2.5.
The regulation of X was governed by the same model as above, with η = 0.16. For each individual, parameters alpha0, alpha1 and K were drawn from the same normal distributions as above, where mean and variance depended on the genotype (A or B). At age 0, a population of 1,000 cells was initiated with X = 5. This population was then evolved by Stochastic Simulation Algorithm [64], with a constant rate of cell death of 0.0001 until the age of 30. The python code implementing this simulation is provided in S2 Methods.
The yeast strains and oligonucleotides used in this study are listed in S2 Table.
To construct the Gal-GFP reporter, we first removed the MET17 promoter of plasmid pGY8 [19] by digestion with restriction enzymes BspEI and SpeI followed by Klenow fill-in and religation. This generated plasmid pGY10. The GAL1 promoter fragment was digested (BglII-BamHI) from pFA6a-His3MX6-PGAL1 [70] and cloned in the BamHI site of pGY10. A small artificial open reading frame upstream GFP was then removed by digestion with EcoRV and BamHI, Klenow fill-in end blunting and religation. This generated plasmid pGY37, carrying a PGAL1-yEGFP-NatMX cassette that could be integrated at the HIS3 genomic locus.
Plasmid pGY37 was linearized at NheI and integrated at the HIS3 locus of strain BY4716 (isogenic to S288c), YEF1946 (a non- clumpy derivative of RM11-1a) and in 61 F1 non-clumpy segregants from BY471xRM11-1a described in [22] to generate strains GY221, GY225, and the S288c x RM11-1a HIS3:PGAL1-yEGFP-NatMX:HIS3 set, respectively.
In parallel, we also constructed a GAL1-GFPPEST reporter coding for a destabilized fluorescent protein [71]. We derived it from pGY334, where GFPPEST was under the control of the PGK promoter. pGY334 was constructed in several steps. The PGK promoter was PCR-amplified from pJL49 (gift from Jean-Luc Parrou) using primers 1A23 and 1A24, digested by BamHI and cloned into the BamHI site of pGY10. The resulting plasmid was digested with EcoRV and XbaI, subjected to Klenow fill-in end blunting and religated, generating plasmid pGY13 carrying a HIS3:PPGK-yEGFP-NatMX:HIS3 cassette. The lox-CEN/ARS-lox sequence from pALREP [20] was amplified by PCR using primers 1I27 and 1I28 and cloned by homologous recombination into pGY13, generating plasmid pGY252. The GFPPEST sequence was PCR-amplified from pSVA18 [71] using primers 1I92 and 1I93 and cloned in vivo into pGY252 (digested by MfeI and DraIII), leading to pGY334. The GAL1 promoter fragment was amplified by PCR from pGY37 using primers 1J33 and 1I42 and cloned into plasmid pGY334 by recombination at homologous sequences flanking the BamHI site of the plasmid. The CEN/ARS cassette of the resulting plasmid was excised by transient expression of the Cre recombinase in bacteria [20], generating the final integrative plasmid pGY338 carrying the HIS3:PGAL1-GFPPEST-NatMX:HIS3 cassette.
pGY338 was linearized by NheI and integrated at the HIS3 locus of BY4724 (isogenic to S288c) and GY1561 to create GY1566 and GY1567 strains, respectively. Strain GY1561 is a non-clumpy derivative of RM11-1a where the KanMX4 cassette was removed. It was obtained by first transforming RM11-1a with an amplicon from plasmid pUG73 [72] obtained with primers 1E75 and 1E76 and selecting a G418-sensitive and LEU+ transformant (GY739) which was then transformed with pSH47 [73] for expression of the CRE recombinase. After an episode of galactose induction, a LEU- derivative was chosen and cultured in non-selective medium (URA+) for loss of pSH47, leading to strain GY744, which was then crossed with GY689 [74] to generate GY1561.
Liquid cultures in synthetic medium with 2% raffinose were inoculated with a single colony and incubated overnight, then diluted to OD600 = 0.1 (synthetic medium, 2% raffinose) and grown for 3 to 6 hours. Cells were then resuspended in synthetic medium with 2% raffinose and 0.5% galactose and grown for the desired time (0, 10, 20, 30, 40, 60, 80, 100, 130, 160, 205 and 250 minutes). Cells were then washed with PBS1X, incubated for 8 min in 2% paraformaldehyde (PFA) at room temperature, followed by 12 min of incubation in PBS supplemented with Glycine 0.1M at room temperature and finally resuspended in PBS. They were then analyzed on a FACSCalibur (BD Biosciences) flow cytometer to record 10,000 cells per sample.
Flow cytometry data was analysed using the flowCore package from Bioconductor [75]. Cells of homogeneous size were dynamically gated and normalized as follows: (i) removal of events with saturated signals (FSC, SSC or FL1 ≥ 1023 or ≤ 0), (ii) correction of FL1 values by subtracting the mean(FL1) observed on the same strain at t = 0, (iii) computation of a density kernel of FSC,SSC values to define a perimeter of peak density containing 60% of events, (iv) cell gating using this perimeter and (v) removal of samples containing less than 3,000 cells at the end of the procedure. The GFP expression values were the corrected FL1 signal of the retained cells.
The DOT6RM allele was amplified by PCR from genomic DNA of the RM strain using primers 1K87 and 1K88. It was then cloned into plasmid pALREP [20] by homologous recombination at sequences flanking the HpaI site of the plasmid. The CEN/ARS cassette of the resulting plasmid was excised by transient expression of the Cre recombinase in bacteria, as previously described [20], generating plasmid pGY389, which was linearized at EcoRI (a unique site within the DOT6 gene) and integrated in strain GY1566 (isogenic to BY, and carrying the HIS3:PGAL1-GFPPEST:HIS3 cassette). The pop-in pop-out strategy was applied as previously described [20] and four independent transformants were selected (GY1604, GY1605, GY1606 and GY1607) where PCR and sequencing validated the replacement of the DOT6 allele.
The yeast morphological data corresponds to the experiments described in [22]. For the present study, raw images were re-analyzed using CalMorph 1.0. The single-cell values and genotypes used are provided in S1 Dataset of this article.
The flow cytometry data corresponding to yeast galactose response is made available from http://flowrepository.org under accession number FR-FCM-ZZPA.
The simulated data of Fig 4 is available as an R package (ptldata) from https://github.com/fchuffar/ptldata.
The scPTL mapping method is made available as an open source R package (ptlmapper) which can be downloaded from https://github.com/fchuffar/ptlmapper. A tutorial of this package explains how to run the analysis on the simulated dataset.
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10.1371/journal.ppat.1003737 | The Inflammatory Kinase MAP4K4 Promotes Reactivation of Kaposi's Sarcoma Herpesvirus and Enhances the Invasiveness of Infected Endothelial Cells | Kaposi's sarcoma (KS) is a mesenchymal tumour, which is caused by Kaposi's sarcoma herpesvirus (KSHV) and develops under inflammatory conditions. KSHV-infected endothelial spindle cells, the neoplastic cells in KS, show increased invasiveness, attributed to the elevated expression of metalloproteinases (MMPs) and cyclooxygenase-2 (COX-2). The majority of these spindle cells harbour latent KSHV genomes, while a minority undergoes lytic reactivation with subsequent production of new virions and viral or cellular chemo- and cytokines, which may promote tumour invasion and dissemination. In order to better understand KSHV pathogenesis, we investigated cellular mechanisms underlying the lytic reactivation of KSHV. Using a combination of small molecule library screening and siRNA silencing we found a STE20 kinase family member, MAP4K4, to be involved in KSHV reactivation from latency and to contribute to the invasive phenotype of KSHV-infected endothelial cells by regulating COX-2, MMP-7, and MMP-13 expression. This kinase is also highly expressed in KS spindle cells in vivo. These findings suggest that MAP4K4, a known mediator of inflammation, is involved in KS aetiology by regulating KSHV lytic reactivation, expression of MMPs and COX-2, and, thereby modulating invasiveness of KSHV-infected endothelial cells.
| Kaposi's sarcoma (KS) is a tumour caused by Kaposi's sarcoma herpesvirus (KSHV) and dysregulated inflammation. Both factors contribute to the high angiogenicity and invasiveness of KS. Various cellular kinases have been reported to regulate the KSHV latent-lytic switch and thereby virus pathogenicity. In this study, we have identified a STE20 kinase family member – MAP4K4 – as a modulator of KSHV lytic cycle and invasive phenotype of KSHV-infected endothelial cells. Moreover, we were able to link MAP4K4 to a known mediator of inflammation and invasiveness, cyclooxygenase-2, which also contributes to KSHV lytic replication. Finally, we could show that MAP4K4 is highly expressed in KS lesions, suggesting an important role for this kinase in tumour development and invasion.
| Kaposi's sarcoma (KS) is a mesenchymal tumour caused by Kaposi's sarcoma herpesvirus (KSHV) [1], which originates from blood and lymphatic vessels and develops under the influence of inflammatory cytokines [2]–[4]. Local or systemic inflammation and immunosuppression are important additional risk factors [5], [6]. In addition to KS, KSHV is involved in the pathogenesis of primary effusion lymphoma (PEL) [7], and the plasma cell variant of multicentric Castleman's disease (MCD) [8].
KS is characterised by multiple patch, plaque or nodular lesions on the skin of the extremities or involving the mucosa and visceral organs [9]. KSHV-infected spindle cells, which were shown to be of vascular or lymphatic endothelial origin, represent the main proliferative element in KS and are the distinctive histological signature of advanced KS tumours [10], [11]. The lesions also contain slit-like neovascular spaces, which represent aberrant new vessels [5], [12]. KS spindle cells were shown to have increased invasiveness [13], which has been attributed to the enhanced expression of several matrix metalloproteinases (MMPs) [14], including MMP-1, MMP-2, MMP-3, MMP-7, MMP-9, and MMP-13 [13], [15], [16]. MMPs are zinc-dependent endopeptidases involved in extracellular matrix remodelling during tumour progression, invasion and metastasis [17], [18]. In addition to MMPs, the key enzyme for inducible prostaglandin synthesis – cyclooxygenase 2 (COX-2) [19] – has also been implicated in KS progression and invasion [20]. Increased COX-2 expression in inflammation-driven tumours contributes to neoangiogenesis and activates MMPs, which promote invasiveness [21], [22]. COX-2 is highly expressed in KS tumour tissue and is involved in KS pathogenesis [20], [23], [24]. Several KSHV proteins were shown to enhance COX-2 expression, including K15 [25], and vGPCR [26]. This could explain how KSHV may increase COX-2 gene expression.
In KS tumours, the majority of KSHV-infected cells harbour latent viral genomes, which are characterised by a restricted viral gene expression pattern that involves the major latent nuclear antigen LANA, homologues of a cellular D-type cyclin and a FLICE inhibitory protein, v-Cyclin and v-FLIP, respectively, and 12 viral miRNAs [6], [27]. However, a minority of infected cells show evidence of productive (‘lytic’) replication and produce not only new virions [28], but also secrete viral or cellular cyto- or chemokines [6], [10], [27], [29], [30]. These are thought to promote the pathological angiogenesis typical for KS lesions, increased invasion, and tumour dissemination [31]. Epidemiological findings also indicate that the prophylactic use of ganciclovir, which inhibits KSHV lytic replication, may reduce the incidence of KS in AIDS patients [32]. In addition, it is thought that the long-term persistence of KSHV in vivo may require periodic reactivation from latency and reinfection of new cells [33]. Experimentally, reactivation of KSHV from latency can be initiated by various chemical agents: these include phorbol esters and histone deacetylase inhibitors, which lead to chromatin remodelling and activation of the viral replication and transcription activator (RTA) [34]–[37]. So far, several signalling pathways were reported to be involved in the reactivation of KSHV from latency: PKCδ [38], b-Raf/MEK/ERK [39], PKA [40], Notch and RBP-Jκ [41], [42], p38 and JNK [43], Pim-1 and Pim-3 [44], PI3K and Akt [45], TLR7/8 signalling [46] and others.
Given the importance of the KSHV lytic cycle in KS pathogenesis and the angiogenic and invasive phenotype of KSHV infected cells, we aimed at identifying ‘druggable’ cellular kinases required for KSHV reactivation from latency. To this end, we screened a library of kinase inhibitors and found the STE20 kinase family member MAP4K4 to be a novel mediator of KSHV lytic reactivation. MAP4K4 is known to play an important role in inflammation, insulin resistance, and invasiveness of several malignancies [3], [47]–[55]. We found that MAP4K4 regulates the expression of COX-2, MMP-7 and -13, and thereby modulates the invasiveness of KSHV infected primary and immortalized endothelial cells. Moreover, we found MAP4K4 to be strongly expressed in KSHV-infected endothelial spindle cells in KS tissue, consistent with a role of MAP4K4 in KS pathogenesis.
Productive replication of KSHV in infected individuals is thought to contribute to viral persistence and the pathogenesis of this virus [56], [57]. Activation of several cellular kinases, involved in different signalling pathways, promotes viral reactivation [58], [59]. In order to identify novel “druggable” cellular kinases required for KSHV reactivation we screened a library of 486 small molecule kinase inhibitors (figure 1A) in a KSHV reactivation assay based on Vero cells infected with the recombinant KSHV strain rKSHV.219 (VK.219) [60]. The activation of productive replication cycle was achieved by treatment with Na-butyrate and infection with a baculovirus expressing KSHV immediate-early protein RTA. Toxicity of the compounds was determined by crystal violet staining of VK.219 and HEK293 cells after treatment. As a result, 105 compounds showed moderate to strong effects on virus production and infectivity without being toxic. Among them, 92 compounds were able to directly inhibit KSHV lytic protein expression in VK.219 cells. The results were validated in BCBL1 [61], and KSHV-infected EA.hy 926 [62] cells. As a result, we identified 18 compounds able to inhibit KSHV lytic protein expression in all three cell lines (figure S1A). Interestingly, among them were 11 compounds identical to, or derived from, known p38 MAP kinase inhibitors, in line with earlier reports on the role of this kinase in KSHV reactivation [43], [58]. When comparing the effects of commercially available p38 inhibitors with compounds in the VICHEM library, we noted that p38 inhibitors SB202190, SB203580, VX745, SKF86002, SB220025, and a derivative of SB220025 (VI18802) differed in their ability to block KSHV reactivation, as shown by their effect on the expression of KSHV envelope glycoprotein K8.1 (figure 1B), although their ability to inhibit the phosphorylation of MK2, a p38 target, seemed comparable (figure 1B). Of these compounds, SB220025 was the most potent with regard to inhibiting K8.1 expression (figure 1B) or virus production (not shown). To validate the effect of SB220025 on KSHV reactivation, we titrated this compound in KSHV-infected endothelial cells (KSHV-infected EA.hy 926) and found it to inhibit KSHV reactivation at submicromolar concentrations (figure 1C). We then determined which other cellular kinases are inhibited by SB220025 and its derivative VI18802. Compound SKF86002, although a strong inhibitor of lytic reactivation, was not included in this comparison, as it reduced the levels of total MK2 (figure 1B). We used a commercial screening assay that measured the ability of these compounds to compete with an immobilized ligand for binding to a panel of 442 recombinant kinases in an in vitro assay (see www.discoverx.com). The list of cellular kinases inhibited by SB220025 and VI18802 is shown in a table presented in figure S1B, which also includes previously published data on compounds SB203580, SB202190 and VX745 [63].
To explore if, apart from p38, any of the other kinases inhibited by SB220025 could account for the strong inhibition of KSHV reactivation observed with this compound, we used small molecule inhibitors or siRNAs against CSNK1D, CSNK1E, CSNK1A1L, MINK, CDC2L1/2, JNK1, MAP4K4, STK36 and TNIK (data not shown). As a result of these experiments we identified the upstream MAP kinase MAP4K4 (data not shown), a member of the STE20 kinase family, which has previously been shown to be involved in inflammation, response to LPS, inflammation-dependent insulin resistance of peripheral tissues, and also invasiveness of several types of cancer cells [47], [48], [53], [64], [65].
In order to explore if MAP4K4 affected KSHV reactivation in a cell type that is known to be infected by KSHV in vivo, we used a pool of siRNAs to silence MAP4K4 expression in the immortalized HUVEC derived cell line EA.hy 926 [62], which we had stably infected with rKSHV.219. As shown in figure 2, silencing of MAP4K4 in these cells significantly reduced production of infectious viral progeny by more than 60% (figure 2A), as well as the expression of immediate-early (RTA), early (KbZIP, ORF45) and late (K8.1) lytic proteins (figure 2B). The effect on K8.1 expression was confirmed using four individual siRNAs targeting MAP4K4, all of which were able to reduce MAP4K4 and K8.1 levels (figure S2A, B). In contrast to other lytic KSHV proteins, the expression of the viral homologue of IL-6, vIL-6, was slightly increased by MAP4K4 knockdown (figure 2B). vIL-6 expression is known to be regulated independently of the productive replication cycle [66] and may therefore not be affected by MAP4K4 silencing. Consistently with the observed decrease in virus production and lytic protein expression, MAP4K4 depletion also reduced KSHV genome replication (figure 2C), similarly to foscarnet, an inhibitor of KSHV DNA polymerase [67]. However, while foscarnet only inhibited the expression of a late viral gene (K8.1), MAP4K4 silencing also affected early KSHV gene (KbZIP) expression (figure 2D), suggesting that this kinase exerts its effect early in the replication cycle. To control whether MAP4K4 knockdown affects transduction or expression of baculovirus RTA, we evaluated the levels of RTA mRNA transcripts before and after MAP4K4 depletion in cells not infected with KSHV that had been treated with baculovirus RTA alone or in combination with Na-butyrate. In these cells, RTA expression was not dependent on MAP4K4 presence (figure S2C–D). Taken together, the observed decrease in KSHV titre, lytic protein expression, and replication in the absence of MAP4K4 suggests that this kinase contributes to the successful completion of the KSHV lytic programme.
MAP4K4 is also known to promote tumour cell migration, invasion, and loss of adhesion [49], [68]. KS tumour derived cells have been reported to show an invasive phenotype [69]. This phenomenon can be studied in vitro in a matrigel-based invasion assay, in which uninfected HuAR2T, a conditionally immortalized HUVEC cell line [70], fails to invade into matrigel, whereas HuAR2T cells infected with rKSHV.219 show increased invasiveness after the treatment with Na-butyrate to induce the KSHV lytic replication cycle (figure 3A–B). Thus, lytic reactivation of the virus promotes invasiveness of these immortalized endothelial cells infected with KSHV. As we observed that MAP4K4 supports the KSHV lytic cycle (figure 2) and since it had been reported to be a promigratory kinase [49], we investigated if its silencing might affect the ability of KSHV-infected endothelial cells to invade matrigel. Indeed, after silencing of MAP4K4 expression with siRNA, KSHV-infected HuAR2T endothelial cells failed to invade matrigel beyond the levels seen in uninfected control cells (figure 3C–D). MAP4K4 and KSHV lytic protein expression was controlled by Western blot analysis as presented in figure 3E. Together, these data suggest a role for MAP4K4 signalling in the KSHV-dependent invasiveness of infected endothelial cells.
In an attempt to understand how MAP4K4 promotes lytic reactivation and leads to the increased invasiveness of KSHV-infected endothelial cells we compared the transcriptome of reactivated KSHV-infected HuAR2T cells, in which the expression of MAP4K4 had been silenced with siRNA, with KSHV-infected, reactivated HuAR2T cells treated with control siRNA. We were able to identify 54 cellular genes that showed at least a 1.5-fold decrease in their expression levels after MAP4K4 knockdown in HuAR2T rKSHV.219 undergoing viral reactivation as compared to control siRNA treated, reactivated HuAR2T rKSHV.219 cells in at least two out of three independent experiments (figure 4A). Successful knockdown of MAP4K4, and the subsequent inhibition of lytic gene expression, was controlled by Western blot analysis (figure S2E). Among the cellular genes regulated by MAP4K4 silencing in KSHV-infected endothelial cells were three that have previously been reported to contribute to the invasive phenotype of tumour cells: PTGS2, encoding cyclooxygenase 2 (COX-2), and the genes coding for matrix metalloproteinases 7 and 13 (MMP-7 and MMP-13) (figure 4A). In order to validate the results of the transcriptome analysis, the expression levels of COX-2 were evaluated by qPCR and Western blot analysis before and after the induction of the lytic cycle. As shown in figure 4B–C, COX-2 mRNA and protein expression is upregulated following induction of the viral lytic cycle and can be reduced by silencing MAP4K4. Likewise, we could show that the expression of both MMP-7 and MMP-13 mRNAs increased after the induction of the lytic cycle and was significantly reduced after MAP4K4 depletion (figure 4B). These data support the notion that MAP4K4 may mediate the increased invasiveness of KSHV-infected endothelial cells due to its ability to modulate not only COX-2, but also MMP-7 and MMP-13 expression.
KS cells are known to express high levels of MMP-1, -2, -3, -7, -9, -13, -19, and previous reports suggest that some of these metalloproteinases may contribute to the invasive phenotype of the tumour [14], [16], [71], [72]. Overexpression of MMP-7 has been reported in several other malignancies [73]–[75], and its depletion with siRNA resulted in a significant decrease in the invasive potential of different cancer cell types [76]–[78]. Similarly, MMP-13 has been reported to confer the ability to penetrate basement membranes and ECM upon malignant cells [79]. Given these proinvasive properties of MMP-7 and MMP-13, and taking into account the ability of MAP4K4 to regulate their expression (figure 4), we addressed the involvement of these metalloproteinases in the invasiveness of KSHV-infected cells in a matrigel-based invasion assay. We found that depletion of both MMP-7 and MMP-13, similarly to MAP4K4 knockdown, led to a significant reduction of the number of invasive KSHV-infected endothelial HuAR2T cells following activation of the viral lytic replication cycle (figure 5A). The efficiency of silencing the expression of MAP4K4, MMP-7 and MMP-13 with siRNA was controlled by Western blot analysis for MAP4K4 (figure 5B) and qPCR for MMP-7 and MMP-13 (figure 5C). We noted that silencing of MAP4K4 led to a reduced expression of the early KSHV protein KbZIP (figure 5B) and its mRNA transcript, as well as K8.1 mRNA expression (figure 5D), whereas silencing of MMP-7 and MMP-13 had no effect on the protein and mRNA levels of KbZIP (figure 5B, 5D) or mRNA levels of K8.1 (figure 5D). These results suggest that MAP4K4 is involved in the activation of the lytic replication cycle, which, in turn, promotes the expression of MMP-7 and MMP-13.
As shown in figure 4, silencing of MAP4K4 reduces the expression of PTGS2, encoding cyclooxygenase 2 (COX-2). COX-2 has previously been shown to be overexpressed in KSHV-infected endothelial cells and to play a role in inflammation, angiogenesis and invasion [20]. The KSHV K15 and vGPCR proteins induce the expression of COX-2 [25], [26]. COX-2 catalyses the production of prostaglandin E2 (PGE2) after stimulation with inflammatory cytokines [80]. Depletion of COX-2 reduced invasiveness of KSHV-infected endothelial cells, similar to MAP4K4 knockdown (figure 6A). Interestingly, both MAP4K4 and COX-2 silencing inhibited KSHV lytic reactivation (figure 6B). To corroborate the effect of COX-2 depletion on KSHV lytic reactivation, we used a specific inhibitor, which does not affect constitutively active COX-1 [81]. Application of this inhibitor, NS-398, led to a dramatic decrease, comparable to the effect of MAP4K4 silencing, in the invasiveness of KSHV-infected endothelial cells undergoing lytic reactivation (figure 6C). NS-398 treatment not only led to a reduction of invasiveness, but also effectively blocked KSHV lytic protein expression (figure 6D–E), as well as the production of viral progeny (figure 6F). This suggests that, in response to MAP4K4 signalling, COX-2 mediated production of PGE2 contributes to the successful completion of KSHV lytic cycle and KSHV driven invasiveness of infected endothelial cells.
To extend our observations, which were obtained with the immortalized endothelial cell line HuAR2T, to primary endothelial cells, we investigated the role of MAP4K4 in the invasiveness of human umbilical vein endothelial cells (HUVECs) following their infection with rKSHV.219 (figure 7). On day 5 after infection, KSHV-infected HUVECs showed a markedly increased invasiveness compared to uninfected cells, and this increased invasiveness depended on the expression of MAP4K4, since silencing of MAP4K4 with siRNA reduced their invasiveness to background levels (figure 7A–B). Similar to KSHV-infected HuAR2T cells, expression of COX-2 increased after infection of HUVECs with KSHV and silencing of MAP4K4 by siRNA reduced COX-2 levels in KSHV-infected primary endothelial cells (figure 7C). We also observed that after infection with KSHV, MAP4K4 protein levels were moderately increased (figure 7C–D). Moreover, KSHV lytic protein expression was inhibited after MAP4K4 depletion in primary cells, similarly to what we had found in immortalized endothelial cells (figure 7D).
To explore if MAP4K4 is expressed in KS tissue and could, therefore, play a role in KSHV-infected cells in vivo and contribute to the pathogenesis of KS, we stained KS biopsies with an antibody to MAP4K4. We observed a strong expression of MAP4K4 in the KS endothelial spindle cells, which are characterised by the expression of CD34 and KSHV LANA (figure 8A). Double staining for LANA and MAP4K4 confirmed the strong cytoplasmic expression of MAP4K4 in LANA-expressing cells (figure 8A). Individual staining for MAP4K4 and LANA of adjacent serial sections of a KS biopsy also indicated the increased expression of MAP4K4 in LANA-expressing KS spindle cells, although a lower level of MAP4K4 expression could also be seen in other cells in the tumour (figure 8B–C), and a basal expression of MAP4K4 was observed in the surrounding connective tissue (figure 8C), in line with another report showing low levels of MAP4K4 cytoplasmic staining in non-neoplastic lung tissues, compared to strong expression in lung adenocarcinomas [82]. We found a moderate to strong expression of MAP4K4 in spindle cells in a total of 13 biopsies, derived from 11 patients (figure 8D), confirming the consistent expression of this kinase in KS tissue. This observation is consistent with a role for MAP4K4 and MAP4K4-dependent signalling pathways in the pathogenesis of KS.
In KS tumours, a small percentage (1–5%) of KSHV-infected cells show evidence of viral lytic replication [31], [83]. Taken together with epidemiological findings indicating a beneficial effect of inhibiting viral lytic replication on the incidence of KS in AIDS patients [32] this suggests that lytic gene products may contribute to the pathogenesis of this disease. On the one hand, lytic replication can be a source of new virions and consequently newly infected cells. This is important, as KSHV does not completely immortalize spindle cells and needs to infect new cells to persist in an infected host [33]. On the other hand, lytic reactivation may lead to the production of autocrine and paracrine signalling molecules, which then promote inflammation, angiogenesis, and invasiveness. KSHV-infected endothelial spindle cells have been shown to have invasive properties [16], [20], [69], [84]–[86]. In order to better understand how KSHV lytic replication cycle contributes to the increased invasiveness of infected endothelial spindle cells we investigated cellular mechanisms underlying the lytic switch of the virus. In contrast to earlier studies that had employed siRNA screens of the human kinome to identify cellular kinases involved in KSHV reactivation and had identified Pim kinases as activators of lytic replication [44], or Tousled-like kinases as negative modulators of KSHV reactivation [87], we screened a library of small molecule kinase inhibitors (figure 1A) to identify positive regulators of KSHV lytic cycle. We found several compounds, known to target p38 MAPK, to inhibit KSHV reactivation after baculovirus RTA and Na-butyrate treatment (figure S1A), in line with previous reports on a role of p38 during de novo infection [88], after induction of productive reactivation [43], and during progression of KSHV through the lytic cycle, when, for instance, vGPCR activates p38 [89]. However, a close comparison of well-characterized p38 inhibitors [90]–[94], showed that these compounds varied with regard to their ability to inhibit KSHV reactivation in endothelial cells, while showing comparable efficacy in inhibiting the phosphorylation of the p38 MAPK target MK2 (figure 1B). This observation suggested that some of these compounds might also target other cellular kinases, which could contribute to KSHV reactivation. Off-target effects of other kinase inhibitors are well known and sometimes improve the biological activity and clinical usefulness of individual compounds [63], [95]. Since compound SB220025, which is known to have anti-inflammatory properties, proved to be the most efficient in reducing KSHV lytic reactivation (figure 1B–C), we profiled this substance together with VI18802, a derivative of SB220025, against 442 kinases using the KINOMEscan platform (DiscoverX). Extending previous reports on the ability of even “specific” p38 inhibitors to bind to other kinases [63], we found a range of other kinases to be inhibited by SB220025 (figure S1B).
By blocking, among others, the p38 cascade, SB220025 inhibits the production of IL-1β and TNF-α [91], [96], and belongs to the CSAID class of cytokine biosynthesis inhibitors [97], [98]. However, p38 is not the only regulator of inflammatory cytokine production. JNKs also regulate the expression and activation of inflammatory mediators, including TNF-α, IL-2, and MMPs [99], [100]. Interestingly, we identified several JNK isoforms and their putative upstream activators MAP4K4 (NCK interacting kinase (NIK) or haematopoietic/germinal centre kinase (HGK)), MINK (Misshapen/NIK related kinase), and TNIK (TRAF2 and NCK interacting kinase) as targets of SB220025 (figure S1B). KSHV is known to activate the JNK pathway during primary infection [101], and JNK is essential for KSHV infection [58], and production of inflammatory cytokines [31], [101], [102]. Considering the important role of inflammation in KS development and progression, and the dependence of KSHV on the JNK pathway, we investigated if upstream regulators of JNK signalling targeted by SB220025 (MAP4K4/NIK, TNIK, MINK) are also critical for KSHV lytic cycle. While siRNA-mediated knockdown of TNIK and MINK did not affect KSHV reactivation (data not shown), MAP4K4 silencing reduced KSHV virus production (figure 2A), lytic protein expression (figure 2B), and KSHV replication (figure 2C) in immortalized, as well as primary endothelial cells (figure 7D). Interestingly, vIL-6 expression levels were increased after MAP4K4 silencing (figure 2B). Although vIL-6 is a lytic gene induced by RTA [103], it is known to be also regulated independently of the lytic switch, for instance by interferon-α [66] and microRNAs, such as miR-1293 [104]. Whether MAP4K4 also regulates the latter factors needs to be further investigated, and perhaps would explain the observed increase in vIL-6 expression in the absence of MAP4K4.
As MAP4K4 was previously shown to be overexpressed in multiple tumour cell lines and cancers [49]–[51], [105], [106], and also implicated in tumour cell invasiveness [49], [106], we investigated its role in previously reported invasiveness of KSHV-infected endothelial cells. We could observe that KSHV-infected immortalized endothelial cells possess a much more invasive phenotype after the induction of the lytic cycle (figure 3A–B). This increased invasiveness could be reduced by MAP4K4 silencing using siRNA (figure 3C–E), demonstrating a role of MAP4K4 in invasive KSHV-infected endothelial cells. Similarly, silencing of MAP4K4 reduced the increased invasiveness of KSHV-infected primary umbilical vein endothelial cells (figure 7A–C).
The role of MAP4K4 in different cellular functions is only incompletely understood. In order to identify genes, regulated by MAP4K4 in the context of KSHV lytic reactivation, we performed a microarray-based analysis after silencing MAP4K4 and inducing the lytic cycle.
Among cellular genes known to affect migration/invasion, we found PTGS2, encoding COX-2, to be downregulated after MAP4K4 knockdown (figure 4A). Its mRNA and protein levels were highly upregulated in induced KSHV-infected cells compared to uninfected cells (figure 4A). Increased levels of PGE2 in Kaposi's sarcoma tissue compared to surrounding tissues were reported already in 1992 [107]. In keeping with this observation, KSHV infected immortalized dermal microvascular endothelial cells display a strong increase in COX-2 expression and PGE2 production early during de novo infection [20], [23], [24], when lytic replication may still take place [108]. Our finding suggests that COX-2 activation is, at least in part, mediated by MAP4K4 and is critical for KSHV lytic cycle progression, as treatment with a specific COX-2 inhibitor NS-398 (figure 6E) or COX-2 depletion (figure 6B) led to a dramatic decrease in expression of KSHV lytic proteins. COX-2 inhibitors are known to also block human cytomegalovirus replication [109], [110], as PGE2 enhances, for instance, CMV promoter activation [111]. COX-2 activation might also play a role in HHV-6 [112], MHV-68 [113], and HSV-1 [114] replication. Of note, MAP4K4 expression levels after COX-2 depletion and its chemical inhibition were slightly reduced (figure 6B, 6D–E). MAP4K4 expression is regulated by TNF-α through TNF receptor α [53], the expression levels of which in turn depend on PGE2 activation [115]. Hence it is conceivable that, when PGE2 production is downregulated by chemical inhibition of COX-2, MAP4K4 levels can also decrease as expression of TNF receptors is reduced.
COX-2 is a known mediator of angiogenesis and tumour cell invasiveness, as it leads to production of inflammatory cytokines, growth factors, angiogenic factors, and MMPs in various tumours, as well as in KSHV infected cells [20], [77], [116]–[122]. We could also show that, similarly to MAP4K4 knockdown, COX-2 silencing or chemical inhibition significantly reduces the invasiveness of KSHV-infected endothelial cells (figure 6A, 6C).
We also found that MAP4K4 mediates the expression of MMP-7 and MMP-13 (figure 4A–B), which both contribute to the invasiveness of KSHV-infected cells (figure 5A). Although matrix metalloproteinases are known to be modulated post-transcriptionally [123]–[125], most of them, including MMP-7 and MMP-13, can be activated also at the transcriptional level, as their promoters harbour several cis-elements, allowing activation by trans-activators, e.g. NF-κB and AP-1 [126], [127]. These MMPs can also be induced at the mRNA level by TNF-α, IL-1 and other cytokines [61], [128]–[131]. Given that MAP4K4 regulates inflammatory cytokine production, such as TNF-α and IL-1β [47], it may therefore also modulate MMP-7 and MMP-13 mRNA expression. Our observation that MAP4K4 regulates MMP-7 and MMP-13 expression illustrates its multifactorial role in the increased invasiveness of KSHV-infected endothelial cells.
Given the reported role of MAP4K4 as an upstream activator of JNK [64], and the role of JNK in KSHV reactivation [43], we also explored if silencing of MAP4K4 in KSHV-infected endothelial cells would alter the levels of JNK 1/2/3 phosphorylation, using phospho-specific antibodies in Western blot analysis. However, we could not detect any prominent effect of MAP4K4 silencing on the levels of JNK phosphorylation (figure S3A), consistent with an earlier report [47]. Searching for other cellular targets that would be phosphorylated in response to MAP4K4, we employed a commercial phosphokinase array and noted a moderate decrease of c-Jun phosphorylation following MAP4K4 silencing (figure S3B–C). This was confirmed in Western Blot analysis using an antibody to c-Jun phosphorylated on S63 (figure S3D–E). Phosphorylation of c-Jun may therefore provide another explanation of how the upstream kinase MAP4K4 exerts its effect on MMP-7 and MMP-13 expression. It might also lead to COX-2 overexpression. However, other possibilities remain to be investigated, as well as the mechanism of how MAP4K4 is activated in KSHV-infected endothelial cells.
Having shown that MAP4K4-dependent signalling pathways are involved in the increased invasiveness of KSHV-infected primary and immortalized endothelial cells, we could demonstrate that MAP4K4 is highly expressed in the pathognomonic KSHV-infected endothelial spindle cells in KS lesions (figure 8A–D), suggesting that it may indeed play a role in vivo in aspects of KSHV-induced pathogenesis. Our findings also provide an explanation for the increased expression of COX-2 in KSHV-infected endothelial cells.
The use of the human biopsies and human umbilical cords for this study was approved by the Hannover Medical School Ethics Committee and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all patients.
HEK293 and EA.hy 926 cells were maintained in Dulbecco's modified Eagle's medium (DMEM), and Vero cells in minimal essential medium (MEM) (Cytogen) supplemented with 10% foetal bovine serum (HyClone), 50 U/ml penicillin, and 50 µg/ml streptomycin (Cytogen) at 37°C in a 5% CO2 incubator. Human umbilical vein endothelial cells (HUVEC) were isolated from freshly obtained human umbilical cords by collagenase digestion of the interior of the umbilical vein as described previously [132] and were cultured in EGM-2MV medium (Lonza) at 37°C in a 5% CO2 incubator. An endothelial cell line HuAR2T, conditionally immortalized with doxycycline dependent human telomerase reverse transcriptase (hTERT) and simian virus 40 (SV40) large T antigen transgene expression [133], were maintained in EGM-2MV medium in the presence of 200 ng/ml doxycycline. Transfection with small interfering RNA (siRNA) was performed using the Neon transfection system according to the manufacturer's instructions (Invitrogen). All siRNAs were microporated at the concentration of 100 pmol into 105 cells. The following siRNAs (siGENOME SMARTpool) were obtained from Dharmacon, Thermo Scientific: Control (Non-targeting siRNA Pool #2, D-001206-14-20), MAP4K4 (M-003971-02-0005), MMP-7 (M-003782-01-0005), MMP-13 (M-005955-01-0005).
Sf9 cells were maintained in Grace's medium (Gibco) supplemented with 10% foetal bovine serum, 100 U/ml penicillin, and 50 µg/ml streptomycin (Cytogen) at 28°C. The generation of the recombinant baculovirus expressing KSHV ORF50/RTA was described previously [60].
To produce virus stocks, Vero cells containing recombinant KSHV (rKSHV.219) [60] were plated at 30–40% confluency in T175 flasks and induced twenty-four hours later with 1 mM Na-butyrate (Sigma-Aldrich) and 10% baculovirus coding for KSHV ORF50/RTA. The supernatant was harvested 72 hours later and 0.45 µm filtered to remove cell debris. The cleared supernatant was collected in centrifuge bottles (230 ml/bottle) and centrifuged at 15000×g at 4°C for 6 hours using a Type19 rotor (Beckman Coulter). The supernatant was then discarded and the pellet resuspended in 250 µl EBM2 basal medium (Lonza) overnight at 4°C. The resuspended virus was kept at 4°C for not longer than three weeks.
For detection and quantification of KSHV titres, 2.3×104 HEK293 cells were plated in a 96-well plate and infected with serially diluted KSHV stocks. GFP-positive cells were counted two days after infection. To determine KSHV titres from EA.hy rKSHV.219 or HuAR2T rKSHV.219 cells after induction of the lytic cycle, the supernatants were cleared from the debris by 0.45 µm filtration and applied to HEK293 cells without dilution.
Protein lysates of cells were prepared in 1× SDS sample buffer (62.5 mM Tris-HCl pH 6.8, 2% w/v SDS, 10% glycerol, 50 mM DTT, 0.01% w/v bromophenol blue) supplemented with cOmplete Ultra protease inhibitor cocktail and PhosSTOP phosphatase inhibitor cocktail (Roche). Proteins were resolved by SDS-PAGE, transferred onto nitrocellulose membranes (GE Healthcare), and detected using the following primary antibodies: rabbit polyclonal MAP4K4 (HGK) antibody (#3485, Cell Signaling Technology), rabbit polyclonal KSHV ORF50/RTA [37], mouse monoclonal KSHV ORF45 antibody (sc-53883, Santa Cruz), mouse monoclonal HHV-8 KbZIP antibody F33P1 (sc-69797, Santa Cruz), rabbit polyclonal KSHV vIL-6 antibody (13-214-050, Advanced Biotechnologies), mouse monoclonal KSHV ORFK8.1A/B antibody (13-212-100, Advanced Biotechnologies), mouse monoclonal β-actin antibody (A5441, Sigma-Aldrich), rabbit monoclonal GAPDH antibody (#2118, Cell Signaling Technology), rabbit polyclonal COX-2 antibody (#4842, Cell Signaling Technology), rat monoclonal KSHV ORF73 (LNA-1) antibody (13-210-100, Advanced Biotechnologies), mouse monoclonal phospho-JNK 1/2/3 antibody 9H8 (sc-81502, Santa Cruz), rabbit polyclonal JNK 1/3 antibody C17 (sc-474, Santa Cruz), mouse monoclonal phospho-p44/42 antibody (#9106, Cell Signaling Technology), mouse monoclonal p44/p42 antibody 3A7 (#9107, Cell Signaling Technology), rabbit polyclonal phospho-p38 antibody (#9211, Cell Signaling Technology), rabbit polyclonal p38 antibody (#9212, Cell Signaling Technology), rabbit monoclonal phospho-MK2 antibody 27B7 (#3007, Cell Signaling Technology), rabbit polyclonal MK2 antibody (#3042, Cell Signaling Technology). All stainings were performed at 4°C overnight with subsequent washing in TBS-T or PBS-T and incubation with a corresponding secondary HRP-labelled antibody (DaKo) at RT for one hour. Following further washing steps, the proteins were detected with SuperSignal West Femto Chemiluminescent Substrate (Pierce, Thermo Scientific).
The “Whole Human Genome Oligo Microarray V2” (G4845A, ID 026652, Agilent Technologies) used in this study contains 44495 oligonucleotide probes covering roughly 27390 human transcripts. Synthesis of Cy3-labeled cRNA was performed with the “Quick Amp Labelling kit, one colour” (#5190-0442, Agilent Technologies) according to the manufacturer's recommendations. cRNA fragmentation, hybridization, and washing steps were carried out exactly as recommended in the “One-Color Microarray-Based Gene Expression Analysis Protocol V5.7” (Agilent). Slides were scanned on the Agilent Micro Array Scanner G2565CA (pixel resolution 5 µm, bit depth 20). Data extraction and processing of raw fluorescence intensity values were performed with the “Feature Extraction Software V10.7.3.1” by using the recommended default extraction protocol file: GE1_107_Sep09.xml.
Processed intensity values of the green channel (“gProcessedSignal” or “gPS”) were globally normalized by a linear scaling approach: All gPS values of one sample were multiplied by an array-specific scaling factor. This scaling factor was calculated by dividing a “reference 75th Percentile value” (set as 1500 for the whole series) by the 75th Percentile value of the particular Microarray (“Array i” in the formula shown below). Accordingly, normalized gPS values for all samples (microarray data sets) were calculated by the following formula: normalized gPSArray i = gPSArray i X (1500/75th PercentileArray i). A lower intensity threshold was defined as 1% of the reference 75th Percentile value ( = 15). All normalized gPS values below this intensity threshold were substituted by the surrogate value of 15.
Data were filtered according to the following criteria: 1) More than 1.5 fold downregulation in lytically induced HuAR2T rKSHV.219 cells after MAP4K4 knockdown compared to control siRNA treated induced cells (each of three experiments). 2) Arithmetic mean intensity of nPS values calculated from both channels that define ratio values >25 (each of three experiments). 3) QC flag entries “gIsNonUnifOL” (determined by the Feature Extraction Software) must have been “0” (indicating reliable performance) (each of six relevant channels of the three experiments). 4) In cases, in which more than one probe directed against the same transcript is present on the microarray, only those transcripts passed the criteria, for which the majority of probes indicate the respective regulation. 5) The respective transcript has to be classified as being functionally characterized and reasonably annotated (for details visit: www.mh-hannover.de/Transcriptomics.html and consult our manual: “Crude probe characterization_RCUT_date.pdf”). Just one representative probe is selected for visualization in figure 4A if many probes directed against the same transcript match the applied criteria.
Total RNA was extracted from the cells with an RNeasy kit (QIAgen) according to the manufacturer's recommendations, followed by DNase treatment and inactivation (Ambion). cDNA was synthesized using BioScript RNase H Low reverse transcriptase (BIO-27036, Bioline) or Expand reverse transcriptase (Roche) in 20 µl reactions. 1 µl of generated cDNA samples (50 ng total RNA equivalents) were used per reaction for real-time PCR with the ABI7500 system (Applied Biosystems). Specific amplification was assured by utilizing TaqMan probes and gene specific primers. Amplification was performed in 10 µl reactions with TaqMan Fast Advanced Master Mix under recommended conditions (Applied Biosystems; #4444557). The following TaqMan gene expression assays (Applied Biosystems: #4331182) were used: Hs00153133_m1 (PTGS2/COX-2), Hs99999908_m1 (GUSB), Hs01042796_m1 (MMP-7), Hs00233992_m1 (MMP-13), Hs02758991_g1 (GAPDH); primer-probe sets for RTA [134], KbZIP [70], K8.1 [135]. The average Ct for each individual amplification reaction was calculated from duplicate measurements by means of the instrument's software in “auto Ct” mode (7500 System Software v.1.3.0). Average Ct values obtained for the analysed transcripts of PTGS2/COX-2, MMP-7 or MMP-13 were normalized by subtraction from the Ct values obtained for GUSB or GAPDH (housekeeping reference). Relative mRNA expression changes were calculated according to the ΔΔCt method.
For quantification of KSHV genome copies, DNA was extracted using the QIAamp DNA Blood Mini Kit (QIAgen) according to the manufacturer's instructions. KSHV genome copy numbers were determined in a TaqMan based qPCR directed against KSHV ORF K6 with normalization to the cellular C-reactive protein (CRP) as described previously [136]. Briefly, qPCR for KSHV was carried out in a total volume of 50 µl containing a ready-to-use master mix (QuantiTect multiplex PCR kit; Qiagen), 0.5 µM concentrations of each primer, 10 µl of DNA from the sample of interest, and 0.4 µM FAM-labeled KSHV K6 probe. Amplification was performed in the Applied Biosystems 7500 thermal cycler and visualized with ABI 7500 software. qPCR of CRP was carried out in a total volume of 20 µl containing a ready-to-use master mix (LightCycler FastStart DNA Master HybProbe; Roche), 0.3 mM MgCl2, 0.5 µM concentrations of each primer, 0.2 µM FAM-labeled CRP probe, and 5 µl of DNA from the sample of interest. Amplification was performed in the LightCycler 2.0 Instrument and analyzed with the LightCycler software. The primers (Sigma) and probes (Eurogentec) used for the quantification of KSHV and CRP had the following sequences: KSHV K6 forward (CGCCTA ATAGCTGCTGCTACGG), HHV8 K6 reverse (TGCATCAGCTGCCTAACCCAG), CRP forward (CTTGACCAGCCTCTCTCATGC), CRP reverse (TGCAGTCTTAGACCCCACCC), K6 probe [5′-(6 FAM)-CAGCCACCGCCCGTCCAAATTC-TAMRA], and CRP probe [5′-(6 FAM)-TTTGGCCAGACAGGTAAGGGCCACC-TAMRA].
Cell invasiveness was measured using Matrigel coated invasion inserts (Growth Factor Reduced Matrigel Invasion Chamber, 8.0 µm; 354483, BD Biosiences). HuAR2T rKSHV.219 cells were microporated with siRNAs twenty hours prior to the induction of the lytic cycle. Twenty-four hours after the induction, the cells were starved in EBM2 supplemented with 2% FCS for twelve hours. Next day, 5×104 cells were plated in the inner chambers in 500 µl of EBM2 basal medium with 2% FCS and 750 µl EBM2 was added to the outer chamber and incubated for twenty-four hours. Before the assay, Matrigel inserts were rehydrated with 500 µl EBM2 for two hours. Cells that were able to degrade the Matrigel layer, migrated to the lower surface of the filter, and were fixed with 4% paraformaldehyde, permeabilised with 0.2% Triton X-100, and nuclei were stained with DAPI (Sigma-Aldrich) and counted under a fluorescent microscope. Four different Matrigel chambers were used for each sample. Four random fields were counted for each chamber, and the average cell number per field in a chamber was calculated using CellProfiler2.0. To quantify the number of cells in the immunofluorescence images we used the CellProfiler software [137]. All pixel intensities were rescaled to 0–1. Using the Otsu Global thresholding method [138] in the DAPI channel, the nuclear area was defined. Clumped nuclei were distinguished based on the intensity. The threshold correction factor was set to 1.3. Cell invasiveness of freshly isolated HUVECs (<p. 2) was measured after infection with rKSHV.219 at MOI 30 for four days. Three days after infection the cells were microporated with siRNAs and starved in EBM2 medium with 2% FCS. After twenty-four hours the cells were seeded onto the Matrigel, and their invasiveness was quantified as described above.
3 µm thin tissue sections were cut from formalin-fixed KS samples and stained with haematoxylin-eosin (HE). KS tumour cells and non-neoplastic endothelial cells were marked immunohistochemically with anti-CD34 antibody (Menarini Corp.) using a 1∶50 dilution. The KSHV latent nuclear antigen (LANA) was marked immunohistochemically with NCL-HHV8-LNA antibody, clone 13B10, purchased from Novocastra, using a 1∶50 dilution. MAP4K4 was stained immunohistochemically with MAP4K4 monoclonal antibody M07, clone 4A5, produced by Abnova Corp. and purchased from Biozol Diagnostica GmbH, applied at a 1∶300 dilution. When performing MAP4K4/LANA double-staining, 1∶20 (LANA) and 1∶100 (MAP4K4) dilutions were applied using the BenchMark Ultra staining machine.
The library of kinase inhibitors was received from Vichem Chemie Research Ltd. (Budapest, Hungary) as lyophilized powders, and stored at room temperature. DMSO (cell culture grade, Applichem) was used to dissolve the inhibitors at a stock concentration of 10 mM. After reconstitution, the inhibitors were stored at room temperature protected from light. VI18802 is a phenoxypyrimidine [93] targeting p38α. As a part of the Vichem Core Validation Library it was handled as described above. SB203580 (#13067, Cayman Chemical), SB202190 (#EI-294-0001, Biomol), SB220025 (#559396, Calbiochem), SKF86002 (#2008, Tocris Bioscience), and VX-745 (#3915, Tocris Bioscience) were reconstituted and stored in working aliquots at −20°C protected from light. To target COX-2, NS-398 (#349254, Calbiochem) was prepared according to the manufacturer's recommendations and applied to the cells six hours before the induction of the lytic cycle. Application of all compounds to the cells was controlled by DMSO treatment.
For evaluation of KSHV reactivation inhibition, 5×103 Vero rKSHV.219 cells per well were plated in 96-well plates twenty-four hours before the treatment with kinase inhibitors. The inhibitors were applied one hour before the induction of the lytic cycle. Forty-eight hours later the supernatants were transferred to HEK293 cells, which were then centrifuged 30 min at 30°C and 500×g and incubated at 37°C for six hours. Medium was exchanged, and the cells were incubated at 37°C for forty-eight hours. The number of infectious particles was evaluated by the mean fluorescence intensity of GFP-positive HEK293 cells. Alternatively, Vero rKSHV.219 or EA.hy rKSHV.219 cells were treated with kinase inhibitors, and subsequently with induction mix to assess the expression levels of KSHV lytic proteins RTA and K8.1.
To assay the viability of cells after treatment with kinase inhibitors, each well of a 96-well plate received 20 µl glutaraldehyde (25%) and was incubated at RT for at least 20 min. After washing with water, the plates were stained with 0.4% crystal violet solution in methanol for 30 min. Absorbance at 590 nm was measured spectrophotometrically with a reference to 405 nm reading.
The KINOMEscan of SB220025 and VI18802 was carried out by DiscoverX as described (www.discoverx.com).
For evaluation of Ser/Thr kinase phosphorylation in endothelial cells, a human phospho-kinase antibody array (ARY003B, R&D Systems) was used according to the manufacturer's recommendations. Briefly, HuAR2T rKSHV.219 cells were transfected with control siRNA or an siRNA pool targeting MAP4K4 twenty-four hours before the induction of the lytic cycle. Cells were lysed twenty-four after lytic cycle induction and diluted lysates were applied to, and incubated overnight with, the nitrocellulose membranes with spotted capture antibodies. The array was washed to remove unbound proteins, followed by incubation with a cocktail of biotinylated detection antibodies, and streptavidin-HRP. Chemiluminescent detection reagents (SuperSignal West Femto Chemiluminescent Substrate, 34096, Thermo Scientific) were applied as recommended. The signal produced at each capture spot corresponded to the amount of phosphorylated protein bound.
Statistical analysis was performed using GraphPad prism software. For the comparison of more than two groups a one-way-ANOVA with Tukey's post-test was applied after using D'Agostino-Pearson's normality test where applicable. P-values <0.05 were considered as significant (*), <0.01 (**), <0.001 (***), and <0.0001 (****). P-values >0.05 were considered non-significant (ns). Error bars were calculated from means ±SD. The qPCR data are shown as means ±SEM, where one replicate is shown as a representative.
Akt (P31749), b-Raf (P15056), CD34 (P28906), CDC2L1 (A4VCI5), c-Jun (P05412), COX-1 (P23219), COX-2 (P35354), CSNK1A1L (Q8N752), CSNK1D (P48730), CSNK1E (P49674), ERK1 (P27361), IFN-α (P01562), IL-1β (P01584), IL-2 (P60568), IL-6 (P05231), JNK1 (P45983), JNK3 (P53779), KSHV K15 (Q9QR69), KSHV K8.1 (D2XQF0), KSHV KbZIP (E5LBX3), KSHV LANA (J9QT20), KSHV ORF45 (F5HDE4), KSHV RTA (F5HCV3), KSHV v-Cyclin (Q77Q36), KSHV v-FLIP (Q76RF1), KSHV vGPCR (Q98146), KSHV vIL-6 (Q98823), MAP4K4 (O95819), MEK1 (Q02750), MINK (Q8N4C8), MK2 (P49137), MMP-1 (P03956), MMP-13 (P45452), MMP-19 (Q99542), MMP-2 (P08253), MMP-3 (P08254), MMP-7 (P09237), MMP-9 (P14780), NF-κB (Q04206), Notch (P46531), p38α (Q16539), PI3K (P42336), Pim-1 (P11309), Pim-3 (Q86V86), PKA (P17612), PKCδ (Q05655), RBP-Jκ (Q06330), STK36 (Q9NRP7), TLR7/8 (D1CS68), TNF-α (P01375), TNFR α (P19438), TNIK (Q9UKE5).
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10.1371/journal.pgen.1003462 | Npc1 Acting in Neurons and Glia Is Essential for the Formation and Maintenance of CNS Myelin | Cholesterol availability is rate-limiting for myelination, and prior studies have established the importance of cholesterol synthesis by oligodendrocytes for normal CNS myelination. However, the contribution of cholesterol uptake through the endocytic pathway has not been fully explored. To address this question, we used mice with a conditional null allele of the Npc1 gene, which encodes a transmembrane protein critical for mobilizing cholesterol from the endolysosomal system. Loss of function mutations in the human NPC1 gene cause Niemann-Pick type C disease, a childhood-onset neurodegenerative disorder in which intracellular lipid accumulation, abnormally swollen axons, and neuron loss underlie the occurrence of early death. Both NPC patients and Npc1 null mice exhibit myelin defects indicative of dysmyelination, although the mechanisms underlying this defect are incompletely understood. Here we use temporal and cell-type-specific gene deletion in order to define effects on CNS myelination. Our results unexpectedly show that deletion of Npc1 in neurons alone leads to an arrest of oligodendrocyte maturation and to subsequent failure of myelin formation. This defect is associated with decreased activation of Fyn kinase, an integrator of axon-glial signals that normally promotes myelination. Furthermore, we show that deletion of Npc1 in oligodendrocytes results in delayed myelination at early postnatal days. Aged, oligodendocyte-specific null mutants also exhibit late stage loss of myelin proteins, followed by secondary Purkinje neuron degeneration. These data demonstrate that lipid uptake and intracellular transport by neurons and oligodendrocytes through an Npc1-dependent pathway is required for both the formation and maintenance of CNS myelin.
| The myelin sheath in the central nervous system is a specialized extension of the oligodendrocyte plasma membrane that serves as an electrical insulator to ensure proper nerve conduction. To accomplish this, myelin is enriched in lipids, particularly unesterified cholesterol, which is an essential and limiting component for myelin formation. Here we determine the contribution of exogenously derived cholesterol to myelination by using a conditional null mutant of the mouse Npc1 gene. Npc1 encodes a transmembrane protein critical for mobilizing exogenously derived cholesterol from late endosomes and lysosomes, and is mutated in patients with Niemann-Pick type C disease, a degenerative disorder caused by impaired intracellular lipid trafficking. We show that mice lacking Npc1 in either neurons or oligodendrocytes exhibit a defect in myelin formation in selected brain regions, with an arrest in oligodendrocyte maturation. In addition, mice with Npc1 deficiency in oligodendrocytes, when aged, show progressive motor dysfunction with myelin breakdown and secondary Purkinje neuron loss. Taken together, our findings demonstrate the role of Npc1 in mediating reciprocal signaling between neurons and glia, and highlight the importance of exogenous cholesterol for CNS myelin formation and maintenance.
| Ensheathment of axons by myelin is an evolutionary feature of the vertebrate nervous system that is accomplished by the extended and specialized plasma membranes of oligodendrocytes in the CNS and Schwann cells in the PNS. Myelin contains at least 70% lipids by dry weight [1], and this high ratio of lipid to protein ensures the insulating properties of myelin to maximize the efficiency of nerve conduction. Among all the lipid species found in the myelin sheath, unesterified cholesterol is a major component [1]. In the mouse CNS, cholesterol in compact myelin represents ∼78% of the total lipid pool [2], and the availability of cholesterol is the rate-limiting step for myelination [3]. Since the CNS is shielded by the blood brain barrier, cholesterol required for myelination comes entirely from local synthesis [2]. Both neurons and glia obtain the cholesterol they need either through endogenous synthesis or by uptake of lipoprotein particles produced and released within the CNS. That endogenously synthesized cholesterol is critical for CNS myelination in mice is demonstrated by deletion in oligodendrocytes of squalene synthase, the first dedicated enzyme of sterol synthesis [3]. These mutant mice exhibit delayed myelination, suggesting that exogenously supplied cholesterol also contributes to CNS myelin formation. However, whether cholesterol from exogenous sources is required for myelin synthesis, or just a compensatory source when endogenous synthesis is lacking in myelinating glia, has not been explored.
An essential component of the pathway through which cholesterol in lipoprotein particles is mobilized from the endolysosomal system is the Npc1 protein [4], [5]. This multipass transmembrane protein resides in late endosomes and lysosomes [6]–[9], and functions cooperatively with the Npc2 protein to facilitate cholesterol efflux [10], [11]. Loss of functional Npc1 disrupts intracellular lipid trafficking, and leads to the sequestration of unesterified cholesterol and glycosphingolipids in late endosomes and lysosomes [12]. Mutations in the human NPC1 gene cause Niemann-Pick type C disease (NPC), a fatal childhood-onset neurodegenerative disorder [13]. Mice with a null mutation in the Npc1 gene (Npc1−/−) recapitulate the human disease, and exhibit progressive CNS neuropathology in which intracellular lipid accumulation, abnormally swollen axons, neuron loss and gliosis underlie the occurrence of ataxia and early death [5], [14]. Notably, both NPC patients and Npc1−/− mice exhibit myelin defects indicative of dysmyelination, particularly in the forebrain [15]–[19]. However, the complex pathology resulting from Npc1 deficiency in both neurons and oligodendrocytes has limited the utility of these global null mutants to provide a detailed understanding of the contribution of exogenous cholesterol to CNS myelination.
Here we use mice with a conditional null allele of the Npc1 gene to achieve temporal and cell type specific deletion in order to define effects on CNS myelin. We show that deletion of Npc1 restricted to neurons unexpectedly recapitulates the dysmyelination phenotype of global null mutants. This effect is mediated by a block in maturation of oligodendrocyte lineage cells that is associated with decreased activation of Fyn kinase, an integrator of axon-glial signals that normally promote myelination. Furthermore, we show that deletion of Npc1 in oligodendrocytes triggers a similar, though less severe impairment of CNS myelination, as well as myelin protein loss and secondary neurodegeneration. Our analyses suggest that exogenous cholesterol entering cells and trafficking through an Npc1-dependent pathway is necessary for both the formation and maintenance of CNS myelin.
To confirm the requirement of Npc1 for proper myelination in mice during early postnatal stages, we utilized mice with a floxed Npc1 allele (Npc1flox) [20]. Cre-mediated deletion yields a null allele that is functionally indistinguishable from the spontaneous null mutation found in Npc1nih mice (Npc1−/−) [5], [20]. To generate mice with Npc1 deletion in the germline, Npc1flox/flox mice were bred with transgenic mice expressing Cre recombinase under the control of the EIIa promoter [21]. Mice mosaic for the conditionally deleted allele were bred with mice carrying the Npc1− allele to generate compound heterozygotes of the conditionally deleted and null Npc1 alleles (Npc1Δ/−). We also generated mice with Npc1 deletion in adults by using a tamoxifen-regulated Cre recombinase under the control of the cytomegalovirus (CMV) promoter (Cre-ERTM+) [22]. Cre-mediated deletion of Npc1 in adults was induced by tamoxifen injections at 6 weeks, an age at which myelination is complete. Mice with adult deletion (Npc1flox/−, Cre-ERTM+) have been shown to recapitulate most features of NPC neuropathology, and reach end-stage by ∼22 weeks [23]. To determine the effect of the timing of Npc1 deletion upon myelination, we compared 7-week-old mice with germline deletion (Npc1Δ/−), 22-week-old mice with adult deletion (Npc1flox/−, Cre-ERTM+) and age matched controls. Myelin basic protein (MBP, a standard marker for mature myelin [1]) and FluoroMyelin (a lipophilic stain for compact myelin) staining of sagittal midline brain sections revealed a dramatic reduction of myelin proteins and lipids in Npc1Δ/− mice, particularly in the forebrain (Figure 1A, 1B). This striking pattern of regionally selective myelin defects is similar to that previously reported in Npc1−/− mice [14], [15], [17]. In contrast, Npc1flox/−, Cre-ERTM+ mice exhibited a staining pattern morphologically similar to that in controls (Figure 1A, 1B). The difference in MBP staining patterns between Npc1Δ/− mice and Npc1flox/−, Cre-ERTM+ mice suggests that Npc1 is required in early postnatal stages for proper myelin formation. Further analysis of myelin-specific proteins demonstrated a decrease in MBP and CNP protein levels in Npc1flox/−, Cre-ERTM+ mice compared to littermate controls, particularly in the cortex (Figure 1C, 1D). We conclude that myelin was properly formed in Npc1flox/−, Cre-ERTM+ mice during postnatal development, but that these mice exhibit loss of myelin proteins at later stages, particularly in the cerebral cortex, after Npc1 deletion at 6 weeks. Axonal loss could contribute to the late stage pathology in Npc1flox/−, Cre-ERTM+ mice, as evidenced by decreased neurofilament levels in these aged mutants (Figure 1C). Taken together, our analysis suggests that lack of myelin in NPC mice is caused by dysmyelination at early postnatal days, followed by loss of myelin proteins at end stage.
We next sought to dissect the contribution of different CNS cell types to NPC dysmyelination. We started by deleting Npc1 specifically in neurons, using transgenic mice expressing Cre recombinase under the control of the Synapsin1 promoter (Syn1-Cre) [24]. We confirmed gene deletion by staining brain sections with filipin, a fluorescent dye that specifically marks accumulation of unesterified cholesterol [25]. NeuN and filipin co-staining verified that Npc1flox/−, Syn1-Cre+ mice, but not Npc1flox/+, Syn1-Cre+ controls [23], developed filipin-positive neurons throughout the brain, including brainstem and cortex (Figure S1A). A subset of neurons remained filipin negative, possibly reflecting mosaic gene deletion. To further verify neuron-specific gene deletion, Syn1-Cre+ mice were crossed to a Rosa reporter line that has been widely used to demonstrate gene deletion in both neurons and oligodendrocytes [26]. LacZ staining revealed widespread positive cells in many brain regions including the cortex, with minimal staining in the corpus callosum, where neuronal cell bodies are lacking (Figure S1B). Co-staining with NeuN or Olig2 showed that these LacZ positive cells were neurons, and not oligodendrocyte lineage cells (Figure S1C), further supporting the notion that we achieved neuron-specific deletion by using Syn1-Cre+ mice.
The effect of Npc1 deficiency in neurons upon myelination was first evaluated by MBP immunofluorescence at 3 different ages. At postnatal day 16 (P16), myelination was actively occurring in the forebrain of Npc1flox/+, Syn1-Cre+ controls, with abundant MBP-positive myelinating oligodendrocytes populating the cortex (Figure 2B). In contrast, Npc1flox/−, Syn1-Cre+ mutants exhibited a severe paucity of myelin in the same region, with most of the MBP positive cells exhibiting the morphology of pre-myelinating oligodendrocytes (Figure 2B). At 7 weeks, myelination was completed in Npc1flox/+, Syn1-Cre+ controls, but was greatly attenuated in the cortex of Npc1flox/−, Syn1-Cre+ mutants. No recovery of myelination was observed in mutants aged to 16 weeks (Figure 2B), which is end stage for these mice [23]. Similarly, FluoroMyelin staining revealed a paucity of compact myelin in the corpus callosum of Npc1flox/−, Syn1-Cre+ mutants at 16 weeks (Figure 2B, bottom panel). Although MBP staining was markedly decreased in the cortex of Npc1flox/−, Syn1-Cre+ mutants, other brain regions exhibited a normal staining pattern, reminiscent of the selective defects in myelination observed after global germline deletion (Figure 1A). Regional-specific dysmyelination was further supported by western blots showing decreased levels of myelin-specific proteins including CNP, MBP and MAG in cortex, but not brainstem of Npc1flox/−, Syn1-Cre+ mutants (Figure 2C). Electron microscopy confirmed that the density of myelinated nerve fibers in the corpus callosum was greatly reduced in Npc1flox/−, Syn1-Cre+ mutants at 3 weeks (Figure 2E). Notably, neurofilament protein levels in the cortex were similar between Npc1flox/+, Syn1-Cre+ controls and Npc1flox/−, Syn1-Cre+ mutants at P16 (Figure 2C), and neurofilament immunofluorescence staining showed no significant axonal pathology (Figure 2D). These data indicate that dysmyelination in the forebrain of Npc1flox/−, Syn1-Cre+ mutants was not secondary to axonal loss.
To characterize the mechanism underlying dysmyelination in Npc1flox/−, Syn1-Cre+ mutants, we assessed oligodendrocyte lineage cells at different stages of differentiation. At P16, Npc1flox/−, Syn1-Cre+ mutants showed a significantly reduced number of CC1-positive mature oligodendrocytes in the forebrain (Figure 3A, 3C) but a normal density of NG2-positive oligodendrocyte precursor cells (OPCs) (Figure 3A, 3B). As previously reported for global null Npc1 mutants [17], this deficit of mature oligodendrocytes was not associated with evidence of increased apoptosis (data not shown). The paucity of mature oligodendrocytes was associated with a reduced number of cells in the corpus callosum expressing Sip1, a signaling protein implicated oligodendrocyte differentiation (Figure 3C) [27]. These data indicated that Npc1 deficiency in neurons triggered a block of oligodendrocyte maturation, and prompted us to determine whether signals known to regulate oligodendrocyte maturation and myelination were perturbed in Npc1flox/−, Syn1-Cre+ mutants. We first examined proteins that mediate signaling between axons and oligodendrocyte lineage cells including PSA-NCAM [28], Lingo1 [29] and Jagged1 [30], and found no differences between Npc1flox/−, Syn1-Cre+ mutants and controls at P16 (Figure S2A). Similarly, we found no evidence of astrocyte activation in the corpus callosum of Npc1flox/−, Syn1-Cre+ mutants at P16 (Figure S2B, S2C), consistent with prior studies showing that astrogliosis is limited to the thalamus of Npc1−/− mice at two weeks [31]. In contrast, activity of the non-receptor tyrosine kinase Fyn [32] was reduced in the cortex of Npc1flox/−, Syn1-Cre+ mutants, as evidenced by decreased levels of the active form (phosphorylated at tyrosine 420) and concurrently increased levels of the inactive form (phosphorylated at tyrosine 531) (Figure 3E). As oligodendroglial Fyn is an integrator of axonal signals that promote myelination [33], the decreased activity of Fyn in Npc1flox/−, Syn1-Cre+ mutants suggests that Npc1 deficiency in axons leads to a disruption of axon-glial signaling that is crucial for oligodendrocyte differentiation and myelination.
Next, we tested if Npc1 deficiency in oligodendrocyte lineage cells contributes to the pathogenesis of dysmyelination in NPC mice. To accomplish this, we used transgenic mice expressing Cre recombinase under the control of the CNP promoter (CNP Cre/+) [34]. In these mice, Cre is abundantly and specifically expressed in postmitotic oligodendrocytes. Co-staining for Cre and Olig2, a marker of both OPCs and postmitotic oligodendrocytes, verified that Cre was specifically expressed in a subset of Olig2+ oligodendrocyte lineage cells in various brain regions including brainstem and cortex (Figure S3B). Filipin staining revealed minimal accumulation of unesterified cholesterol in Npc1flox/−, CNPCre/+ mutants (Figure S3A), a finding both consistent with a previous report showing no detectable cholesterol accumulation in oligodendrocytes of Npc1−/− mice [35] and indicative of the cell lineage specificity of this Cre line.
Deletion of Npc1 in oligodendrocytes resulted in a dysmyelination phenotype that was initially similar to that caused by Npc1 deletion in neurons. At P16, Npc1flox/−, CNPCre/+ mutants expressed markedly reduced levels of myelin-specific proteins including MBP, CNP and MAG in the cortex (Figure 4A, 4B). Similarly, compact myelin levels by FluoroMyelin staining were decreased in Npc1flox/−, CNPCre/+ mutants (Figure 4A). This dysmyelination phenotype partially recovered by 7 weeks (Figure 4A), a finding that indicates oligodendrocyte deletion delayed myelination and contrasts with the block produced by neuronal deletion. Myelination in the brainstem of Npc1flox/−, CNPCre/+ mutants was minimally affected (Figure 4B) despite robust Cre expression in this region (Figure S3B, S3C). Electron microscopy confirmed diminished density of myelinated nerve fibers in the corpus callosum of Npc1flox/−, CNPCre/+ mutants at 3 weeks (Figure 4D). Similar to neuron-specific mutants, dysmyelination in Npc1flox/−, CNPCre/+ mutants occurred without significant axonal pathology (Figure 4B, 4C). The requirement of Npc1 in oligodendrocytes for proper myelination was further confirmed by using an independent line in which Cre was highly expressed in OPCs (Olig2Cre/+ mice, Figure S4) [36]. Similar to Npc1flox/−, Syn1-Cre+ mutants, Npc1flox/−, CNPCre/+ mutants at P16 showed reduced density of mature oligodendrocytes (Figure 5A, 5C), with normal numbers of OPCs in the forebrain (Figure 5A, 5B), indicating arrest of oligodendrocyte maturation.
As the Npc1flox/−, CNPCre/+ mutants aged, they developed progressive motor deficits (Figure 6C), although weight was not affected (Figure 6A, 6B). This led us to examine myelin protein levels in 23-week-old Npc1flox/−, CNPCre/+ mutants. We found decreased levels of myelin proteins not only in cortex, but also in brainstem and cerebellum (Figure 7A), where myelination in early postnatal days was nearly normal (Figure 4B). This suggested that myelin loss was taking place in several brain regions of the aged Npc1flox/−, CNPCre/+ mutants. We found this was associated with only mild changes in the pattern of MBP staining (Figure 7B). Interestingly, the total number of Olig2+ oligodendrocyte lineage cells in the cerebellar white matter was unchanged in aged mutants (Figure 7C, 7D), suggesting that loss of Npc1 did not affect the survival of oligodendrocytes in adult mice. This loss of myelin proteins was associated with secondary neuron loss in the cerebellum. We detected Purkinje cell loss in anterior lobules of 23-week-old but not 7-week-old Npc1flox/−, CNPCre/+ mutants, as demonstrated by calbindin staining of sagittal midline sections (Figure 7E, 7G) and by loss of calbindin staining on western blot (Figure 7F). Importantly, no filipin-positive Purkinje neurons were identified in these mice (not shown), supporting the conclusion that Purkinje cell loss was a consequence of non-cell autonomous toxicity. We conclude that Npc1 acts in oligodendrocytes both to promote normal myelination and to ensure the maintenance of myelin in the adult CNS.
Here we used Npc1 conditional null mice to establish the critical role of Npc1 in both neurons and oligodendrocytes for proper CNS myelination. Our findings demonstrate that deletion of Npc1 in neurons alone is sufficient to recapitulate the dysmyelination phenotype that occurs following global germline deletion. These mice display a severe phenotype, particularly in the forebrain, characterized by a lack of mature oligodendrocytes but a normal density of OPCs, indicating that Npc1 deficiency in neurons triggers an arrest of oligodendrocyte maturation. Our data also demonstrate that deletion of Npc1 in oligodendrocytes leads to similar but milder forebrain dysmyelination that largely recovers by 7 weeks, consistent with a delay rather than a block in myelination. Furthermore, we demonstrate that these oligodendrocyte-specific mutants develop ataxia as they age, and that this is associated with decreased myelin proteins and Purkinje cell loss in anterior cerebellar lobules, establishing the occurrence of secondary neurodegeneration. Our results highlight the importance of Npc1 in both neurons and oligodendrocytes for the formation and maintenance of CNS myelin.
Significant effort has been devoted to defining the contribution of specific cell types to NPC neuropathology. Studies in chimeric mice, a conditional knock-out model, and several neuron-specific transgenic rescue experiments all demonstrate that neuronal loss can be a consequence of cell autonomous neurotoxicity [20], [23], [37]–[39]. Furthermore, these analyses indicate that brain inflammation is a consequence rather than a driver of neuron loss [20], [23], [38], [40]. The role of astroglial cells in NPC neuropathology has been more controversial. While in vitro data suggest that Npc1 deficient astrocytes fail to fully support cultured neurons [41], both conditional knockout and transgenic rescue experiments failed to establish a significant role for astrocytes in pathogenesis [23], [38]. A transgenic line that highly over-expresses Npc1 from the GFAP promoter does show some rescue [42], but the extent of cell type restricted expression in these mice remains incompletely defined. The effects of Npc1 deficiency restricted to oligodendrocytes had not been previously explored. As for effects on CNS myelin, prior transgene rescue experiments using the NSE promoter to drive Npc1 expression demonstrated partial rescue of myelination [39]. These findings are consistent with our observation that neuronal expression of Npc1 plays an important role in oligodendrocyte maturation and myelination. Finally, we note that aged, oligodendrocyte-specific null mutants show evidence of neuron loss. While prior studies firmly establish that neuronal deficiency of Npc1 is sufficient to mediate neurotoxicity [20], [23], the data reported here indicate that non-cell autonomous pathways arising from oligodendrocytes also contribute to neuropathology.
Oligodendrocyte differentiation and myelination are highly dynamic processes controlled by both intrinsic factors and extrinsic mechanisms [43]. Recent studies of axon-glial communication have identified a series of axonal signals important for regulating myelination. Oligodendroglial Fyn, a Src family kinase, has been suggested to play a central role in integrating diverse axonal signals to initiate myelination [33]. Downstream signaling from activated Fyn kinase promotes oligodendrocyte survival, alters cytoskeleton polarity and increases the expression of myelin genes. Our analysis of neuron-specific Npc1 mutants reveals decreased Fyn activity and a regionally-restricted dysmyelination phenotype similar to that of Fyn knockout mice [44]. We suggest that Npc1 deficiency in neurons disrupts an axon-glial signal vital for promoting myelination. The axonal ligand responsible for oligodendroglial Fyn activation remains elusive. The requirement of Npc1 for Fyn activation raises the possibility that a lipid species, such as cholesterol or a sphingolipid, may contribute to this signal. Additionally, recent neuron-glial co-culture studies demonstrate the role of action potentials in stimulating myelination through Fyn-dependent mechanisms [45]. It is therefore also possible that defective Fyn activation results from decreased electrical activity of axons in Npc1flox/−, Syn1-Cre+ mutants. Recently, a similar role in myelination has been demonstrated for neuron-restriction expression of the PI(3,5)P(2) phosphatase Fig4 [46], suggesting that defects in axon-glial signaling may underlie dsymyelination in several disorders.
Animal studies of cholesterol metabolism in myelinating glia have highlighted the importance of cell-autonomous production of cholesterol for myelin formation. Mice lacking oligodendroglial squalene synthase, an enzyme required for cholesterol synthesis, exhibit perturbed CNS myelination in early postnatal days [3]. Similarly, deletion of SCAP (SREBP-cleavage-activating protein) in Schwann cells, a protein that complexes with SREBP to regulate the expression of genes promoting cholesterol synthesis and lipoprotein uptake, leads to PNS hypomyelination [47]. It is notable that both mouse models partially recover at later stages, suggesting that myelinating glia have the capacity to overcome the lack of endogenous cholesterol production, probably through increased uptake. Here we present in vivo evidence indicating an important contribution of exogenous cholesterol to myelin synthesis. Our findings show that deletion of Npc1 in oligodendrocytes, which eliminates their utilization of cholesterol from the endocytosis of LDL or similar lipoprotein particles, leads to perturbed myelin formation in the CNS. Npc1 deficiency also impairs intracellular trafficking of sphingolipids [48] and endogenously synthesized cholesterol [49]. Nonetheless, the blockade of exogenous cholesterol utilization and the essential role that cholesterol plays in myelination leads us to favor the conclusion that the effects observed here are due to a disruption in the availability of exogenous cholesterol. As shown for other cell types [12], we speculate that the synthesis of endogenous cholesterol may be up-regulated in Npc1 deficient oligodendrocytes yet insufficient to overcome the lack of exogenous cholesterol, especially during the peak phase of myelination. This suggests that extracellularly-derived cholesterol is indispensible for normal CNS myelination.
Although Npc1flox/−, CNPCre/+ mutants form myelin in the brainstem and cerebellum during postnatal development, these regions exhibit loss of myelin proteins in adults. Biochemical studies have shown that in the adult CNS, myelin production and cholesterol turnover decrease to very low levels [2]. It is therefore unlikely that the loss of myelin proteins in these adult mutants results from impaired access to exogenous cholesterol as a consequence of Npc1 deficiency. Rather, we speculate that late-stage pathology stems from the unstable nature of the myelin sheath produced by mutant oligodendrocytes. Studies of cellular models of NPC have shown that cholesterol content is decreased in the plasma membrane of mutant cells [50], [51]. This change may impact myelin by disrupting membrane fluidity, altering lipid rafts or modulating the function of membrane proteins, and thereby increase the vulnerability of aged mutants. Further analysis of the biochemical composition of the myelin sheath generated by Npc1-deficient oligodendrocytes will help define the mechanism mediating late-onset loss of myelin proteins. Axonal degeneration and neuron loss in these mutants highlights the important role of oligodendrocytes in supporting neuron function and survival. Similar observations have been made in mice over-expressing alpha-synuclein in oligodendrocytes [52]. While this effect may be mediated in part through loss of myelin, other studies have shown that oligodendroglia support axons through metabolic pathways independent of myelination [53]. It is currently unclear which of these mechanisms accounts for Purkinje neuron loss in Npc1flox/−, CNPCre/+ mutants.
In summary, the data reported here extend our understanding of the role of cholesterol metabolism in myelination, and demonstrate that exogenous cholesterol is needed by both neurons and oligodendrocytes for the formation and maintenance of CNS myelin. A characteristic feature of Npc1 deficient mice, both global nulls and cell-specific knockouts, is the regionally severe dysmyelination that occurs during early postnatal stages. Fate-mapping studies have established that OPCs originate from heterogeneous regions of the subventricular zone, under the influence of different signaling pathways [54]. We speculate that these regional differences in oligodendrocyte lineage cells lead to distinct responses to axonal signals or to the need for exogenously-derived cholesterol for proper myelination, contributing to severe dysmyelination particularly in the forebrain of Npc1 mutants. While the precise mechanism underlying this regional selectivity remains to be defined, our data establish a critical role for Npc1 in both myelin formation and maintenance. Our findings have important implications for understanding the pathogenesis of NPC disease and may also inform our knowledge of other dysmyelinating/demyelinating disorders.
Animal use and procedures were approved by the University of Michigan Committee on the Use and Care of Animals.
Npc1flox/flox and Npc1Δ/− mice were generated as previously described [20]. Other mice used include tamoxifen-inducible CMV-Cre (Cre-ERTM+) (#004682, Jackson Laboratories), Sny1-Cre (#003966, Jackson Laboratories), CNPCre/+ mice [34], Olig2Cre/+ mice [36] and Rosa reporter mice (#003474, Jackson Laboratories). All mouse strains were maintained on the C57BL6/J background, except Olig2Cre/+ mice which were maintained on the 129/C3H mixed background.
Tamoxifen (Sigma) was dissolved in corn oil (Sigma) at 20 mg/ml and stored at −20C in the dark. The stock solution was warmed to 37C before injection. 6-week-old mice were injected intraperitoneally with 3 mg tamoxifen per 40 g body weight for 5 consecutive days.
Motor function was measured using the balance beam test as described previously [20].
Brain lysates were homogenized in RIPA buffer (Thermo Scientific) containing Complete protease inhibitor cocktail (Roche) and phosphatase inhibitors (Thermo scientific) using a motor homogenizer (TH115, OMNI International). Samples were resolved by 4–20% Tris-glycine gradient gel and transferred to nitrocellulose membranes (BioRad) on a semidry transfer apparatus. Immunoreactivity was detected by Immobilon chemilluminescent substrate (Thermo Scientific). Antibodies used were rat anti-MBP (1∶2000, Abcam), mouse anti-CNP (1∶2000, Millipore), mouse anti-MAG (1∶5000, Millipore), mouse anti-Neurofilament 200 (1∶5000, Millipore), rabbit anti-NG2 (1∶1000, Millipore), rabbit anti-GAPDH (1∶5000, Santa Cruz), mouse anti-Cre (1∶1000, Millipore), rabbit anti-GFAP (1∶5000, Dako), mouse anti-PSA-NCAM (1∶1000, Millipore), goat anti-Jagged1(1∶1000, Santa Cruz) and rabbit anti-Lingo1 (1∶1000, Abcam).
200 µg brain lysates were immunoprecipitated with 10 µg anti-Fyn antibody (FYN3, Santa Cruz) overnight at 4C, followed by incubation with 20 µl Protein A beads (Santa Cruz) for 1 h at 4C. The immunoprecipitates were then washed 4 times with protein lysis buffer before being boiled with 2× sample buffer at 100C for 5 min. For the subsequent western blot analysis, anti-Fyn (FYN3, Santa Cruz), Src pY418 and pY529 antibodies (Life technologies) were used to detect total Fyn and phosphorylation of Fyn at Y420 and Y531, respectively.
Mice were perfused with 0.9% normal saline followed by 4% paraformaldehyde. Brains were removed and post-fixed in 4% paraformaldehyde overnight. Brains were bisected, with the right hemisphere processed for paraffin embedding and the left hemisphere processed for frozen sections. Prior to freezing, brain tissue was cryoprotected in 30% sucrose for 48 hr at 4C. Brains were frozen in isopentane chilled by dry ice and embedded in OCT (Tissue-Tek). Frozen sections were prepared at 14 µm in a cryostat and used for LacZ staining and subsequent eosin counter staining or immunohistochemical staining for Olig2 (1∶500, Millipore) and NeuN (1∶500, Millipore). For filipin staining, frozen sections were first used for immunofluorescence staining for NeuN or Olig2, followed by incubation for 90 min in PBS with 10% fetal bovine serum plus 25 µg/ml filipin (Sigma). For FluoroMyelin staining, frozen sections were rehyrated in PBS for 20 min, incubated with FluoroMyelin solution (1∶300, Life Technologies) at room temperature for 2 hours, and then cleared with four 30-minute washes with PBS. Paraffin-embedded sections were prepared at 5 µm and used for staining with H&E staining or MBP (1∶100, Abcam), SMI-31P (1∶200, Covance), NG2 (1∶100, Millipore), CC1 (1∶200, Calbiochem), Calbindin (1∶1000, Sigma), Sip1 (1∶100, Santa Cruz) and GFAP (1∶1000, Dako) immunofluorescence. For visualization of staining, secondary antibodies conjugated to Alexa Fluor 594 or Alexa Fluor 488 (Molecular Probes) were used and images were captured on a Zeiss Axioplan 2 imaging system. For NG2 and CC1 co-staining and Olig2 staining, images were captured on an Olympus FluoView 500 Confocal Microscope system. Quantification of CC1+ or Olig2+ cells was performed using NIH ImageJ software. Quantification of Purkinje cell loss was performed on H&E stained sections. Counts were normalized to the length of the Purkinje layer, as measured by NIH ImageJ software, and reported as Purkinje cell density.
Mice were perfused with 0.9% normal saline followed by 3% paraformaldehyde and 2.5% glutaraldehyde in 0.1 M Sorensen's buffer. The corpus callosum was removed and post-fixed in perfusion solution overnight, followed by fixation in 1% osmium tetroxide solution for 1 h at room temperature. After dehydration, tissues were embedded in epoxy resin. For transmission electron microscopy, ultrathin sections were cut, and images were captured on a Philips CM-100 imaging system at 10,500× magnification.
Statistical significance was assessed by unpaired Student's t test. Statistics were performed using the software package Prism 5 (GraphPad Software). P values less than 0.05 were considered significant.
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10.1371/journal.pntd.0005505 | Rapid Surveillance for Vector Presence (RSVP): Development of a novel system for detecting Aedes aegypti and Aedes albopictus | The globally important Zika, dengue and chikungunya viruses are primarily transmitted by the invasive mosquitoes, Aedes aegypti and Aedes albopictus. In Australia, there is an increasing risk that these species may invade highly urbanized regions and trigger outbreaks. We describe the development of a Rapid Surveillance for Vector Presence (RSVP) system to expedite presence- absence surveys for both species.
We developed a methodology that uses molecular assays to efficiently screen pooled ovitrap (egg trap) samples for traces of target species ribosomal RNA. Firstly, specific real-time reverse transcription-polymerase chain reaction (RT-PCR) assays were developed which detect a single Ae. aegypti or Ae. albopictus first instar larva in samples containing 4,999 and 999 non-target mosquitoes, respectively. ImageJ software was evaluated as an automated egg counting tool using ovitrap collections obtained from Brisbane, Australia. Qualitative assessment of ovistrips was required prior to automation because ImageJ did not differentiate between Aedes eggs and other objects or contaminants on 44.5% of ovistrips assessed, thus compromising the accuracy of egg counts. As a proof of concept, the RSVP was evaluated in Brisbane, Rockhampton and Goomeri, locations where Ae. aegypti is considered absent, present, and at the margin of its range, respectively. In Brisbane, Ae. aegypti was not detected in 25 pools formed from 477 ovitraps, comprising ≈ 54,300 eggs. In Rockhampton, Ae. aegypti was detected in 4/6 pools derived from 45 ovitraps, comprising ≈ 1,700 eggs. In Goomeri, Ae. aegypti was detected in 5/8 pools derived from 62 ovitraps, comprising ≈ 4,200 eggs.
RSVP can rapidly detect nucleic acids from low numbers of target species within large samples of endemic species aggregated from multiple ovitraps. This screening capability facilitates deployment of ovitrap configurations of varying spatial scales, from a single residential block to entire suburbs or towns. RSVP is a powerful tool for surveillance of invasive Aedes spp., validation of species eradication and quality assurance for vector control operations implemented during disease outbreaks.
| Aedes (Stegomyia) vectors of dengue, Zika and chikungunya viruses utilize artificial and natural containers as larval habitats. Adults do not usually disperse far (< 500 m) from these larval habitats in urban and peri-urban environments. Highly heterogeneous distributions raise significant logistic challenges to conduct informative surveillance. Public health imperatives require contemporaneous vector mosquito presence-absence data for highly urbanized regions that are both vulnerable to invasions and have frequent exposure to viremic travellers. We developed a promising tool to expedite presence-absence surveillance of Aedes aegypti and Aedes albopictus by integrating molecular diagnostics with ovitraps and automated egg quantification software. The high sensitivity of the molecular assays enabled samples from multiple ovitraps to be pooled and processed for each diagnostic test. This innovation resolves the considerable logistic constraints inherent in traditional ovitrap surveillance programs. Proof of concept was evaluated in field trials in Queensland geographies where Ae. aegypti is considered either absent, present or at the margin of its range (Brisbane, Rockhampton and Goomeri, respectively). Aedes aegypti was detected in Goomeri and Rockhampton and not detected in Brisbane. Further investigation is required to address the inaccuracy of automated egg counting software whenever contaminants are present. RSVP can accommodate varied ovitrap designs and deployment configurations, improves efficiency in laboratory and labor costs for high volumes of samples, and enables a rapid turnaround of results. The RSVP system can innovate surveillance programs for early-warning of invasion, eradication, and quality assurance for vector control in disease response contexts.
| Aedes aegypti and Aedes albopictus are invasive mosquito species and global vectors of Zika (ZIKV) [1], dengue (DENVs) [2] and chikungunya (CHIKV) [3] viruses. Both species can coexist in the same ecological niche [4, 5] and share characteristics that are likely to make their detection difficult in the early stages of stochastic invasions, including heterogeneous distributions [6–8], limited dispersal capability of adults [9–13], and often low population densities [14–16]. Ovipositing females cement eggs inside a wide variety of water-bearing containers and these eggs can resist desiccation for several months [17, 18]. Transport to new regions or countries can occur via the shipment of immature stages in freight, such as used tires [19–21] and lucky bamboo [22, 23], or as adults sequestered in aircraft [24].
In Australia, quarantine authorities intercept both species at international seaports and airports [16, 25], with the frequency of detections increasing dramatically since 2012. However, there is a concurrent threat of range expansion from endemic Queensland populations. Aedes aegypti occurs throughout most of Queensland, although dengue outbreaks only occur in north Queensland [26–29]. Aedes albopictus has not colonized the Australian mainland following the rapid invasion of island communities of the Torres Strait, north Queensland [16, 30], largely due to suppression programs at transport hubs [31].
Importantly, both species are considered to be absent from southeast (SE) Queensland (population 3.4 million), where ≈ 70% of the Queensland population reside. This status is not based on systematic entomological baseline monitoring, but rather on the lack of local disease transmission following the importation of ZIKV, DENV and CHIKV in viremic travellers, coupled with negative results from ad hoc larval surveys and trapping of peridomestic mosquitoes. Southeast Queensland is considered to be receptive to invasion by both species. Indeed, Ae. aegypti was present in this region until the mid-1950s [29], whilst predictive models indicate the eastern seaboard of Australia could be colonized by Ae. albopictus [25, 32]. Receptivity is considered to be increasing [33, 34], partly due to the recent proliferation of water-storage containers, such as rainwater tanks [35]. In the future, ineffective mosquito-proofing of rainwater tanks or rainwater harvesting structures may increase the number of available larval habitats [36–38].
Contemporaneous baseline monitoring is essential to increase certainty that vector species are absent in geographies that are vulnerable to invasion [39] to minimize the risk of cryptic disease outbreaks. However, existing surveillance options for peridomestic mosquito species have operational weaknesses. Larval and pupal surveys are labor-intensive [40–42] and can be compromised by inaccessible premises or larval habitats, cryptic containers [43, 44], or timing a survey when mosquito abundance is low [16]. In terms of adult traps, some designs (e.g., Biogents BG-Sentinel trap) are highly sensitive [45] but require electricity and incur significant procurement, servicing, and maintenance costs [46, 47]. Novel adult traps (BG-Gravid Aedes Trap) are cheaper but less sensitive [48–50] and may be expensive to deploy in extensive arrays over a large spatial scale [6, 47, 51, 52]. Ovitraps provide a cheap and sensitive tool for presence-absence surveys of peridomestic species [17, 53, 54] but require an investment in time and laboratory resources to count eggs, rear and identify larvae [54, 55].
We report the development of a sensitive, easy-to-use, and cost-effective system, which we call Rapid Surveillance for Vector Presence (RSVP), to expedite presence-absence surveys of invasive Aedes species. The RSVP provides a powerful validation tool to confirm a species is absent at various spatial scales (property, suburban block, town or region), within an eradication program, and potentially as a quality assurance measure for vector control strategies implemented in response to ZIKV, DENV or CHIKV outbreaks.
Eggs were hatched overnight by submerging ovistrips in 200 mL of Milli-Q water in rectangular 750 mL polyethylene containers, to which ≈ 10 mg of brain-heart powder was added to stimulate hatching. Ovistrips were removed and larvae extracted from the water via a vacuum filtration protocol. Specifically, the membrane and support pad of a MicroFunnel 300 filter funnel (Pall Life Sciences, Ann Arbor, MI, USA) was replaced with an FTA card (GE Healthcare Life Sciences, PA, USA) trimmed to fit the base of the funnel. The filter funnel was then placed on top of a vacuum flask. The water containing the larvae was gently agitated before it was poured into the cylinder of the filter funnel and a vacuum applied. The filter funnel and hatching container were rinsed with Milli-Q water until there were no larvae visible. The cards were then removed from the base of the funnel and air-dried overnight at room temperature.
The protocol of Hall-Mendelin et al. [56] was used to prepare the FTA cards and dried larvae for nucleic acid extraction. Briefly, the cards were cut into 4–5 strips and placed in a 5 mL vial containing 1 mL of Milli-Q water. Vials were placed on wet ice and vortexed every 5 min for 15 sec for a total of 20 min. The cards were then placed in a 5 mL syringe from which the plunger had been removed. The plunger was used to squeeze the liquid from the cards into a 2 mL vial. Nucleic acids were extracted from 140 μL of the eluate using the Bio Robot Universal System (Qiagen, Hilden, Germany) and the QIAamp Virus BioRobot MDx Kit (Qiagen, Clifton Hill, Australia) according to the manufacturer’s instructions.
Real-time TaqMan reverse transcription-polymerase chain reaction (RT-PCR) assays were used to detect target Ae. aegypti and Ae. albopictus against a background of endemic species. The Ae. aegypti ribosomal RNA was detected using real-time TaqMan RT-PCR with forward GCAGTCAGATGTTTGATACCGC and reverse GGTTGACGTATTATCAGGTCACACTA primers at 500 nM, and probe FAM-TGGGCGCCTCGGTGTCCCG-TAMRA at 300 nM. The Ae. albopictus ribosomal RNA was detected using forward CCGACAAGGCAATATGTC and reverse ACGCGTACGGACATTG primers at 300 nM, and probe FAM-TTCCCTCCGATCAGCGAACTC-TAMRA at 200 nM. The cycling conditions consisted of one cycle at 50°C for 5 min, one cycle at 95°C for 2 min, and 40 cycles at 95°C for 3 sec and 60°C for 30 sec. A positive result, indicating the presence of target species RNA, corresponded to cycle threshold values of ≤ 40 cycles. Reaction controls always included a no-template and synthetically generated template samples. The specificity of the 2 TaqMan RT-PCR assays was tested using 4th instar larval samples of Ae. aegypti and Ae. albopictus, as well as other peridomestic species, including Aedes katherinensis, Aedes notoscriptus, Aedes palmarum, Aedes scutellaris, Aedes tremulus and Culex quinquefasciatus.
Pools of varying sizes were produced to test the sensitivity of the TaqMan RT-PCR assays in detecting Ae. aegypti and Ae. albopictus. The Ae. aegypti eggs used to optimize the sensitivity of the assay were obtained from the F2-4 generations of a colony originating from Townsville, Australia, while Fo Ae. notoscriptus were collected in ovitraps deployed in Brisbane, Australia. The Ae. albopictus were from a colony established from eggs collected from Hammond Island (Torres Strait, Australia) and had been in colony for < 10 generations. Following hatching as described above, single 1st instar Ae. aegypti or Ae. albopictus larvae were added to batches of 1st instar Ae. notoscriptus or Ae. aegypti larvae, respectively, to produce pool sizes of 10, 100, 1,000 and 5,000 larvae. In addition, a pool comprised of a single Ae. aegypti or Ae. albopictus 1st instar larvae, and pools containing only Ae. notoscriptus or Ae. aegypti 1st instar larvae were produced. The pools were processed using the vacuum filtration method described above and Ae. aegypti and Ae. albopictus detected using the species-specific TaqMan RT-PCR assays.
We assessed ImageJ software [63] as a platform to count egg numbers on ovistrips in situ from Brisbane ovitraps deployed during the 2013–14 summer. Using a stereo microscope, each ovistrip was inspected, eggs counted and relative contamination assessed to ascertain whether the ovistrip was of sufficient quality to be analyzed by ImageJ (Fig 2). Quality of the ovistrips was especially important, as ImageJ cannot differentiate Aedes eggs from other objects or contaminants, such as debris, leaves, insect cadavers and/or fungal growth, on the ovistrip. This could potentially produce an inaccurate count of the eggs on the ovistrips. Consequently, an arbitrary grade for each ovistrip was assigned to determine suitability for ImageJ analysis and comprised suitable (Fig 2A), suitable with image manipulation (Fig 2B) and unsuitable (Fig 2C).
An image of each ovistrip was then captured using a standard flatbed scanner (Epson Perfection V370 Photo, Suwa, Nagano, Japan). Images were saved in .jpg format under standardized conditions of ovistrip presentation, background, debris removal and a fixed resolution (1200 dpi). We created a macro to analyze each image individually within a directory of imagery. The image was initially split into red, green and blue colour channels. In the red channel, eggs appeared as dark regions on a light background. Accurate selection and measurement of dark regions required the image to be smoothed with a median filter (radius 2 pixels) and inverted (eggs appear as light regions on a dark background). A threshold was then applied (intensity 140) to binarize the image so that egg regions were rendered white (intensity 255) on a black background (intensity 0).
ImageJ produces two estimates of egg number; the number of distinct/disjoint eggs or egg regions (the unit method), and the total area (in pixels) of egg regions (the area method). For the latter method, an average value of 168 pixels per Ae. notoscriptus egg (n = 25) was calculated.
The script was automated upon selection of a directory and dual results (a spread sheet containing quantification for each image and binary images showing the regions selected by the macro as being egg-covered) placed in a subdirectory. The binary images provided a quality control mechanism to check that the macro functions correctly and egg regions are located accurately.
The Ae. aegypti TaqMan RT-PCR assay was able to detect single 1st instar larvae in all pools that contained increasing numbers of Ae. notoscriptus up to a pool size of at least 5,000 (Table 1). The Ae. albopictus assay was not as sensitive, with the limit of detection being a single 1st instar larva in a pool of 1,000 Ae. aegypti.
A total of 477 ovitraps were deployed in Brisbane of which 400 (83.9%) collected >75,000 Aedes spp. eggs. The processing of 25 pools, representing an estimated ≈ 54,400 eggs, using the Ae. aegypti TaqMan assay did not detect Ae. aegypti.
A total of 45 ovitraps were deployed in Rockhampton, of which 18 (40.0%) collected an estimated total of ≈ 1,700 Aedes spp. eggs. Aedes aegypti was detected in 4 of 6 pools.
A total of 62 ovitraps were deployed in Goomeri, of which 32 (51.6%) collected an estimated ≈ 4,200 eggs. Aedes aegypti was detected in 5 of 8 pools.
Due to an absence of visual contaminants, the eggs on 107 of 193 (55.4%) of ovistrips were able to be accurately quantified using ImageJ. Of the remainder, accretion of contaminants rendered 41 of 193 (21.2%) of ovistrips unsuitable for analysis. The eggs on 45 of 193 (23.3%) ovistrips could be quantified with manipulation, such as removing debris or digitally enhancing the image.
The RSVP system is a powerful and flexible tool for presence-absence surveillance of invasive Aedes (Stegomyia) mosquito species. RSVP utilizes sensitive molecular diagnostics to resolve logistical and time constraints typically associated with ovitrap surveillance. Molecular diagnoses take approx. 48 h, including hatching of eggs, nucleic acid extraction and real-time RT-PCR. This represents an appreciable time saving, considering that 1,000s of larvae from multiple ovitraps can be processed concurrently. In comparison, ovitraps require approx. 7–10 days to rear cohorts to 4th instar larvae or adults before each specimen is identified by the microscopic examination of key morphological characters. Molecular analyses mitigates the risk of human error in detecting a small number of specimens of an invasive species that are obscured by many 1,000s of larvae from endemic species [64]. Hatching eggs to 1st instar larvae prior to RT-PCR analysis removes requirements for a specific ovitrap format (design, size, material), ovistrip size [65] or substrate (e.g., cloth, paper, germination paper, wood). Shipment of large numbers of egg samples by postal service is also potentially more logistically viable than couriering water samples for newly developed environmental DNA analysis [66].
When viewed in isolation, this application of molecular diagnostics may seem expensive, with a single test costing approx. AU$50.00. However, compared to morphological identification, the saving in time and labor to perform one molecular diagnosis for 10 pooled samples (or AU$5.00 per ovitrap) more than offsets the cost of performing the molecular assays. Furthermore, the cost to perform each test will decrease as the number of cards tested increases. Molecular diagnostics can detect multiple target species (e.g. Ae. albopictus and Ae. aegypti) once the nucleic acids have been extracted, with only a marginal cost increase to include an additional species. Other real-time TaqMan assays can be developed to identify other invasive species, such as Aedes japonicus and Aedes koreicus, which have recently colonized North America and Europe, respectively [67].
Estimates of egg abundance on each ovistrip will guide the aggregation of ovistrips into pool sizes that are below the TaqMan RT-PCR assay’s threshold of detection, and enable comparisons between trap sites or to generate spatial ‘heat maps’ [7]. Egg counting software has the potential to remove the need for manual counting and improve the accuracy and consistency of subjective rapid visual estimates [68]. We demonstrated that systems developed to count eggs harvested under laboratory conditions [69, 70] may not be suitable for rapid analyses of field collections due to greater exposure of ovistrips to contaminants. It should be noted that eggs on any contaminated ovistrips would remain viable for molecular analyses. Pre-trial testing in Brisbane determined that a 2 week ovitrap deployment minimized the accretion of contaminants, such as plant debris and/or bacterial and fungal growth. Whilst the relatively high number of contaminated ovistrips limited its accuracy for automated egg counting in our trials, ImageJ or other image processing software warrants further investigation.
The spatial scale of an RSVP ovitrap network can be adjusted to service early-warning, eradication or quality assurance programs. RSVP was trialled by inexperienced field staff in locations where Ae. aegypti is present (Rockhampton), absent (Brisbane) and near the margin of its geographic range (Goomeri). The simplicity, operational flexibility and the rapidity of diagnosis that RSVP provided was well received by these staff. The RSVP system is currently being translated into an expanded local government program in southeast Queensland. Furthermore, the RSVP is also providing a diagnostic platform for a pilot citizen-science project in southeast Queensland. This pilot will emulate citizen-science programs that monitor invasive mosquitoes in Europe [71] and North America (Invasive Mosquito Project), and may complement community-release programs for novel biological control strategies, such as Wolbachia-based programs [72–74].
RSVP may provide a powerful quality assurance tool [75] for ZIKV, DENV or CHIKV responses, either through rapid analyses of eggs from lethal ovitraps [60, 76] or non-lethal ovitraps. Vector presence-absence data within small spatial scales (e.g. residential block) [27, 77, 78] may facilitate risk-based assessment for persistence of local transmission and direct subsequent control operations toward blocks that are infested. The RSVP has potential to be integrated with the roll-out of Wolbachia-based programs. For example, ovitraps were used to assess the rate of invasion by Ae. aegypti infected with the endosymbiont Wolbachia pipientis in open field releases in north Queensland [78]. RSVP may also provide a tool to facilitate the measurement of the heterogeneity of Ae. aegypti in the assessment of candidate sites before releases [79], or aid the assessment of Wolbachia invasion during and after releases for interventions aimed either at the reduction of vector populations [80–82] or for control of ZIKV [83, 84], DENV [77, 85, 86] and CHIKV [87, 88] outbreaks.
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10.1371/journal.pntd.0006980 | Insights into antitrypanosomal drug mode-of-action from cytology-based profiling | Chemotherapy continues to have a major impact on reducing the burden of disease caused by trypanosomatids. Unfortunately though, the mode-of-action (MoA) of antitrypanosomal drugs typically remains unclear or only partially characterised. This is the case for four of five current drugs used to treat Human African Trypanosomiasis (HAT); eflornithine is a specific inhibitor of ornithine decarboxylase. Here, we used a panel of T. brucei cellular assays to probe the MoA of the current HAT drugs. The assays included DNA-staining followed by microscopy and quantitative image analysis, or flow cytometry; terminal dUTP nick end labelling to monitor mitochondrial (kinetoplast) DNA replication; antibody-based detection of sites of nuclear DNA damage; and fluorescent dye-staining of mitochondria or lysosomes. We found that melarsoprol inhibited mitosis; nifurtimox reduced mitochondrial protein abundance; pentamidine triggered progressive loss of kinetoplast DNA and disruption of mitochondrial membrane potential; and suramin inhibited cytokinesis. Thus, current antitrypanosomal drugs perturb distinct and specific cellular compartments, structures or cell cycle phases. Further exploiting the findings, we show that putative mitogen-activated protein-kinases contribute to the melarsoprol-induced mitotic defect, reminiscent of the mitotic arrest associated signalling cascade triggered by arsenicals in mammalian cells, used to treat leukaemia. Thus, cytology-based profiling can rapidly yield novel insight into antitrypanosomal drug MoA.
| African trypanosomes cause devastating and lethal diseases in humans and livestock. These parasites are transmitted among mammals by tsetse flies and circulate and grow in blood and tissue fluids. There are several drugs available to treat patients but, despite their use for many decades, we know relatively little about how they work. We reasoned that exposure of trypanosomes to each drug, followed by microscopic examination of cellular structures, would reveal the major cellular compartments, structures or growth phases affected. For example, we examined two major DNA structures, and cellular compartments known as mitochondria. We found that two drugs thought to act in mitochondria did indeed disrupt this compartment, but in completely different ways. Another drug stopped cell growth at a specific point in the cycle. An arsenic-based drug, related to anti-leukaemia drugs, perturbed the nuclear DNA division cycle, indicating that arsenicals may kill parasites and cancer cells by similar mechanisms. Thus, the ‘chemical-biology’ profiles we observe illuminate distinct killing mechanisms. A similar approach can now be used to assess new drugs, and the insights may help to develop improved anti-parasite therapies.
| Chemotherapy is central to the control of the neglected tropical diseases caused by African trypanosomes (Trypanosoma brucei spp), South American trypanosomes (Trypanosoma cruzi) and Leishmania spp; the related kinetoplastid parasites [1]. The current drugs suffer problems with complex administration, efficacy, toxicity and resistance [2]. There are a small number of drugs in clinical trials for these diseases but there remains a desperate need for new and improved drugs. An understanding of drug mode-of-action (MoA) would aid the development of these new drugs, but our knowledge of how the current antitrypanosomals work is lacking [1]. This has also been the case for drugs currently in clinical trials and for other promising compounds that emerged from phenotypic cell-based screening campaigns. These gaps in knowledge will only become more acute given the current trend of phenotypic screening and the typically high attrition rate during the development of compounds that emerge from target-based screening [1].
In the case of Human African Trypanosomiasis (HAT), there are five current drugs, and two in clinical trials [1]. The disease progresses from stage 1 to stage 2, when parasites invade the central nervous system, which is typically lethal without treatment. The current drugs are eflornithine, melarsoprol, nifurtimox, pentamidine and suramin. Nifurtimox and eflornithine were introduced most recently and are used in combination to treat stage 2 disease in West Africa [3]; eflornithine, however, is particularly challenging to administer. Melarsoprol use has been largely phased out, as it is highly toxic [4]. Resistance to melarsoprol, due to the disruption of a parasite aquaglyceroporin [5,6,7], is also now widespread, but this drug remains the only available treatment for stage 2 disease in East Africa; the parasite sub-species found in this region is not susceptible to eflornithine [8]. Pentamidine and suramin are used to treat stage 1 disease in West and East Africa, respectively. The new orally active drugs in clinical trials are acoziborole / SCYX-7158 [9] and fexinidazole [10].
With the exception of eflornithine and acoziborole, the mechanisms of action for the antitrypanosomal drugs above are not understood in any detail. Eflornithine enters trypanosomes via the amino-acid transporter AAT6 [11] and inhibits ornithine decarboxylase [12] and, only recently, acoziborole was shown to target an mRNA maturation factor known as CPSF3 [13]. Almost all antitrypanosomal drugs emerged based on an ability to selectively target trypanosomes or to reduce parasite viability in cell culture or in animal models. Rather than differences between host and parasite intracellular targets, the selective efficacy of suramin, melarsoprol and pentamidine is at least partly due to trypanosome-specific uptake mechanisms [14]. Suramin enters trypanosomes via an endocytic pathway involving a bloodstream stage-specific invariant surface glycoprotein ISG75 [14], and variant surface glycoprotein [15]. The action of suramin is enhanced by the import of ornithine, via an amino acid transporter [16], and its metabolism by ornithine decarboxylase, such that eflornithine is antagonistic [14]. Suramin inhibits pyruvate kinase in vitro but the drug may also occupy ADP/ATP binding sites in other enzymes [17], none of which have been validated as targets in vivo. Melarsoprol is an arsenical drug that enters trypanosomes via an adenosine transporter [18] and an aquaglyceroporin, AQP2 [5], acting primarily by forming a stable adduct, known as Mel T, with the antioxidant, trypanothione [19]. Pentamidine, like melarsoprol, enters trypanosomes via AQP2 [5]. Indeed, pentamidine inhibits the glycerol permeability of AQP2 [20] but this particular activity has little impact on parasite viability. Rather, this DNA-binding drug [21] becomes highly concentrated in the cell and collapses trypanosome mitochondrial membrane potential [22]. Pentamidine remains a low nanomolar antitrypanosomal agent against parasites lacking mitochondrial (kinetoplast) DNA, however, which display only 2.5-fold resistance [23,24]. Nifurtimox and fexinidazole are both nitro pro-drugs that are activated by a putative ubiquinone nitroreductase (NTR) located in parasite mitochondria [14,25,26], but it is unknown whether these drugs kill parasites primarily by disrupting mitochondrial functions or whether the toxic metabolites access targets outside the mitochondria.
Cytology-based profiling can facilitate antibiotic discovery efforts [27] and a selection of cellular assays have been previously applied to antitrypanosomal compounds [28]; but this previous study examined only one of the current HAT compounds (pentamidine) and employed only two of the assays described below. We now report cytology-based profiling for T. brucei to probe the MoA of all five antitrypanosomals used in patients. We describe a panel of assays that assess cell cycle progression, nuclear and mitochondrial DNA content, mitochondrial DNA replication, nuclear DNA damage, mitochondrial membrane potential, and lysosome structure and function. Using these assays, we show that each drug tested induces specific and distinct cellular perturbations, yielding novel insight into the MoA of the antitrypanosomal agents. Follow-up studies revealed a melarsoprol-induced mitotic defect that is dependent upon a specific set of kinases.
The potency of the antitrypanosomal drugs used in the clinic varies widely. The 50% effective growth inhibitory concentration (EC50) determined against bloodstream-form T. brucei in culture is in the low nM range for pentamidine (2.5 nM), suramin (27 nM) and melarsoprol (7 nM) but is in the low μM range for eflornithine (15 μM) and nifurtimox (2.6 μM); a 6,000-fold differential between the most potent (pentamidine) and least potent (eflornithine). It is important to note that, since EC50 values are typically determined over three to four days, they may fail to reflect the rate at which growth is inhibited or whether the compound is cytocidal or cytostatic at a particular concentration.
We examined the growth profiles of bloodstream-form trypanosomes treated with each of the five clinical antitrypanosomal drugs at 1 x EC50 and 5 x EC50; see Materials and methods. All drugs had a relatively moderate impact on growth at 1 x EC50, as expected (Fig 1). In contrast, growth at 5 x EC50 revealed a clear difference between eflornithine, which is known to be cytostatic [29], and the other drugs, which were all demonstrably cytocidal over four days (Fig 1). We selected 5 x EC50 exposure for 24 hours for subsequent assays. These concentrations and this time-point were selected to achieve a balance between allowing robust primary phenotypes to develop but to minimise the emergence of secondary effects associated with loss of viability.
Our first cellular assay was a simple examination of each of the five populations of drug-treated cells for defects in gross cellular morphology by phase-contrast microscopy. We found that the majority of suramin-treated cells became enlarged and distorted (see below). We also looked for the ‘BigEye’ phenotype, which is associated with an endocytosis-defect and observed following treatment with the N-myristoyltransferase inhibitor DDD85646 [30], however this phenotype was not seen.
Arguably the simplest and most widely used fluorescent-staining method for T. brucei is DAPI-staining of DNA followed by microscopy. This is particularly informative for T. brucei because cells in which the single mitochondrial genome, or kinetoplast, has segregated are easily visualised and scored, alongside the unsegregated or segregated nuclear genome [31]. We, therefore, examined DAPI-stained cells by microscopy following drug-exposure. Cells were scored according to the number of nuclei (n) and kinetoplasts (k). In non-treated cells (Fig 2A, NT), we found that ~80% of cells displayed a 1n1k pattern (primarily G1 + S-phase), ~15% of cells were 1n2k (primarily G2) and ~5% of cells were 2n2k (post-mitosis). We exercised some caution when analysing these data for drug-treated cells, and focused on only robust phenotypes that preceded or were coincident with loss-of-viability. The analysis revealed a major increase in the proportion of cells with more than two nuclei following suramin treatment (79%), while eflornithine treatment also yielded an appreciable increase, with 11% of cells having more than two nuclei (Fig 2A, yellow bars). In addition, we noted ~5% of cells lacking a kinetoplast (0k) following pentamidine treatment (Fig 2A, orange bar, see below). This loss of kinetoplast DNA is consistent with previous observations [28].
DNA staining followed by flow cytometry allows the rapid analysis of large numbers of cells and can reveal relative cellular (primarily nuclear) DNA-content. Accordingly, we next examined propidium iodide stained cells by flow cytometry following drug-exposure. Untreated cells showed the characteristic profile, with a G2/M population that was approximately half the magnitude of the G1 population (Fig 2B, top-left and red profiles). In this assay, both nifurtimox-treated and pentamidine-treated cells conformed to the profile observed for untreated cells. In contrast, and consistent with the microscopy analysis above, we observed >70% of cells with >2n DNA content following suramin treatment; eflornithine treatment also yielded an appreciable increase in this category (Fig 2B, blue profiles). Only melarsoprol treatment yielded a second, distinct and notable perturbation using this assay; the G2/M population increased from 51% to 83%, relative to the G1-population (Fig 2B, blue profile). Since we had not scored an increase in the proportion of 2n cells by microscopy following melarsoprol-treatment, this particular flow-cytometry profile indicated an increase in cells with a replicated but unsegregated nuclear genome. Thus, DNA-staining with microscopy and flow cytometry suggested that suramin inhibited cytokinesis and that melarsoprol inhibited mitosis (see below).
The TUNEL (Terminal deoxynucleotidyl transferase dUTP Nick End Labelling) assay allows the fluorescent labelling and detection of blunt DNA ends, following programmed cell-death (PCD) for example; notably, conventional PCD is not thought to operate in trypanosomatids [32]. Using this assay, TUNEL-signals were not readily detected in trypanosome nuclei. In contrast, and as previously reported [33], we observed robust signals associated with kinetoplasts (Fig 3A). We found approximately 25% of control cells to be TUNEL-positive (Fig 3A). Eflornithine, nifurtimox and melarsoprol-treatment did not significantly alter the proportion of cells with detectable TUNEL-signals when compared to cells that had not been exposed to drug (Fig 3A), but the proportions of TUNEL-positive cells were significantly reduced following pentamidine or suramin-treatment (Fig 3A). This was likely due to loss of kinetoplast DNA following pentamidine-treatment (see above) and may have been due to limited repeated rounds of kinetoplast DNA replication in multi-nucleated cells following suramin-treatment (see below).
TUNEL signals were found to be cell cycle dependent (Fig 3B), consistent with the high concentration of transient nicked DNA ends expected to be present during the replication of thousands of minicircles [34]. Indeed, the majority (91%) of elongated kinetoplasts (in S-phase cells) and a substantial proportion (22%) of segregated kinetoplasts (in G2 cells) were TUNEL-positive, while we observed very few TUNEL-positive kinetoplasts in G1 or post-mitotic cells (Fig 3C). The TUNEL-signals were consistently observed at opposite poles of extended kinetoplasts and, when present, on both segregated kinetoplasts. Notably, the appearance of kinetoplast-associated TUNEL-signals remained synchronised even in those suramin-treated cells with four kinetoplasts; all four were either negative or positive in each cell (Fig 3D). Thus, TUNEL revealed those cells that are progressing through kinetoplast S-phase and indicated continued synchronisation, even in cells with four kinetoplasts.
We previously identified trypanosome γH2A, a modified histone H2A that is phosphorylated at the C-terminus and that accumulates at sites of nuclear DNA double-strand breaks [35]. To assess nuclear DNA damage in drug-treated cells, we used a γH2A antibody in an immunofluorescence assay; methyl methanesulfonate (MMS) is a radiomimetic DNA-damaging agent and was included as a positive control (Fig 3E). We found no significant differences in the proportion of cells with nuclear γH2A foci compared to untreated cells (Fig 3E), suggesting that none of the current drugs kill trypanosomes by forming double-strand breaks in nuclear DNA. Notably, the nitro pro-drugs, nifurtimox and fexinidazole, although found to be mutagenic by Ames test (Salmonella typhimurium based assay), are not thought to be genotoxic to mammalian cells [36,37].
MitoTracker fluorescence staining is dependent upon mitochondrial membrane potential. We treated trypanosomes with antitrypanosomal drugs and scored cells by microscopy for an extended MitoTracker signal, as observed in non-treated cells (Fig 4A, NT panels). Significantly fewer cells scored positive for extended signals following either nifurtimox or pentamidine-treatment, compared to non-treated cells (Fig 4A). There were also qualitative differences in signals resulting from drug treatments; whereas pentamidine eliminated the detectable signal, nifurtimox-treatment produced an intense and discreet signal adjacent to the kinetoplast (Fig 4A). Notably, mitochondrial membrane potential was maintained following suramin-treatment (Fig 4A), despite the gross morphological perturbations observed in these cells.
LysoTracker is highly selective for acidic organelles. We treated trypanosomes with antitrypanosomal drugs and scored cells for LysoTracker signals by microscopy. Non-treated cells displayed a single signal between the nucleus and kinetoplast (~60%), as expected for the trypanosome lysosome (Fig 4B, NT panels). Melarsoprol substantially reduced the proportion of LysoTracker-positive cells (Fig 4B), but this failed to achieve statistical significance. In the case of suramin, which is known to accumulate in trypanosomes through receptor-mediated endocytosis [14], acidification of the lysosomal compartment does not appear to be perturbed (Fig 4B). The variability we observe in this assay suggests either that additional replicates will be desirable in future, or that this particular assay will be of value only when major lysosomal perturbation occurs.
Using the DAPI, TUNEL and MitoTracker assays following pentamidine-treatment, we observed kinetoplast-loss, a reduced proportion of positive cells and a loss of mitochondrial membrane potential, respectively. To quantify these effects, we scored each phenotype following 12, 24, and 48 h of drug-exposure (Fig 5A–5C). These analyses revealed a sharp increase in kinetoplast-negative cells in the 48-h DAPI-stained population (Fig 5A) and a progressive decline in TUNEL-positive (Fig 5B) and MitoTracker-positive cells (Fig 5C) at each time-point.
DAPI-stained cells appeared to reveal progressively diminished kinetoplast DNA rather than loss in one step. For a quantitative and objective assessment of kinetoplast DNA-staining intensity, we adapted an ImageJ-based approach [31]; see Materials and methods. ImageJ efficiently identified nuclei and kinetoplasts in untreated cells (Fig 5D, red and green, respectively in the upper panel), but failed to identify very low-intensity kinetoplasts in pentamidine-treated cells (Fig 5D, open green circle in the lower panel). A quantitative analysis indicated that the kinetoplasts that are still detected following pentamidine-treatment display reduced surface area and signal intensity relative to the control population; values are expressed relative to nuclei in the same cell (Fig 5D). These results confirm progressive loss of kinetoplast DNA induced by pentamidine.
Nifurtimox-treatment produced an intense and discreet MitoTracker signal adjacent to the kinetoplast (Fig 4A). To explore this effect further, we stained the nuclear-encoded F1β-subunit of the mitochondrial ATP-synthase (Tb927.3.1380) in drug-treated and MitoTracker-stained cells and examined these cells by microscopy. The ATP-synthase signal was largely coincident with the extended MitoTracker signal in control cells (Fig 6A, NT panels) and was also coincident with the focal MitoTracker signal in nifurtimox-treated cells (Fig 6A, Nif panels), possibly indicating a structural defect in mitochondria in the latter cells. We next assessed F1β-subunit expression by protein blotting of whole-cell extracts, which revealed a substantial and specific reduction in abundance following nifurtimox-treatment (Fig 6B). Depletion of the ATP-synthase component may reflect a more widespread depletion of mitochondrial proteins, consistent with activation of this pro-drug by a mitochondrial NTR [14,25,26]. Using the same assays, pentamidine-treated cells displayed a diffuse and cytosolic, rather than mitochondrial, ATP-synthase signal (Fig 6A, Pen panels). In this case, protein import into mitochondria [38] may be defective as a result of loss of mitochondrial membrane potential, as indicated by the diminished MitoTracker signal (Fig 6A, Pen panels). Consistent with this hypothesis, the total cellular F1β signal detected by protein blotting remained constant following pentamidine treatment (Fig 6B).
DNA staining followed by microscopy or flow cytometry suggested that melarsoprol inhibited mitosis. Indeed, a closer inspection of DAPI-stained cells revealed a sub-set (>10%) with “conjoined” nuclei (Fig 7; top panels, double-arrowheads). For an objective assessment of nuclear DNA-staining intensity in these cells, we used the quantitative ImageJ-based approach; see Materials and methods. In this case, nuclei were compared to kinetoplasts in ‘1n2k’ cells, revealing both increased nuclear surface area and signal intensity following melarsoprol-treatment (Fig 7). These quantitative results confirmed the mitotic defect resulting from melarsoprol treatment.
Cytology-based profiling may be combined with orthogonal approaches to probe antitrypanosomal compound MoA. We have found high-throughput genetic screening to be particularly informative [13,14]. A prior RNA interference (RNAi) Target Sequencing (RIT-seq) screen revealed a highly significant (P = 2.3 × 10−9) over-representation of kinases, comprising three hits among the top seven in this screen; these were a putative mitogen-activated protein kinase (MAPK11, Tb927.10.12040), a putative MAPK kinase (MKK4, Tb927.8.5950) and a putative MAPK kinase kinase (STE11, Tb927.10.1910) [14]. It is also notable that trypanosome CDC14 (Tb927.11.12430) was a hit in this screen, since human Cdc14 stabilises Wee1, a key kinase that inhibits mitosis [39]. Thus, knockdowns that promote mitosis may partially counteract the inhibitory effects of melarsoprol, but the RIT-seq screening hits noted above have not previously been independently validated nor further characterised. To explore our hypothesis, we further characterised these kinases. All three were expressed with a C-terminal 12-myc epitope tag and the tagged proteins were all found to localise to the trypanosome cytosol (Fig 8A). Since RIT-seq screening fails to yield clonal knockdown strains [14], we assembled pairs of independent knockdown strains for two of the kinases (Tb927.10.12040 and Tb927.8.5950). Both knockdowns, confirmed by monitoring the epitope-tagged proteins (Fig 8B), reproducibly and significantly (P < 0.0001) increased melarsoprol EC50 by 1.7 +/- 0.07 fold and 1.5 +/- 0.01 fold, respectively (Fig 8C), as predicted by the RIT-seq screen [14]. Finally, we determined whether knockdown alleviated the melarsoprol-induced, conjoined-nuclei phenotype and found that this was indeed the case for both kinases (Fig 8D). Thus, the melarsoprol-induced mitotic defect is kinase-dependent.
Cytocidal or cytostatic compounds identified using phenotypic approaches may target proteins, nucleic acids, membranes or other metabolites. They may also exhibit polypharmacology, killing cells by perturbing multiple pathways. Even compounds developed as target-based therapies may kill trypanosomes by ‘off-target’ mechanisms. Determining mechanism of action remains a major challenge for these drugs and compounds and an improved understanding of how they kill parasites could present new opportunities in terms of developing more potent compounds, delivering compounds to their targets more effectively or devising combination therapies that minimise the likelihood of resistance. Eflornithine kills trypanosomes by inhibiting ornithine decarboxylase but the mode-of-action of the other four current antitrypanosomal drugs is not known. Our studies indicate that cytology-based profiling can provide a rapid and effective means to yield insight into drug mode-of-action and we now present additional insights into the mode-of-action for all current drugs used to treat human African trypanosomiasis.
Suramin was found to produce cells with more than two nuclei, indicating a defect in cytokinesis with continued mitosis. Multi-nucleated cells were still stained by MitoTracker, indicating that this organelle retained membrane potential. DAPI-staining and TUNEL (terminal dUTP nick end labelling) assays indicated that nuclear:nuclear and nuclear:kinetoplast replication remained synchronised in these cells. Indeed, although the TUNEL assay failed to reveal any nuclear DNA damage induced by the drugs used here, it did provide an excellent marker for the kinetoplast replication cycle, producing robust signals consistent with the presence of DNA-ends at antipodal sites on the kinetoplast during minicircle DNA replication [34]. In this regard, TUNEL provides a useful marker for kinetoplast S-phase.
Pentamidine is a DNA-binding drug [21] that collapses trypanosome mitochondrial membrane potential [22] and induces loss of kinetoplast DNA [28]. Data from Saccharomyces cerevisiae indicated pentamidine accumulation in the mitochondrion but also inhibition of translation in these cells [40]. In addition, metabolomic studies indicated that pentamidine is unlikely to act through the inhibition of any specific metabolic pathways [41]. We find that pentamidine-induced loss of kinetoplast DNA is progressive, suggesting progressive loss of maxi- and minicircles; the latter are present in thousands of copies per kinetoplast [34]. Kinetoplast DNA loss is revealed by both DAPI-staining and TUNEL-assay. Loss of kinetoplast DNA is expected to disrupt membrane-potential since the A6-subunit of the ATP-synthase, required to maintain this potential, is encoded by kinetoplast DNA [42]. Thus, we suggest that kinetoplast DNA is indeed a primary target for pentamidine, involving inhibition of mitochondrial type II topoisomerase action, as previously suggested [43]. What remains unclear is whether loss of mitochondrial membrane-potential is solely a consequence of the kinetoplast defect or whether this reflects an independent response to pentamidine.
Nifurtimox is a pro-drug that is activated by a mitochondrial NTR [26], but it is unknown whether the toxic metabolite(s) access(es) targets outside the mitochondrion, or whether parasite killing is primarily due to disruption of mitochondrial functions. We found that nifurtimox, like pentamidine, also disrupted the MitoTracker signal. Kinetoplast defects were not observed, however, and the MitoTracker staining pattern was distinct to that seen with pentamidine. Nifurtimox also induced loss of a nuclear-encoded ATP-synthase subunit, while data from pentamidine-treated cells indicated that ATP-synthase was still expressed, but not imported, when membrane potential was perturbed. Thus, we suggest severe disruption of mitochondrial structure and function by nifurtimox, consistent with damage to targets in the organelle where the drug is activated. Fexinidazole is another nitro pro-drug, currently in clinical trials, that is also activated by mitochondrial NTR [25]. These drugs may act through similar antitrypanosomal mechanisms.
Melarsoprol forms potentially toxic adducts with trypanothione [19], and we now show that this drug increases the proportion of cells with a replicated but unsegregated nuclear genome. This indicates a defect in mitosis. The identification of multiple putative kinases in a loss-of-function screen for melarsoprol-resistance suggested a role for a signalling cascade in melarsoprol susceptibility [14]. The current findings led us to consider a more specific role for these putative kinases in the control of mitosis. Indeed, these proteins are putative MAPK, MAPKK and MAPKKKs and our results now indicate that the mitotic defect is less pronounced when the putative MAPK (Tb927.10.12040) and MAPKK (Tb927.8.5950) are depleted. We suggest that these kinases negatively control mitosis, as part of normal quality control following DNA replication. Bypass of this surveillance could allow cells to continue to grow, possibly accumulating, but tolerating, melarsoprol-induced (oxidative) damage. Thus, knockdowns that promote mitosis may partially counteract the inhibitory effects of melarsoprol.
Arsenicals are used to treat leukemia but are also themselves mutagens. There is indeed evidence that arsenic induces DNA damage in yeast [44]. Notably, the MoA in yeast involves activation of a MAP kinase pathway [45] and this also appears to be the case in mammalian cells [46], where mitotic arrest occurs as a result of induction of a mitotic spindle checkpoint [47]. We did not detect any evidence for nuclear DNA damage in T. brucei following melarsoprol treatment but, as detailed above, did find a melarsoprol-dependent and kinase-dependent mitotic defect. Thus, arsenical MoA may operate through a common kinase signalling cascade leading to mitotic arrest in both trypanosomes and mammalian cells. Notably, like the Myt1 kinase, which phosphorylates Cdc2 and controls mitosis in Xenopus [48], Tb927.10.1910 has a putative transmembrane domain; Tb927.10.1910 also has a putative guanylate cyclase domain. These findings illustrate how cytology and genetic approaches can converge to yield insights into drug MoA in trypanosomes. In this case, we propose a common MoA for arsenicals in human cells and in trypanosomes.
Cytology-based approaches can provide rapid and cost-effective methods for quantitative profiling of cellular responses to drugs. The drugs can also be considered as chemical probes for exploring parasite biology. Taking a systematic cytology-based approach with antitrypanosomal drugs of uncertain MoA, our findings indicate target organelles and structures for pentamidine, nifurtimox and melarsoprol; the kinetoplast, the mitochondrion and the nucleus, respectively. Further analysis, guided by the primary assays, indicated destruction of mitochondrial ATP-synthase by nifurtimox and a mitotic defect induced by melarsoprol. These assays should also provide novel insights into MoA when applied to cells treated with other antitrypanosomal compounds, including those with known primary targets. Common profiles will allow compounds to be clustered based on their primary MoA. New assays and bespoke assays can be incorporated as appropriate and these may be guided by outputs from orthogonal genetic, proteomic, metabolomic or computational approaches. The approach may also be usefully applied to the other parasitic trypanosomatids, T. cruzi and Leishmania. These and other approaches should reveal those particularly susceptible pathways that can be prioritised and targeted by antitrypanosomal therapies.
Lister 427 derived T. brucei (clone 221a) bloodstream form cells were grown in HMI-11 in the presence of antitrypanosomal drugs. Cultures were initiated at 2 x 105 cells.ml-1 and incubated at 37°C in a humidified 5% CO2 atmosphere. The drugs were applied at five times the EC50, as determined using a standard AlamarBlue assay [49]; for EC50 determination, drug exposure was for 66–67 h and AlamarBlue incubation was for 5–6 h. Plates were read on an Infinite 200 Pro plate-reader (Tecan). The EC50 values of the antitrypanosomal drugs used were: Eflornithine 15 μM; Melarsoprol 7 nM; Nifurtimox 2.6 μM; Pentamidine 2.5 nM; Suramin 27 nM. Methyl methanesulfonate (MMS, Sigma) was used at 0.0003%. To monitor cumulative trypanosome growth, cultures were grown in the presence of drug, with four technical replicates of cell density counted at 6, 9, 24, 48, 72 and 96 h. Cultures were diluted with fresh media containing the appropriate drug, such that cell density never exceeded 2 x 106 cells.ml-1. All tagging and RNAi constructs were transfected into 2T1 T. brucei cells [50]. RNAi was induced with tetracycline (1 μg/ml) for three days prior to initiating EC50 determination.
Cells were grown in the presence of drug at 5 x EC50 for 24 h, unless stated otherwise. Cells were then fixed in 2% v/v paraformaldehyde for 30 min at 4°C before being washed three times in PBS. Fixed cells were dried onto slides before staining with antibodies (outlined below). Slides were washed in PBS and mounted in Vectashield containing DAPI (Vector Laboratories); 4, 6-diamidino-2-phenylindole. Scoring of phenotypes was carried out by counting 100 cells per condition, and by two of us, with the data combined. For immunofluorescence analysis (IFA), cells dried onto slides were permeablised in 0.5% Triton X100 / PBS for 20 min and washed three times in PBS before blocking in 50% foetal bovine serum FBS / PBS. Primary antibody (α-γH2A) [35] was diluted 1:250 and applied for 1 h, slides were washed three times in PBS, and secondary antibody (fluorescein-conjugated goat anti-rabbit) was diluted 1:100 and then applied for 1 h. Antibodies were applied in 3% FBS / PBS. The F1β-subunit of the mitochondrial ATP-synthase was similarly detected using a polyclonal rabbit antiserum directed against the Crithidia fasciculata ATP synthase (1:500), which cross-reacts with the T. brucei orthologue [51,52]. All phase and epifluorescence images were captured on an Eclipse E600 microscope (Nikon) using a Coolsnap FX (Photometrics) charged coupled device camera and processed in Metamorph 5.4 (Photometrics).
MitoTrackerRed (Invitrogen) was added to cultures at 100 nM. Cultures were incubated for 5 min under standard conditions before parasites were harvested by centrifugation at 1000 x g for 10 min before fixing as outlined above.
LysoTracker (Invitrogen) was added to cultures at 50 nM. Cultures were incubated under standard conditions for 1 h before parasites were harvested by centrifugation at 1000 x g for 10 min before fixing as outlined above.
Cells on slides were fixed, dried and permeablised as described above for IFA. Reaction mix from the ‘In Situ Cell Death Detection Kit (fluorescein)’ (Roche) was applied to cells for 1 h as per the manufacturer’s instructions.
Cells were harvested and washed in ice cold PBS and resuspended in 300 μl PBS before the addition of 700 μl of methanol and stored at 4°C for 12 h. Cells were then harvested at 400 x g for 10 min at 4°C and resuspended in 1 ml PBS. 10 μg/ml RNAse A and 10 μg/ml propidium iodide was added to each sample and incubated at 37°C for 45 min in the dark. Analysis was performed on an LSRII flow cytometer (BD Biosciences), and data analysis was conducted in FlowJo (Tree Star).
All images of DAPI fluorescence were captured at 40 ms exposure time for consistency and to avoid overexposure. They were analysed using an ImageJ plug-in [31] modified to enable cell cycle analysis from DAPI and phase contrast images. DAPI alone was suitable for the identification of nuclei and kinetoplasts, since kinetoplasts do not generally overlap with nuclear DNA signals in bloodstream form T. brucei. As in the original ImageJ plug-in, two kinetoplasts are counted only when no longer linked by pixels with signal. A subset of the original macros were retained and modified, as necessary: Measure K/N Signal, Cell Analysis, K/N Count Summary, and Save Analysis. First, the Measure K/N Signal tool thresholds images using built-in ImageJ functions, and extracts object area values from DAPI images. Next, the script applies the K-means clustering algorithm [31] to separate kinetoplasts and nuclei into respective clusters, returning values for the maximum kinetoplast area and minimum nucleus area. These values are then passed to the Cell Analysis tool which finds objects in phase images, creates binary copies of the phase and DAPI image and identifies kinetoplasts and nuclei, counting how many lie within each cell. K/N Count Summary and Save Analysis tools function identically to the original macros described in [31]. Macro scripts are available on request.
For tagging native Tb927.8.5950, Tb927.10.12040 and Tb927.10.1910 alleles with 12-myc epitope tags at the C-terminus, we used the following primer-pairs, to amplify 893, 1038 and 769 bp fragments, respectively: 5950MF (GATCAAGCTTGATCCATGTGTAGTTGAC) and 5950MR (GATCTCTAGAGGATACTGGTGAACCATC); 12040MF (GATCAAGCTTGGCACACTTTCACCACGAT) and 12040MR (GATCTCTAGACTCAACGGAACCCACATATT); 1910MF (GATCAAGCTTGCGTGTATCTAGGCATGGA) and 1910MR (GATCTCTAGAAAGGGAAAAAAGTG). These primer-pairs incorporate HindIII and XbaI sites, respectively (italics). The resulting fragments were cloned in the pNATx12MYC construct [53]. The resulting pNAT5950-12myc, pNAT12040-12myc and pNAT1910-12myc constructs were linearized by digestion with Bstz171, EcoRV and EcoRV prior to transfection, respectively. For knockdown of Tb927.8.5950 or Tb927.10.12040 using RNA interference, we used the following primer-pairs, respectively: 5950RF2 (GATCTCTAGAGGATCCAAACGACCCAAGTTGGAGAG) and 5950RR2 (GATCGGGCCCGGTACCGCTTCCAGCGTCCATGTATT); 12040RF2 (GATCTCTAGAGGATCCATTCTTGGTGAGTTGCTGGG) and 12040RR2 (GATCGGGCCCGGTACCACTCTCATCATACACCGCCC). These primer-pairs incorporate XbaI / BamHI and ApaI / KpnI sites, respectively (italics). The resulting fragments were cloned in the pRPaiSL construct [53]. The resulting pRPa5950-RNAi and pRPa12040-RNAi constructs were linearized by digestion with AscI prior to transfection.
Total cell extracts were separated on SDS-polyacrylamide gels and subjected to standard western blotting analysis. Duplicate gels were generated and one was stained with Coomassie and the other was used to produce the nitrocellulose blot. Blots were blocked in 5% milk in TBST and washes were performed in TBST (0.05% Tween). Blots were then probed with mouse α-c-Myc primary antibody (1:5000; 9E10, Source Biosciences) or the ATP synthase primary antibody (1:500). Following incubation with the appropriate secondary antibodies (1:2000; Pierce), membrane was washed and visualised using the Amersham enchanced chemiluminescence (ECL) detection system (GE Healthcare Life Sciences) according to the manufacturer instructions.
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10.1371/journal.pgen.1007032 | Dopamine negatively modulates the NCA ion channels in C. elegans | The NALCN/NCA ion channel is a cation channel related to voltage-gated sodium and calcium channels. NALCN has been reported to be a sodium leak channel with a conserved role in establishing neuronal resting membrane potential, but its precise cellular role and regulation are unclear. The Caenorhabditis elegans orthologs of NALCN, NCA-1 and NCA-2, act in premotor interneurons to regulate motor circuit activity that sustains locomotion. Recently we found that NCA-1 and NCA-2 are activated by a signal transduction pathway acting downstream of the heterotrimeric G protein Gq and the small GTPase Rho. Through a forward genetic screen, here we identify the GPCR kinase GRK-2 as a new player affecting signaling through the Gq-Rho-NCA pathway. Using structure-function analysis, we find that the GPCR phosphorylation and membrane association domains of GRK-2 are required for its function. Genetic epistasis experiments suggest that GRK-2 acts on the D2-like dopamine receptor DOP-3 to inhibit Go signaling and positively modulate NCA-1 and NCA-2 activity. Through cell-specific rescuing experiments, we find that GRK-2 and DOP-3 act in premotor interneurons to modulate NCA channel function. Finally, we demonstrate that dopamine, through DOP-3, negatively regulates NCA activity. Thus, this study identifies a pathway by which dopamine modulates the activity of the NCA channels.
| Dopamine is a neurotransmitter that acts in the brain by binding seven transmembrane receptors that are coupled to heterotrimeric GTP-binding proteins (G proteins). Neuronal G proteins often function by modulating ion channels that control membrane excitability. Here we identify a molecular cascade downstream of dopamine in the nematode C. elegans that involves activation of the dopamine receptor DOP-3, activation of the G protein GOA-1, and inactivation of the NCA-1 and NCA-2 ion channels. We also identify a G protein-coupled receptor kinase (GRK-2) that inactivates the dopamine receptor DOP-3, thus leading to inactivation of GOA-1 and activation of the NCA channels. Thus, this study connects dopamine signaling to activity of the NCA channels through G protein signaling pathways.
| Heterotrimeric G proteins modulate neuronal activity in response to experience or environmental changes. Gq is one of the four types of heterotrimeric G protein alpha subunits [1] and is a positive regulator of neuronal activity and synaptic transmission [2–4]. In the canonical Gq pathway, Gq activates phospholipase Cβ (PLCβ) to cleave the lipid phosphatidylinositol 4,5-bisphosphate (PIP2) into diacylglycerol (DAG) and inositol trisphosphate (IP3), which act as second messengers. In a second major Gq signal transduction pathway, Gq directly binds and activates Rho guanine nucleotide exchange factors (GEFs), activators of the small GTPase Rho [5–8]. Rho regulates many biological functions including actin cytoskeleton dynamics and neuronal development, but less is known about Rho function in mature neurons. In C. elegans, Rho has been reported to stimulate synaptic transmission through multiple pathways [9–11]. We recently identified the C. elegans orthologs of the NALCN ion channel, NCA-1 and NCA-2, as downstream targets of a Gq-Rho signaling pathway [12]. We aim to understand the mechanism of activation of this pathway.
The NALCN/NCA ion channel is a nonselective cation channel that is a member of the voltage-gated sodium and calcium channel family [13–15]. The NALCN channel was proposed to be the major contributor to the sodium leak current that helps set the resting membrane potential of neurons [16], though there is controversy whether NALCN is indeed a sodium leak channel [17–19]. In humans, mutations in NALCN or its accessory subunit UNC80 have been associated with a number of neurological symptoms, including cognitive and developmental delay [20–33]. In other organisms, mutations in NALCN/NCA or its accessory subunits lead to defects in rhythmic behaviors [16,34–42]. Specifically in C. elegans, the NCA channels act in premotor interneurons where they regulate persistent motor circuit activity that sustains locomotion [43]. In addition to the Gq-Rho pathway described above, two other mechanisms have been reported to regulate the activity of the NALCN channel: a G protein-independent activation of NALCN by G protein-coupled receptors [44,45] and a G protein-dependent regulation by extracellular Ca2+ [46]. Here we identify a molecular cascade downstream of dopamine in the nematode C. elegans that involves the D2-like dopamine receptor DOP-3 and the G protein-coupled receptor kinase GRK-2 to modulate activity of the NCA-1 and NCA-2 ion channels.
G protein-coupled receptor kinases (GRKs) are protein kinases that phosphorylate and desensitize G protein-coupled receptors (GPCRs). Mammalian GRKs have been divided into three groups based on their sequences and function: 1) GRK1 and GRK7, 2) GRK2 and GRK3, and 3) GRK4, GRK5 and GRK6 [47]. C. elegans has two GRKs: GRK-1 and GRK-2, orthologs of the GRK4/5/6 and GRK2/3 families respectively [48]. Mammalian GRK2 is ubiquitously expressed [49,50] and GRK2 knock-out mice die as embryos [51]. In C. elegans, grk-2 is expressed in the nervous system and required for normal chemosensation [52] and egg-laying [53]. In this study, we find that C. elegans grk-2 mutants have locomotion defects due to decreased Gq signaling. We identify the D2-like dopamine receptor DOP-3 as the putative GRK-2 target and find that GRK-2 acts through DOP-3 to inhibit Go signaling. This in turn leads to activation of the NCA channels through the Gq-Rho signaling pathway. We also find that GRK-2 and DOP-3 exert their effect by acting in the premotor interneurons, where the NCA channels also act to regulate persistent motor neuron activity [43].
The D2-like receptors are GPCRs that couple to members of the inhibitory Gi/o family [54]. In mammals, GRK2 has been connected to the regulation of D2-type dopamine receptors, but the reported results are based mainly on effects of GRK2 overexpression in heterologous expression systems [55–59]. The results reported here provide a direct connection between GRK-2 and D2-type receptor signaling in a behaviorally relevant in vivo system. In C. elegans, dopamine, through dop-3, causes the slowing of the worm’s locomotion rate on food [60]; DOP-3 signals through Go to inhibit locomotion [61]. Here we find that dopamine, through activation of DOP-3, negatively modulates the activity of the NCA channels. This suggests a model in which dopamine signaling negatively regulates NCA channel activity and sustained locomotion through G protein signaling acting in premotor interneurons.
To identify regulators of Gq signaling, we performed a forward genetic screen in the nematode C. elegans for suppressors of the activated Gq mutant egl-30(tg26) [62,63]. The egl-30(tg26) mutant is hyperactive and has a tightly coiled “loopy” posture (Fig 1A and 1B). These phenotypes were suppressed by the yak18 mutation isolated in our screen (Fig 1A). When outcrossed away from the egl-30(tg26) mutation, yak18 mutant animals are shorter than wild-type animals, have slow locomotion (Fig 1C, Right), and are egg-laying defective.
We mapped yak18 to the left arm of Chromosome III and cloned it by whole-genome sequencing and a complementation test with the deletion allele grk-2(gk268) (see Methods). yak18 is a G to A transition mutation in the W02B3.2 (grk-2) ORF that leads to the missense mutation G379E in the kinase domain of GRK-2. GRK-2 is a serine/threonine protein kinase orthologous to the human GPCR kinases GRK2 and GRK3 [52]. The deletion allele grk-2(gk268) also suppresses the loopy posture and hyperactive locomotion of activated Gq (Fig 1A and 1B) and causes defects in locomotion, egg-laying, and body-size similar to grk-2(yak18) (Fig 1C Left, S1A and S1B Fig). We also found that grk-2 mutant animals are defective in swimming (S2 Fig), a locomotion behavior that has distinct kinematics to crawling [64]. Additionally, grk-2 mutants restrict their movements to a limited region of a bacterial lawn, whereas wild-type animals explore the entire lawn (S1C Fig).
Our data suggest that GRK-2 regulates locomotion and is a positive regulator of Gq signaling. The standard model of GRK action is that GPCR phosphorylation by GRK triggers GPCR binding to the inhibitory protein beta-arrestin; binding of arrestin blocks GPCR signaling and mediates receptor internalization [65]. We tested whether loss of arrestin causes defects similar to loss of grk-2 by using a deletion allele of arr-1, the only C. elegans beta-arrestin homolog. We found that arr-1(ok401) mutant animals do not have slow locomotion (S3A Fig). To test whether an arr-1 mutation suppresses activated Gq, we constructed an egl-30(tg26) mutant strain carrying an arr-1 mutation in trans to a closely linked RFP marker (that is, an egl-30(tg26); arr-1/RFP strain). Surprisingly, this strain segregated few viable non-red animals, suggesting that egl-30(tg26); arr-1 double mutants are subviable. The few egl-30(tg26); arr-1 viable animals looked similar to the egl-30(tg26) single mutant (S3B Fig), but died as young adults. These results suggest that GRK-2 acts independently of arrestin to regulate locomotion rate and Gq signaling.
In addition to phosphorylation of GPCRs, mammalian GRK2 can also regulate signaling in a phosphorylation-independent manner [66,67]. Thus, we tested whether the kinase activity of GRK-2 is required for proper locomotion and Gq signaling by assaying whether a kinase-dead GRK-2[K220R] mutant [48,68] is capable of rescuing the grk-2(gk268) and egl-30(tg26); grk-2(gk268) mutants. Wild-type GRK-2 rescued the locomotion defect of grk-2(gk268) mutants (Fig 1C, Left), but the kinase-dead GRK-2[K220R], although it was properly expressed (Fig 2G), did not rescue either the locomotion defect or the suppression of activated Gq (Fig 1C and 1D). We conclude that GRK-2 acts as a kinase to regulate locomotion rate and Gq signaling.
To examine whether GRK-2 acts as a GPCR kinase to control locomotion, we took a structure-function approach (Fig 2A). We took advantage of previously-described mutations that disrupt specific activities of GRK-2, but do not disrupt GRK-2 protein expression or stability [48]. These mutations all affect conserved residues in well-characterized domains of GRK-2 [48].
Although GRKs act as kinases for activated GPCRs, mammalian GRKs have been shown to interact with and phosphorylate other molecules as well [66,67]. Therefore, although the kinase activity of GRK-2 is required for locomotion, it is possible that the relevant targets are proteins other than GPCRs. To examine whether phosphorylation of GPCRs is required for GRK-2 function in locomotion, we expressed GRK-2 with mutations (D3K, L4K, V7A/L8A, and D10A) that have been shown to reduce mammalian GRK2 phosphorylation of GPCRs, but that do not affect phosphorylation of other targets [69]. These N-terminal residues of mammalian GRKs form an amphipathic α-helix that contributes specifically to GPCR phosphorylation [70–74]. grk-2(gk268) mutants expressing any of these mutant GRK-2 constructs had slow locomotion like grk-2(gk268) (Fig 2B, 2C and 2G), indicating that GPCR phosphorylation is required for GRK-2 function in locomotion in vivo.
In mammalian GRKs, interaction of the N-terminal region with the kinase domain stabilizes a closed and more active conformation of the enzyme, important for phosphorylation of GPCRs and other substrates [70–72]. Specifically, mutation of mammalian GRK1 Arg191 disrupted phosphorylation of target substrates in addition to GPCRs, suggesting that this residue is critical for conformational changes important for GRK function as a kinase [71]. To determine whether the analogous residue in GRK-2 is required for its function in locomotion, we expressed GRK-2[R195A] in grk-2(gk268) mutants. GRK-2[R195A] did not rescue the grk-2(gk268) locomotion phenotype (Fig 2D and 2G), further supporting the model that GRK-2 acts as a GPCR kinase to regulate locomotion.
The RH (Regulator of G protein Signaling Homology) domain of mammalian GRK2 (Fig 2A) does not act like other RGS domains as an accelerator of the intrinsic GTPase activity of the Gq subunit, but instead interacts with Gq and participates in the uncoupling of GPCRs linked to Gq via a phosphorylation-independent mechanism [67,74]. To examine whether the Gq-binding residues of the RH domain are needed for GRK-2 function in locomotion, we expressed GRK-2[R106A], Y109I, and D110A that correspond to mutations previously shown to disrupt mammalian GRK2 binding to Gq/11 [75]. All three mutant GRK-2 constructs rescued the slow locomotion defect of grk-2(gk268) (Fig 2E and 2G). These results suggest that GRK-2 binding to Gq and phosphorylation-independent desensitization of GPCR signaling are not required for GRK-2 function in locomotion.
The pleckstrin homology (PH) domain of mammalian GRK2 (Fig 2A) mediates interactions of GRK2 with membrane phospholipids and Gβγ subunits [67,76–78]. To examine whether these activities are required for GRK-2 function in locomotion, we expressed GRK-2[K567E] that disrupts phospholipid binding [79] and GRK-2[R587Q] that disrupts binding to Gβγ [79]. Neither of these GRK-2 mutants rescued the locomotion defect of the grk-2(gk268) mutant (Fig 2F and 2G), suggesting that both phospholipid and Gβγ binding through the PH domain of GRK-2 are required for GRK-2 function in locomotion.
GRK-2 is broadly expressed in body and head neurons [52]. To determine where GRK-2 acts to control locomotion, we expressed the grk-2 cDNA under the control of neuron-specific promoters. Expression of grk-2 under the pan-neuronal (Prab-3) or acetylcholine neuron (Punc-17) promoters fully rescued grk-2(gk268) mutant locomotion (Fig 3A). Interestingly, expression in ventral cord acetylcholine motor neurons (Pacr-2) did not rescue the locomotion phenotype, but expression driven by an unc-17 promoter derivative that is expressed mainly in the head acetylcholine neurons (Punc-17H [80,81]) rescued the locomotion phenotype (Fig 3A). Additionally, expression driven in a number of interneurons and head motorneurons by the glr-1 promoter did not rescue (Fig 3A). To exclude the possibility that the described role of GRK-2 in chemosensation [52] contributes to the slow locomotion phenotype of grk-2 mutants, we expressed grk-2 under ciliated sensory neuron promoters (Pxbx-1 and Posm-6). Expression of grk-2 in ciliated sensory neurons did not rescue the slow locomotion of grk-2 mutants (Fig 3A). We conclude that grk-2 acts in head acetylcholine neurons to regulate locomotion.
To determine if grk-2 also acts in head acetylcholine neurons to regulate Gq signaling, we expressed the grk-2 cDNA in the head acetylcholine neurons of egl-30(tg26); grk-2 double mutants. Expression in head acetylcholine neurons reversed the grk-2 suppression of the loopy posture and hyperactive locomotion of activated Gq−that is, the egl-30(tg26); grk-2 double mutants expressing grk-2 cDNA in the head acetylcholine neurons resemble the activated Gq single mutant (Fig 3B and 3C). These results suggest that grk-2 acts in head acetylcholine neurons to positively regulate Gq signaling.
To confirm that grk-2 is expressed in the head acetylcholine neurons, we coexpressed tagRFP fused to GRK-2 driven by the grk-2 promoter (grk-2::tagRFP) and GFP driven by the head acetylcholine neuron promoter (Punc-17H::GFP). We observed that grk-2::tagRFP is expressed broadly in head neurons and colocalizes with GFP in several head acetylcholine neurons (Fig 3D). We conclude that GRK-2 is expressed in head acetylcholine neurons to regulate locomotion and Gq signaling.
Our results suggest that GRK-2 acts as a GPCR kinase to regulate locomotion. If GRK-2 were a kinase for a GPCR coupled to Gq (EGL-30 in C. elegans) then we would expect GRK-2 to negatively regulate Gq, which does not agree with our data. Alternatively, GRK-2 could be a kinase for a GPCR coupled to Go (GOA-1 in C. elegans). The C. elegans Gq and Go pathways act in opposite ways to regulate locomotion by controlling acetylcholine release [82]. EGL-30 is a positive regulator of acetylcholine release whereas GOA-1 negatively regulates the EGL-30 pathway through activation of the RGS protein EAT-16 and the diacylglycerol kinase DGK-1. egl-30 loss-of-function mutants are immobile whereas egl-30 gain-of-function mutants are hyperactive and have a loopy posture [83,84]. goa-1 and eat-16 mutants have locomotion phenotypes opposite those of egl-30; they are hyperactive and have a loopy posture [85–87]. dgk-1 loss-of-function mutants are hyperactive but do not have a loopy posture [88]. To test whether GRK-2 acts on a Go-coupled GPCR, we examined whether goa-1 mutations suppress grk-2 mutants. We found that the goa-1; grk-2 double mutant is hyperactive and has a loopy posture like the goa-1 single mutant (S4A, S4C and S4D Fig), indicating that GRK-2 acts upstream of goa-1. This result suggests that GRK-2 could be acting on GPCR(s) coupled to GOA-1.
To further dissect the GRK-2 pathway, we examined whether grk-2 mutations suppress the hyperactive phenotypes of eat-16 and dgk-1 mutants. The eat-16; grk-2 double mutant is hyperactive and has a loopy posture like the eat-16 single mutant (S4A, S4C and S4D Fig) indicating that eat-16, like goa-1, acts downstream of GRK-2. By contrast, the grk-2; dgk-1 double mutant is similar to grk-2 (S4B Fig). Expression of the kinase-dead GRK-2[K220R] in grk-2; dgk-1 mutants does not restore dgk-1 hyperactive locomotion (S4E Fig). In addition, expression of GRK-2 under a head acetylcholine neuron promoter in grk-2; dgk-1 mutants restores dgk-1 hyperactive locomotion (S4F Fig). Thus, GRK-2 regulation of the locomotion rate, Gq signaling, and DAG signaling all depend on the GRK-2 kinase activity and a function of GRK-2 in head acetylcholine neurons.
In a search for potential Go-coupled GPCR targets for GRK-2, we considered the Go-coupled D2-like dopamine receptor DOP-3. In C. elegans, dopamine is required for the “basal slowing response”, a behavior in which wild-type animals slow down when on a bacterial lawn [89]. This behavior is mediated by the mechanosensory activation of dopamine neurons caused by physical contact of the worm body with bacteria. cat-2 mutants that are deficient in dopamine biosynthesis [90] or dop-3 mutants that lack the D2-like dopamine receptor DOP-3, are defective in basal slowing [61,89]. DOP-3 has been proposed to act through Go in ventral cord acetylcholine motor neurons to decrease acetylcholine release and promote the basal slowing response [61].
If grk-2 acts in the dopamine pathway to mediate proper locomotion, possibly by phosphorylating and inactivating DOP-3, then mutations in dop-3 and cat-2 should suppress the grk-2 locomotion phenotype. Indeed, the grk-2 mutant slow locomotion phenotype was suppressed by mutations in dop-3 and cat-2 (Fig 4A). A dop-3 mutation also suppressed the swimming defect of the grk-2 mutant (S2 Fig). In addition, the dop-3 and cat-2 mutations reversed the grk-2 suppression of the loopy posture and hyperactive locomotion of activated Gq−that is, the triple mutants resemble the activated Gq single mutant (Fig 4B–4E and S5 Fig). These results suggest that GRK-2 acts in the dopamine pathway to regulate locomotion and Gq signaling by negatively regulating the D2-like dopamine receptor DOP-3.
Our results suggest that GRK-2 acts in head acetylcholine neurons to regulate locomotion. To test if DOP-3 acts in the same neurons as GRK-2, we expressed the dop-3 cDNA under a pan-neuronal promoter (Prab-3), an acetylcholine neuron promoter (Punc-17), a head acetylcholine neuron promoter (Punc-17H), and an acetylcholine motor neuron promoter (Pacr-2) in the grk-2; dop-3 double mutant. Expression driven by the pan-neuronal, acetylcholine neuron, and head acetylcholine neuron promoters reversed the dop-3 suppression of the slow locomotion of grk-2(gk268) mutant animals—that is, grk-2; dop-3 mutants expressing dop-3 cDNA by these three promoters resemble the grk-2 mutant (S6A Fig). By contrast, expression of the dop-3 cDNA by an acetylcholine ventral cord motor neuron promoter did not reverse the grk-2; dop-3 locomotion phenotype (S6A Fig) or the hyperactive locomotion and loopy posture of egl-30(tg26); grk-2; dop-3 mutant animals (S6C–S6E Fig). We conclude that dop-3, like grk-2, acts in head acetylcholine neurons, consistent with the model that GRK-2 acts directly on DOP-3. Moreover, dop-3 expression under the grk-2 promoter reversed the dop-3 suppression of the slow locomotion of grk-2 mutants (S7A Fig), supporting the idea that GRK-2 and DOP-3 act in the same neurons.
We observed that grk-2; dop-3 and cat-2; grk-2 double mutant animals still retain some of the characteristic grk-2 phenotypes: the animals have shorter bodies and are egg-laying defective. In addition, grk-2 mutants do not fully explore a bacterial lawn and this behavior remains in the grk-2; dop-3 double mutant (S1C Fig). Thus, GRK-2 has additional neuronal functions that do not depend on dop-3.
The D1-like dopamine receptor DOP-1 has been shown to act antagonistically to DOP-3 to regulate the basal slowing response: dop-1 mutations suppress the dop-3 basal slowing phenotype [61]. By contrast, we found that DOP-1 is not involved in the GRK-2 and DOP-3 pathway that regulates locomotion rate because dop-1 mutations do not affect the locomotion rate of the grk-2; dop-3 double mutant (S6B Fig). Thus, the role of DOP-3 in GRK-2-regulated locomotion is independent of its role in the basal slowing response.
Exposure of C. elegans to exogenous dopamine causes DOP-3-dependent paralysis—dop-3 mutants are significantly resistant to the paralytic effects of exogenous dopamine [61]. If GRK-2 negatively regulates DOP-3, then grk-2 mutants might be hypersensitive to dopamine due to increased DOP-3 activity. Indeed, we found that grk-2 mutants are hypersensitive to dopamine and this hypersensitivity depends on dop-3 (Fig 4F).
In an effort to dissect the molecular mechanism by which grk-2 regulates DOP-3 activity, we expressed GFP-tagged DOP-3 under the grk-2 promoter in dop-3 and grk-2; dop-3 mutant animals and examined the levels of expression of DOP-3::GFP both by Western and by fluorescence microscopy (S7 Fig). Although Pgrk-2::DOP-3::GFP fully reversed the dop-3 suppression of the slow locomotion of grk-2 mutants (S7A Fig), we did not observe any difference in the level of DOP-3 expression in a grk-2 mutant (S7B–S7D Fig) nor did we observe any obvious change in the subcellular localization of DOP-3::GFP in a grk-2 mutant (S7C Fig). However, one caveat is that we do not have the resolution to distinguish between DOP-3 localization on the plasma membrane or in an intracellular compartment.
In addition to genes within the canonical Gq-PLCβ pathway, our screen for suppressors of activated Gq also identified the Trio RhoGEF (UNC-73 in C. elegans) as a new direct Gq effector [8]. Recently, we identified the cation channels NCA-1 and NCA-2 as downstream targets of this Gq-Rho pathway. Specifically, we found that mutations in genes that encode accessory subunits of the NCA channels (unc-79, unc-80) or in the NCA channels per se (nca-1; nca-2) suppress the neuronal phenotypes of activated Gq and activated Rho [12]. Moreover, mutations in the Rho-NCA pathway suppress the loopy posture of the activated Gq mutant more strongly than do mutations in the canonical PLCβ pathway [12]. Like mutations in the Rho-NCA pathway, grk-2 mutants also strongly suppress the loopy posture of an activated Gq mutant (Figs 1A, 4D and 4E), suggesting that grk-2 may affect signaling through the Rho-NCA pathway.
To further examine whether grk-2 affects Rho-NCA signaling, we built double mutants of grk-2 with an activated Rho mutant (G14V), referred to here as Rho*, expressed in acetylcholine neurons. Rho* has a loopy posture and slow locomotion and a grk-2 mutation partially suppresses these phenotypes (Fig 5A and 5B), consistent with grk-2 affecting signaling through the Rho-NCA pathway.
We also built double mutants of grk-2 and a dominant activating mutation in the NCA-1 channel gene, nca-1(ox352), referred to here as Nca* [12,24]. Like Rho*, Nca* mutants have a loopy posture and slow locomotion. However, grk-2 mutants do not suppress either of these phenotypes because Nca*; grk-2 double mutants behave identically to Nca* mutants (Fig 5C and 5D). This suggests that grk-2 acts upstream of NCA.
C. elegans has two proteins that encode pore-forming subunits of NCA channels, NCA-1 and NCA-2. Mutations that disrupt both NCA-1 and NCA-2 channel activity cause a characteristic “fainter” phenotype in which worms suddenly arrest their locomotion and acquire a straightened posture [35]. Our genetic experiments indicate that GRK-2 affects Rho-NCA signaling, but grk-2 mutants are not fainters. Given that grk-2 partially suppresses Rho*, we hypothesized that GRK-2 is not absolutely required for Rho-NCA signaling, but provides modulatory input. To test this hypothesis, we built double mutants between grk-2 and nlf-1, which is partially required for localization of the NCA-1 and NCA-2 channels and has a weak fainter mutant phenotype [12,91]. A grk-2 mutation strongly enhanced the weak fainter phenotype of an nlf-1 mutant so that the double mutants resembled the stronger fainter mutants that completely abolish NCA-1 and NCA-2 channel activity (Fig 6A and 6B). Moreover, double mutants between grk-2 and the RhoGEF Trio unc-73 were also strong fainters, supporting the hypothesis that GRK-2 modulates the Rho-NCA pathway (Fig 6C). By contrast, double mutants between grk-2 and the egl-8 PLCβ do not have a fainter phenotype (Fig 6C). These results suggest that GRK-2 is a positive modulator of NCA-1 and NCA-2 channel activity.
If GRK-2 modulates the NCA channels by acting as a negative regulator of Go then we would expect that mutations in other proteins that act as negative regulators of Go might enhance the fainter phenotype of nlf-1 mutants. Indeed, a mutation in egl-10, encoding the RGS that negatively regulates Go [92], strongly enhances the nlf-1 fainter phenotype (Fig 6D). As controls, mutations in genes involved in dense-core vesicle biogenesis (eipr-1 and cccp-1), that cause locomotion defects comparable to grk-2 or egl-10 [63,80], did not enhance the nlf-1 fainter phenotype, indicating that the interactions of grk-2 and egl-10 with nlf-1 are specific.
grk-2 acts in head acetylcholine neurons to mediate locomotion. We recently used the same Punc-17H promoter construct to show that nlf-1 also acts in head acetylcholine neurons and not in ventral cord motor neurons to regulate locomotion [12]. Therefore, we predicted that expression of an activated Go mutant under a head acetylcholine neuron promoter would enhance the fainter phenotype of nlf-1 mutants. Indeed, we found that expression of the activated Go mutant GOA-1[Q205L] in head acetylcholine neurons makes the animals slow (Fig 6E) and significantly enhances the fainter phenotype of nlf-1 mutants (Fig 6F). These results support the model that GRK-2 negatively regulates Go, and that Go negatively regulates NCA-1 and NCA-2 channel activity.
Our results are consistent with the model that GRK-2 acts in locomotion by negatively regulating DOP-3 and that GRK-2 is a positive modulator of NCA-1 and NCA-2 activity. These data predict that DOP-3 would be a negative modulator of NCA-1 and NCA-2 channel activity. Consistent with this model, mutations in cat-2 and dop-3 almost fully suppress the nlf-1 fainter phenotype during forward movement (Fig 7A and 7B). Additionally, dop-3 mutants partially suppress the strong grk-2; nlf-1 fainter phenotype, consistent with the model that DOP-3 is a substrate for GRK-2 (Fig 7C). These results suggest that dopamine, through DOP-3, negatively modulates NCA-1 and NCA-2 channel activity.
To more directly test whether grk-2 and dop-3 modulate the NCA channel per se, we created double mutants between grk-2 and the pore-forming subunit gene nca-1. nca-1 mutants have a low penetrance, very weak backward-fainting phenotype that is strongly enhanced in a grk-2 mutant background (S3C and S6F Figs). arr-1 mutants, on the other hand, do not enhance the nca-1 phenotype, further supporting the conclusion that arrestin does not play a role in this pathway (S3C Fig). As expected, dop-3 suppresses the enhanced fainting phenotype of the grk-2; nca-1 double mutant (S6F Fig).
Our data suggest that GRK-2 and DOP-3 play modulatory and not essential roles in the regulation of NCA-1 (and possibly NCA-2) channel activity. By contrast, UNC-80 is necessary for the stability and function of NCA-1 and NCA-2, so unc-80 mutants are strong fainters [36,39]. As expected for a modulatory role in regulating NCA-1 and NCA-2 activity, mutations in dop-3 and cat-2 do not suppress the strong fainter phenotype of unc-80 mutants (S8A and S8B Fig).
We showed above that grk-2 mutants are hypersensitive to the paralytic effects of dopamine. We also found that low concentrations of dopamine do not paralyze grk-2 mutants but instead cause them to faint, and that this effect depends on dop-3 (Fig 7D). This is consistent with the model that dopamine acts through DOP-3 to negatively modulate NCA-1 and NCA-2.
In C. elegans, the NCA channels act in premotor interneurons [12,43,91]. To determine whether grk-2 acts in a cell autonomous way to regulate NCA, we identified the head acetylcholine neurons where GRK-2 is expressed. We coexpressed GRK-2 fused to tagRFP driven by the grk-2 promoter (grk-2::RFP) and nuclear YFP driven by the choline transporter cho-1 promoter (Pcho-1fosmid::SL2::YFP::H2B), which is expressed in all acetylcholine neurons [93]. We found that grk-2 is expressed in the following head acetylcholine neurons: the AVA, AVB, AVD, and AVE premotor interneurons; SMD and RMD head motor neurons; and in the AIN, AIY, SIA, SIB, and SAA interneurons (Fig 8A).
To further determine where GRK-2 acts to control locomotion, we expressed the grk-2 cDNA under additional neuron-specific promoters in grk-2 mutants. We used a cho-1 promoter fragment for expression in the SMD and RMD head motor neurons [94], the ceh-24 promoter for expression in the SIA and SIB interneurons, and a ttx-3 promoter fragment for AIY-specific expression [95]. For expression in premotor command interneurons we used a combination of the nmr-1 promoter for AVA, AVD and AVE (also PVC and RIM) expression together with the sra-11 promoter for AVB (also AIY and AIA) expression, as previously described [91]. We found that grk-2 expression in command interneurons fully rescued the slow locomotion of grk-2 mutants, but expression in the other neuron types failed to rescue (Fig 8B–8E). However, expression of grk-2 in only sra-11 or only nmr-1 expressing neurons did not rescue the slow locomotion defect (S9 Fig). Additionally, grk-2 expression in the command interneurons was sufficient to rescue the enhanced fainting phenotype of grk-2; nlf-1 mutants (Fig 8F). Similarly, dop-3 expression in the command interneurons was sufficient to reverse the dop-3 suppression of the slow locomotion of grk-2 mutants (Fig 8G). Given that the fainting phenotypes of nlf-1 mutants and nca mutants were also rescued by expression in command interneurons [43,91], our results suggest that GRK-2, DOP-3, and the NCA channels act in the same neurons. However, we did not see rescue of the grk-2 locomotion phenotype using the glr-1 promoter (Fig 3A), which in principle is also expressed in the command interneurons, and similarly we did not observe statistically significant rescue of the nlf-1 fainting phenotype using the same glr-1 promoter [12]. The different results seen between the glr-1 and the nmr-1 + sra-11 promoters may be due to different levels of expression or because of expression in some different neuron types.
In this study we identified a pathway that modulates the activity of the NCA-1 and NCA-2 channels through dopamine and Gq signaling (Fig 9). We found that dopamine acts through the D2-like receptor DOP-3 to negatively modulate NCA-1 and NCA-2. Furthermore, we identified the GPCR kinase GRK-2 as a positive (indirect) regulator of Gq and negative regulator of NCA-1 and NCA-2. Our results suggest that GRK-2 mediates its regulatory effects by inhibiting DOP-3.
In C. elegans, GRK-2 was previously found to act in sensory neurons to regulate chemosensation [52]. Here we found that GRK-2 acts in command interneurons to regulate locomotion and Gq signaling. Using a structure-function approach, we found that GPCR phosphorylation, Gβγ-binding, and membrane-binding are required for GRK-2 function in locomotion, but binding to Gq is not required. Similar results were reported for the function of GRK-2 in chemosensation [48], suggesting that in both cases GRK-2 acts as a GPCR kinase and that membrane localization is critical for its function. Additionally, GRK-2 seems to act independently of arrestin to regulate both locomotion and chemosensation [52]. Because cat-2 and dop-3 mutants are hypersensitive to the aversive odorant octanol [96–98] and grk-2 mutants are insensitive to octanol [52], GRK-2 might act as a GPCR kinase for DOP-3 in chemosensory neurons as well.
GRK-induced phosphorylation of GPCRs induces endocytosis, which leads to their sorting to either lysosomes for degradation or to recycling endosomes. GRK-dependent recruitment of arrestins to the phosphorylated receptor is typically required for endocytosis, but GRK2 was also reported to utilize arrestin-independent mechanisms to mediate receptor internalization [66]. GRK2 associates with a large number of proteins with known roles in receptor internalization and signaling. For example, the C-terminus of GRK2 directly binds clathrin and this interaction has been proposed to be involved in arrestin-independent internalization [99]. Our data suggest an arrestin-independent role for C. elegans GRK-2 in GPCR regulation, supporting the idea that the role of GRK-2 extends beyond just the recruitment of arrestin.
The D2-type dopamine receptors, like DOP-3, are GPCRs that couple to members of the inhibitory Gi/o family. Mammalian GRK2 and GRK3 (the orthologs of GRK-2) have been connected to the desensitization, internalization, and recycling of D2-type dopamine receptors [55–59,100]. Interestingly, some of the effects of GRK2 on D2 receptor function may be independent of receptor phosphorylation [57,58,100], though one caveat of these studies is that they involve GRK2 overexpression in heterologous cells. Our structure-function approach indicates that GPCR phosphorylation is important for GRK-2 function in locomotion and Gq signaling in C. elegans, although we cannot exclude the possibility that phosphorylation of additional substrates may also be required. In vivo studies of the role of mammalian GRKs in the regulation of dopamine receptors have focused on the analysis of behaviors that are induced by psychostimulatory drugs such as cocaine that elevate the extracellular concentration of dopamine [59]. Mice with a cell-specific knockout of GRK2 in D2 receptor-expressing neurons have altered spontaneous locomotion and sensitivity to cocaine [101], though the cellular mechanisms underlying these behavioral effects are not known. Our findings provide evidence of a direct association between GRK-2 and D2-type receptor signaling that regulates locomotion in an in vivo system.
In C. elegans, the Gq and Go pathways act in opposite ways to regulate locomotion by controlling synaptic vesicle release [82]. Gq acts as a positive regulator of acetylcholine release while Go negatively regulates Gq signaling, through activation of the Gq RGS EAT-16 and the diacylglycerol kinase DGK-1. DGK-1 phosphorylates the second messenger DAG and thus inhibits its action. Using genetic epistasis, we demonstrated that GRK-2 acts upstream of GOA-1/Go and EAT-16 to positively regulate locomotion and body posture. Given this result, our cell-specific rescue data, and our data indicating that GRK-2 acts as a GPCR kinase for a locomotion-related GPCR, we propose that GRK-2 acts as a kinase for the Go-coupled GPCR DOP-3 in premotor interneurons. In this model, GRK-2 driven phosphorylation of DOP-3 reduces Go signaling and thereby promotes Gq signaling (Fig 9). Inhibition of Go by GRK-2 could promote Gq-Rho signaling by two mechanisms: (1) by inhibiting the Gq RGS EAT-16 and thus activating Gq itself, and (2) by inhibiting DGK-1 which acts in parallel to Gq-Rho to regulate DAG levels (Fig 9).
Interestingly, a grk-2 mutant is suppressed by mutations in goa-1 and eat-16, but not by dgk-1. This finding supports other literature that suggests that goa-1 and eat-16 have similar interactions with Gq signaling, but that dgk-1 is distinct [87,102]. GOA-1 and EAT-16 act upstream of Gq to inhibit Gq signaling. DGK-1, on the other hand, acts downstream of Gq to reduce the pool of the Gq-generated second messenger DAG. Adding to the complexity, DAG levels may be controlled by both the PLCβ and Rho branches of the Gq pathway (Fig 9). Previously, it has been shown that mutations in dgk-1 partially suppress the strong locomotion defect of egl-30/Gq loss-of-function mutations [102]. Surprisingly, we found that a grk-2 mutation fully suppresses a dgk-1 mutant. This suggests that the effect of GRK-2 on locomotion is more complex and may be partially independent of Gq signaling and of Gq-generated DAG. This agrees with our data showing that GRK-2 has additional neuronal functions that do not depend on DOP-3.
Gq signaling regulates several genetically separable aspects of locomotion behavior including locomotion rate and waveform. The Gq-PLCβ signaling pathway has been reported to act in ventral cord motor neurons to regulate acetylcholine release and locomotion rate [103], whereas the Gq-Rho pathway has been reported to act in at least two different classes of neurons including head acetylcholine neurons to regulate locomotion rate, waveform, and fainting behavior [12]. Our data here further suggest that DOP-3, GRK-2, and the Gq-Rho pathway all act together in the premotor command interneurons to regulate activity of the NCA channels. The command interneurons have been previously shown to regulate several aspects of locomotion behavior including the propensity to go forward or reverse [104,105] and the tendency of the worm to sustain persistent locomotion [43]. Our data here suggest that the command interneurons also regulate the locomotion rate and the posture of the animals. As we reported previously, mutations in the Rho-NCA pathway suppress both the locomotion rate and loopy posture of activated Gq mutants whereas mutations in the PLCβ pathway suppress mainly the locomotion rate [12]. Thus, Gq acts through both the PLCβ pathway and the Rho-NCA pathway to regulate locomotion rate, probably by acting in different neurons. By contrast, Gq acts primarily through the Rho-NCA pathway to regulate the posture of the worms. This agrees with our data showing that grk-2 mutations, which affect signaling through the Rho-NCA pathway, strongly suppress the loopy posture of activated Gq.
The identification of GRK-2 as a putative DOP-3 kinase and positive modulator of Gq-Rho signaling connects dopamine signaling to modulation of the NCA channels (Fig 9). NCA channels have been shown in recent years to be important for neuronal excitability and a number of rhythmic behaviors [16,34–42]. In humans, mutations affecting the NCA channel NALCN cause neurological diseases [20–33]. However, despite the relevance of this channel to neuronal function it is unclear how it is gated and activated. Two studies have shown that NALCN-dependent currents can be activated by G protein-coupled receptors in a G protein independent way [44,45] whereas another study showed that NALCN can be activated by low extracellular calcium via a G protein-dependent pathway [46], but the specific mechanisms remain unknown. Our results suggest that dopamine acts through the DOP-3 G protein-coupled receptor and downstream G protein signaling pathways to modulate activity of the NCA channels in a physiologically relevant setting. This is the first study connecting dopamine to the activation of these important channels.
Strains were maintained at room temperature or 20° on the OP50 strain of E. coli [106]. The Supplementary Information contains full genotypes of all the strains we used (S1 Table; List of strains).
The grk-2(yak18) mutant was isolated in an ENU screen as a suppressor of the hyperactive locomotion and loopy posture of the activated Gq mutant egl-30(tg26) [63]. We mapped the yak18 mutation to the left arm of Chromosome III (between -27 and -21.8 m.u.) using a PCR mapping strategy that takes advantage of PCR length polymorphisms due to indels in the Hawaiian strain CB4856 (Jihong Bai, personal communication). Using whole-genome sequencing (see below), we found that yak18 is a G to A transition mutation in the W02B3.2 (grk-2) ORF that creates a G379E missense mutation in the kinase domain of GRK-2. We confirmed the gene identification by performing a complementation test between grk-2(yak18) and the grk-2(gk268) deletion mutant, finding that they fail to complement for the slow locomotion phenotype.
Genomic DNA from grk-2(yak18) animals was isolated and purified according to the Worm Genomic DNA prep protocol from the Hobert lab website (http://hobertlab.org/wp-content/uploads/2013/02/Worm_Genomic_DNA_Prep.pdf). The sample was sequenced using Ion Torrent sequencing (DNA Sequencing Core Facility, University of Utah). The sequencing data were uploaded to the Galaxy web platform and were analyzed as described [107].
The Supplemental Information contains a complete list of constructs used (S2 Table; List of plasmids). All constructs made in this study were constructed using the multisite Gateway system (Invitrogen). Specifically, a promoter region, a gene region (cDNA), and an N- or C-terminal 3’UTR or fluorescent tag (GFP or tagRFP) fused to a 3’UTR were cloned into the destination vector pCFJ150. For the cell-specific rescuing experiments, an operon GFP was included in the expression constructs downstream of the 3’UTR [108]. This resulted in expression of untagged grk-2, dop-3, or goa-1, but allowed for confirmation of proper promoter expression by monitoring GFP expression. The cho-1 fosmid reporter construct otIs534 carries an SL2-spliced nuclear localized YFP::H2B immediately after the stop codon of the cho-1 gene [93].
Extrachromosomal arrays were made by standard injection and transformation methods [109]. In all cases we injected 5–10 ng/ul of the expression vector and isolated multiple independent lines. At least two lines were tested that behaved similarly.
We made a construct driving expression of the grk-2 cDNA fused to tagRFP under the grk-2 promoter and generated worms with extrachromosomal arrays. For the grk-2 promoter region, we PCR amplified 2892 bp upstream of the start codon using genomic DNA as a template and the following set of primers: forward primer 5’cacgacagtttccatagtgattgg3’ and reverse primer 5’tttttgttctgcaaaatcgaattg3’. grk-2 was expressed in neurons in the head, ventral cord, and tail, consistent with the published expression pattern [52]. Neurons were identified by the stereotypical positions of cells expressing the acetylcholine neuron reporter cho-1fosmid::SL2::YFP::H2B [93,110] that colocalized with grk-2::tagRFP.
For most experiments, we measured locomotion rate using the body bend assay. Specifically, first-day adults were picked to a three-day-old lawn of OP50 and stimulated by poking the tail of the animal with a worm pick. Body bends were then immediately counted for one minute. A body bend was defined as the movement of the worm from maximum to minimum amplitude of the sine wave [102]. Specifically for the experiment described in Fig 5D we used a radial locomotion assay. Animals were placed in the center of 10 cm plates with thin one to two-day-old lawns of OP50 and left for one hour. The position of each worm was marked and the radial distance from the center of the plate was measured (cm travelled/h).
Egg-laying assays were performed as described [80]. L4 larvae were placed on plates with OP50 at 25°C overnight. The next day, five animals were moved to a fresh plate and allowed to lay eggs at 25°C for two hours. The number of eggs present on the plate was counted.
First-day adult animals were placed on an OP50 plate and allowed to move forward until when they had completed five to ten tracks. Each animal's tracks were imaged at 40X magnification using a Nikon SMZ18 microscope with the DS-L3 camera control system. Period and 2X amplitude were measured using the line tool in Image J. For each worm, five period/ amplitude ratios were averaged and five worms were used per experiment.
The fainting phenotype is characterized by frequent arrest of locomotion, accompanied by a straightening of the anterior part of the body. We scored fainting as a sudden halt in movement accompanied by a straightened posture.
First-day adults were transferred to plates with two to three-day-old lawns of OP50 and left undisturbed for one minute. Animals were then poked either on the head (for backward movement) or on the tail (for forward movement), and we counted the number of body bends before the animal faints. If the animal made ten body bends, the assay was stopped and we recorded ten as the number. Thus, animals that never faint (for example, wild-type) are scored as 10 in these experiments. Specifically for the experiment described in Fig 7D the number reported was the percentage of animals that fainted before making 10 body bends.
Single, first-day adults were transferred to a 25 ul drop of M9 buffer at the center of an empty NGM plate and video recorded for 30 sec. The swimming behavior was analyzed as described [38,64].
First-day adults were mounted on 2% agarose pads and anesthetized in M9 buffer containing 50 mM sodium azide for ten minutes. The image of each animal was obtained using a Nikon 80i wide-field compound microscope. Body size was measured using ImageJ software.
We used a method similar to the one described [61]. Specifically, first-day adults were transferred to plates containing dopamine (5 mM, 10 mM, 15 mM, 20 mM, 40 mM) and incubated for 20 min at room temperature. Animals were then poked using a worm-pick and the number of body bends was counted, stopping the assay at 10 body bends. We report the percent of animals that moved 10 body bends without stopping (Percent of animals moving). A body bend was defined as the movement of the worm from maximum to minimum amplitude of the sine wave. Dopamine plates were prepared fresh just before use, as described [61].
For the Western analysis shown in Fig 2G, worm lysates were prepared as follows. Ten transgenic animals from each strain were transferred to a 6 cm OP50 plate and grown until most of their progeny had reached adult stage. Animals from five such plates were washed off with M9, collected in a 15 ml Falcon tube, and spun down at 2000 rpm for 3 min. Animals were washed twice with M9. The pelleted worms were then resuspended in 2X SDS loading dye and lysed by incubation at 95°C for 20 min. For the Western analysis shown in S7B Fig, worm lysates were prepared as follows. Two hundred transgenic worms were individually picked and transferred in a microfuge tube in 10 ul M9. An equal volume of 2X SDS loading dye was added to the tube and the animals were lysed by incubation at 95°C for 20 min.
Samples were resolved on 10% SDS-polyacrylamide gels and blotted onto PVDF membranes. To detect the desired proteins, we added the following primary antibodies: monoclonal anti-GRK2/3, clone C5/1.1 (1:1000, EMD Millipore #05–465), monoclonal anti-beta-tubulin antibody (1:1000, ThermoFisher, BT7R, #MA5-16308), rabbit polyclonal anti-GFP (1:1000, Santa Cruz #sc-8334), and monoclonal anti-mCherry (1:50, a gift from Jihong Bai and the Fred Hutchinson Cancer Research Center antibody development shared resource center). The secondary antibodies were an Alexa Fluor 680-conjugated goat anti-mouse antibody (1:20,000, Jackson Laboratory #115-625-166) and an Alexa Fluor 680-conjugated goat anti-rabbit antibody (1:20,000, Jackson Laboratory #111-625-144). A LI-COR processor was used to develop images.
For fluorescence imaging, first-day adult animals were mounted on 2% agarose pads and anesthetized with 50 mM sodium azide for ten minutes before placing the cover slip. The images shown in Fig 3D and S7C Fig were obtained using an Olympus FLUOVIEW FV1200 confocal microscope. The images shown in Fig 8A were acquired using a Zeiss confocal microscope (LSM880) with Z-stack analysis and reconstruction performed using the ZEN software tool.
For pictures of worms, first-day adult animals were placed on an assay plate and photographed at 50 or 60X using a Nikon SMZ18 dissecting microscope with a DS-L3 camera control system. The images were processed using ImageJ.
P values were determined using GraphPad Prism 5.0d (GraphPad Software). Normally distributed data sets requiring multiple comparisons were analyzed by a one-way ANOVA followed by a Bonferroni or Dunnett test. Normally distributed pairwise data comparisons were analyzed by two-tailed unpaired t tests. Non-normally distributed data sets with multiple comparisons were analyzed by a Kruskal-Wallis nonparametric ANOVA followed by Dunn’s test to examine selected comparisons. Non-normally distributed pairwise data comparisons were analyzed by a Mann-Whitney test. For the experiments shown in S3C and S6F Figs a chi-square test for multiple comparisons was used.
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10.1371/journal.ppat.1006301 | A novel Meloidogyne graminicola effector, MgGPP, is secreted into host cells and undergoes glycosylation in concert with proteolysis to suppress plant defenses and promote parasitism | Plant pathogen effectors can recruit the host post-translational machinery to mediate their post-translational modification (PTM) and regulate their activity to facilitate parasitism, but few studies have focused on this phenomenon in the field of plant-parasitic nematodes. In this study, we show that the plant-parasitic nematode Meloidogyne graminicola has evolved a novel effector, MgGPP, that is exclusively expressed within the nematode subventral esophageal gland cells and up-regulated in the early parasitic stage of M. graminicola. The effector MgGPP plays a role in nematode parasitism. Transgenic rice lines expressing MgGPP become significantly more susceptible to M. graminicola infection than wild-type control plants, and conversely, in planta, the silencing of MgGPP through RNAi technology substantially increases the resistance of rice to M. graminicola. Significantly, we show that MgGPP is secreted into host plants and targeted to the ER, where the N-glycosylation and C-terminal proteolysis of MgGPP occur. C-terminal proteolysis promotes MgGPP to leave the ER, after which it is transported to the nucleus. In addition, N-glycosylation of MgGPP is required for suppressing the host response. The research data provide an intriguing example of in planta glycosylation in concert with proteolysis of a pathogen effector, which depict a novel mechanism by which parasitic nematodes could subjugate plant immunity and promote parasitism and may present a promising target for developing new strategies against nematode infections.
| Post-translational modification (PTM) is a tool used by prokaryotic and eukaryotic cells to regulate protein activity, and many unique and important functions of proteins depend on appropriate PTMs. Evidence is emerging that plant pathogen effectors can utilize the host post-translational machinery to mediate their PTM and regulate their activity to facilitate parasitism. However, these biochemical modifications have been described only for a limited number of plant-parasitic nematode effectors. In this report, we identified the novel Meloidogyne graminicola effector MgGPP, which is important for nematode parasitism. We found that the effector MgGPP is secreted into host tissues and is subjected to glycosylation in concert with proteolysis in rice. Furthermore, we have shown that the proteolytical processing of MgGPP could change the subcellular trafficking of MgGPP, and the N-glycosylation of MgGPP can activate its function to suppress resistance gene (RBP-1/Gpa2)-mediated cell death, revealing a strategy of host-mediated PTM that is cleverly exploited by plant-parasitic nematodes to subjugate plant immunity and thereby promote parasitism.
| Root-knot nematodes (RKNs) are one of the most economically important plant-parasitic nematodes (PPNs), infecting more than 5500 plant species [1,2]. The soil-borne RKNs devastate varieties of crop plants, resulting in about $70 billion losses in worldwide agriculture annually [3]. Generally, the second-stage juveniles (J2s) of RKNs penetrate host roots and migrate intercellularly towards the vascular cylinder, where they transform five to seven cells around their head into large and multinucleated feeding cells called giant cells that provide RKNs with nutrients and are essential for their development and reproduction [4]. RKNs have evolved numerous effectors that originate from the nematode esophageal gland cells and are secreted into host plant tissues, playing key roles in root invasion and the formation and maintenance of giant cells, resulting in the successful parasitism of RKNs [5].
Decades of research have demonstrated the roles of some effectors of PPNs. For example, extracellular effectors, such as ß-1,4-endoglucanase and pectate lyase, can degrade and depolymerize the main structural polysaccharide constituents of the plant cell wall [6,7], and cyst nematode-secreted CLAVATA3/ESR CLE-like proteins (CLEs) mimic endogenous host-plant CLE peptides [8]. Recently, one of the most exciting discoveries has been that effectors of PPNs are capable of suppressing host defenses directly [9–12]. Previous studies showed that plants have evolved a set of immune system defenses against plant pathogens [13]. When the immune system detects pathogens, a series of immune responses, such as Ca2+ spikes, callose deposition, reactive oxygen species bursts, a localized hypersensitive response (HR) and the induction of pathogenesis-related gene expression, can be activated [14,15]. PPNs therefore have also evolved a class of effectors to suppress the host immune system for survival. The first nematode-secreted effector that was found to have the ability to suppress the plant defense responses is the calreticulin Mi-CRT identified in M. incognita [11]. Subsequently, several effectors, mainly cyst nematode-secreted and root-knot nematode-secreted effectors, such as SPRYSEC-19 and GrUBCEP12 in Globodera rostochiensis, Ha-ANNEXIN in Heterodera avenae, MeTCTP in M. enterolobii and MiMsp40 in M. incognita, were demonstrated to suppress host defense responses directly [9,12,16]. Of these effectors, GrUBCEP12 was found to be cleaved in planta [12], suggesting that nematode-secreted effectors may be subjected to post-translational modification (PTM) in planta.
PTM, including phosphorylation, acetylation, glycosylation, proteolysis and ubiquitination, is a tool used by prokaryotic and eukaryotic cells to regulate protein activity or promote protein/protein interactions [17,18]. Of the various types of PTM, glycosylation and proteolysis are the two important modes of protein modification in plant cells. N-glycosylation has been widely identified and found to play vital roles in diverse aspects of development and physiology, such as the regulation of protein folding, salt tolerance, cellulose biosynthesis, environmental stress responses and plant immunity [19,20]. Intriguingly, the N-glycosylation of plant pathogen effectors also plays a role in the infection and parasitism of pathogens [19]. Pathogens can secrete glycoproteins directly into host plants or utilize the host post-translational machinery to form glycosylated effectors, avoiding the plant immunity and promoting pathogenesis of pathogens. For example, a secreted LysM protein, Slp1, was shown to function in Magnaporthe oryzae as an effector protein that suppresses host immunity by binding chitin oligosaccharides; however, incomplete N-glycosylation of Slp1 led to a dramatic reduction in its chitin-binding capability [19].
Proteolysis is a selective mechanism that can either be co-translational or act in concert with other PTMs in many cellular processes, such as the stress response, maturation of inactive hormones, neuropeptides and growth factors, and targeting of intracellular proteins [21,22]. Post-translational proteolytic processing of plant pathogen effectors in planta has been reported. For example, the effector AvrRpt2 from Pseudomonas syringae was delivered into host cells via the type III secretion system, where it was specifically cleaved to generate a functional C-terminal end [23].
Previous studies on fungal and bacterial effectors have partly contributed to the understanding of PTM of plant pathogen effectors in planta. However, only three nematode-secreted effectors have been found to be post-translationally modified in planta as yet. In addition to GrUBCEP12 mentioned above, a CLE effector from G. rostochiensis and the effector protein 10A07 from Heterodera schachtii were glycosylated and phosphorylated in planta, respectively [24,25]. It is essential to explore more nematode-secreted effectors with PTM capabilities in planta to understand their roles during nematode parasitism.
Rice is the staple food of more than half of the world’s population, and it is also an excellent model system for studying physiological and molecular interactions between plants and PPNs [26,27]. Meloidogyne graminicola, one of the most important RKNs, is considered to be a major threat to rice and has caused substantial destruction to up to 87% of the production [28]. Transcriptomes of the rice root-knot nematode M. graminicola have been obtained [29,30], greatly facilitating the exploration of candidate effectors. However, little is known about M. graminicola effectors.
Here, we report the cloning and characterization of a novel gene from M. graminicola. We present several lines of evidence to show that this novel gene affects M. graminicola parasitism. Additionally, we also provide evidence that the effector encoded by the novel gene can be secreted into host cells, transported from the endoplasmic reticulum (ER) to the nucleus, and post-translationally glycosylated and proteolytically cleaved in host cells. Moreover, only the glycosylated effector is capable of suppressing the host defense response. The effector protein was named MgGPP because of its glycosylation in concert with proteolysis in planta.
A 759-bp genomic fragment, designated MgGPP, was obtained. The MgGPP gene includes an open reading frame (ORF) of 675 bp (GenBank accession number KY113086), separated by two introns of 43 bp and 41 bp (S1A Fig). The intron/exon boundaries have a conserved 5’-GT-AG-3’ intron splice-site junction [31]. The ORF encodes a 224-amino-acid polypeptide with a predicted molecular size of 25.5 kDa. The protein contains a secretion signal peptide of 20 amino acids at its N-terminus according to the SignalP program and has no putative transmembrane domain based on TMHMM, suggesting that MgGPP may be a secreted protein. MgGPP is predicted to have one N-glycosylation site at Asn-110 (Asn-Asp-Ser-Asp, NDSD) and contain a putative SV40-like nuclear localization signal (NLS) domain (21EIKKYKP27) (S1B Fig), and based on PSORTII, MgGPP is predicted to have a 73.9% probability of being located in the nucleus. Southern blot analysis showed that MgGPP is a single-copy gene in the genome of M. graminicola (S2 Fig).
A BLAST search did not reveal any significant MgGPP homologues at the nucleotide level in other organisms but showed matches with several Meloidogyne avirulence protein family (MAPs) at the peptide level. However, the shared identities between MgGPP and the MAPs were only 37.3%-41.1%. Moreover, the conserved double-psi beta-barrel domain and repetitive motifs of 13 and 58 aa that exist in the MAPs were not found in MgGPP according to InterProScan. These observations suggest that MgGPP is a novel gene of M. graminicola.
The tissue localization of MgGPP in M. graminicola was investigated using in situ hybridization. Strong signals from accumulated transcripts were observed in the subventral esophageal gland cells of M. graminicola preparasitic second-stage juveniles (pre-J2s) after hybridization with the digoxigenin-labeled antisense ssDNA probe. No signal was detected in pre-J2s when using the sense ssDNA probe as a negative control (Fig 1A and 1B).
The transcriptional expression of the MgGPP gene in different stages was analyzed using quantitative real-time PCR (qRT-PCR). The expression level of MgGPP at the egg stage was set at one as a reference for calculating the relative fold changes in the other stages. The transcription levels in pre-J2s and parasitic second-stage juveniles (par-J2s) at 3 and 5 days post-infection (dpi) were relatively high. The transcript expression reached a maximum at 3 dpi, with a 789-fold increase in expression compared with the egg stage. The relative fold changes for MgGPP transcripts in par-J2s at 5 dpi and in pre-J2s were approximately 655 and 470, respectively, compared with that in the egg stage. After the par-J2 stage, the transcript level of MgGPP was dramatically reduced and reached a minimum at the female stages, where only a 38-fold higher transcript level was found compared with the transcripts in the egg stage (Fig 1C). These findings suggested that MgGPP may be a secretory protein and play a role in the early stages of M. graminicola parasitism.
To determine whether MgGPP is actually secreted within host plants, immunolocalization was performed on the gall sections from rice plants at 5 dpi with M. graminicola using an antiserum against MgGPP. Western blot analysis was used to determine the serum specificity to MgGPP, which showed a clear hybridizing band with the expected size of ~25.5 kDa in the total protein samples from pre-J2s but not in the protein sample from healthy rice roots. By contrast, the control western blot hybridized with pre-immune serum did not generate any visible band from the nematode and rice root total protein samples (S3 Fig). The results showed that the anti-MgGPP serum can specifically recognize MgGPP of M. graminicola.
The localization of the MgGPP protein (5 dpi) was consistently observed in giant cell nuclei. In some sections, MgGPP was also observed along the cell wall of adjacent giant cells around the nematode head or accumulated on the nematode head, at the stylet and inside the lumen of the anterior esophagus (Fig 2). No signal was observed in the giant cells of either gall sections containing nematodes without hybridization or incubated with pre-immune serum or in root sections of an uninfected healthy plant hybridized with anti-MgGPP serum (S4 Fig). These findings suggested that MgGPP is secreted in the early stages of M. graminicola parasitism, injected into the root tissue, and targeted to the host cell nuclei.
To intensively study the subcellular localization of MgGPP in host cells, a transient protein expression assay was performed using protoplasts from rice roots. The full-length MgGPP sequence without the signal peptide region was fused with enhanced green fluorescent protein (eGFP) and transformed into rice root protoplasts. The eGFP was fused to either the N-terminus (eGFP:MgGPPΔsp) or C-terminus (MgGPPΔsp:eGFP) of MgGPP (Fig 3A). Since MgGPP was confirmed to be secreted inside host cells, the exclusion of the signal peptide should allow the MgGPP effector to be tested for its function in host cells. At ~8 h after culture, the fusion protein eGFP:MgGPPΔsp was detected to be colocalized in the ER with the ER marker HDEL in ~41% of the transformed cells. After ~48 h, the exclusive nuclear localization of eGFP:MgGPPΔsp in ~42% of the transformed cells was detected (Fig 3B). Unexpectedly, cells transformed with the fusion protein MgGPPΔsp:eGFP consistently displayed both cytoplasmic and nuclear accumulation of the fluorescent signal (Fig 3C). As a control, the transformed cells expressing eGFP alone also showed cytoplasmic and nuclear accumulation of the GFP signal (Fig 3D).
To demonstrate the correct expression of eGFP-tagged MgGPP in protoplast cells, the proteins extracted from the transformed cells were analyzed by western blot using an anti-GFP antibody. Unexpectedly, when MgGPPΔsp:eGFP was transiently expressed in protoplasts from rice roots, the anti-GFP antibody specifically detected an accumulated protein of ~27 kDa, which was much smaller than the expected size of MgGPPΔsp:eGFP (~50 kDa) but identical to the size of eGFP, indicating that the C- terminus of MgGPP may be proteolytically cleaved, and the fluorescence in the cytoplasm and nucleus could be the free eGFP. In contrast, when eGFP:MgGPPΔsp was transiently expressed in protoplasts, two protein forms of ~43 and ~39 kDa were detected, which was one extra protein form than we expected. Moreover, they were both smaller than the expected size of eGFP:MgGPPΔsp (~50 kDa) (Fig 3E), indicating that MgGPP may be processed and cleaved.
In silico analysis showed that MgGPP has one N-glycosylation site at Asn-110, showing that MgGPP may be glycosylated. An anti-MgGPP antibody specifically detected a band with a size of ~25.5 kDa in the total protein samples from pre-J2s, par-J3s/J4s and females with or without treatment of the deglycosylation enzyme PNGase F (Fig 4A), indicating that MgGPP is not glycosylated in nematodes. Therefore, we speculated that the host plants mediated the N-glycosylation and C-proteolysis of MgGPP. To verify this speculation, first, total protein samples of rice protoplasts and tobacco leaves expressing eGFP:MgGPPΔsp were treated with the deglycosylation enzyme PNGase A. Western blot analysis showed that the protein form of ~43 kDa was successfully cleaved by PNGase A (Fig 4B). Second, we also generated an MgGPP allele in which the N110Q site was mutated and expressed this mutant in rice protoplasts and tobacco leaves. Western blotting analysis showed that the protein form of ~43 kDa also disappeared (Fig 4B). Based on these results, we conclude that the secreted effector protein MgGPP is N-glycosylated in host cells.
Because MgGPP may be subjected to C-proteolysis, our original goal was to determine the cleavage site in MgGPP. To achieve this, MgGPPΔsp_Δ201–224:eGFP, MgGPPΔsp_Δ161–224:eGFP, MgGPPΔsp_Δ141–224:eGFP and MgGPPΔsp_Δ121–224:eGFP (Fig 5A) were generated and transiently expressed in rice protoplasts. Western blot analysis indicated that the anti-GFP antibody specifically detected a band with the size of ~27 kDa, which is identical to the size of free eGFP, in plants expressing MgGPPΔsp_Δ201–224:eGFP, MgGPPΔsp_Δ161–224:eGFP and MgGPPΔsp_Δ141–224:eGFP. However, plants expressing MgGPPΔsp_Δ121–224:eGFP produced a ~39 kDa band that corresponded to the molecular weight of the fusion protein of eGFP plus MgGPPΔsp_Δ121–224 (Fig 5B). Furthermore, MgGPPΔsp_Δ121–140:eGFP, MgGPPΔsp_Δ121–160:eGFP and MgGPPΔsp_Δ121–200:eGFP (Fig 5A) were constructed and transiently expressed in rice protoplasts. Similarly, the anti-GFP antibody specifically detected a band of ~27 kDa that is the same size as free eGFP (Fig 5C). These results suggested that MgGPP was cleaved at multiple loci from 121 to 224 aa, and the cleavage position nearest to the N-terminus should be between 121 aa and 140 aa. Then, MgGPPΔsp_Δ122–224:eGFP, MgGPPΔsp_Δ123–224:eGFP, MgGPPΔsp_Δ124–224:eGFP, MgGPPΔsp_Δ125–224:eGFP and MgGPPΔsp_Δ126–224:eGFP were constructed (Fig 5A) and transiently expressed in rice protoplasts. Western blot analysis showed that the expression of MgGPPΔsp_Δ122–224:eGFP and MgGPPΔsp_Δ123–224:eGFP generated a ~39 kDa band, but the expression of MgGPPΔsp_Δ124–224:eGFP, MgGPPΔsp_Δ125–224:eGFP and MgGPPΔsp_Δ126–224:eGFP produced a ~27 kDa band (Fig 5D). These results showed that the secreted effector protein MgGPP is processed proteolytically after the 122-aa position in host cells.
The 123-aa to 224-aa C-terminal sequence of MgGPP is processed proteolytically when MgGPP enters host plant cells. Additionally, the analysis of the subcellular localization of MgGPP showed that MgGPP translocated from the ER to the nucleus. It has been considered that proteolytic processing may function in the proper trafficking of effectors to their cellular targets [32], and we therefore speculated that the MgGPP123-224 region may be required for MgGPP trafficking to the ER. As mentioned above, the 123-aa to 224-aa C-terminal sequence of MgGPP is processed proteolytically, which motivated us to study the role of this region. Thus, eGFP:MgGPPΔsp_Δ123–224 was generated (Fig 6A) and transiently expressed in rice protoplasts. The results show that no ER localization signal was observed in any transformed cells at ~8 h after culture, while the fluorescent signal was observed in both the nucleus and cytoplasm (Fig 6B). Moreover, the anti-GFP antibody specifically detected an accumulated protein of ~39 kDa (Fig 6C). As a control, nearly half of the eGFP:MgGPPΔsp was observed in the ER (Fig 6D). These results indicated that MgGPP lacking the sequence of 123 aa to 224 aa could not be imported into the ER and glycosylated.
N-Glycosylation of proteins in eukaryotic cells usually occurs in the ER [33]. We therefore speculated that the MgGPP123-224 region is required for ER import of MgGPP, resulting in the glycosylation of MgGPP in the ER. To verify this, we constructed WAK2ss:eGFP:MgGPPΔsp:HDEL and WAK2ss:eGFP:MgGPPΔsp_Δ123–224:HDEL (Fig 7A) to ensure that MgGPP was transported to and retained in the ER. When the two vectors were transiently expressed in rice protoplasts, the fluorescent signal of WAK2ss:eGFP:MgGPPΔsp_Δ123–224:HDEL was consistently observed in the ER (Fig 7B), and WAK2ss:eGFP:MgGPPΔsp:HDEL was observed in the ER of ~50% of the transformed cells at ~8 h after culture and in the nucleus of ~40% of the transformed cells after ~48 h (Fig 7C). The results demonstrated that the proteolytic cleavage of MgGPP at site 123 releases MgGPP21-122 from the C-terminal part containing the ER-retention signal, and therefore this amino-terminal part can leave the ER. Western blot analysis showed that two bands of ~43 and ~39 kDa were detected, indicating that these two fusion proteins were both glycosylated in the ER (Fig 7D). Thus, we conclude that the MgGPP123-224 region is required for MgGPP trafficking to the ER, and the effector is then glycosylated in the ER.
To assess the role of MgGPP in nematode parasitism, transgenic rice lines expressing MgGPP without the signal peptide under the control of the maize ubiquitin promoter were generated. Southern blot analysis confirmed the integration of the target gene into the rice genome, and five single-copy transgenic lines were selected (S5A Fig). The expression of MgGPP transcripts in the five transgenic lines was confirmed by qRT-PCR analysis (S5B Fig). Meanwhile, western blot analysis was also used to examine the expression of MgGPP using the anti-MgGPP antibody (Fig 8A). The western blot analysis showed that two bands of ~12 kDa and ~16 kDa were detected, which is consistent with the expected molecular weight of the protein, due to PTM of MgGPP in rice plants. MgGPP expression seemed to have no measurable impact on transgenic plant growth and development by phenotype analysis. Then, the susceptibility of these transgenic rice lines to nematodes was tested. The results indicated that the average number of adult females increased by 49.8% and 42.9%, 33.6% and 27.5%, 62.2% and 58.8%, 26% and 21.3%, and 73% and 66% at 12 dpi in lines 4, 5, 6, 9 and 39, respectively, compared to the wild-type (WT) and empty vector (EV) plants. In conclusion, transgenic rice lines overexpressing MgGPP were more susceptible to nematode infection (Fig 8B).
To further confirm the findings of MgGPP overexpression, host-mediated gene silencing was performed to knock down MgGPP expression during the parasitism of M. graminicola using transgenic rice lines expressing a hairpin dsRNA of MgGPP. Four single-copy transgenic rice plants were confirmed using southern blot analysis (S5C Fig). By RT-PCR and qRT-PCR, these transgenic plants were confirmed to carry the MgGPP dsRNA, that is, the RNAi cassette (a 363-bp GUS intron fragment) was detected in the four single-copy transgenic lines (S5D Fig and Fig 9A) and was not amplified in the WT and EV controls. According to phenotype analyses, the transgenic lines expressing the hairpin dsRNA of MgGPP had no apparent differences in plant growth and development compared to the WT control lines. The expression level of MgGPP in nematodes after silencing by host-mediated RNAi was measured by qRT-PCR. The transcription of MgGPP was reduced significantly in M. graminicola feeding on the roots of RNAi lines at 3 dpi compared to those feeding on control plants. Therefore, the host-mediated gene silencing of MgGPP was effective. Two other genes, Mg-CRT and Mg-expansin, which have similar transcriptional expression patterns and somewhat similar nucleotide sequences to those of MgGPP, were used to verify the specificity of this MgGPP-targeting RNAi by qRT-PCR analysis. The results showed that Mg-CRT and Mg-expansin were not affected by the MgGPP-targeting RNAi treatment (Fig 9B). Importantly, the four transgenic rice lines had 50%-72.2% fewer adult females than the WT lines and EV plants at 12 dpi (Fig 9A). These findings suggest that MgGPP plays a role in nematode parasitism.
There is evidence that secreted effectors originating from the nematode esophageal gland cells can directly suppress the plant defense system that is responsible for the parasitism of nematodes [9,10,12,16,34]. Bax, INF1 and Gpa2/RBP-1 can trigger cell death [9,12,35]; therefore, to determine whether MgGPP actually has the ability to suppress the plant immune system, Bax, INF1 and Gpa2/RBP-1 systems were used to investigate the function of MgGPP in programmed cell death (PCD). Agrobacterium strains carrying flag:MgGPPΔsp were constructed (Fig 10A). In addition, the empty vector pCAMBIA1305:flag was generated as a negative control, and pCAMBIA1305:GrCEP12 that can suppress the HR induced by Gpa2/RBP-1 [12] was used as the positive control. These constructs were infiltrated into Nicotiana benthamiana leaves 24 h prior to infiltration of an Agrobacterium strain carrying Bax, INF1 and Gpa2/RBP-1. At 5 days after the last infiltration, we found that MgGPP and the positive control GrCEP12 could suppress cell death mediated by Gpa2/RBP-1, with a necrosis index of 2.6 and 3.0, respectively. As controls, all points infiltrated with buffer and flag followed by Gpa2/RBP-1 showed a necrosis index of around 7 (Fig 10B and 10C), whereas infiltration with buffer, MgGPP or flag alone did not induce necrosis (S6 Fig). In addition, none of the treatments, including infiltration of MgGPP, could suppress the HR induced by Bax and INF1 (S6 Fig).
We have shown above that MgGPP is subjected to N-glycosylation and C-proteolysis in plants, and the MgGPP123-224 region is required for MgGPP trafficking to the ER, where MgGPP is glycosylated. The glycosylation of effectors may suppress plant immunity [19]. To discover if glycosylation of MgGPP affected its ability to suppress the HR, we constructed the pCAMBIA:flag:MgGPPΔsp_Δ123–224 and pCAMBIA:flag:MgGPPΔsp-N110Q mutants to generate non-glycosylated MgGPP (Fig 10A). It was found that all points infiltrated with pCAMBIA:flag:MgGPPΔsp_Δ123–224 and pCAMBIA:flag:MgGPPΔsp-N110Q followed by Gpa2/RBP-1 showed necrosis (Fig 10B and 10C). The expression of all genes was verified by RT-PCR (Fig 10D) and western blot analysis (Fig 10E). These results showed that the glycosylated MgGPP protein can suppress the cell death induced by Gpa2/RBP-1.
In this study, our original aim was to amplify a Meloidogyne avirulence proteins (MAPs) gene based on contigs that were annotated as Map from a previously reported transcriptome of M. graminicola [29]. Although the amplified gene exhibited the highest match with MAPs, the shared identities were no more than 41.1%. Moreover, this gene does not possess a conserved RlpA-like protein domain and internal repetitive motifs, which are common in MAPs [36,37]. These observations showed that the gene is not the counterpart of the reported Map genes of Meloidogyne and is a novel gene that we have called MgGPP.
Some evidence from this study indicated that the novel nematode effector MgGPP is secreted into host plants and plays a role in nematode parasitism. First, in silico analysis demonstrated that MgGPP contains an N-terminal signal peptide, which is considered to be a character of secreted proteins [38]. In addition, in situ hybridization indicated that MgGPP was expressed exclusively in the subventral esophageal glands, which are one of the origins of nematode secretory effector proteins and are thought to be involved in the early parasitic stages of RKNs [5]. Second, qRT-PCR analysis showed that the transcription of MgGPP was obviously up-regulated during the early parasitic stages of the nematodes, reaching a maximum level at 3 dpi, which is consistent with the results of its spatial expression, suggesting a potential role in the early parasitism stage of nematodes. Third, we affirmed, using an immunocytochemical method, that MgGPP accumulated in host giant cell nuclei, showing that MgGPP could indeed be secreted into host plant cells. Fourth, rice transgenic lines overexpressing MgGPP became substantially more susceptible to the nematode infection than wild-type plant controls, and conversely, silencing of MgGPP using an in planta RNAi approach significantly attenuated nematode parasitism, demonstrating that MgGPP promoted M. graminicola parasitism.
The identification of the subcellular compartments targeted by nematode-secreted effectors could assist in their functional characterization [39]. MgGPP was found to contain one NLS in the N-terminus. It is usually considered that nematode effectors with one or more NLS are most likely to be a nuclear-localized protein [38,40,41]. However, it has also been reported that nematode effectors possessing NLSs were not located in the nucleus [39]. The immunocytochemical technique is a good tool for studying the actual localization of nematode-secreted effectors in planta [42]. Two effectors, Mi-EEF1 and MjNULG1a, secreted by M. incognita and M. javanica, respectively, both containing NLSs, were confirmed to be nuclear-localized using the immunocytochemical method [40,41]. Utilizing this technique, we confirmed that MgGPP was actually targeted to giant cell nuclei. Meanwhile, it was noticed that the MgGPP signal was also observed in the cell-wall regions around the head of the nematode. It was reported that the stylet comes into contact with the plasma membrane without perforation and delivers nematode-secreted proteins in the cytoplasm [43]. Two RKNs-secreted effectors have been observed in the apoplast and to target to the nuclei [40,41], raising the possibility that PPNs effectors could be translocated from the apoplasm to the cytoplasm of plant cells., although little is known about transport mechanisms of PPNs effectors. Interestingly, the effectors from oomycetes and fungi were shown capable of further moving into plant cells after entering the apoplast [44]. For example, the oomycete effectors’ RXLR motif mediated entry of the effectors into cells by binding to the phospholipid, phosphatidylinositol-3-phosphate that is abundant on the outer surface of plant cell plasmamembranes. Therefore, it is thought that RxLR effector entry involves lipid raft-mediated endocytosis [45,46]. Regardless of how the effector MgGPP enters plant cells from the apoplast, our results suggest that MgGPP is probably secreted by the nematode into the apoplast, then entering into cells and targeting into the cell nucleus during the process of nematode parasitism. However, it is interesting that transiently expressed MgGPP was not always located in the nucleus. At ~8 h after culture, MgGPP can be observed in the ER of rice root protoplasts, and finally in the nucleus at ~48 h. With all these considered, it is possible that MgGPP may be secreted into the plant cell apoplast first and then enter into host cells and target to the ER, before finally being transported to the nucleus.
In this study, most importantly, MgGPP was found to be post-translationally modified in host plants. Thus far, only three nematode-secreted effectors have been found to be post-translationally modified, including GrUBCEP12 and CLE from G. rostochiensis and 10A07 from H. schachtii, which were proteolytically cleaved, glycosylated and phosphorylated in planta, respectively [12,24,25]. Detailed studies have shown that MgGPP was subjected to both N-glycosylation and C-terminal proteolysis in host cells. Recent studies showed that glycosylation is one of the pathogen effector PTMs [47,48]. More often, the glycosylation of effectors occurred in the plant pathogen itself, and the subsequent glycoprotein was secreted into host plants. For example, the secreted effectors BAS4, CBH1 and PCIPGII were shown to undergo N-glycosylation in M. oryzae, Trichoderma reesei and Phytophthora capsici, respectively, and then translocate into the host cytoplasm [19,49–51]. Only a few effectors have been found to be first secreted into host plants and then glycosylated in the plants, such as the CLE effector from G. rostochiensis mentioned above [25].
Previous reports indicated that the glycosylation of effectors may be related to plant immunity [19]. For example, N-glycosylation of the effector Slp1 of M. oryzae enhanced its ability to suppress the host immunity by binding chitin oligosaccharides [19,52], while N-glycosylation of the effectors Avr4 and Avr9 of Cladosporium fulvum improved their capability to induce effector-triggered defense responses [53]. In this study, we confirmed that N-glycosylated MgGPP can consistently suppress the cell death induced by effector-triggered immunity (ETI) proteins Gpa2/RBP-1, while non-N-glycosylated MgGPP cannot. Additionally, neither N-glycosylated MgGPP nor non-N-glycosylated MgGPP can suppress the cell death induced by the PAMP-triggered immunity (PTI) protein INF1 and the pro-apoptotic protein Bax. Consistent with this notion, many plant pathogen effectors have been shown to selectively suppress the host cell death responses induced by several elicitors. For example, allergen-like proteins of cyst nematodes selectively suppress the activation of programmed cell death by surface-localized immune receptors, and different effectors of Phytophthora sojae could selectively suppress the programmed cell death induced by different elicitors [34,54]. Thus, it seems that glycosylation of MgGPP contributed to the its ability to selectively suppress the defense-related host cell death, which is one possible mechanism underlying its contribution to M. graminicola virulence.
In this study, subcellular localization assays showed that MgGPP can translocate from the ER to the nucleus. It has been suggested that PTM of effectors has an effect on their subcellular localization in plant cells [55]. For example, the cytoplasmically localized effector Hs10A07 is translocated to the nucleus after being phosphorylated [24]. Some studies also showed that proteolytic processing plays a role in the proper trafficking of effectors to their cellular targets. For example, the cyst nematode effector HsUbil was cleaved in plants, leading to the transport of the C-terminal domain into the nucleus [56]. Autoproteolysis of the P. syringae effector AvrPphB occurred inside plant cells to expose a myristoylation motif, and AvrPphB was then transported from the cytoplasm to the plasma membrane [32]. Interestingly, the C-terminal truncation mutation assays in our study showed that the C-terminal region of 123 aa to 224 aa of MgGPP was proteolytically cleaved in planta. Subcellular localization assays showed that eGFP:MgGPPΔsp_Δ123–224 was not located in the ER, WAK2ss:eGFP:MgGPPΔsp_Δ123–224:HDEL was always located in the ER, and WAK2ss:eGFP:MgGPPΔsp:HDEL was translocated from the ER to the nucleus. The C-terminal HDEL is a well-known signal for the retention of secretory proteins in the ER [57]; therefore, these observations demonstrated that the C-terminal region of 123 aa to 224 aa plays a role in the trafficking of MgGPP to the ER. MgGPPΔsp_Δ123–224 was exported from the ER due to the proteolytic processing of the C-terminal region (123 to 224) in the ER. Moreover, our data indicated that MgGPPΔsp lacking the C-terminal region, i.e., MgGPPΔsp_Δ123–224, cannot be glycosylated, but the intact MgGPPΔsp and WAK2ss:eGFP:MgGPPΔsp_Δ123–224:HDEL can be glycosylated. Glycosylated MgGPP can suppress the HR induced by Gpa2/RBP-1, but non-glycosylated MgGPP cannot. MgGPPΔsp lacking the C-terminal region cannot suppress the host HR either. It has been reported that N-glycosylation of proteins in eukaryotic cells usually occurs in the ER [33]. These provide further evidence that the MgGPP123-224 region is required for MgGPP to translocate to the ER, and the effector MgGPP was glycosylated only when it was in the ER, after which the glycosylated MgGPP could be activated to suppress the HR.
In summary, we obtained a novel effector, MgGPP, from M. graminicola. Our experimental evidence suggests that MgGPP may be secreted into the host plants during parasitism, first into the cell apoplast, then entering into cells and targeted to the ER, where N-glycosylation and C-terminal proteolysis occurs, and it is finally translocated from the ER to the nucleus. N-glycosylated MgGPP suppressed host defenses and promoted the parasitism of M. graminicola. Our data provide an intriguing example of host-dependent proteolysis in concert with the glycosylation of a pathogen effector, suggesting that plant pathogen effectors can recruit the host post-translational machinery to mediate their PTM and regulate their activity to facilitate parasitism.
No specific permissions were required for the nematode collected for this study in Hainan Province, China. The field for nematodes collection was neither privately owned nor protected, and did not involve endangered or protected species.
Meloidogyne graminicola were collected from rice in Hainan, China, purified using a single egg mass, and reared on rice (Oryza sativa cv. ‘Nipponbare’) in a greenhouse at 28°C under 16:8 h light:dark conditions. Pre-J2 and parasitic stage nematodes were collected as described previously [29]. Rice (including wild-type and transgenic lines) and tobacco (N. benthamiana) were grown in a glasshouse at 28°C and 23°C, respectively, under 16:8 h light:dark conditions [29].
Genomic DNA and total RNA were isolated from freshly hatched pre-J2s using the Genomic DNA Purification Kit (Shenergy Biocolor, Shanghai, China) and TRIzol reagent (Invitrogen, California, USA), respectively. Based on M. graminicola transcriptome data [29], the full-length cDNA sequence of MgGPP was obtained by rapid amplification of cDNA ends using a BD SMART RACE cDNA Amplification Kit (Clontech, California, USA) according to the manufacturer’s instructions. The genomic DNA was amplified using the primers MgGPP-gDNA-F/MgGPP-gDNA-R. All primers used in this study were synthesized by Invitrogen Biotechnology Co. Ltd. and are listed in S1 Table.
The sequence homology of the predicted proteins was analyzed using a BLASTx, BLASTn or tBLASTn search of the nonredundant and Expressed Sequence Tags database of the National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov/BLAST/). The signal peptide was predicted using SignalP 4.0 (http://www.cbs.dtu.dk/services/SignalP/). Putative transmembrane domains were predicted based on TMHMM (http://www.cbs.dtu.dk/services/TMHMM/). Molecular mass was predicted using ProtParam, and motif analyses were performed using InterProScan [58]. Glycosylation was analyzed using NetNGlyc1.0 (http://www.hiv.lanl.gov/content/sequence/GLYCOSITE/glycosite.html). Nuclear localization signal (NLS) domains were predicted as previously described [59].
First, the CaMV35S-promotor of the pCAMBIA1305.1 vector was replaced with the maize ubiquitin promoter to generate the binary vector pUbi (S7A Fig). Subsequently, for overexpression constructs, the coding sequence of MgGPPΔsp was cloned into the pUbi vector (S7B Fig). For MgGPP silencing constructs, the fragment of 251 to 469 bp within the MgGPP sequence was selected as the RNAi target and confirmed to have no contiguous 21-nucleotide identical hit in other genes. MgGPP251-469 was inserted into the pMD-18T vector (Takara, Tokyo, Japan) in both sense and antisense orientations. The sense and antisense fragments were separated by a GUS intron. Then, the entire RNAi fragment was inserted into the pUbi vector to generate transgenic rice lines expressing hairpin dsRNA (S7C Fig). The overexpression and RNAi constructs were transformed into A. tumefaciens strain EHA105 and used to transform the rice calli. The transgenic seedlings were screened on N6 Medium containing 50 mg/L hygromycin [60].
The expression levels of MgGPP in each transgenic rice line were determined by qRT-PCR. RNAi transgenic lines were confirmed using the gus intron fragment as target and the MgGPP expressions were determined in nematodes extracted from roots of RNAi lines and the control lines. The OsUBQ and Mg-ACT2 genes were selected as the reference gene for qRT-PCR, and two other genes, Mg-CRT and Mg-expansin, were used to verify the specificity of MgGPP-targeting RNAi by qRT -PCR analysis. Three technical replicates of each reaction were performed in all experiments and three independent experiments were performed. Expression levels of the transgenic lines were calculated using the 2-ΔΔCT method. Western blot analysis was performed to determine MgGPP expression in transgenic lines. Total proteins were extracted from each transgenic lines, control lines and Meloidogyne graminicola.
Ten micrograms of M. graminicola total genomic DNA were separately digested with HindIII (no cleavage site within MgGPP) or SphI (one cleavage site located at 389 to 394 bp) before separation by electrophoresis and then transferred to Hybond N+ membranes (Amersham Biosciences, Buckinghamshire, UK). The probe hybridization and signal detection were performed as previously described [10]. The digoxigenin-labeled DNA probe targeting the region from 91 bp to 366 bp of the MgGPP gene was synthesized using a PCR DIG probe synthesis kit (Roche Applied Science, Rotkreuz, Switzerland).
RNA samples were prepared from approximately 100 M. graminicola nematodes at different life stages as indicated using the RNA prepmicro kit (Tiangen Biotech, Beijing, China). The cDNA was synthesized using TransScript All-in-One First-Strand cDNA Synthesis SuperMix (Transgen Biotech, Beijing, China). qRT-PCR was performed using the primer pairs Mg-qPCR-F/Mg-qPCR-R and Mg-ACT2-F/Mg-ACT2-F-R for amplifying the MgGPP gene and the endogenous reference gene Mg-ACT2, respectively [29]. qRT-PCR was performed using the SYBR Premix Ex Taq II (Tli RNaseH Plus) (Takara, Tokyo, Japan) on a Thermal Cycler Dice Real Time System (Takara, Tokyo, Japan). Three technical replicates for each reaction were performed in all experiments, and three independent experiments were performed. The relative changes in gene expression were determined using the 2-ΔΔCT method [61].
For in situ hybridization, approximately 10,000 freshly hatched M. graminicola pre-J2s were collected as described previously [29]. Digoxigenin (DIG)-labeled probes were synthesized as described above. The nematode sections were hybridized as described previously [41] and examined by microscopy using a Nikon ECLIPSE Ni microscope (Nikon, Tokyo, Japan). Three independent experiments were performed.
The anti-MgGPP polyclonal serum was obtained as described previously [39]. Briefly, the MgGPP protein was expressed in BL21 (DE3) cells and purified using Ni2+NTA agarose (Merck) according to the user manual. The amount and the purity of the purified protein were determined by the BCA method (Tiangen Biotech, Beijing, China) and sodium dodecyl sulfate–polyacrylamide gel electrophoresis (SDS-PAGE). The anti-MgGPP polyclonal serum was obtained by immunizing rabbits.
For immunolocalization on sections of rice galls, rice galls infected with M. graminicola for 5 days were dissected, fixed, dehydrated and embedded in paraffin as described previously [42]. Sections were incubated in dimethylbenzene and an alcohol gradient to remove the paraffin. Then, the sections were treated with an MgGPP primary antibody at room temperature for 2 h in a humid box. They were washed three times for 5 min using PBS and then incubated with Goat anti-Rabbit Superclonal Secondary Antibody, Alexa Fluor 488 conjugate (Thermo Fisher Scientific, San Jose, CA, USA) at room temperature for 2 h in a humid box. Finally, the sections were mounted with Fluoromount-G (SouthernBiotech, Birmingham, UK) containing DAPI and observed with a Nikon ECLIPSE Ni microscope.
For constructing the MgGPPΔsp:eGFP, eGFP:MgGPPΔsp and eGFP plasmids, the sequences of MgGPPΔsp and eGFP were amplified and cloned into pUbi. As a control, mCherry was cloned into WAK2ss:HDEL to generate WAK2ss:mCherry:HDEL. The protoplasts of rice root tissues were obtained as previously described [62]. Then, 50 μg of the MgGPPΔsp:eGFP, eGFP:MgGPPΔsp and eGFP plasmids were added to 1 mL (approximately 2 × 103 cells) of rice protoplasts, respectively. The protoplasts were then incubated in the dark at room temperature for ~8 and ~48 h and examined under a Nikon ECLIPSE Ni microscope.
For verification of the intact fusion protein, western blot analysis was performed as described previously [9]. Briefly, total proteins from rice root protoplasts and tobacco leaves were separately extracted using RIPA lysis buffer (2% SDS, 80 mM Tris/HCl, pH 6.8, 10% glycerol, 0.002% bromophenol blue, 5% β-mercaptoethanol and Complete protease inhibitor cocktail). Approximately 20 μg of total proteins were separated on a 12% SDS-polyacrylamide gel electrophoresis (SDS-PAGE) gel and transferred to a nitrocellulose membrane (PALL, Washington, NY, USA). The membranes were blocked with 5% (w/v) nonfat dry milk, incubated with a primary mouse anti-GFP antibody (Transgene Biotech, Beijing, China) at a 1:3000 dilution, and then incubated with an anti-mouse horseradish peroxidase-conjugated secondary antibody at a 1:2000 dilution (Biosynthesis Biotechnology Co., Beijing, China). The proteins were visualized using the Immobilon Western Chemiluminescent system with Pierce ECL Western Blotting Substrate (Thermo Fisher Scientific, San Jose, CA, USA).
For glycosylation analysis, the cDNA of MgGPPΔsp was amplified and cloned into the pCAMBIA1305.1 and pUbi vectors. The two constructs, pCAMBIA:eGFP:MgGPPΔsp and eGFP:MgGPPΔsp, were expressed in tobacco leaves and rice root protoplasts, respectively. Total protein extracted with RIPA lysis buffer was digested with PNGase A (New England Biolabs, Beverly, MA, USA) for 1 h before being mixed with the loading buffer. Meanwhile, the total protein samples from pre-J2s, para-J2s/J3s and females of M. graminicola were treated with or without PNGase F (New England Biolabs, Beverly, MA, USA). PNGase F and PNGase A were used for glycosylation analysis of MgGPP following the manufacturer’s instructions. The extracted protein samples from three independent transformations were analyzed by western blot.
For glycosylation site analysis, MgGPPΔsp was used to mutate the putative N-glycosylation site at Asn-110 to Gln by a PCR-mediated approach. Subsequently, the mutant MgGPPΔsp-N110Q was cloned into the pCAMBIA1305.1 and pUbi vectors. Finally, the constructs pCAMBIA:eGFP:MgGPPΔsp-N110Q and eGFP:MgGPPΔsp-N110Q were obtained and expressed in tobacco leaves and rice root protoplasts, respectively. The extracted protein samples from three independent transformations were analyzed by western blot.
Different cDNAs of MgGPP mutants as indicated were amplified and cloned into the pUbi vector. In addition, MgGPPΔsp and MgGPPΔsp_Δ123–224 were cloned into WAK2ss:HDEL to generate the WAK2ss:eGFP:MgGPPΔsp:HDEL and WAK2ss:eGFP:MgGPPΔsp_Δ123–224:HDEL constructs. All of the splicing- and mutagenesis-generated MgGPP mutants in this work were obtained by PCR-driven overlap extension [63]. These mutant constructs were expressed in rice root protoplasts and examined under a Nikon ECLIPSE Ni microscope. The extracted protein samples from three independent transformations were analyzed by western blot.
Twelve-day-old rice plants were inoculated with 200 M. graminicola pre-J2s. At 12 dpi, the roots were collected, washed and stained by acid fuchsin, and the number of females was counted. Each experiment was performed three times. Statistically significant differences between treatments were determined by an unadjusted paired t-test (P<0.05) with SAS version 9.2 (SAS Institute, North Carolina, USA).
N. benthamiana plants were grown at 23°C for 4 weeks in 16 h light: 8 h dark conditions in a greenhouse. All mutant sequences of MgGPP were cloned into the pCAMBIA1305.1 vector to generate the pCAMBIA:flag:MgGPPΔsp, pCAMBIA:flag:MgGPPΔsp_Δ123–224 and pCAMBIA:flag:MgGPPΔsp-N110Q fusion constructs. The Gpa2, RBP–1, INF1 and GrCEP12 sequences were synthesized by Generay Biotech Co., Ltd. and cloned into pCAMBIA1305.1 to generate the expression constructs pCAMBIA1305:GrCEP12, pCAMBIA1305:Gpa2, pCAMBIA1305:INF1:HA and pCAMBIA1305:RBP-1:HA. The other elicitor of programmed cell death, the pCAMBIA1305:Bax construct, was generated as described previously [9]. All constructs were introduced into A. tumefaciens GV3101, and the transformed bacteria were then suspended in a buffer containing 10 mM 2-(N-morpholino) ethanesulfonic acid (MES) (pH 5.5) and 200 μM acetosyringone at an absorbance at 600 nm (OD600) of 0.3. A. tumefaciens cells carrying the constructs pCAMBIA:flag:MgGPPΔsp, pCAMBIA:flag:MgGPPΔsp_Δ123–224 and pCAMBIA:flag:MgGPPΔsp-N110Q were infiltrated into N. benthamiana leaves as described previously [9,12]. After 24 h, the same infiltration sites were injected with A. tumefaciens cells carrying the constructs pCAMBIA1305:Gpa2/pCAMBIA1305:RBP-1:HA, pCAMBIA1305:Bax and pCAMBIA1305:INF1:HA. As controls, the buffer and A. tumefaciens carrying the empty vector pCAMBIA1305 and pCAMBIA1305:GrCEP12 were separately infiltrated in parallel. Photographs were taken for symptom analysis 5 days after the last infiltration. The cell-death phenotype was scored by a necrosis index [64].
To confirm gene expression, western blotting and RT-PCR were performed. Western blot analysis was performed as described above. RT-PCRs were performed using the gene-specific primers MgGPP-F/MgGPP-R, Bax-F/Bax-R, RBP-1-F/RBP-1-R, Gpa2-F/Gpa2-R, INF1-F/INF1-R and NbEF1α-F/NbEF1α-R to amplify MgGPP, Gpa2, RBP-1, INF1 and the control NbEF1α [65], respectively.
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10.1371/journal.pgen.1005565 | The Dedicated Chaperone Acl4 Escorts Ribosomal Protein Rpl4 to Its Nuclear Pre-60S Assembly Site | Ribosomes are the highly complex macromolecular assemblies dedicated to the synthesis of all cellular proteins from mRNA templates. The main principles underlying the making of ribosomes are conserved across eukaryotic organisms and this process has been studied in most detail in the yeast Saccharomyces cerevisiae. Yeast ribosomes are composed of four ribosomal RNAs (rRNAs) and 79 ribosomal proteins (r-proteins). Most r-proteins need to be transported from the cytoplasm to the nucleus where they get incorporated into the evolving pre-ribosomal particles. Due to the high abundance and difficult physicochemical properties of r-proteins, their correct folding and fail-safe targeting to the assembly site depends largely on general, as well as highly specialized, chaperone and transport systems. Many r-proteins contain universally conserved or eukaryote-specific internal loops and/or terminal extensions, which were shown to mediate their nuclear targeting and association with dedicated chaperones in a growing number of cases. The 60S r-protein Rpl4 is particularly interesting since it harbours a conserved long internal loop and a prominent C-terminal eukaryote-specific extension. Here we show that both the long internal loop and the C-terminal eukaryote-specific extension are strictly required for the functionality of Rpl4. While Rpl4 contains at least five distinct nuclear localization signals (NLS), the C-terminal part of the long internal loop associates with a specific binding partner, termed Acl4. Absence of Acl4 confers a severe slow-growth phenotype and a deficiency in the production of 60S subunits. Genetic and biochemical evidence indicates that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 localizes to both the cytoplasm and nucleus and it has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our findings indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assembly site in the nucleus.
| Ribosomes are the molecular machines that generate proteins from mRNA templates. The biogenesis of eukaryotic ribosomes is an outstandingly complex process, in which around 80 ribosomal proteins and four ribosomal RNAs are accurately pieced together. Actively growing yeast cells must produce more than 160’000 ribosomal proteins per minute in order to meet the cellular demand for new ribosomes. Many ribosomal proteins are prone to aggregation and need therefore to be protected on their path from the cytoplasm to their mostly nuclear incorporation sites within ribosome precursors. Recent evidence has highlighted that specific binding partners, referred to as dedicated chaperones, may ensure the soluble expression, nuclear import and/or correct assembly of ribosomal proteins. Here, we have identified such a dedicated chaperone, termed Acl4, which exclusively interacts with and accompanies the ribosomal protein Rpl4 to its nuclear assembly site. Notably, Acl4 has the capacity to recognize Rpl4 as it is synthesized by the ribosome. Our findings emphasize that co-translational capturing of ribosomal proteins by dedicated chaperones is an advantageous strategy to provide sufficient amounts of assembly-competent ribosomal proteins. A detailed knowledge of eukaryotic ribosome assembly is instrumental to eventually understand and treat ribosomopathies, diseases frequently caused by altered functionalities of ribosomal proteins.
| The biogenesis of ribosomes is a fundamental cellular process whose main principles are conserved from the lower eukaryote Saccharomyces cerevisiae to mammalian organisms. S. cerevisiae 80S ribosomes are composed of two unequal subunits, a small 40S (SSU) and a large 60S (LSU) ribosomal subunit (r-subunit), which contain 33 ribosomal protein (r-proteins) and the 18S ribosomal RNA (rRNA) or 46 r-proteins and the 25S, 5.8S, and 5S rRNA, respectively [1]. The synthesis of ribosomes basically consists in the ordered assembly of the r-proteins with the rRNAs; however, the efficient and accurate assembly of r-subunits vitally depends on a multitude (>200) of transiently acting biogenesis factors [2,3,4,5]. Ribosome assembly takes successively place in the nucleolus, nucleoplasm, and cytoplasm. Within the nucleolus, it is initiated by the transcription of the ribosomal DNA (rDNA) into a long, polycistronic 35S precursor rRNA (pre-rRNA), which contains the 18S, 5.8S, and 25S rRNAs, and a pre-5S rRNA [6]. Concomitant to transcription, the 35S pre-rRNA already associates with several SSU r-proteins and early-associating biogenesis factors to give birth to the first detectable pre-ribosomal particle, referred to as 90S or SSU-processome [2,3,4,5]. In a predominantly co-transcriptional process, the pre-rRNA within this initial pre-ribosomal particle undergoes cleavage at processing site A2 (for a pre-rRNA processing scheme, see S1 Fig) [7,8], thus leading to the formation of nuclear 43S pre-ribosomal particles containing the 20S pre-rRNA. These pre-40S ribosomes are rapidly exported to the cytoplasm, where they are converted in a series of concerted events, including processing of 20S pre-rRNA to 18S rRNA, into mature 40S subunits [2,3,4,5,9,10]. The first pre-60S ribosome, termed 66S particle, also assembles on nascent pre-rRNA and contains, upon termination of transcription, the 27SA2 pre-rRNA [2,8]. This 66S pre-ribosomal particle is associated with selected r-proteins, mainly binding to domains I and II of 25S rRNA, that promote the initial compaction of the emerging LSU and a characteristic set of early-acting biogenesis factors [2,3,11]. Maturation of and pre-rRNA processing within nuclear pre-60S particles proceeds in a hierarchical manner and involves the sequential recruitment of r-proteins, which shape and stabilize the pre-60S subunit with the aid of specific biogenesis factors. Affinity purifications have revealed the protein and pre-rRNA/rRNA composition of pre-60S particles and, accordingly, the existence of several distinct nuclear intermediates could be established [2,3,4,5,12]. Pre-60S subunits acquire export competence by associating with several export-promoting factors; at this stage, the particles contain most r-proteins and only relatively few biogenesis factors [2,3,4,13]. Upon appearance in the cytoplasm, a cascade of sequential events leads to the dissociation of the export and biogenesis factors and the incorporation of the final eight 60S r-proteins; thus enabling subunit joining and engagement of 80S ribosomes in translation [2,3,4,5,10].
An exponentially growing yeast cell must initially synthesize at least 2’000 molecules of each r-protein per minute to meet the demand for assembly-competent r-proteins [14]. Many r-proteins exhibit rather unique folds and they often contain, besides featuring in many cases a globular domain, disordered loops and extensions that stabilize the tertiary structure of rRNA [1,15,16]. Notably, eukaryotic r-subunits have acquired, when compared to their bacterial counterparts, additional rRNA elements, referred to as expansion segments (ES), and 46 eukaryote-specific r-proteins [1,15]. Moreover, many of the evolutionarily conserved r-proteins contain eukaryote-specific extensions, which, together with the ES and the eukaryote-specific r-proteins, form most of the solvent-side surface of eukaryotic ribosomes [15,17]. Conversely, the evolutionarily conserved loops and extensions mostly penetrate into the interior of the rRNA cores of the r-subunits [15,16]. In agreement with their prevalent role in mediating interactions with the negatively charged rRNA phosphate backbones, most r-proteins are very basic with their loops and extensions being especially rich in lysine and arginine residues [16]. Since ribosome assembly mainly occurs in the nucle(ol)us, most r-proteins have to undertake the cumbersome journey from their cytoplasmic site of synthesis to the nucleus. However, due to their structural characteristics and highly basic nature, they need, prior to their ribosome incorporation, to be protected from engaging in illicit interactions with non-cognate RNAs or polyanions that may promote their aggregation [18]. Despite their small size, nuclear import of r-proteins largely depends on active transport across the nuclear pore complex (NPC) [19,20,21]. Notably, importins may not only act as transporter receptors for r-proteins, but as they were shown to prevent their aggregation, a chaperone role for importins has been put forward [18].
As already implied above, the equimolar synthesis of assembly-competent r-proteins represents a major challenge for the cell. Each non-assembled r-protein likely exhibits a distinct intrinsic stability and propensity for aggregation, as suggested by the occurrence of different folds and the unequal partitioning into globular domains and disordered extensions [15]. Moreover, proper folding of a large number of, but apparently not all, r-proteins especially depends on two functionally collaborating ribosome-associated chaperone systems [22], consisting of the chaperone triad SSB/ribosome-associated complex (RAC) and the nascent polypeptide-associated complex (NAC) [23]. Since r-proteins associate at different spatiotemporal entry points with the evolving pre-ribosomal subunits, their non-assembled forms, which may in many cases be unstable [24,25], are exposed for different durations to the hostile intracellular environment before being finally stabilized by encountering the cognate rRNA binding context at the pre-ribosome. Therefore, it is not surprising that additional mechanisms, besides the general chaperone and transport systems, evolved to ensure the stable expression of r-proteins and the subsequent delivery to their assembly site.
While one such strategy, utilized by two r-proteins, comprises the initial synthesis as a precursor protein carrying an N-terminal ubiquitin moiety [26,27,28,29], an emerging and prevalent theme involves the association of r-proteins with specific binding partners, also referred to as dedicated chaperones. Recent evidence revealed that such binding partners prevent r-proteins from aggregation, promote their nuclear import and/or coordinate their assembly into pre-ribosomal particles. The ankyrin-repeat protein Yar1 interacts specifically with Rps3 (uS3 according to the recently proposed r-protein nomenclature [30]) and acts as an anti-aggregation chaperone that may accompany Rps3 to its nuclear assembly site [31,32]. The nuclear Tsr2 promotes the safe transfer of importin-bound Rps26 (eS26) to the 90S pre-ribosome [33]. The transport adaptor Syo1 binds simultaneously to Rpl5 (uL18) and Rpl11 (uL5) and mediates, upon nuclear import of the trimeric complex via the transport receptor Kap104, their synchronized delivery to the 5S rRNA by serving as a 5S RNP assembly platform [19,34,35]. Additionally, the eight-bladed WD-repeat β-propeller protein Sqt1 and the predicted WD-repeat β-propeller protein Rrb1 are dedicated chaperones of Rpl10 (uL16) and Rpl3 (uL3), respectively [36,37,38,39,40]. While Syo1, Sqt1, and Rrb1 recognize the N-terminal extensions of their binding partners [35,38], Yar1 mainly binds to the solvent-exposed side of the first α-helix within the N-terminal globular domain of Rps3 [31]. Interestingly, and in line with a protective function, these four chaperones were recently shown to have the capacity to capture their r-protein clients at the earliest possible moment in a co-translational manner [38].
We are interested in unravelling the assembly paths, from stable cytoplasmic synthesis, along nuclear import to ribosome incorporation, of r-proteins and in understanding if and how dedicated chaperones contribute to these events. The essential 60S r-protein Rpl4 (uL4) is a particularly interesting candidate for studying its assembly path, since it associates very early with pre-60S particles and displays remarkable structural features [2,15,41,42,43]. Rpl4 is mostly located on the solvent-side surface of the mature 60S r-subunit and is composed of a universally conserved globular domain and a prominent eukaryote-specific C-terminal extension (see Fig 1) [15,41]. Notably, the globular domain contains an insertion of a long internal loop, which penetrates deep into the interior of the 60S core and whose tip region forms part of the constriction point within the polypeptide exit tunnel [15,41]. Additionally, a small internal loop also emanates from the surface-exposed globular domain into the 60S subunit. While the globular domain almost exclusively interacts with conserved, interconnected rRNA segments of domains I and II of the 25S rRNA, the eukaryote-specific extension spans across more than half the width of the solvent-side 60S surface and thereby engages in an intricate network of interactions, primarily with eukaryote-specific rRNA and r-protein moieties (Fig 1A and 1B) [11,15,17]. The first part of the eukaryote-specific extension is accommodated by Rpl18 (eL18) and ES15L (H45), and the second part is sandwiched between Rpl7 (uL30), mostly by its long, eukaryote-specific N-terminal α-helix, and helices ES7Lc/ES7Lb of ES7L (Fig 1B). Moreover, the eukaryote-specific r-proteins Rpl20 (eL20) and Rpl21 (eL21) contact the C-terminal residues of Rpl4.
In this study, we show that both the long internal loop and the C-terminal eukaryote-specific extension are essential features of Rpl4. We further reveal that Rpl4 contains at least five distinct nuclear localization signals (NLS), which map to different regions of Rpl4, including the long internal loop and the C-terminal extension. Notably, we have identified a previously uncharacterized protein, termed Acl4, as a specific binding partner of Rpl4. Acl4 interacts with the C-terminal part of the long internal loop of Rpl4 and both genetic and biochemical evidence suggests that Acl4 can be considered as a dedicated chaperone of Rpl4. Furthermore, we show that Acl4, which localizes to the cytoplasm and the nucleus, has the capacity to capture nascent Rpl4 in a co-translational manner. Taken together, our data indicate that the dedicated chaperone Acl4 accompanies Rpl4 from the cytoplasm to its pre-60S assembly site in the nucleus.
In S. cerevisiae, the paralogous RPL4A and RPL4B genes encode the essential r-protein Rpl4 of 362 amino acids length whose two versions, Rpl4a and Rpl4b, only differ at amino acid position 356 (threonine versus alanine). Notably, Rpl4 is composed of a globular domain, which contains a small (amino acids 184–205) and a long internal loop (amino acids 44–113), and a eukaryote-specific C-terminal extension (amino acids 264–362) (see Introduction and Fig 1). To determine the contribution of the long internal loop and the C-terminal extension to Rpl4 function, we selected Rpl4a since Δrpl4a null mutant cells, but not Δrpl4b null mutant cells, showed a moderate growth defect and reduced steady-state levels of 60S subunits, as evidenced by a shortage of free 60S subunits and the accumulation of half-mer polysomes (S2 Fig). For phenotypic analysis, plasmids encoding the Rpl4a deletion variants were transformed into a Δrpl4a/Δrpl4b strain harbouring an instable URA3/ADE3 plasmids containing RPL4A (RPL4 shuffle strain). Importantly, expression of wild-type Rpl4a under the control of its cognate promoter from a monocopy plasmid in the RPL4 shuffle strain conferred, upon plasmid shuffling on 5-FOA containing plates, almost wild-type growth and resulted only in a very minor 60S deficiency (S2 Fig). Complete deletion of the C-terminal extension (N264 construct; i.e.: Rpl4a deletion variant consisting of amino acids 1–264) did not support growth (Fig 1C). To map more precisely the important regions within the C-terminal extension, we generated a series of progressive C-terminal deletion variants. This analysis revealed that the last 30 amino acids (N332 construct) of Rpl4 are completely dispensable; conversely, removal of the C-terminal 71 amino acids (N291 construct) resulted in lethality (Fig 1C). The first rpl4 truncation mutant showing a slow-growth phenotype lacked the last 37 amino acids (N325 construct) and further deletion led to a severe slow-growth phenotype (N312 and N301 constructs) (Fig 1C and S3 Fig). Moreover, the severity of the observed growth defects correlated with the extent of the deficiency in 60S subunits, as indicated by the more dramatic reduction in polysome content in Rpl4.N312 and Rpl4.N301 expressing cells (S3 Fig). All the viable C-terminal deletion variants localized, as also observed for N-terminally yEGFP-tagged Rpl4a, mainly to the cytoplasm (S3 Fig). While removal of the small internal loop (deletion of amino acids 185–200) did not affect growth, deletion of the long internal loop (deletion of amino acids 46–110) resulted in a non-functional Rpl4 protein (Fig 1C). We conclude that, in agreement with its central location within 60S subunits, the presence of the long internal loop is strictly required for the synthesis of functional 60S subunits. Concerning the role of the eukaryote-specific C-terminal extension, we conclude that its interaction with Rpl20 and Rpl21 is dispensable for full functionality of Rpl4. More importantly, the interaction network formed by the second part of the C-terminal extension (from amino acids 308 onwards) with Rpl7 and ES7Lb/c contributes majorly, as evidenced by the severe slow-growth and the temperature sensitivity of the Rpl4a.N301 and Rpl4a.N312 constructs (Fig 1C and S3 Fig), to the efficient recruitment of Rpl4 and/or the assembly of functional 60S subunits. Further deletion of Rpl4 sites (amino acids 292–301) that mediate some of the interactions with Rpl18 and ES15L conferred lethality; thus, indicating an additional important role of these contacts for incorporation of Rpl4, pre-60S assembly and/or the functional integrity of 60S subunits (see also Discussion). At this point however, it cannot be ruled out that the lethality of Rpl4a.N291 and Rpl4a.N264 variants may not be simply due to their inefficient nuclear import.
To determine whether the lethal C-terminal deletion variants of Rpl4 could still enter the nucleus, we expressed them from plasmid, under the transcriptional control of the cognate promoter, as fusion proteins with a C-terminal yEGFP in a wild-type strain containing the nucleolar marker protein Nop58-yEmCherry. While Rpl4.N291 localized almost exclusively to the nucleus, Rpl4.N264 showed clear nuclear enrichment but also some cytoplasmic signal (Fig 2A). Further C-terminal deletion revealed that constructs Rpl4.N210 and Rpl4.N173 exhibited a complete or partial nuclear accumulation. On the other hand, and as observed above (S3 Fig), the viable C-terminal deletion constructs displayed a mainly cytoplasmic localization (Fig 2A), indicating that they are assembled into mature 60S subunits. Conversely, an Rpl4 construct lacking the long internal loop displayed a striking nuclear enrichment, suggesting that the presence of the long internal loop is required for the incorporation of Rpl4 into and/or the nuclear maturation of pre-60S subunits (see below and Discussion). Furthermore, we conclude that the Rpl4 variants lacking completely or more than two-thirds of the C-terminal extension (N264 and N291 constructs) enter the nucleus but are presumably not efficiently assembled into pre-60S subunits.
To obtain a complete overview of the different Rpl4 regions that may confer nuclear localization, we next wished to precisely map the individual NLSs. To reduce passive diffusion across the NPC, we fused a C-terminal triple yEGFP (3xyEGFP) preceded by a short (GA)5 linker, consisting of five glycine-alanine repeats, to the different Rpl4 fragments. Since an Rpl4 fragment consisting of the complete C-terminal extension (263C construct; amino acids 263–362) localized to the nucleus (Fig 2B), we first determined the NLS region(s) within the C-terminal extension. This analysis revealed that the C-terminal extension contains two distinct, but partially overlapping regions, consisting of amino acids 263–325 and 312–341, which conferred nuclear targeting, albeit slightly less efficiently than amino acids 263–362 (Fig 2B). These two regions can be considered as minimal NLSs since their further N- or C-terminal shortening resulted in a mostly cytoplasmic signal (S4 Fig). In agreement with the exclusive nuclear localization of the N210 construct (Fig 2A), we could map a strong NLS to amino acids 183–210, which basically encompasses the small internal loop region of Rpl4 (Fig 2B). Despite the finding that the N173 construct only displayed a partial nuclear enrichment, we could identify therein two quite efficient, but again partially overlapping, NLS regions consisting of amino acids 43–114 and 101–173 (Fig 2B). Since their further N- and/or C-terminal shortening strongly decreased the intensity of the nuclear signal (S4 Fig), these two regions likely correspond to minimal NLSs. Notably, the NLS region defined by amino acids 43–114 corresponds to the long internal loop of Rpl4. Taken together, our comprehensive analysis revealed that Rpl4 contains at least five distinct NLSs, with four of these being part of two larger, overlapping NLS regions (Fig 2A and S4 Fig). However, due to the enormous combinatorial complexity—there are ten importin-β proteins in yeast, which, moreover, often display significant overlap in substrate specificity [44]–we have not attempted to assign the importin(s) responsible for the nuclear import of the five individual NLSs within Rpl4.
To explore a possible role of the C-terminal extension for assembly of Rpl4 into pre-60S particles, we first expressed the partially functional (N325 and N301 constructs) and lethal (N291 and N264 constructs) C-terminal deletion variants from plasmid, under the transcriptional control of the cognate promoter, in a wild-type strain and examined their effects on growth. While additional expression of Rpl4a did not affect the growth of wild-type cells, all of the above C-terminal deletion variants conferred a similar slow-growth phenotype and decrease in 60S and polysome content (Fig 3A and 3B). Moreover, related growth defects were observed when these C-terminal deletion variants were overexpressed from a galactose-inducible promoter (S5 Fig). In agreement with their cytoplasmic localization, the partially functional Rpl4.N325 and Rpl4.N301 proteins, expressed as fusion proteins with an N-terminal TAP tag in wild-type cells, got incorporated into 60S, 80S, and translating ribosomes (Fig 3B). Conversely, the almost exclusively nuclear Rpl4.N291 only showed a very minor 60S association and was mostly detectable in the soluble fractions (Fig 3B), while Rpl4.N264, as already suggested by its dual cytoplasmic and nuclear localization, was both present in the soluble and ribosome-associated fractions (Fig 3B). We conclude that the C-terminal extension contributes to the efficient assembly of Rpl4 into nuclear pre-60S particles. Interestingly, expression of Rpl4.N291, which is not stably incorporated into 60S subunits, confers a severe slow-growth phenotype to wild-type cells. This observation raised the possibility that free Rpl4.N291 may titrate a protein that acts positively on wild-type Rpl4. Finally, the Rpl4 protein lacking the long internal loop mainly migrated in the fractions surrounding the 60S peak and exhibited a clearly reduced 80S and polysome distribution when compared to Rpl3 (Fig 3B). Considering that the Rpl4(Δ46–110) protein localized predominantly to the nucleus (Fig 2A), we conclude that this Rpl4 variant gets efficiently incorporated into pre-60S subunits and, therefore, that the long internal loop is not a critical determinant for the assembly of Rpl4. Moreover, expression of Rpl4(Δ46–110) in wild-type cells not only conferred a slow-growth phenotype but also some decrease in 60S subunits and a drastic reduction in polysome content (Fig 3A and 3B); thus, pointing to a possible role of the long internal loop in maturation events that are necessary for the productive assembly of export-competent pre-60S subunits (see Discussion).
In order to corroborate the sucrose gradient fractionation data and, if possible, to identify the anticipated binding partner of free Rpl4, we performed tandem-affinity purifications (TAP) of N-terminally TAP-tagged Rpl4.N264 and Rpl4.N291. In agreement with its dual gradient localization, purification of NTAP-Rpl4.N264 not only yielded the bait protein but also sub-stoichiometric amounts of r-proteins (Fig 4A). Most strikingly, a prominent band, corresponding to a protein migrating at around 43 kDa, was observed in the final EGTA eluates of both purifications (Fig 4A). Mass spectrometric analysis revealed that this band contained the previously uncharacterized protein Ydr161w, which was recently assigned the name Acl4 (Assembly Chaperone of RpL4) [45]. Moreover, both purifications contained low levels of early 60S biogenesis factors (Fig 4A), indicating that both Rpl4.N264 and Rpl4.N291 have the capacity to be incorporated into nucleolar pre-60S particles. In further validation of an association with early pre-60S particles, N-terminally GFP-tagged Rpl4a.N264 precipitated the 27SA2 and 27SB pre-rRNAs (S6 Fig). To address whether Acl4 was also associated with full-length Rpl4 under normal conditions, we expressed N-terminally TAP-tagged full-length Rpl4a from plasmid, under the transcriptional control of the cognate promoter, in Δrpl4a/Δrpl4b cells. In agreement with the good functionality of this construct (S7 Fig), the purification revealed that NTAP-Rpl4a was efficiently incorporated into mature 60S subunits (Fig 4B). Notably, significant amounts of Acl4 could be co-purified with the NTAP-Rpl4a bait. Next, we purified in a reciprocal experiment C-terminally TAP-tagged Acl4, which was expressed as a fully functional protein from its genomic locus (S7 Fig), thereby revealing that Acl4 specifically co-purified free Rpl4 (Fig 4C). Moreover, the only other prominent band, migrating slightly above 70 kDa, contained the Hsp70 chaperones Ssa1, Ssa2, Ssa3, and Ssa4. However, we have not further explored the functional significance of their co-enrichment with the Acl4-TAP bait in this study, since Hsp70 chaperones, belonging to the Ssa and Ssb subfamilies, are rather commonly found in TAP purifications. Altogether, these in vivo purifications provide strong evidence that Acl4 is a specific binding partner of non-ribosome bound Rpl4.
Acl4 is an acidic protein (pI 4.15) of 387 amino acids with a calculated molecular mass of 42.94 kDa. The eukaryote-specific Acl4 is predicted to contain 16 α-helices that, based on our bioinformatics analysis, may form up to eight tetratrico peptide repeats (TPR) or TPR-like repeats. Notably, TPR domains build the scaffolds that mediate protein-protein interactions and the assembly of multiprotein complexes in a versatile manner [46,47]. Acl4 is not only conserved in fungi, such as filamentous ascomycetes (e.g.: Chaetomium thermophilum, Accession: XP_006691536) and fission yeasts (e.g.: Schizosaccharomyces pombe, Accession: NP_596496), as orthologues can also be found in protists (e.g.: Trypanosoma brucei, Accession: XP_011776831), arachnids (e.g.: Stegodyphus mimosarum, Accession: KFM57536 and Metaseiulus occidentalis, Accession: XP_003742882), ray-finned and coelcanth fish (e.g.: Danio rerio, Accession: XP_009290679 and Latimeria chalumnae, Accession: XP_006002768), and amphibians (e.g.: Xenopus tropicalis, Accession: XP_002934631). However, there are no orthologues in the evolutionary more advanced classes of reptiles, birds, and mammals. Moreover, Acl4 is not present in plants (e.g.: Arabidopsis thaliana), insects (e.g.: Drosophila melanogaster), and nematodes (e.g.: Caenorhabditis elegans), indicating that Acl4 can also be specifically lost in earlier evolutionary branches.
To address the functional significance of the identification of Acl4 as an Rpl4 binding protein, we first determined whether Acl4 contributed to the biogenesis of 60S subunits. Haploid cells with a chromosomally disrupted ACL4 gene (Δacl4) were viable, but displayed a severe slow-growth phenotype at all tested temperatures (Fig 4D). In agreement with an involvement in 60S biogenesis, polysome profile analysis revealed that Δacl4 cells contained reduced levels of 60S subunits, as evidenced by a shortage of free 60S subunits and the accumulation of half-mer polysomes, resulting in a substantial decrease in overall polysome content (Fig 4E). Compared to cells that were genetically depleted for Rpl4, which showed as previously reported a striking decrease in 27SB and 7S pre-rRNAs (S8 Fig) [43,48], the reduction of these pre-rRNA species was clearly observable but less pronounced in Δacl4 cells (S8 Fig). In line with the in vivo purification, sucrose gradient fractionation revealed that Acl4-TAP was exclusively present in the soluble fractions (Fig 4F); thus, providing further evidence that Acl4 is not stably associated with pre-60S or mature 60S subunits. Finally, Acl4-GFP, expressed from its genomic locus as a functional protein at 30°C (S7 Fig), localized to both the nucleus and cytoplasm (Fig 4G). Since Acl4 lacks a predicted NLS, it is highly likely that Acl4 may already bind to Rpl4 in the cytoplasm and may be imported in complex with Rpl4, which contains several experimentally established NLS regions (Fig 2B).
To precisely map the binding site of Acl4 on Rpl4, we first conducted yeast two-hybrid (Y2H) interaction assays (Fig 5A). As already indicated by the co-purification of Acl4 with NTAP-Rpl4.N264 (Fig 4A), the C-terminal extension was neither required for nor sufficient to mediate the interaction with Acl4 (Fig 5A). Further C-terminal deletion analysis revealed that the first 114 amino acids (N114 construct) were sufficient for a robust interaction, while the Rpl4.N104 construct did not show any interaction with Acl4 (Fig 5A). We therefore tested next whether Acl4 recognized the long internal loop and, indeed, a strong Y2H interaction was observed between Acl4 and amino acids 43–114 of Rpl4. By shortening the long internal loop from the N-terminal side, amino acids 88–114 were identified as the minimal, albeit less efficient, interaction fragment. However, amino acids 88–264 resulted in an interaction that was almost as strong as the one between Acl4 and full-length Rpl4, thus indicating that the region around amino acid 88 likely corresponds to the N-terminal border of the minimal interaction fragment. In further support of this notion, amino acids 96–114 or 96–264 of Rpl4 were insufficient to promote an interaction with Acl4. Finally, deletion of the long internal loop from full-length Rpl4 (deletion of amino acids 46–110) completely abolished the Y2H interaction. To corroborate the Y2H data, we turned to in vitro binding assays (Fig 5B). To this end, we co-expressed C-terminally (His)6-tagged Rpl4 or fragments thereof with full-length Acl4-Flag in Escherichia coli and subsequently performed Ni-affinity purification of the different Rpl4 baits. These binding assays confirmed that the long internal loop contained the Acl4 binding site and defined amino acids 72–114 of Rpl4 as the minimal region conferring a robust interaction (Fig 5B). In contrast to the Y2H data, however, amino acids 88–114 of Rpl4 were insufficient to mediate the interaction in vitro. Finally, the Rpl4 bait lacking the long internal loop did not yield co-purification of Acl4 (Fig 5B).
To corroborate the in vitro binding data obtained with the S. cerevisiae proteins and to obtain, if possible, structural insight into the Acl4-Rpl4 interaction, we turned to the orthologous proteins from the thermophilic, filamentous ascomycete C. thermophilum (ct). Proteins from this organism often display excellent biochemical properties and are well suited for structural studies [49]. In agreement with being a functional orthologue, ctAcl4 complemented the slow-growth phenotype of Δacl4 mutant cells to the wild-type extent (S9 Fig). Ni-affinity purification of ctRpl4-(His)6 yielded stoichiometric amounts of co-expressed ctAcl4-Flag (S9 Fig). Moreover, these in vitro binding assays also confirmed that the long internal loop is sufficient to mediate the interaction. Most notably and in contrast to the in vitro binding data of the S. cerevisiae proteins, amino acids 89–115 of ctRpl4 resulted in an efficient co-purification of ctAcl4-Flag (S9 Fig), suggesting that these residues indeed constitute the minimal, evolutionary conserved binding site for Acl4. Unfortunately however, we could so far not gain structural insights into the interaction mode, since our attempts to obtain crystals of full-length ctAcl4 or co-crystals of the ctRpl4(89–115)/ctAcl4 complex were not yet successful.
To confirm that the C-terminal part of the long internal loop (amino acids 88–114) is required for the interaction in S. cerevisiae and to delineate important residues, we next generated four different Rpl4 variants harbouring non-overlapping, consecutive alanine substitutions (block-I: F90A/N92A/M93A/C94A/R95A, block-II: R98A/M99A/F100A, block-III: P102A/T103A/K104A/T105A, and block-IV: W106A/R107A/K108A/W109A) (see Fig 6A). Contrary to the complete deletion of the long internal loop (Fig 1C), all four alanine-block substitution mutants were viable, albeit displaying different degrees of growth deficiencies (S10 Fig). Moreover, these mutant Rpl4 proteins were similarly expressed as wild-type Rpl4 and their expression did not confer a slow-growth phenotype to wild-type cells (S10 Fig). Subsequent Y2H analyses and in vitro binding assays revealed that none of these four Rpl4 variants retained the capacity to interact with Acl4 (Fig 6B and 6C). Taken together, we conclude that Acl4 recognizes Rpl4 by directly interacting with the C-terminal part of the long internal loop (amino acids 88–114) and that, notably, residues dispersed along the entire, linear interaction segment are critical binding determinants.
Having established Acl4 as a physical interaction partner of Rpl4, we next wished to assess the functional relevance of their association. Genetic analyses revealed that the slow-growth phenotype of Δacl4 cells could be efficiently suppressed by overexpression of Rpl4a from a monocopy plasmid (Fig 7A). Moreover, cells simultaneously lacking Acl4 and Rpl4a exhibited a pronounced synthetic enhancement phenotype, as evidenced by their severely reduced growth rate compared to the one of Δacl4 and Δrpl4a single mutant cells (Fig 7B). These findings point to an important function of Acl4 in ensuring that cells are provided with sufficient amounts of assembly-competent Rpl4. Along these lines, we observed that overexpression of Acl4 suppressed the growth defects associated with the expression of Rpl4.N264 and Rpl4.N291 (Fig 7C), which efficiently associate with Acl4 (Fig 4A) and therefore likely compete with endogenous Rpl4 for Acl4 binding. Finally, newly synthesized Rpl4-2xHA, expressed for 20 min from a copper-inducible promoter, was only found to be soluble in the presence, but not in the absence, of Acl4 (Fig 7D). Taken together, the genetic and biochemical evidence indicates that Acl4 can be considered as a specific chaperone of Rpl4.
Acl4, which lacks a predicted NLS, localizes both to the cytoplasm and nucleus (Fig 4G). Moreover, we observed that overexpression of Acl4 from a galactose-inducible promoter led to the nuclear accumulation of Rpl4 (Fig 7E). Therefore, Acl4 may already bind to Rpl4 in the cytoplasm and travel in complex with Rpl4 to the nucleus. To obtain evidence for a cytoplasmic interaction, which would most efficiently already be established during translation of Rpl4, we assessed whether Acl4 was recruited to nascent Rpl4. To this end, we employed, as recently described [38], a method coupling chaperone purification to the detection of associated mRNAs by real-time quantitative reverse transcription PCR (real-time qRT-PCR). Briefly, we purified Acl4-TAP and, as a control, Syo1-FTpA by IgG-Sepharose pull-down from extracts of cells that were, prior to harvesting, treated with cycloheximide in order to preserve the translating ribosomes on the mRNAs (see Methods). The purified chaperones were then released from the IgG-Sepharose beads by TEV cleavage and the associated RNA was extracted and transcribed into cDNA, which was used as the template for the assessment of the levels of the different r-protein mRNAs (RPL3, RPL4, RPL5 and RPL11) by real-time qRT-PCR. As recently reported [38], purification of Syo1-FTpA specifically enriched the mRNA encoding Rpl5, while the amounts of the RPL4 mRNA were similar to the one of the negative control mRNA RPL3 (Fig 8). Interestingly, the mRNA encoding Rpl11, which forms together with Syo1 and Rpl5 a co-imported complex [34,35], was not enriched, indicating that only Rpl5, but not Rpl11, is captured by Syo1 in a co-translational manner. On the other hand, purification of Acl4-TAP yielded a specific and robust enrichment of the RPL4 mRNA (Fig 8). Notably, the fold enrichment (around 150 fold) of the RPL5 or RPL4 mRNAs, compared to the non-specific r-protein mRNAs, was similar in the case of the Syo1-FTpA and Acl4-TAP purification, respectively. We conclude that Acl4 has the capacity to recognize Rpl4 in a co-translational manner.
Recent evidence has highlighted that r-proteins rely in several cases on specific binding partners, also referred to as dedicated chaperones, which favour the soluble expression of r-proteins, promote their nuclear import and/or coordinate their assembly into pre-ribosomal subunits (see Introduction). In the present study, we have identified such a specific binding partner, termed Acl4, which exclusively interacts with the LSU r-protein Rpl4. The genetic and biochemical data reported here indicate that Acl4 can be considered as a dedicated chaperone of Rpl4. Notably, Acl4 has the capacity to recognize, by directly interacting with the C-terminal part of the long internal loop, nascent Rpl4 in a co-translational manner. Further, the identification of several NLS regions within Rpl4 suggests that Acl4, which lacks a predicted NLS, gets imported into the nucleus in complex with Rpl4 and accompanies Rpl4 to its nucleolar pre-60S incorporation site. Finally, we show that both the eukaryote-specific C-terminal extension and the long internal loop are essential features of Rpl4, which are required for the assembly of Rpl4 into and the nuclear maturation of pre-60S subunits. While we compiled our manuscript, the Hurt laboratory, in collaboration with the Hoelz group, independently reported the identification and characterization of Acl4 as a binding partner and assembly chaperone of Rpl4 [45]. Notably, their study also described the partial crystal structure of C. thermophilum Acl4, revealing that, in agreement with our bioinformatics prediction, the central core of Acl4 is made up of 6.5 TPR repeats [45]. Moreover, the Woolford laboratory investigated in a recent study the contribution of the long internal loop and the eukaryote-specific C-terminal extension of Rpl4 to the assembly of 60S subunits [48]. By assimilating the data from these three different studies, we outline an integrated model, whose main steps are discussed below, describing how Acl4 may ensure the synthesis of assembly-competent Rpl4 and how the incorporation of Rpl4 into early pre-60S particles may proceed (Fig 9).
Several lines of evidence indicate that Acl4 can be considered as a dedicated ‘holding’ chaperone of Rpl4. First, Acl4 specifically binds to free Rpl4 and is therefore only, if at all, very transiently associated with pre-60S particles during the initial docking phase of Rpl4 (Fig 4C and 4F). Second, genetic experiments reveal an important function of Acl4 in ensuring that cells are provided with sufficient amounts of assembly-competent Rpl4 (Fig 7A and 7B) [45]. Suppression of the growth defects, entailed by the absence of a dedicated chaperone, by overexpression of the respective r-protein partner has been previously observed for other r-protein/chaperone pairs [32,35,38]. Moreover, we observed that cells simultaneously lacking Acl4 and Rpl4a are substantially sicker than the individual single mutants (Fig 7B). Third, by binding to the long internal loop of Rpl4, which penetrates deeply into the rRNA core of the 60S subunit (see Fig 1), Acl4 may shield this highly basic region from engaging in illicit interactions with polyanions and, hence, aggregation prior to its final insertion into the cognate rRNA environment within nascent pre-60S subunits. Fourth, and in line with a protective function, Acl4 is required for the soluble expression of newly synthesized Rpl4 in yeast cells (Fig 7D).
Elegant experiments from the Görlich laboratory have demonstrated that r-proteins are prone to aggregation in the presence of polyanions, such as tRNA [18]. Notably, precipitation of selected r-proteins could be reversed by incubation with specific subsets of importins, thus establishing, besides their classical role as nuclear transport receptors, an anti-aggregation function for importins. It has therefore been postulated that, in order to be most efficiently protected, newly synthesized r-proteins should be immediately, possibly even co-translationally, shielded [18]. While it has not yet been revealed whether importins might already be recruited to nascent r-proteins, we have recently shown that four dedicated chaperones capture their specific r-protein partner in a co-translational manner [38]. In this study, we provide evidence that, likewise, Acl4 has the capacity to recognize Rpl4 as it is synthesized by the ribosome (Fig 8). Contrary to the other four cases, where the N-terminal regions of the r-proteins constitute the chaperone binding sites, Acl4 binds to an internal region, the C-terminal part of the long internal loop, of Rpl4. Further support for an early, cytoplasmic interaction is provided by the finding that Rpl4 is already bound by Acl4 within 5 min of its induced expression in an experiment combining the pulse-chase epitope labelling of Rpl4 with its affinity purification [45,50]. The necessity to protect Rpl4 as early as possible is also highlighted by the observation that it is susceptible to aggregation, like many other r-proteins, in the absence of the ribosome-associated chaperone systems (SSB/RAC and NAC) [22]. Collectively, this raises interesting questions about the coordination of the different co-translational processes, e.g.: how is a nascent r-protein, such as Rpl4, transferred from the general co-translational chaperones to its dedicated chaperone in order to ensure its productive synthesis as a soluble protein?
In agreement with cytoplasmic formation and nuclear co-import of the Acl4-Rpl4 complex, in vitro reconstitution experiments revealed that the importin Kap104 is capable of forming a stoichiometric trimeric complex with Acl4 and Rpl4 [45]. Notably, the association of the Acl4-Rpl4 heterodimer with Kap104 is dependent on the presence of Rpl4’s C-terminal extension, which harbours a complex NLS region consisting of two partially overlapping NLSs (Fig 2B) [45]. However, Rpl4 lacking the C-terminal extension is still targeted to the nucleus (Fig 2A), indicating that the other three identified NLSs are sufficient to mediate nuclear import. Since binding of Acl4 covers a region of Rpl4, encompassing amino acids 101–114, that is a critical nuclear-targeting determinant for two of these NLSs (Fig 2B and S4 Fig), it can be inferred that this NLS region should only be responsible for the import of free Rpl4 and not the Acl4-Rpl4 complex. Taken together and considering the quasi-essential nature of Acl4, we propose that the co-import of the Acl4-Rpl4 complex, mediated by binding of Kap104 to the C-terminal extension of Rpl4, represents the major import and pre-60S assembly pathway (Fig 9). However, it is very likely that other, yet to be determined importins may also recognize the complex NLS region within Rpl4’s C-terminal extension since its nuclear localization is not abolished in kap104-16 mutant cells (S11 Fig). Given that yeast cells still grow, albeit at a very slow rate, in the absence of Acl4 (Fig 4D) [45], we suggest the existence of alternative, Acl4-independent import and pre-60S assembly routes for Rpl4 (Fig 9). In these minor import pathways, nuclear transport of Rpl4 is likely promoted by importin binding to one of the two internal or the C-terminally located NLS regions. Irrespective of the import route, Ran-GTP binding to the importin will release free or Acl4-bound Rpl4, which can then be incorporated into early pre-60S particles. Additionally, it is also possible that Rpl4, imported via an Acl4-independent route, will encounter and be transferred to an Acl4 molecule in the nucleoplasm. In this scenario, Acl4 would act as an escortin, as recently proposed to be the role of the Rps26 chaperone Tsr2 [33], connecting the nuclear import of Rpl4 to its fail-safe deposition on pre-60S subunits. Finally, it has to be assumed that most Acl4 molecules, in order to sustain the enormous demand for newly synthesized and assembly-competent Rpl4, should travel back, after pre-60S delivery of Rpl4, from the nucleus to the cytoplasm. However, it remains to be determined whether translocation of Acl4 across the NPC occurs by facilitated diffusion or relies on an active export mechanism.
Contrary to the result obtained by the Woolford laboratory [48], our study, as well as the one from the Hurt laboratory [45], clearly revealed that the eukaryote-specific extension of Rpl4 harbours an essential function (Fig 1C). Due to the importance of the eukaryote-specific C-terminal extension in coupling Acl4 release with the incorporation of Rpl4 into nascent pre-60S subunits, it was proposed that this region delivers the Acl4-Rpl4 complex to the pre-ribosomal particle by contacting co-evolved, eukaryote-specific sites [45]. Given that the efficient assembly of Rpl4 relies on critical hydrophobic contacts between residues of the C-terminal extension (e.g.: Ile289, Ile290, and Ile295) with Rpl18, a hierarchical assembly model has been put forward [45]. Accordingly, assembly of Rpl18 would precede and mediate, by contributing to the recruitment of the eukaryote-specific C-terminal extension, the initial docking of Rpl4, which would then be followed by the insertion of the long internal loop and the concomitant release of Acl4 [45]. However, several lines of evidence indicate that a complete understanding of the order of the assembly events will require further clarification. Contrary to the above-described contribution of certain hydrophobic residues of Rpl4 to its pre-60S incorporation and release from Acl4, Rpl4 gets, while only showing a moderate increase in Acl4 association, efficiently assembled into mature 60S subunits in cells expressing an Rpl18 variant (L32E, V129D) with mutations in the Rpl4 interaction surface [45]. Moreover, our C-terminal deletion analysis revealed that interactions of Rpl4, besides the contacts with Rpl18, with Rpl7 and ES7L, involving the middle part of the C-terminal extension (amino acids 302–332), are required for optimal cell growth and production of 60S subunits (Fig 1C and S3 Fig). Nevertheless, these mutant Rpl4 proteins (N325 and N301 constructs) get, in the presence of wild-type Rpl4, incorporated into mature 60S subunits and translating ribosomes (Fig 3B). More strikingly even, we observed in the same experimental setting that Rpl4 lacking the complete C-terminal extension (N264 construct) can be assembled, albeit less efficiently, into mature 60S subunits (Fig 3B); thus, indicating the possibility of an independent pre-60S assembly of the universally conserved part of Rpl4. However, the assembly of the globular domain and C-terminal extension seems to be tightly interconnected, as suggested by our observation that their separate expression confers growth to yeast cells and leads to the stable incorporation of both protein fragments into mature 60S subunits (S12 Fig). Further, given that the globular domain and the N-terminal part of the long internal loop of Rpl4 engage in a significant number of interactions with LSU rRNA domain I, while Rpl18 almost exclusively forms polar contacts with rRNA domain II, the initial association of Rpl4 with nascent pre-rRNA may occur prior to Rpl18 recruitment. Taken together, an alternative model for Rpl4 assembly, involving a series of interconnected steps, may be envisaged: (i) initial docking of Rpl4 is established by contacts of the globular domain and parts of the long internal loop with rRNA domain I; (ii) the C-terminal extension of Rpl4 facilitates Rpl18 and Rpl7 recruitment, thereby enabling formation of the eukaryote-specific interaction network and, thus, fortifying Rpl4 association and promoting correct pre-rRNA folding within this pre-60S region; (iii) either concomitantly or subsequently, the long internal loop of Rpl4 gets completely inserted into the rRNA core of pre-60S subunits, thereby leading to the dissociation of Acl4 from its Rpl4 binding site. These highly complex assembly events, coupling stable Rpl4 incorporation with Acl4 release, are expected to occur very fast since Acl4 is not detectably associated with pre-60S particles (Fig 4C and 4F). Moreover, the cellular Acl4 levels seem to influence the efficiency of Rpl4 association with pre-60S subunits, by affecting the equilibrium between Acl4-bound and pre-60S-associated Rpl4, since overexpression of Acl4 confers a strong slow-growth phenotype to wild-type cells (S13 Fig); this observation also suggests that Acl4 does not actively promote Rpl4 assembly.
The combined data from the three studies also provide compelling evidence for a role of the essential long internal loop of Rpl4 during late pre-60S maturation events that are necessary for the productive assembly of export-competent pre-60S subunits. In contrast to depletion of Rpl4, which entails early pre-60S assembly defects (S8 Fig) [43,48], it was shown that Rpl4 depleted cells expressing an Rpl4 protein lacking its long internal loop display a strong accumulation of the 7S pre-rRNA [48]. In line with such a requirement during late pre-rRNA processing steps leading to the formation of mature 5.8S rRNA, purification, upon pulse-chase epitope labelling, of Rpl4 lacking the tip region (amino acids 63–87) of the long internal loop yielded late pre-60S ribosomes, as inferred by the co-enrichment of the export adaptor Nmd3 and the GTPases Nog1 and Lsg1 [45]. Consistently, Rpl4 lacking the long internal loop, when expressed in wild-type cells, confers a slow-growth phenotype and, as revealed by sucrose gradient fractionation, gets efficiently incorporated into pre-60S subunits (Fig 3A and 3B). While the other two studies did not investigate in which compartment these aberrant pre-60S subunits are stalled, our report notably reveals, as suggested by the predominant nuclear localization of Rpl4 lacking the long internal loop (Fig 2A and 2B), that maturation of pre-60S particles is blocked at a nuclear step. Future experiments will be required to better define the specific step at which assembly of these pre-60S subunits is halted and how the long internal loop of Rpl4 promotes progression of late pre-60S maturation.
An interesting, yet puzzling observation is that the eukaryote-specific Acl4, despite its almost essential function in yeast, is not conserved in all evolutionary more advanced classes, including mammals, and also conspicuously absent from certain early evolutionary branches; thus, indicating that other proteins may relatively easily replace Acl4. Comparison of the Acl4 binding site within the long internal loop of Rpl4 between archaea and yeast reveals that this region, which has only limited sequence similarity, notably displays eye-catching differences with respect to the electrostatic surface properties (S14 Fig). This observation suggests that Acl4 might have arisen due to the necessity to shield the more positively charged, eukaryotic surface during transport of Rpl4 to its nuclear pre-60S assembly site. Given that certain importins (the importin-β/importin-7 heterodimer and importin-9) were shown to counteract the aggregation of mammalian rpL4 in the presence of polyanions [18], it is reasonable to speculate that these importins may fulfil a dual role as dedicated chaperone and transport receptor of rpL4 in mammalian cells.
In conclusion, our study has identified the previously uncharacterized Acl4 as a dedicated chaperone of the 60S subunit r-protein Rpl4 and has revealed that recognition may already occur during the cytoplasmic synthesis of Rpl4. Our findings underscore the necessity to protect r-proteins on their path to their ribosomal incorporation site and further validate co-translational capturing of r-proteins by dedicated chaperones as an advantageous and prevalently used concept to efficiently fulfil this task. Clearly, future work will be required to decipher the molecular details of the Acl4-Rpl4 interaction and to unveil the precise mechanisms that promote the stable assembly of Rpl4 into pre-60S subunits.
The S. cerevisiae strains used in this study are listed in Supporting S1 Table; all strains, unless otherwise specified, are derivatives of W303 [51]. For yeast two-hybrid analyses the reporter strain PJ69-4A was used [52]. Deletion disruption and C-terminal tagging at the genomic locus were performed as described [53,54]. Preparation of media, yeast transformation, and genetic manipulations were done according to established procedures. For the experiments involving induction of expression by addition of copper sulfate, media were prepared with copper-free yeast nitrogen base (FORMEDIUM). All recombinant DNA techniques were according to established procedures using Escherichia coli DH5α for cloning and plasmid propagation. Codon-optimized (for E. coli expression) C. thermophilum ctACL4 and ctRPL4 genes were generated by custom DNA synthesis (Eurofins). All cloned DNA fragments generated by PCR amplification were verified by sequencing. More information on the plasmids, which are listed in Supporting S2 Table, is available upon request.
For Y2H-interaction assays, plasmids expressing bait proteins, fused to the Gal4 DNA-binding domain (G4BD), and prey proteins, fused to the Gal4 activation domain (G4AD), were co-transformed into reporter strain PJ69-4A. Y2H interactions were documented by spotting representative transformants in 10-fold serial dilution steps onto SC-Trp-Leu, SC-Trp-Leu-His (HIS3 reporter), and SC-Trp-Leu-Ade (ADE2 reporter) plates, which were incubated for 3 d at 30°C. Growth on SC-Trp-Leu-His plates is indicative of a weak/moderate interaction, whereas only relatively strong interactions permit growth on SC-Trp-Leu-Ade plates.
Live yeast cells were imaged by fluorescence microscopy using an Olympus BX54 microscope. Nop58-yEmCherry, expressed from the genomic locus under the control of the cognate promoter, was used as a nucleolar marker. The Image J software was used to process the images.
Cells expressing Acl4-TAP and NTAP-Rpl4 were grown at 23°C in 4 l YPD medium to an optical density (OD600) of 2. Wild-type cells expressing NTAP-Rpl4a.N264 and NTAP-Rpl4a.N291 from plasmid were grown at 30°C in 4 l SC-Leu medium to an OD600 of 1.5. Cell extracts were obtained by glass bead lysis with a Pulverisette (Fritsch). Tandem-affinity purifications were performed in a buffer containing 50 mM Tris-HCl pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 5% glycerol, and 0.1% NP-40 as described [55]. The EGTA eluates were precipitated by the addition of TCA to a final concentration of 10% and, after an acetone wash, dissolved in 80 μl of 3x SDS sample buffer. Protein samples were separated on NuPAGE 4–12% Bis-Tris 12-well gels (Novex), run in 1x MES SDS running buffer, and subsequently stained with Brilliant Blue G Colloidal Coomassie (Sigma). The identity of the proteins contained in Coomassie-stained bands was determined by mass spectrometric analysis of peptides obtained by digestion with trypsin.
For in vitro binding assays between Rpl4-(His)6 and Acl4-Flag or between ctRpl4-(His)6 and ctAcl4, proteins were co-expressed from pETDuet-1 (Novagen) in Rosetta(DE3) (Novagen) or BL21(DE3) (Novagen) E. coli cells, respectively. Cells were grown in 200 ml of lysogeny broth (LB) medium at 37°C and protein expression was induced at an OD600 of around 0.6 to 0.8 by the addition of IPTG to a final concentration of 0.5 mM. After 3 h of growth at 30°C, cells were harvested and stored at -80°C. Cells were resuspended in 25 ml lysis buffer (50 mM Tris-HCl pH 7.5, 200 mM NaCl, 1.5 mM MgCl2, 5% glycerol) and lysed with a M-110L Microfluidizer (Microfluidics). The lysate (30 ml volume) was adjusted by the addition of 300 μl 10% NP-40 to 0.1% NP-40 (note that from here onwards all buffers contained 0.1% NP-40). An aliquot of 100 μl of total extract (sample T) was taken and mixed with 100 μl of 6x loading buffer. The total extract was then centrifuged at 4°C for 20 min at 14’000 rpm. The soluble extract was transferred to a 50 ml Falcon tube and, as above, an aliquot of 100 μl of soluble extract (sample S) was taken and mixed with 100 μl of 6x loading buffer. The insoluble pellet fraction (sample P) was resuspended in 3 ml of lysis buffer and 10 μl thereof were mixed with 90 μl of lysis buffer and 100 μl of 6x loading buffer. The soluble extract (30 ml) was adjusted to 15 mM imidazole by adding 180 μl 2.5 M imidazole pH 8. Upon addition of 250 μl of Ni-NTA Agarose slurry (Qiagen), samples were incubated for 2 h on a turning wheel at 4°C. Then, the Ni-NTA Agarose beads were pelleted by centrifugation at 4°C for 2 min at 1’800 rpm, resuspended in 2 ml of lysis buffer, and transferred to a 2 ml Eppendorf tube. The Ni-NTA Agarose beads were first washed five times with 1 ml of lysis buffer containing 15 mM imidazole and then two times for 5 min, by rotation on a turning wheel at 4°C, with 1 ml lysis buffer containing 50 mM imidazole. Elution of bound proteins was carried out by incubation of the Ni-NTA Agarose beads with 1 ml of lysis buffer containing 500 mM imidazole for 5 min on a turning wheel at 4°C. The eluate (sample E) was transferred to a 1.5 ml Eppendorf tube and 100 μl thereof were mixed with 100 μl of 6x loading buffer. Protein samples (5 μl of samples T, P, S, and E) were separated on NuPAGE 4–12% Bis-Tris 15-well gels (Novex), run in 1x MES SDS running buffer, and subsequently stained with Brilliant Blue G Colloidal Coomassie (Sigma). For Western analysis, appropriate dilutions of the above samples were separated on Bolt 4–12% Bis-Tris Plus 15-well gels (Novex), run in 1x MES SDS running buffer, and proteins were subsequently blotted onto nitrocellulose membranes (GE Healthcare).
Cell extracts for polysome profile analyses were prepared as previously described [56] and eight A260 units were layered onto 10–50% sucrose gradients that were centrifuged at 38’000 rpm in a Sorvall TH-641 rotor at 4°C for 2 h 45 min. Sucrose gradients were analysed using an ISCO UA-6 system with continuous monitoring at A254. For the fractionation experiments, five A260 units were subjected to sucrose gradient centrifugation for 2h 45 min and 20 fractions of around 500 μl were collected and processed as described [57]. Precipitated proteins were resuspended in 50 μl 3x sample buffer and 5 μl each fraction was separated on NuPAGE 4–12% Bis-Tris 26-well gels (Novex), run in 1x MES or 1x MOPS SDS running buffer, and subsequently analyzed by Western blotting. As an input control, 0.05 A260 units of total cell extract was run alongside the fractions.
Total yeast protein extracts were prepared as previously described [58]. Cultures were grown to an OD600 of around 0.8 and protein extracts were prepared from an equivalent of one OD600 of cells. Western blot analysis was carried out according to standard protocols. The following primary antibodies were used in this study: mouse monoclonal anti-FLAG (1:2’000–1:10’000; Sigma), anti-GFP (1:2’000; Roche), anti-HA (1:3’000; BAbCO), anti-His6 (1:500; Roche), and anti-Rpl3 (1:5’000; J. Warner, Albert Einstein College of Medicine, New York); rabbit polyclonal anti-Adh1 (1:50’000; obtained from the laboratory of C. De Virgilio, University of Fribourg), anti-CBP (1:15’000; Open Biosystems), anti-Rpl5 (1:5’000; S.R. Valentini, São Paulo State University, Araraquara), and anti-Rps3 (1:20’000; M. Seedorf, ZMBH, University of Heidelberg, Heidelberg). Secondary goat anti-mouse or anti-rabbit horseradish peroxidase-conjugated antibodies (Bio-Rad) were used at a dilution of 1:10’000. For detection of TAP-tagged proteins, the Peroxidase-Anti-Peroxidase soluble complex was used at a dilution of 1:20’000 (Sigma). Immobilized protein-antibody complexes were visualized by using enhanced chemiluminescence detection kits (Amersham ECL, GE Healthcare; PicoDetect, Applichem; WesternBright Sirius, Advansta).
Total RNA was extracted from exponentially grown cells (10 OD600 units) by the acid-phenol method and equal amounts of total RNA (5 μg) were separated on 1.2% agarose gels containing 6% formaldehyde or on 7% polyacrylamide gels containing 8 M urea. Northern hybridization was performed as previously described [59], utilizing the following oligonucleotides as probes:
Probe b (18S) 5’-CATGGCTTAATCTTTGAGAC-3’
Probe c (D/A2) 5-GACTCTCCATCTCTTGTCTTCTTG-3’
Probe d (A2/A3) 5’-TGTTACCTCTGGGCCC-3’
Probe e (5.8S) 5’-TTTCGCTGCGTTCTTCATC-3’
Probe f (E/C2) 5’-GGCCAGCAATTTCAAGTTA-3’
Probe g (C1/C2) 5’-GAACATTGTTCGCCTAGA-3’
Probe h (25S) 5’-CTCCGCTTATTGATATGC-3’
Probe 5S 5’-GGTCACCCACTACACTACTCGG-3’
The radioactive signals on the hybridized membranes were revealed using the Typhoon FLA 9400 imaging system and the supplied software (GE Healthcare).
For the determination of the rRNA composition of ribosomal particles, GFP-tagged Rpl4a was precipitated by a one-step GFP-Trap_A procedure that was slightly modified from the one suggested in the manufacturer’s instructions (ChromoTek). Briefly, wild-type cells expressing untagged Rpl4a (negative control) or N-terminally yEGFP-tagged Rpl4a or Rpl4a.N264 from plasmid were grown in 200 ml SC-Leu medium to an OD600 of 0.8. Cells were then washed twice with ice-cold water and finally resuspended in 500 μl of ice-cold lysis buffer (20 mM Tris-HCl pH 8.0, 5 mM Magnesium acetate, 200 mM KCl, 0.2% Triton X-100) supplemented with 1 mM DTT and containing a protease inhibitor cocktail (Complete, Roche). Cells were disrupted with glass beads by vigorous vortexing at 4°C for 12 min. Lysates were clarified by centrifugation in a microfuge at the maximum speed (approximately 16’100x g) for 15 min at 4°C. To each of the resulting total cell extracts, 30 μl of GFP-Trap_A beads, equilibrated with the same buffer, were added and the mixture was incubated for 1 h 30 min at 4°C with end-over-end tube rotation. After incubation, the beads were extensively washed seven times with 1 ml of the same buffer at 4°C and finally collected. RNA was extracted from the beads and the total cell extracts as previously described [60], and the extracted RNA was analysed by Northern blotting as above.
The Δacl4 mutant cells, either containing empty vector or a centromeric plasmid expressing Acl4 from the ADH1 promoter, were grown in a volume of 100 ml to an OD600 of around 0.7 and expression of C-terminally 2xHA-tagged Rpl4a was induced for 20 min from the CUP1 promoter with 500 μM copper sulfate. After harvesting, cells were lysed with glass beads in a buffer containing 50 mM Tris-HCl pH 7.5, 100 mM NaCl, 1.5 mM MgCl2, 5% glycerol, and 0.1% NP-40 and cell extracts were centrifuged for 3 min at 3’000 rpm. Then, total cell extracts, 10 A260 units in a final volume of 500 μl, were subjected to centrifugation at 200’000 g for 1 h. Pellets were resuspended in 100 μl lysis buffer and equal amounts of the total extracts (T), soluble extracts (S), and pellet fractions (P) were analyzed by SDS-PAGE and Western blotting using an anti-HA antibody.
Co-translational association of Syo1-FTpA and Acl4-TAP with nascent r-proteins was assessed by IgG-Sepharose pull-down and real-time quantitative reverse transcription PCR (real-time qRT-PCR) as previously described [38]. Oligonucleotide pairs for the specific amplification of DNA fragments, corresponding to the RPL3 and RPL5 mRNA, from the input cDNAs, obtained from total RNA or chaperone-associated RNA, have been previously described [38]. The following oligonucleotide pairs were used for the specific amplification of DNA fragments corresponding to the RPL4 and RPL11 mRNAs:
RPL4-I-forward 5’-ACCTCCGCTGAATCCTGGGGT-3’
RPL4-I-reverse 5’-ACCGGTACCACCACCACCAA-3’ (amplicon size 72 bp)
RPL11-I-forward 5’-ACACTGTCAGAACTTTCGGT-3’
RPL11-I-reverse 5’-TTTCTTCAGCCTTTGGACCT-3’ (amplicon size 81 bp)
Multiple sequence alignments of orthologous proteins were generated in the ClustalW output format with T-Coffee using the default settings of the EBI website interface [61]. Secondary structure prediction was performed with the PSIPRED v3.3 prediction method available at the PSIPRED website interface [62]. Potential tetratrico peptide repeats within Acl4 were identified by using the TPRpred website interface [63], in combination with secondary structure prediction, multiple sequence alignments, and visual inspection of the occurrence of TPR consensus residues [46,47]. To identify orthologues of S. cerevisiae Acl4, the sequence of the protein YD161_YEAST was searched for orthologues against the OMA database for orthology prediction (http://omabrowser.org/cgi-bin/gateway.pl?f=DisplayEntry&p1=YD161_YEAST) [64]. OMA identified 1:1 orthologues in 23 species in the first group, with orthologues in fungi, parasites, and fishes. A total of 97 orthologous sequences from eight groups (OMA groups: 351324, 181749, 130365, 204390, 227596, 336561, 539094, and 370534), as well as their corresponding NCBI taxid, were extracted from the orthoXML file. One group (273573 with six sequences) was excluded since it did not contain the characteristic TPR-repeat domain of this family. The sequences were aligned with MAFFT [65] and viewed with Jalview [66]. The taxids were pasted into the phyloT web server (http://phylot.biobyte.de) to generate the tree and forwarded to the iTOL web site to visualize the tree [67]. As shown by the multiple sequence alignment and the tree, the gene encoding Acl4 orthologues is found mainly in fungi, but some dispersed branches kept it (e.g.: some fishes, invertebrates, and parasites). Analysis and image preparation of three-dimensional structures, downloaded from the PDB archive, was carried out with the PyMOL (PyMOL Molecular Graphics System; http://pymol.org/) or Chimera (http://www.cgl.ucsf.edu/chimera) software. The coordinates of the following ribosome structures were used: S. cerevisiae 60S subunit (PDB 3U5H and 3U5I; [15]), S. cerevisiae 80S ribosome (PDB 4V88; [15]), and Haloarcula marismortui 50S subunit (PDB 4V9F; [68,69]. The representation of the electrostatic surface potential of the universally conserved part of yeast and archaeal Rpl4 was generated with Chimera by coulombic surface colouring.
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10.1371/journal.pgen.1002307 | A Noncoding Point Mutation of Zeb1 Causes Multiple Developmental Malformations and Obesity in Twirler Mice | Heterozygous Twirler (Tw) mice develop obesity and circling behavior associated with malformations of the inner ear, whereas homozygous Tw mice have cleft palate and die shortly after birth. Zeb1 is a zinc finger protein that contributes to mesenchymal cell fate by repression of genes whose expression defines epithelial cell identity. This developmental pathway is disrupted in inner ears of Tw/Tw mice. The purpose of our study was to comprehensively characterize the Twirler phenotype and to identify the causative mutation. The Tw/+ inner ear phenotype includes irregularities of the semicircular canals, abnormal utricular otoconia, a shortened cochlear duct, and hearing loss, whereas Tw/Tw ears are severely malformed with barely recognizable anatomy. Tw/+ mice have obesity associated with insulin-resistance and have lymphoid organ hypoplasia. We identified a noncoding nucleotide substitution, c.58+181G>A, in the first intron of the Tw allele of Zeb1 (Zeb1Tw). A knockin mouse model of c.58+181G>A recapitulated the Tw phenotype, whereas a wild-type knockin control did not, confirming the mutation as pathogenic. c.58+181G>A does not affect splicing but disrupts a predicted site for Myb protein binding, which we confirmed in vitro. In comparison, homozygosity for a targeted deletion of exon 1 of mouse Zeb1, Zeb1ΔEx1, is associated with a subtle abnormality of the lateral semicircular canal that is different than those in Tw mice. Expression analyses of E13.5 Twirler and Zeb1ΔEx1 ears confirm that Zeb1ΔEx1 is a null allele, whereas Zeb1Tw RNA is expressed at increased levels in comparison to wild-type Zeb1. We conclude that a noncoding point mutation of Zeb1 acts via a gain-of-function to disrupt regulation of Zeb1Tw expression, epithelial-mesenchymal cell fate or interactions, and structural development of the inner ear in Twirler mice. This is a novel mechanism underlying disorders of hearing or balance.
| Twirler (Tw) mice have a combination of abnormalities that includes cleft palate, malformations of the inner ear, hearing loss, vestibular dysfunction, obesity, and lymphoid hypoplasia. In this study, we show that the underlying mutation affects the Zeb1 gene. Zeb1 was already known to encode a protein normally expressed in mesenchymal cells, where it represses expression of genes that are uniquely expressed in epithelial cells. The Tw mutation is a rare example of a single-nucleotide substitution in a region of a gene that does not encode protein, promoter, or splice sites, so we engineered a mouse model with the mutation that confirmed its causative role. The Tw mutation disrupts a consensus DNA binding site sequence for the Myb family of regulatory proteins. We conclude that this mutation leads to abnormal expression of Zeb1, structural malformations of the inner ear, and a loss of hearing and balance function. A similar mechanism may underlie other features of Twirler, such as obesity and cleft palate.
| Twirler (Tw) spontaneously arose in a crossbred stock of mice segregating multiple recessive mutant alleles [1]. Heterozygous Tw mice develop obesity after three months of age, and exhibit stereotypic behavior that includes waltzing, spinning, and horizontal head-shaking [1]. This behavior is thought to result from malformed vestibular labyrinths that include hypomorphic or absent lateral semicircular canals, irregular contours of the anterior and posterior semicircular canals, and absent otoconia in the utricle and saccule [1]. In contrast, all homozygous Tw mice are born with cleft palate and die soon after birth [1].
Tw is located on proximal chromosome 18 but the causative mutation has not been identified [1], [2]. A transgene insertional mutant, Tg9257, exhibits a similar inner ear phenotype and is also located on proximal chromosome 18, raising the possibility that these phenotypes are allelic [3]. However, complementation testing is inconclusive [3]. Similarly, the Irxl1 gene, located within a broad critical map interval for Tw and expressed in developing palate, has also been ruled out as a candidate for Tw [4].
Zeb1 is also located on proximal chromosome 18 and encodes a transcription factor, Zeb1, that binds E-box-like elements to either repress [5], [6], or activate transcription [7]–[9]. Mice that are homozygous for a targeted deletion of exon 1 of Zeb1 (Zeb1ΔEx1) die soon after birth with cleft palate, limb defects and other skeletal abnormalities, and T-cell deficiency [10], whereas heterozygous Zeb1ΔEx1/+ mice are viable and adult females show increased adiposity [11]. This partial phenotypic overlap with Twirler does not include stereotypic vestibular behavior or inner ear malformations, although these were likely not examined in Zeb1ΔEx1 mice. Ectopic expression of Zeb1 in neoplastic epithelium has been implicated in the epithelial-to-mesenchymal transition (EMT) leading to local tumor invasion and metastasis [12]. In normally developing mesenchymal tissue, Zeb1 is thought to repress epithelial-specific genes such as E-cadherin and activate mesenchyme-specific genes such as collagen, smooth muscle actin and myosin [9]. Genome-wide expression profiling reveals a probable similar role for Zeb1 in the regulation of gene expression in developing mouse inner ear mesenchyme [13]. In humans, heterozygous mutations of ZEB1 cause posterior polymorphous corneal dystrophy, characterized by an epithelial transition and abnormal proliferation of corneal endothelium [14]. Zeb1ΔEx1/+ mice also show corneal abnormalities and further implicate Zeb1 in the suppression of an epithelial phenotype [15].
In the current study we show that Twirler is caused by a noncoding point substitution in the first intron of Zeb1. The mutation does not affect splicing, but does disrupt a consensus binding site sequence for Myb proteins [16]. The maintenance of inner ear mesenchyme- and epithelium-specific gene expression is disrupted in Twirler inner ears [13], demonstrating a novel mutation and developmental mechanism for the pathogenesis of hearing or balance disorders.
Heterozygous Tw/+ adult mice had smaller spleens (38±2 mg vs. 68±7 mg, P<0.013) in comparison to wild type littermates. Tw/+ thymi were also smaller although the difference was not significant (13±2 mg vs. 31±6 mg, P<0.06). Tw/+ mice had lower counts of white blood cells (1×103/µl vs. 7.2×103/µl, P<0.0001), lymphocytes (0.5×103/µl vs. 5.9×103/µl, P<0.0004) and polymorphonuclear neutrophils (0.4×103/µl vs. 1.3×103/µl, P<0.04). No abnormalities were found in other adult Tw/+ tissues. Histopathological examination of P0 animals revealed no abnormalities in the thymus or spleen of wild type, Tw/+ or homozygous Tw/Tw mice. Tw/Tw mice had cleft palates.
There was no significant difference in average body weight between Tw/+ and wild type littermates of either sex until 12 weeks of age (Figure 1A and 1B). Beginning at seven weeks of age, Tw/+ mice consumed approximately 15 to 20% more food than wild type littermates (Figure 1C and 1D). There was a significant increase in the percentage of body fat and slightly reduced lean body mass in Tw/+ mice of both sexes (Table 1), indicating that fat accounts for the increased body mass. Body weight-adjusted energy expenditure, estimated from oxygen consumption, revealed a reduced metabolic rate in Tw/+ mice that did not reach statistical significance (Table 1). Tw/+ mice had normal serum glucose levels but elevated levels of serum free fatty acids, triglycerides, insulin, leptin, corticosterone and adiponectin (Table 1). Insulin and glucose tolerance tests of 15-week-old females showed insulin resistance and slight glucose intolerance in Tw/+ mice (Figure 1E and 1F), consistent with data for other obese mice with hyperinsulinemia [17].
We evaluated the morphology of mutant inner ears using the paint-filling technique (Figure 2). The Tw/+ inner ears had grossly intact semicircular canals and neurosensory cristae ampullaris, but the contours of the canals were irregular due to small bulges and projections (Figure 2B). The most anatomically consistent malformation was found at the non-ampullated end of the lateral canal where it normally narrows to join the vestibule in wild type ears (Figure 2D). In contrast, the non-ampullated ends of Tw/+ lateral canals were irregular or constricted (Figure 2E). Tw/Tw inner ears have more severe malformations that include absence of the lateral semicircular canal, truncation of the posterior semicircular canal, and shortening of the cochlear duct (Figure 2C, 2F and 2I).
The average length of Tw/+ cochlear ducts (Figure 2H) was 91% (±5%) that of wild type ears (P<0.00002; Figure 2G). Binaural average ABR thresholds were elevated for Tw/+ mice in comparison to wild type controls at one month of age (33±1.6 dBSPL vs. 55±5.3 dBSPL at 8 kHz, p<0.0006; 33±1.8 dBSPL vs. 46±4 dBSPL at 16 kHz, p<0.01; 29±1.9 dBSPL vs. 39±3.6 dBSPL at 32 kHz, p<0.023; Figure 2J). Tw/+ mice showed no significant change in ABR thresholds measured at three months of age in comparison to thresholds measured at one month of age (not shown).
Tw/+ utricles had giant otoconia that were transparent by light microscopic examination but visible by scanning electron microscopy (Figure 2N). In contrast, Tw/+ saccular otoconia appeared normal (Figure 2L).
We screened 1679 [(C57BL/6J-Tw/+ x CAST/Ei)F1-Tw/+ x C57BL/6J]N2 progeny for recombinations. Recombination locations were refined with additional markers to narrow the Tw interval to 814 kb between D18Nih6 and D18Nih42 (Figure 3A). This interval was five Mb proximal to the Tg9257 transgene insertion site [3]. The Tw interval contained three genes: Zeb1, Zeb1os (Zeb1 opposite strand transcript, annotated in MGI as predicted gene Gm10125) and Zfp438 (Figure 3A). Zeb1 encodes a transcription factor with two zinc finger motifs and one homeobox motif. Zeb1os is predicted to encode a long noncoding RNA of unknown function. It is located on the opposite strand of Zeb1 where the two overlapping genes share parts of their first introns. Finally, Zfp438 is predicted to encode a zinc finger protein whose biological function is unknown [18].
Zeb1 was a good candidate for the gene mutated in Tw based upon the phenotype associated with a targeted deletion allele, Zeb1ΔEx1. Homozygous Zeb1ΔEx1 mice are born with cleft palate, skeletal and thymus abnormalities, and die shortly after birth [10]. We observed that Zeb1ΔEx1/+ heterozygotes have inner ear morphology and hearing thresholds that are indistinguishable from those of wild type littermates, whereas Zeb1ΔEx1/ΔEx1 homozygotes have a subtle constriction of the midportion of the lateral semicircular canal that differs in location and severity from that observed in Tw/+ mice (Figure S1). This difference is probably not due to genetic background since both lines were congenic on a C57BL/6J background.
To determine if Tw and Zeb1ΔEx1 can complement to form a normal palate or inner ear, we crossed heterozygous Tw and heterozygous Zeb1ΔEx1 mice. We observed an approximate Mendelian ratio of genotypes: five +/+, five Tw/+, seven Zeb1ΔEx1/+ and eight Tw/Zeb1ΔEx1. All Tw/Zeb1ΔEx1 mice were born with normal palates and developed into adults with circling behavior typical of Tw/+ mice. The lateral semicircular canals resembled those of Tw/+ mice (Figure S1). These results suggest these mutations exert their effects via different genes or mechanisms. While the Zeb1 pathway may be altered in Twirler mice, it is unlikely to be due to a loss-of-function allele of Zeb1.
To identify the Tw mutation, we first used 5′-RACE and 3′-RACE to identify novel exons of Zeb1, Zeb1os and Zfp438. 5′-RACE revealed Zeb1 transcripts with each of five additional alternative first exons (designated 1b, 1c, 1d, 1e and 1f) between exon 1 (heretofore termed exon 1a) and exon 2 (Figure S2). We amplified and sequenced all novel and annotated exons of Zeb1, Zeb1os and Zfp438 from genomic DNA of Tw/Tw, Tw/+ and wild type mice. We also amplified and sequenced cDNA transcripts of these genes from embryonic mRNA. All major transcripts of these genes were amplified from mice with each genotype. We found no sequence differences in the cDNAs or genomic exons. Sequence analysis of the 192-bp region of overlap of Zeb1 and Zeb1os revealed a single nucleotide substitution (G>A) 181 bp downstream of Zeb1 exon 1 and 12 bp downstream of Zeb1os exon 1 in Tw (Figure 3B). We designated this Tw variant as c.58+181G>A, which was the only sequence variation we detected. The wild type variant c.58+181G was conserved among 13 normal control inbred mouse strains as well as other vertebrate species (Figure 3B). In silico analyses (NNsplice, GeneSplicer, Net2Gene) predict that c.58+181G>A does not affect splicing of the adjacent splice donor site for exon 1 of Zeb1os. Sequence analysis of Zeb1 and Zeb1os cDNA transcripts confirmed no effect of c.58+181G>A on splicing.
c.58+181G>A disrupts a predicted site for Myb protein binding (Figure 3B)[16]. To test if this change can alter the binding of a Myb protein, recombinant mouse C-Myb was expressed and purified for an electrophoretic mobility shift assay (EMSA) of its binding to oligonucleotide probes containing either c.58+181G or c.58+181A and the flanking genomic sequences. There was a shift of the mobility of the wild type DNA probe in the presence of C-Myb, while the Tw DNA probe mobility was unchanged (Figure 4A). The binding of C-Myb to wild type DNA was inhibited by both the wild type probe and a mim-1 control probe which has been shown to interact with C-Myb [19], but not by the Tw probe (Figure 4B). These data provide in vitro evidence that the Tw mutation can disrupt binding of a Myb protein (C-Myb) to the mutated first intronic sequence of Zeb1.
We analyzed mRNA expression levels of Zeb1, Zeb1os and Zfp438 from inner ears of Tw/Tw, Tw/+ or wild type mice at E13.5. We performed the same analysis with Zeb1ΔEx1 heterozygotes, homozygotes, and wild type littermates. We designed primer pairs to specifically amplify Zeb1 transcripts starting from each of exons 1a, 1b, 1c, 1d, 1e or 1f. One primer pair for constitutively spliced exons 2 and 3 was designed to amplify all Zeb1 transcripts. The levels of Zeb1 transcripts containing exon 1b, 1c, 1d, 1e, or 1f, as well as the Zeb1os and Zfp438 transcripts, were too low to be reliably quantified by RT-PCR. The levels of transcripts containing exons 1a and 2, as well as exons 2 and 3, were significantly increased from the Tw allele of Zeb1 (Zeb1Tw) in comparison to wild type Zeb1 (Figure 5A). In contrast, Zeb1ΔEx1 expressed no Zeb1 transcripts containing exons 1a and 2, and nearly non-detectable levels of any other Zeb1 transcripts containing other exons (Figure 5B). Transcripts levels for the closely related Zeb2 gene were unchanged among all three Zeb1 genotypes (Figure 5B). These results indicate that Zeb1ΔEx1 is a loss-of-function allele whereas Zeb1Tw is likely to act via gain-of-function.
To confirm the pathogenic effect of c.58+181G>A, we generated two knockin mouse lines: KIA segregates the Tw variant c.58+181A and KIG segregates the wild type variant c.58+181G (Figure 6). Compound heterozygous KIG/KIA mice consumed more food and grew heavier with increased adiposity in comparison to KIG/KIG control males and females (Figure 7A–7D, Table 2). The energy expenditure and circulating hormone levels in KIG/KIA mice recapitulated the Tw/+ phenotype (Table 2). The reduction in body weight-adjusted energy expenditure reached statistical significance in KIG/KIA female mice, whereas it did not in Tw/+ females (Table 1). Insulin and glucose tolerance tests showed insulin resistance and slight glucose intolerance in KIG/KIA mice (Figure 7E and 7F). Although KIG/KIA mice showed neither circling behavior nor constricted semicircular canals, the semicircular canals were irregular (Figure 8B) and the utricles contained giant otoconia (Figure 8N). Average ABR thresholds for KIG/KIA and KIG/KIG mice were not significantly different (Figure 8J). KIA/KIA and KIA/Tw inner ears displayed the same malformations as Tw/Tw ears (Figure 8C, 8F, 8I, and Figure 9B, 9D, 9F). KIG/KIA average spleen weight was decreased by 15% (P<0.05) but average thymus weight did not differ relative to KIG/KIG littermates (Table 2). We observed cleft palate with or without cleft lip in KIA/KIA and KIA/Tw mice with 50% and 90% penetrance, respectively (not shown). We did not observe cleft palate or cleft lip in KIG/KIA, KIG/KIG or KIG/Tw mice, indicating that the recapitulation of the Tw phenotype is specific.
The different phenotypic severity and penetrance of KIA in comparison to Tw could result from genetic background differences, since Tw arose on a different undefined stock. However, we serially backcrossed Tw to wild type C57BL/6J for over 30 generations, and KIA was generated from C57BL/6-derived Bruce4 ES cells and maintained on an isogenic C57BL/6J background. Therefore the differences in severity and penetrance could result from closely linked sequence variation, the residual loxP site in KIA, or a combination of these effects.
To determine if Zeb1 protein is expressed from the Tw allele, we stained inner ears of Tw/Tw mice with anti-Zeb1 antibodies (Figure 10A and 10B). We observed Zeb1 expression in non-epithelial (mesenchymal) cells surrounding Tw/Tw inner ears in which epithelial and mesenchymal tissue compartments could be microanatomically differentiated (Figure 10B). Other Tw/Tw inner ears had poorly preserved microarchitecture, precluding a differentiation of epithelium versus mesenchyme (Figure 10C). We conclude that Zeb1 protein is expressed in Tw/Tw ears, consistent with the result of real-time RT-PCR.
To determine if Zeb1 protein levels are altered by Tw, we performed a western blot analysis of inner-ear or whole-head protein extracts from E13.5 mice. We compared Tw/Tw, Tw/+ and wild type littermates, as well as KIG/KIG, KIG/KIA and KIA/KIA littermates. We were unable to detect Zeb1 in inner-ear protein extracts, but were able to reliably detect it in samples from whole heads. Total Zeb1 protein levels appeared to be slightly increased by Tw in comparison to wild type littermates (Figure 10D). This difference was not significant (ANOVA, P>0.05), possibly due to small numbers of animals and the degree of variation of Zeb1 band intensities within genotypes (Figure 10F). In contrast, Zeb1 protein levels in KIG/KIA and KIA/KIA mice were 2- to 3-fold higher than in KIG/KIG littermates (Figure 10E). The variation within knockin genotype groups was smaller, resulting in differences between knockin genotype groups that were significant (P<0.05) (Figure 10G).
This study demonstrates that the phenotype of Twirler is caused by a noncoding nucleotide substitution within a shared first intron of the Zeb1 and Zeb1os genes on mouse chromosome 18. This is a rare example of a Mendelian noncoding point mutation that does not affect a splice site or promoter. Our results demonstrate the potential for complex phenotypic effects of noncoding point variants, which are increasingly implicated in association studies of genetically complex traits. Our recombinant knockin mouse model and wild type knockin control for testing the pathogenic potential of the Tw mutation may be a useful paradigm to explore the effects of other noncoding variants of unknown pathogenic potential. The altered penetrance potentially associated with a residual loxP site in the Tw knockin line serves a cautionary note to include a wild type knockin control.
Although the initial study by Lyon [1] described abnormal development of the sensory neuroepithelium in the cristae ampullaris of some semicircular canals of Tw/+ mice, we have not observed the same alteration. Instead we observed a highly penetrant constriction of the non-ampullated end of the lateral semicircular canal that could impede or prevent the flow of endolymph and disrupt neurosensory detection of angular acceleration. A difference in strain background [1] may account for the different result. Moreover, Lyon reported that utricular otoconia were absent in Tw/+ ears whereas we observed giant utricular otoconia. This difference could also result from the strain background difference, loss of giant otoconia during the dissection process, or our use of scanning electron microscopy in addition to light microscopy. Nevertheless, either of the described utricular phenotypes could impair the detection of linear acceleration by Tw/+ utricles. We conclude that our observed semicircular canal and utricular anomalies underlie the vestibular behaviors of Tw/+ mice, although we cannot estimate their relative contributions to the observed vestibular functional phenotype. Correlating mouse vestibular structural or functional abnormalities with behavior is difficult due to a complex interrelationship between vestibular behavior and anxiety that is also dependent upon strain background [20].
The cause of hearing loss observed in some Tw/+ mice also remains obscure. Postmortem examination of middle ears did not reveal otitis media or developmental malformations of the external or middle ears that could account for the hearing loss. Although severe hearing loss has been observed in other mouse mutants with much shorter cochlear ducts [21], the severity of hearing loss in Tw/+ mice was highly variable but the degree of shortening of the cochlear duct was nearly constant. This lack of correlation leads us to conclude that associated physiologic defects or undetected structural anomalies underlie hearing loss in Tw/+ mice.
The Tw/+ phenotype includes hyperphagia with elevated levels of circulating corticosterone and adiponectin that are similar to those in a corticotropin-releasing factor (CRF) transgenic mouse model of Cushing syndrome [22], [23]. Other phenotypic similarities of that model to Tw/+ include increased body weight and adiposity, alopecia, atrophy of the thymus and spleen, and muscle wasting. This may suggest that Tw disrupts, at least in part, the hypothalamus-pituitary-adrenal axis. This is consistent with expression of Zeb1 in the pituitary gland [24]. However, Zeb1 protein is also expressed in adipose tissue and increases during adipogenesis in cell culture [11]. Moreover, Zeb1ΔEx1/+ mice develop obesity that is not associated with hyperphagia [11], unlike Tw/+ mice (Figure 1C and 1D). Therefore different mechanisms or tissues may underlie obesity phenotypes associated with Zeb1ΔEx1 and Zeb1Tw. The pathogenetic mechanism for one or both of these Zeb1 alleles may also underlie a locus for susceptibility to obesity on human chromosome 10p11 [25]-[28], which includes the human ZEB1 gene.
The results presented here and in Hertzano et al. [13], in combination with the body of published data on Zeb1 in cancer and normal development, show that Zeb1 is a master regulator of mesenchyme-specific gene expression in the developing mouse ear. Twirler is a novel example of a disorder of hearing or balance caused by a disruption of mesenchymal-epithelial identities or interactions. A similar lateral semicircular canal phenotype is seen in other hyperactive circling mice, including epistatic circler mice [29] and mice segregating a gene-trap allele of Chd7 [30]. Chd7 encodes a chromodomain protein required for the development of multipotent migratory neural crest cells [31], which includes an epithelial-to-mesenchymal transition. An auditory-vestibular phenotype approximating that of Twirler and Chd7 mutant mice is also observed in human patients with CHARGE syndrome and mutations of the human CHD7 gene [32], [33]. Semicircular canal formation is also known to require Bmp4 [34] and heterozygosity for a knockout allele of mouse Bmp4 primarily affects the lateral semicircular canal [35]. Bmp4 is a member of the transforming growth factor-β (TGF-β) super-family [36], providing another link to Zeb1 since Zeb1 and Zeb2 have been implicated in TGF-beta/BMP signaling [8].
Why do Twirler mice have a different inner ear phenotype than Zeb1ΔEx1 mice? Genetic background differences seem unlikely to account for this difference since Zeb1ΔEx1 and Twirler were both maintained on a congenic C57BL/6J background. It is possible that other Zeb1 transcripts could compensate for the loss of exon 1 in Zeb1ΔEx1 ears, but our quantitative RT-PCR and expression profiling results [13] render this hypothesis unlikely. Alternatively, the closely related Zeb2 gene may be able to compensate for the loss of Zeb1 expression in the inner ear, but not other affected tissues such as the palate or lymphoid system. However, our quantitative RT-PCR results revealed no compensatory change in Zeb2 transcript levels in the mutants. It is also possible that disruption of Zeb1os may contribute to the Tw phenotype. Ectopic expression of an analogous long noncoding antisense RNA in epithelial cells leads to altered Zeb2 RNA splicing, increased Zeb2 protein levels, and epithelial-to-mesenchymal transition [37]. However, Zeb1os RNA levels were too low for us to reliably detect and monitor by either qRT-PCR or Northern blot analyses to confidently address this possibility (data not shown). Finally, perhaps Twirler does not exert its pathogenic effect via Zeb1. This also seems unlikely since there is significant phenotypic overlap of Twirler with Zeb1ΔEx1, including abnormalities of the semicircular canals associated with both mutant alleles. Furthermore, the phenotypic effects of compound heterozygosity for Zeb1ΔEx1 and Tw are consistent with the conclusion that Zeb1ΔEx1 is an amorphic or hypomorphic allele whereas Twirler acts as a hypermorphic or neomorphic allele to misregulate Zeb1 expression.
Our electrophoretic mobility shift experiment (Figure 4) suggests that Tw exerts its pathogenic effect by disruption of binding of C-Myb or other Myb proteins to the first intron of Zeb1. There are also published observations supporting the general hypothesis that loss of Myb protein binding leads to de-repression of Zeb1Tw and inner ear malformations: First, C-Myb can function as either an activator or repressor of gene transcription [38] and is thought to function in regulation of epithelial-mesenchymal cell identity [39]. Second, a pathogenic effect of up-regulation of developmental transcription factors has been demonstrated for Pax6 in the eye [40] and Tbx1 in the inner ear [41]. In the inner ear, increased expression of Tbx1 can cause malformations that include incomplete coiling and reduced extension of the cochlear duct [41]. Furthermore, Tbx1 expression in the periotic mesenchyme is required for cochlear duct outgrowth [42], suggesting a potential link to the observed inner ear phenotype of Twirler. Taken together, these observations and our results support the hypothesis that Twirler disrupts inner ear development via mis-regulation of Zeb1.
The cell type-specific gene expression profiles of Twirler ears [13] suggest that a pathologic disruption of epithelial and mesenchymal cell identities underlies the inner ear malformations. This could arise from a loss of mesenchymal cell identity leading to mesenchymal-epithelial transition (MET), a loss of epithelial cell identity leading to epithelial-mesenchymal transition (EMT), or a combination of these mechanisms. Although the gene expression profiles [13] seem consistent with MET, it is difficult to conceive a simple MET pathway that does not invoke a loss-of-function mechanism in Tw mesenchyme. In contrast, EMT would involve a gain-of-function with ectopic expression of Zeb1 in Tw inner ear epithelium. Indeed, ectopic expression of Zeb proteins in other epithelial tissues has been shown to lead to EMT in other neoplastic and developmental processes [43]. Distinguishing among EMT and MET mechanisms may be difficult if they involve complex regulatory pathways mediated by Zeb1os, Zeb2, microRNAs or other genes.
In summary, we have identified the pathogenic mutation of Twirler as a noncoding point mutation that leads to over- or mis-expression of Zeb1, pathologic alterations of gene expression [13], cell fate and interactions in the developing inner ear. The ultimate result is a gross alteration of the structure and function of the vestibular and auditory organs. Disruption of epithelial-mesenchymal identity or interactions may be a shared pathogenetic mechanism underlying phenotypes that primarily affect development of the lateral semicircular canals, extension of the cochlear duct, or both.
Mice were maintained on a 12∶12-h light-dark cycle. All experiments and procedures were approved by the Animal Care and Use Committees of the National Institute of Diabetes and Digestive and Kidney Diseases, National Institute of Neurological Disorders and Stroke and National Institute on Deafness and Other Communication Disorders. Twirler mice were a kind gift from Drs. Miriam Meisler and Siew-Ging Gong at the University of Michigan and were maintained on a C57BL/6J background by backcrossing heterozygous Tw males to C57BL/6J females for at least 30 generations. Zeb1ΔEx1 mice [10] were a generous gift from Dr. Douglas Darling and were serially backcrossed to C57BL/6J to maintain the line.
Bacterial artificial chromosome (BAC) clone RP23-135A18 containing mouse genomic DNA encoding exon 1 of Zeb1 was digested with PacI/SphI and SphI to yield 7.6-kb and 2.6-kb homology arms, respectively, for targeting constructs (Figure 6). Each targeting construct included loxP sites flanking a splice acceptor site and internal ribosomal entry site (IRES) (pGT1.8IresBgeo, provided by Austin Smith at University of Edinburgh) [44], E. coli lacZ, and a reverse-oriented pPGK-neomycin resistance cassette cloned into the pPNT plasmid [45] (Figure 6). The wild type (KIG) and Twirler (KIA) 7.6-kb PacI/SphI homology arms contained G and A at position c.58+181, respectively. Bruce4 embryonic stem (ES) cells [46] were electroporated with the KIG or KIA targeting constructs and grown in the presence of G418 and ganciclovir, using standard protocols at the University of Michigan Transgenic Animal Model Core [47]. G418-resistant ES clones were screened for homologous recombination by PCR and Southern blot analyses. At least three recombinant ES cell lines for each targeting construct were injected into C57BL/6 blastocysts. Chimeric males were mated with C57BL/6 females and offspring were analyzed by Southern blot and PCR analyses for germline transmission of KIG or KIA (Figure S3). Table S1 shows PCR primer pairs used to genotype KI alleles before and after neomycin cassette removal (Figure 6). Mice transmitting KIG or KIA in the germline were crossed to Cre recombinase-expressing mice (C57BL/6-TgN(Zp3-Cre)93Knw, Jackson laboratory, ME) to delete the IRES-lacZ-neomycin resistance cassette, leaving a single loxP site 606 bp downstream from Zeb1 exon 1.
A comprehensive gross anatomical, histological, and serological analysis of three 15-week-old Tw/+ and three wild type littermate males was performed as described [48]. Tissue sections from two Tw/Tw, two Tw/+ and two wild type mice at postnatal day 0 (P0) were analyzed.
Heterozygous Tw males and females were mated. Pregnant females were identified by the presence of a vaginal plug and gestational stage was estimated by defining that morning as 0.5 days post-conception (dpc). Embryos at 14.5 dpc were harvested and processed for paint-filling as described [49]. The length of the cochlear duct was measured along its outer contour from a ventral view [50]. For scanning electron microscopy (SEM), whole-mounted inner ears were fixed in 2.5% glutaraldehyde in 0.1 M sodium cacodylate with 2 mM CaCl2 for 90 min. The organ of Corti, saccule, utricle, and crista ampullaris were dissected free in water and dehydrated with a serial dilution series of acetone. Samples were critical point-dried and sputter-coated followed by visualization with a field-emission scanning electron microscope (S-4800, Hitachi). Auditory brainstem response (ABR) thresholds were measured in response to click or pure-tone stimuli of 8, 16, or 32 kHz as described [51].
Six Tw/+ male, six Tw/+ female, six wild type male and six wild type female mice were housed individually with regular mouse chow and water provided ad libitum. Body weights were measured weekly from 5 weeks of age. Weekly food intake was measured from weeks 6 through 22 to calculate average daily food intake. At 23 weeks of age, mice were transferred to the NIDDK Mouse Metabolism Core Laboratory for measurement of oxygen consumption, carbon dioxide production and motor activity as described [52]. Body composition was measured using Echo3-in-1 NMR analyzer (Echo Medical Systems, Houston, TX). Tail vein blood was used for serologic analyses. Fifteen-week-old female mice (eight Tw/+, six wild type) were tested for glucose and insulin tolerance as described [52]. All data are expressed as a mean ± SEM. Student's t-test was used to identify statistically significant differences between genotype groups.
Twirler males (C57BL/6J-Tw/+) were crossed with DBA/2J or Castaneus (CAST/Ei) females since Twirler females are poor caretakers of offspring. Male (C57BL/6J-Tw/+ x DBA/2J)F1-Tw/+ or (C57BL/6J-Tw/+ x CAST/Ei)F1-Tw/+ progeny were backcrossed with DBA/2J or C57BL/6J females, respectively, to generate 337 and 1679 N2 backcross progeny, respectively. Progeny were scored for circling behavior or obesity by visual inspection.
We genotyped short tandem repeat (STR) markers on 337 DBA/2J N2 backcross progeny to identify two STR markers (D18Mit65, D18Mit64, D18Mit19 and D18Umi1) flanking each side of Tw. These markers were genotyped in the 1679 CAST/Ei N2 backcross progeny to identify recombinations in the Tw region. The Tw map interval was defined by genotypes of additional markers in the recombinants. We genotyped MIT markers between D18Mit65 and D18Umi1, as well as 40 novel STR markers (denoted D18Nih1 through D18Nih44; PCR primer sequences listed in Table S1) located between D18Mit19 and D18Mit219.
Genomic DNA of Tw/Tw, Tw/+ and wild type mice were isolated for PCR amplification as described [53]. The primers were designed to amplify and sequence all of the annotated exons of the Zeb1, Zeb1os (MGI predicted gene Gm10125) and Zfp438 genes in the Tw critical interval. Additional novel exons were identified by 5′ and 3′- RACE (5′ and 3′ rapid amplification of cDNA ends) of the Zeb1, Zeb1os and Zfp438 genes. This revealed multiple alternative first exons for Zeb1 that were also sequenced. Reverse transcription (RT)-PCR was performed to amplify and sequence full-length cDNA clones of the three genes using whole body mRNA collected from embryonic Tw/Tw, Tw/+ and wild type littermates.
PCR reaction conditions were modified to amplify and sequence the overlapping genomic region of Zeb1 and Zeb1os. Fifty-µl PCR reactions contained 50 to 100 ng of genomic DNA, 5 pmol each of forward and reverse primers, 200 mM each dNTP, 0.5 M betaine, 10% dimethyl sulfoxide (DMSO), 2.5 mM MgCl2, and 0.5 U of thermostable polymerase. Thermal cycling conditions were: 95°C for 1 min; 33 cycles of 20 s at 95°C, 20 s at 57°C, and 45 s at 72°C; and a final 2-min extension at 72°C. For sequencing, 50 µl PCR reaction products were purified with a QIAquick PCR purification kit (Qiagen, Hilden, Germany) and eluted with 30 µl elution buffer. Three µl of purified products were added to a 10-µl sequencing reaction containing 3.2 pmol primer, 0.25 µl Big Dye Terminator Ready Reaction mix (PE Biosystems), sequencing buffer and 10% DMSO. Cycling conditions were 96°C for 2 min and 33 cycles of 96°C for 10 s, 55°C for 10 s, and 60°C for 4 min. We also amplified and sequenced the overlapping genomic region of Zeb1 and Zeb1os from normal mouse control strains 129/J, AKR/J, BALB/cJ, C3H/HeJ, C57BL/6J, C58/J, CBA/J, CE/J, DBA/2J, P/J, RF/J, SEA/GnJ and SWR/J DNA.
Double-stranded oligodeoxyribonucleotide probes were synthesized to encode genomic sequences containing c.58+181G (5′-TGCTGGACTGGACCGTTATGTCTTACCTGC and 5′-GCAGGTAAGACATAACGGTCCAGTCCAGCA), c.58+181A (5′-TGCTGGACTGGACCATTATGTCTTACCTGC and 5′-GCAGGTAAGACATAATGGTCCAGTCCAGCA), or a C-Myb binding site control from the mim-1 gene [19] (5′-GCTCTAAAAAACCGTTATAATGTACAGATATCTT and 5′-AAGATATCTGTACATTATAACGGTTTTTTAGAG). Probes were end-labeled with [γ-32P]ATP by T4 Polynucleotide Kinase (New England Biolabs). Mouse C-Myb cDNA was cloned in pET-41a(+) (Novagen), and the protein was expressed in E.coli strain BL21(DE3)pLys (Invitrogen) and purified with Ni-NTA columns (Qiagen). Twenty-μl reactions were performed with the EMSA Accessory Kit (Novagen). Unlabeled oligonucleotide competitors were added at 25- or 50-fold molar excess. Binding reaction products were separated by 6% DNA retardation gel electrophoresis (Invitrogen) and visualized with a Typhoon Trio+ (GE Healthcare).
Inner ears with adjacent mesenchyme were microdissected from E13.5 offspring of Tw/+ x Tw/+ matings. Total RNA was isolated from inner ears using PicoPure (Applied Biosystems, Foster City, CA). Total RNA from 10 to 14 ears of the same genotype was pooled and purified with the RNAeasy MinElute Cleanup kit (Qiagen). RNA integrity was measured with an Agilent 2100 Bioanalyzer (Applied Biosystems). One µg of total RNA was reverse-transcribed with oligo(dT) primers and SuperScriptIII (Invitrogen, Carlsbad, CA, USA). For TaqMan real-time PCR, PCR primers were designed to amplify Zeb1 exons 1a to 2, 1b to 2, 1c to 2, 1d to 2, 1e to 2, 1f to 2, and 2 to 3, Zeb1os exons 1 to 2, and Zfp438 exons 3 to 4 with ZEN double-quenched probes containing a 5′ FAM fluorophore, 3′ IBFQ quencher, and an internal ZEN quencher (IDT, Coralville, IA). Sequences for the primers and probes are listed in Table S1.
Comparative TaqMan assays were performed in triplicate on an ABI 7500 real-time PCR system (Applied Biosystems). PCR reactions were performed in a 50-µl volume containing 5 µl cDNA, 5 µl primer mix (IDT), and 25 µl of Universal PCR Master Mix (Applied Biosystems). Cycling conditions were 50°C for 2 min, 95°C for 10 min, followed by 40 cycles of 15 s at 95°C and 1 min at 60°C. Relative expression was normalized as the percentage of β-actin expression, and calculated using the comparative threshold cycle method of 2−ΔΔCT. Data are presented as mean values ± S.D. from six technical replicates. ANOVA was used to identify statistically significant differences between genotype groups (P<0.05).
Proteins were extracted from E13.5 mouse inner ears or whole heads with NE-PER Nuclear and Cytoplasmic Extraction Reagents (Pierce Biotechnology) in the presence of Halt Protease Inhibitor Cocktail (Thermo Fisher Scientific Inc.). Proteins were separated by SDS-PAGE in 4–20% NuPage Bis-Tris gels followed by transfer to PVDF membranes (Millipore Corp., Billerica, MA). Proteins were detected with primary antibodies for Zeb1 (ab64098, Abcam, 1∶200) and β-actin (A2228, Sigma-Aldrich, 1∶1000). Secondary antibodies were conjugated with Cy 3 or Cy 5 (GE Healthcare) and detected with a Typhoon Trio+ (GE Healthcare). Band density was measured using ImageQuant TL software. β-actin levels were used for normalization. ANOVA analysis of two to six biological replicates from each genotype group was used to identify statistically significant differences (P<0.05).
Mouse inner ear sections were harvested, processed and immunostained with anti-Zeb1 or anti-CD326 antibodies as described in Hertzano et al. [13]. CD326 is also known as epithelial cell adhesion/activating molecule (EpCAM) that serves as a specific antigenic marker for epithelial cells [13].
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10.1371/journal.pbio.0060015 | Inhibitory Phosphorylation of Separase Is Essential for Genome Stability and Viability of Murine Embryonic Germ Cells | Activity of separase, a cysteine protease that cleaves sister chromatid cohesin at the onset of anaphase, is tightly regulated to ensure faithful chromosome segregation and genome stability. Two mechanisms negatively regulate separase: inhibition by securin and phosphorylation on serine 1121. To gauge the physiological significance of the inhibitory phosphorylation, we created a mouse strain in which Ser1121 was mutated to Ala (S1121A). Here we report that this S1121A point mutation causes infertility in mice. We show that germ cells in the mutants are depleted during development. We further demonstrate that S1121A causes chromosome misalignment during proliferation of the postmigratory primordial germ cells, resulting in mitotic arrest, aneuploidy, and eventual cell death. Our results indicate that inhibitory phosphorylation of separase plays a critical role in the maintenance of sister chromatid cohesion and genome stability in proliferating postmigratory primordial germ cells.
| Higher eukaryotes rely on a separate cell lineage, the germline, to pass genetic information from generation to generation. To ensure faithful transmission of genetic information, cell cycle checkpoint mechanisms are engaged during mitotic and meiotic divisions of germ cells. The identity and function of these checkpoints is not well understood. In mammals, the germline is specified early in embryogenesis as primordial germ cells (PGCs) at the epiblast stage (around embryonic day 5.0 in mice). PGCs then migrate out from their birthplace and arrive at the genital ridge several days later. In the genital ridge, PGCs undergo a great expansion in number through mitosis. During this expansion, PGCs critically depend on the inhibitory phosphorylation of separase to prevent premature separation of sister chromatids and hence progeny with abnormal chromosome number. Separase is a protease which cleaves the Scc1 subunit of sister chromatid cohesin complex. Its activity must be suppressed before all sisters are aligned at the metaphase plate. Two mechanisms are known that can inhibit separase: phosphorylation and binding by securin, both of which are activated at the spindle assembly checkpoint. Although these two mechanisms are redundant in somatic cells, our results indicate that the inhibitory phosphorylation of separase is uniquely required in the germline.
| Faithful transmission of duplicated genetic material is of fundamental importance to the viability of all organisms. In eukaryotes, sister chromatids are closely connected by cohesin complexes established during S phase. The core cohesin complex is composed of the protein subunits Smc1, Smc3, Scc1, and Scc3 [1] and is believed to form a ring-like structure enclosing the two sister chromatids [2]. Prior to anaphase, the majority of cohesin on chromosomal arms is removed by Plk1- and Aurora B–mediated phosphorylation of cohesin subunit Scc3 [3–7]. However, the final separation of sister chromatids in anaphase depends on separase-mediated cleavage of Scc1 [8,9]. To prevent premature separation of sister chromatids, separase must be tightly regulated. In yeast, this occurs through direct inhibition by securin [10]. In vertebrates, inhibitory phosphorylation of separase provides an additional layer of regulation [11]. In humans, phosphorylation of Ser1126 and Thr1326 inhibits separase activity by allowing Cdk1/cyclin B1 to bind and inhibit the protease [12].
Loss of securin is lethal in fission yeast and Drosophila [13–15] and causes chromosomal instability in budding yeast [10]. However, securin-deficient mice are viable and apparently normal [16]. Mammalian cells lacking securin do not show obvious growth defects and maintain sister chromatid cohesion when challenged with spindle microtubule poisons [16–18]. These results suggest redundancy in the inhibition of separase by securin and by inhibitory phosphorylation. Indeed, our previous studies demonstrate that murine embryonic stem cells carrying a nonphosphorylatable separase allele and with securin deleted are sensitive to nocodazole and cannot maintain sister chromatid cohesion in response to microtubule disruption [19]. However, we do not know whether these two separase-regulating mechanisms are redundant at the level of the organism or if the inhibitory phosphorylation of separase plays any role in development.
Infertility in humans has a strong genetic contribution. It is estimated that genetic etiology is responsible for approximately 15% male and 10% female sterility [20], which certainly is an underestimation because many of the idiopathic sterilities in clinic may have unidentified genetic causes. Chromosome abnormalities or aneuploidy in germ cells are often cited as the cause for failed conceptions. Aneuploidy can result from errors in mitosis or meiosis of germ cells. Yet, we know little about the molecular mechanisms that ensure genome stability in germ cells, during their development, and in meiosis. In this study, we report the analysis of mice carrying a nonphosphorylable separase allele. We show that these mice are sterile due to the depletion of germ cells during embryogenesis, demonstrating a unique role of inhibitory phosphorylation of separase in germline development.
Mouse separase is encoded by 31 exons spanning about 90 kb on Chromosome 15 near the telomere. The two inhibitory phosphorylation sites Ser1121 (Ser1126 in human) and Thr1341 (Thr1346 in human) are located in exon 18. Because phosphorylation of Ser1121 contributes the most to separase inhibition [11], we reasoned that mutating this serine residue to alanine would destroy this separase-inhibiting mechanism. We first generated a number of mouse embryonic stem cell clones in which one separase allele was modified by introducing the S1121A point mutation through homologous recombination [19]. We selected for the knock-in through a floxed Puro marker in intron 17 and named this allele S1121A-flox-Puro separase. The point mutation is expressed only after the removal of the Puro marker through the action of Cre recombinase. The modified allele is dominant, and when combined in ES cells with a securin deletion, causes the failure of sister chromatid cohesion upon treatment with nocodazole [19]. The S1121A-flox-Puro separase allele was successfully transmitted through the germ line. The resulting separase+/S1121A-flox-Puro mice are normal and fertile. Intercrosses of separase+/S1121A-flox-Puro mice demonstrated that the homozygous S1121A-flox-Puro separase allele is lethal, and the embryos died at the same developmental stage as reported for separase knockouts [21,22], suggesting that S1121A-flox-Puro separase is a null allele. To activate the point mutation, we crossed separase+/S1121A-flox-Puro mice with Meox2+/Cre mice which express Cre as early as 5.5 days post-conception (dpc) in the epiblast, causing Cre-mediated recombination in most lineages including the germ line [23]. Thus, the resulting Meox2+/CreSeparase+/S1121A-flox-Puro mice are essentially Meox2+/CreSeparase+/S1121A because the vast majority of the cells in these animals should have the Puro marker deleted from the separase locus by Cre-mediated recombination. PCR genotyping using tail DNA confirmed the conversion from S1121A-flox-Puro to S1121A.
Meox2+/CreSeparase+/S1121A mice are indistinguishable from their littermates, which are Meox2+/+Separase+/+, Meox2+/+Separase+/S1121A-flox-Puro, or Meox2+/CreSeparase+/+. They do not show any overt abnormalities up to 2 y of age. However, both male and female Meox2+/CreSeparase+/S1121A mice are sterile. To rule out the possibility that sterility in the mutant mice is a combined effect of the separase S1121A point mutation and Meox2 heterozygosity (due to the Cre knock-in), we used another Cre strain, EIIa-Cre, in which Cre is expressed as early as the zygote stage [24]. Separase+/S1121A/EIIa-Cre mice were also infertile (unpublished data), indicating the infertile phenotype in the mutant mice is caused by the separase point mutation rather than by heterozygosity for Meox2 or an effect of Cre. Because of sexual dimorphism arising from the separase point mutation, the analysis of the female phenotype will be reported elsewhere.
At 6 wk of age, mutant testes are much smaller than the controls (Figure 1A). Histological examination revealed that the mutant seminiferous tubules are devoid of spermatocytes and spermatids (Figure 1B), suggesting spermatogenesis failure in separase S1121A mutant mice. Spermatogenesis in mice starts around 10 d post-partum (dpp) when spermatocytes are first produced, followed by spermatids around 20 dpp and spermatozoa around 35 dpp [25]. We examined testes isolated from mice ranging from 4 dpp up to 2 wk old. We could not detect histologically any differences between the control and the mutant before the age of 2 wk (Figure 1C). However, at 2 wk when the first wave of spermatogenesis had started in the control, we found no sign of spermatogenesis in the mutant (Figure 1C). The most likely reason for this result is the lack of spermatogonia in the mutant testes. To determine if that was the case, we carried out immunostainings for Tra98, a germ cell marker [26], and Sox9, a marker for Sertoli cells [27,28]. As shown in Figure 1D, we could not detect Tra98 in the mutant testes at 4 d after birth, whereas Sox-9 was readily detected. Absence of Tra98 was confirmed by western blot analysis (Figure 1E). Three additional germ cell markers, Plzf [29], Sohlh1 [30], and GCNA [31] were also absent in the mutant testes (Figures 1–3 and S1–S3), indicating that the loss of inhibitory phosphorylation of separase leads to spermatogonia cell depletion.
Spermatogonia originate from primordial germ cells (PGCs). PGCs emerge as a group of about 45 founder cells at the base of the future allantois in the epiblast at 7.5 dpc [32–36]. PGCs can be recognized by their high levels of expression of tissue-nonspecific alkaline phosphatase [37]. From 8.5 to 10.5 dpc, PGCs migrate towards the genital ridge and increase their numbers through mitotic divisions [38]. In the mouse, most PGCs enter the genital ridge at 11.5 dpc, lose their locomotive ability, and expand from about 3,000 to over 25,000 cells [32,33,38]. This expansion ends between 13.5 and 14.5 dpc. Immunostaining (Figure 2A) and western blot (Figure 2B) analyses of genital ridges from mutant and control mice at 14.5 dpc showed that separase point mutant testes lack the early germ cell marker Mvh (mouse vasa homologue) [39], suggesting the absence of PGCs at this stage.
The germ cell deficiency in separase point mutant testes could arise from a failure of PGCs to reach the genital ridge or a failure to proliferate after reaching the genital ridge. To distinguish between these two possibilities, we took advantage of the high expression levels of tissue nonspecific alkaline phosphatase (AP) [37] by PGCs to analyze their fate during migration. At 11.5 dpc, AP staining showed similar number of AP-positive cells in genital ridges from control and mutant mice (Figure 2C), indicating that mutant PGCs are not impaired in migration. However, while control PGCs expanded greatly in the genital ridge, mutant PGCs did not (Figure 2D). Although mutant PGCs increased in number between 11.5 and 12.5 dpc, the increase was significantly smaller than that of control PGCs (Figure 2D) and their number diminished rapidly over time, as determined by Mvh immunostaining. These data indicate that the separase phosphorylation mutant S1121A causes proliferation failure in the germ cells between 11.5 and 14.5 dpc when the PGCs proliferate rapidly in the genital ridge. Therefore, the loss of germ cells during embryonic development in separase point mutants explains the observed absence of germ cells in the postnatal testes.
Because the point mutation in separase could generate a constitutively active enzyme, we reasoned that mutant PGCs might prematurely separate their sister chromatids before the onset of anaphase. The precociously separated sister chromatids would not be able to align at the metaphase plate nor will they generate tension at their kinetochores, leading to activation of the spindle assembly checkpoint and mitotic arrest [40,41]. One prediction of this hypothesis is that mutants would have more PGCs in mitosis than controls due to spindle checkpoint-mediated mitotic arrest. Therefore, we determined the mitotic indices of the germ cells in genital ridges by quantitating the number of Mvh-positive cells that contained condensed chromosomes. As shown in Figure 3A, the mitotic indices of mutant PGCs were slightly higher than controls at 11.5 dpc and were significantly higher than controls at 12.5, 13.5, and 14.5 dpc, whereas the mitotic indices of non–germ cells were the same between the control and the mutant (Figure 3B). We also measured the expression levels of phospho-histone H3 and cyclin B1 in 13.5 dpc genital ridges by western blotting. Again, these two mitotic markers were expressed at higher levels in the mutant than in the control (Figure 3C). Furthermore, we observed that the majority of mutant mitotic figures were abnormal and showed misaligned chromosomes (Figure 3D), suggesting premature separation of sister chromatids. To visualize precocious sister separation, we cultured PGCs isolated from 11.5 dpc genital ridges and treated the cells with nocodazole for 6 h to enrich mitotic cells. As shown in Figure 3E, the mutant PGCs showed complete separation of sister chromatids while the control did not, indicating that inhibitory phosphorylation of separase is essential in maintaining sister chromatid cohesion in PGCs. Taken together, these data demonstrate that the point mutation in separase causes precocious sister chromatid separation and the accumulation of PGCs in mitosis due to spindle checkpoint activation.
Over time, mammalian cells adapt to or escape from spindle checkpoint and become tetraploid [42]. Cells with compromised spindle checkpoint function often escape from microtubule poison-induced mitotic arrests to enter tetraploid G1 with micronuclei [43,44]. We observed a large number of mutant PGCs with micronuclei (Figure 4A and 4B), most likely formed by the misaligned and prematurely separated chromosomes once the cells adapted to the spindle checkpoint. When the DNA content of the germ and non–germ cells was determined through laser scanning cytometry and averaged without grouping the cells into G1, S, or G2/M phases, we found that control and mutant non–germ cells contained the same amount of DNA with similar statistical distributions (Figure 4C). By contrast, germ cells in controls contained higher levels of DNA compared with somatic cells, suggesting that cell cycle profiles differ between germ cells and somatic cells and that more germ cells than somatic cells are in G2/M phase (Figure 4C). Consistent with this result, the mitotic indices of germ cells were 2-fold higher than those of somatic cells from 11.5 to 13.5 dpc (Figure 4D). In contrast, germ cell DNA content in mutants was much higher compared with controls and was greater than twice the amount in somatic cells (Figure 4C). Given that the difference in cell cycle profiles between somatic cells and germ cells, the average DNA content of somatic cells can be considered as 2N. It follows then that the DNA content in mutant germ cells is greater than 4N, suggesting that some mutant germ cells might have undergone or initiated another round of DNA synthesis as tetraploid cells. Indeed, some of the mutant PGCs with micronuclei contained more than two centrosomes, which were identified through γ-tubulin staining (Figure 4E), strongly supporting the notion that the mutant germ cells had entered another round of cell division. Taken together, these results strongly suggest that separase S1121A mutant PGCs undergo premature sister chromatid separation, mitotic arrest, and adaptation to the spindle checkpoint, resulting in aneuploidy. This phenotype resembles what was previously seen in securin–/–separase+/S1121A ES cells released from nocodazole arrest [19] and in HeLa cells overexpressing human S1126A mutant separase [45].
To further demonstrate the mitotic arrest phenotype, we analyzed the expression of Aurora B kinase in PGCs. Aurora B is a chromosome passenger protein required for spindle assembly checkpoint [46,47]. In normal mitosis, Aurora B first associates with the metaphase chromosomes, departs the chromosomes and translocates to the cleavage furrow at anaphase, becomes concentrated in the mid-body during cytokinesis, and finally disappears in G1. When we immunostained with antibodies against Aurora B, we found that this mitotic kinase formed foci in all mutant PGCs with abnormal nuclear morphologies, although the cells were no longer in mitosis as their chromosomes had already decondensed (Figure 5A). Western blot analysis confirmed the immunostaining result (Figure 5B). These data suggest that mutant PGCs adapt to the spindle assembly checkpoint but retain Aurora B expression. To determine if the retention of Aurora B expression is always associated with cells having undergone adaptation to spindle checkpoint, we treated a panel of cell lines with nocodazole, let the cells adapt, and analyzed the pattern of Aurora B expression. All cell lines tested, including HeLa, U2OS, and mouse embryonic fibroblasts, showed the same nuclear Aurora B staining pattern and abnormal nuclear morphologies as did mutant PGCs (Figure 5C, results from HeLa cells are shown). We conclude that the formation of nuclear Aurora B foci is a common feature of mammalian cells adapted to the spindle assembly checkpoint. However, at present, it is unclear if the retention of Aurora B expression is necessary for adaptation.
We next evaluated whether apoptotic cell death could account for the loss of PGCs during embryonic development in separase point mutant mice, since apoptosis has been shown to be the type of cell death involved in naturally occurring PGC demise [48]. We found that mutant cells displaying nuclear morphologies consistent with checkpoint adaptation (Figure 4A) were also positive for the apoptotic marker active capase-3 (Figure 5D). Western blot analysis confirmed the presence of higher levels of activated caspase-3 in the mutant genital ridge than in the control (Figure 5B). In addition, we found higher levels of p53 and its pro-apoptotic target Bax in the mutant genital ridge (Figure 5B), suggesting that apoptosis of mutant PGCs is p53-related. In this context, it was reported previously that overexpression of Aurora B can lead to p53 activation [49]. It is therefore plausible that the persistent expression of Aurora B in the mutant PGCs undergoing abnormal mitosis may activate p53, resulting in the elimination of these abnormal germ cells.
The above analyses demonstrate that the inhibitory phosphorylation of separase plays a critical role in the maintenance of male embryonic germ cell genome stability by preventing premature separation of sister chromatids. Besides sterility, mice carrying one S1121A separase allele are essentially normal, suggesting redundancy between inhibitory phosphorylation and securin in the regulation of separase in somatic cells. Indeed, elimination of both inhibitory mechanisms causes early lethality (Figure 6A). However, inhibitory phosphorylation is uniquely required for germline development, since lack of securin causes no appreciable depletion of germ cells (Figure 6B) nor infertility [16,19]. The question that remains open is the origin of the germline vulnerability to the loss of the inhibitory phosphorylation of separase. One possibility is that post-migratory germ cells express relatively low levels of securin so that inhibitory phosphorylation is the primary mechanism that inhibits separase in these cells. To test this possibility, we examined the expression of securin in genital ridges. Immunostaining analysis showed that securin levels were undetected in both germ cells and somatic cells (Figure 6C). However, securin was readily detected in postnatal testes (Figure 6C). In agreement with these results, western blotting showed much lower levels of securin expression in the fetal testes than in the postnatal testes (Figure 6D). Furthermore, securin levels in genital ridges were lower than those observed in whole embryos or embryo heads, whereas the level of separase expression was similar in fetal genital ridges and other parts of the body (Figure 6E). Since somatic cells in separase point mutant genital ridges appear normal and do not display any mitotic defects, these data suggest that the stoichiometric ratio of securin to separase in postmigratory germ cells is lower compared with somatic cells, and these cells must rely completely on the inhibitory phosphorylation of separase to prevent premature activation of the protease.
The dependence of postmigratory germ cells on inhibitory phosphorylation of separase raises the question of why this mechanism of separase regulation is not required in migratory PGCs. Most likely, it is because of securin. To test that, we isolated embryos at 10.5 dpc from crosses between mice that were securin+/−separase+/S1121A-flox-Puro and those that were securin−/−Meox2+/Cre and performed whole mount AP assays on the embryos. As shown in Figure 7, the number of migratory PGCs was the same between securin+/−separase+/+ and securin−/−separase+/+ mice, indicating that securin is not required in these cells, consistent with what we found at a later time (Figure 6B). Strikingly, we could hardly detect any germ cells in the double mutants (Figure 7), demonstrating that securin and the inhibitory phosphorylation are redundantly required in PGCs prior to their arrival at the genital ridge. We do not currently know, however, if the PGCs in the double mutants died during migration, or died shortly after their birth. The exact time of their death needs further investigation.
To prevent unscheduled or precocious sister chromatid separation, separase must be kept inactive prior to anaphase. This is achieved primarily through two negative regulatory mechanisms: securin binding and phosphorylation. In budding yeast, PDS1 (budding yeast securin) is essential for the prevention of sister separation in response to spindle checkpoint [10]. However, we have found that securin knockout mice are normal and cells lacking it maintain an intact spindle assembly checkpoint [16]. It was also demonstrated in human cells that securin is not required for the maintenance of sister cohesion when microtubule spindle is disrupted [17]. These results indicate that the spindle assembly checkpoint in higher eukaryotes does not solely rely on the inhibition of separase by securin to block sister separation. There must be another mechanism that can keep separase in check, which was found to be phosphorylation [11]. Mammalian separases are phosphorylated at multiple sites. These sites resemble Cdk/MAP kinase phosphorylation sites and are most likely phosphorylated by Cdk1/cyclin B1 in mitosis. More recent work indicates that the phosphorylation itself is not inhibitory, but the phosphorylation is necessary for the binding and inhibition by Cdk1/cyclin B1 complex [12].
Given the redundancy between securin and the inhibitory phosphorylation, it is not surprising that mice carrying a nonphosphorylatable separase allele are essentially normal. However, it is surprising that these mice are sterile. Our analyses demonstrate that the sterility stems from the fact that the postmigratory germ cells are depleted during development in separase point mutant mice. What makes the germline particularly vulnerable to this phosphorylation point mutation in separase? One possibility is that securin may be expressed at relatively low levels in postmigratory germ cells so that stoichiometrically there is more separase than securin. Our results (Figure 6) do support this possibility.
The fact that the number of separase S1121A mutant PGCs at 11.5 dpc is about the same as in the control indicates that migratory PGCs are not affected by the mutation. A likely reason is that in the migratory PGCs, securin is still functioning to inhibit separase. Indeed, we found that germ cells were largely eliminated in securin and separase double mutants (Figure 7), demonstrating a redundancy between securin and the inhibitory phosphorylation in migratory PGCs. The redundancy no longer exists in postmigratory germ cells. At present, we do not know why this is the case. Perhaps securin levels are higher in migrating PGCs and are reduced once the germ cells take residency in genital ridges. Securin overexpression was found highly correlated with the metastatic potential of breast tumors [50], suggesting that the protein might have a role in cell migration separated from its role in inhibiting separase. Since securin null PGCs do not display any significant migratory defects, the role of securin (if any) in PGC migration must be nonessential, however. Once arrived at the genital ridge, PGCs may shut off the expression of securin along with the entire migratory program, making the postmigratory germ cells vulnerable to nonphosphorylatable separase. Thus, the dependence on the inhibitory phosphorylation of separase is another property that differentiates postmigratory PGCs from migratory PGCs, supporting the notion that PGCs undergo a major phenotypic change once they take residence in the gonads [51], including, perhaps, a switch to low levels of securin expression.
Cell cycle control in migratory and postmigratory PGCs is poorly understood. To date, few genes have been shown to negatively affect PGC proliferation in vivo [25]. Our results suggest that postmigratory male PGCs have a strong and functional spindle checkpoint and that the mechanisms governing sister chromatid separation during anaphase in PGC mitoses are distinct from those of somatic cells, and more importantly, from those of migratory PGCs. Although the early germ cell depletion observed in separase mutant males precluded studies in meiosis, loss of separase in oocytes has been shown to cause failure to resolve chiasma in MI, indicating a requirement for separase in the separation of homolog chromosomes [52]. The relative contribution of securin and the inhibitory phosphorylation to the control of separase activity during meiosis awaits further investigation.
The S1121A-flox-Puro separase allele [19] was transmitted through the germline. The resulting separase+/S1121A-flox-Puro mice were crossed with Moex2+/Cre mice [23] to generate Separase+/S1121A mice. Offspring genotyping was performed with PCR analysis using the following primers: for separase, pz228a: 5′-cct tct cta acc cag gta gg-3′, pz228b: 5′-aag agc tct acc tac ctc agg-3′, and pz228c: 5′-atc gca tcg agc gag cac gta ctc-3′; for Meox2, Cre1: 5′-aag atg tgg aga gtt cgg ggt ag-3′, Cre2: 5′-ggg acc acc ttc ttt tgg ctt c-3′, and Cre3: 5′-cca gat cct cct cag aaa tca gc-3′. pz228a/b amplifies the S1121A allele, and pz228b/c S1121A-flox-Puro. Embryo sex determination was performed by a PCR analysis of Sry, a gene only present on the Y chromosome [53] on genomic DNA.
Standard histological procedures were followed to prepare testes and fetal gonads for examination. In brief, tissues were fixed in Bouin's solution or 10% neutral buffered formalin (Sigma). The specimens were dehydrated through a graded series of ethanol washes, cleared in Histo-Clear, embedded in paraffin, and sectioned (4 μm thick). The sections were dewaxed, rehydrated, and stained with hematoxylin and eosin. Immunostainings were carried out as described [54].
The primary antibodies used were rat anti-GCNA1 (a kind gift of Dr. George C. Enders, University of Kansas Medical Center, Kansas City), rabbit anti-Sox-9 (CHEMICON), mouse anti-β-tubulin (clone E7, Developmental Studies Hybridoma Bank), mouse anti-cyclin B1/mouse anti-p53/rabbit anti-Plzf/rabbit anti-BAX (Santa Cruz Biotechnology), rabbit anti-active Caspase-3/mouse anti-Aurora B (BD Biosciences PharMingen), rabbit anti-phospho-histone H3 (S10) (Cell Signaling Technology), mouse anti-γ–tubulin (Sigma), mouse anti-securin (Novocastra), rabbit anti-Mvh (a kind gift from Dr. Toshiaki Noce, Mitsubishi-Kasei Institute of Life Science, Tokyo, Japan), rabbit anti-Tra-98 (a kind gift from Dr. Yoshitake Nishimune, Osaka University, Japan), and anti-Sohlh1 (from Dr. Aleksandar Rajkovic, Baylor College of Medicine, Houston).
The following secondary antibodies were used: Cy3- or FITC-conjugated anti-rat IgG, Cy3- or FITC-conjugated anti-rabbit IgG, and Cy3- or FITC-conjugated anti-mouse IgG (all from Jackson ImmunoResearch Laboratories).
Equal amounts of proteins in extracts prepared from testes, gonads, or other tissues were separated with SDS-PAGE and blotted onto polyvinylidene difluoride membrane (Bio-Rad). The blots were probed with the indicated primary and appropriate secondary antibodies (Bio-Rad) and detected with an ECL chemiluminescence kit (GE Healthcare).
Dissected genital ridges were fixed in 4% paraformaldehyde, washed with PBS, and stained for AP activity with α-naphthyl phosphate (Sigma)/fast red TR (Sigma) solution as described [35].
11.5 dpc genital ridges were minced and treated with 0.25% Trypsin/EDTA for 30 min at 37 °C to dissociate the cells. The resulting cell suspensions were plated on a feeder layer formed by irradiated LIF-expressing SNL fibroblasts in 15% FBS/DMEM for 2 d. The cells were harvested via trypsin digestion after 6-h nocodazole (65 ng/ml) treatment and subjected to AP staining. AP-positive cells collected through a mouth-pipette were treated for chromosome spread as described [19].
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10.1371/journal.pgen.1004412 | A Population Genetic Signal of Polygenic Adaptation | Adaptation in response to selection on polygenic phenotypes may occur via subtle allele frequencies shifts at many loci. Current population genomic techniques are not well posed to identify such signals. In the past decade, detailed knowledge about the specific loci underlying polygenic traits has begun to emerge from genome-wide association studies (GWAS). Here we combine this knowledge from GWAS with robust population genetic modeling to identify traits that may have been influenced by local adaptation. We exploit the fact that GWAS provide an estimate of the additive effect size of many loci to estimate the mean additive genetic value for a given phenotype across many populations as simple weighted sums of allele frequencies. We use a general model of neutral genetic value drift for an arbitrary number of populations with an arbitrary relatedness structure. Based on this model, we develop methods for detecting unusually strong correlations between genetic values and specific environmental variables, as well as a generalization of comparisons to test for over-dispersion of genetic values among populations. Finally we lay out a framework to identify the individual populations or groups of populations that contribute to the signal of overdispersion. These tests have considerably greater power than their single locus equivalents due to the fact that they look for positive covariance between like effect alleles, and also significantly outperform methods that do not account for population structure. We apply our tests to the Human Genome Diversity Panel (HGDP) dataset using GWAS data for height, skin pigmentation, type 2 diabetes, body mass index, and two inflammatory bowel disease datasets. This analysis uncovers a number of putative signals of local adaptation, and we discuss the biological interpretation and caveats of these results.
| The process of adaptation is of fundamental importance in evolutionary biology. Within the last few decades, genotyping technologies and new statistical methods have given evolutionary biologists the ability to identify individual regions of the genome that are likely to have been important in this process. When adaptation occurs in traits that are underwritten by many genes, however, the genetic signals left behind are more diffuse, and no individual region of the genome is likely to show strong signatures of selection. Identifying this signature therefore requires a detailed annotation of sites associated with a particular phenotype. Here we develop and implement a suite of statistical methods to integrate this sort of annotation from genome wide association studies with allele frequency data from many populations, providing a powerful way to identify the signal of adaptation in polygenic traits. We apply our methods to test for the impact of selection on human height, skin pigmentation, body mass index, type 2 diabetes risk, and inflammatory bowel disease risk. We find relatively strong signals for height and skin pigmentation, moderate signals for inflammatory bowel disease, and comparatively little evidence for body mass index and type 2 diabetes risk.
| Population and quantitative genetics were in large part seeded by Fisher's insight [1] that the inheritance and evolution of quantitative characters could be explained by small contributions from many independent Mendelian loci [2]. While still theoretically aligned [3], these two fields have often been divergent in empirical practice. Evolutionary quantitative geneticists have historically focused either on mapping the genetic basis of relatively simple traits [4], or in the absence of any such knowledge, on understanding the evolutionary dynamics of phenotypes in response to selection over relatively short time-scales [5]. Population geneticists, on the other hand, have usually focused on understanding the subtle signals left in genetic data by selection over longer time scales [6]–[8], usually at the expense of a clear relationship between these patterns of genetic diversity and evolution at the phenotypic level.
Recent advances in population genetics have also allowed for the genome-wide identification of individual recent selective events either by identifying unusually large allele frequency differences among populations and environments or by detecting the effects of these events on linked diversity [9]. Such approaches are nonetheless limited because they rely on identifying individual loci that look unusual, and thus are only capable of identifying selection on traits where an individual allele has a large and/or sustained effect on fitness. When selection acts on a phenotype that is underwritten by a large number of loci, the response at any given locus is expected to be modest, and the signal instead manifests as a coordinated shift in allele frequency across many loci, with the phenotype increasing alleles all on average shifting in the same direction [10]–[14]. Because this signal is so weak at the level of the individual locus, it may be impossible to identify against the genome-wide background without a very specific annotation of which sites are the target of selection on a given trait [15], [16].
The advent of well-powered genome wide association studies with large sample sizes [17] has allowed for just this sort of annotation, enabling the mapping of many small effect alleles associated with phenotypic variation down to the scale of linkage disequilibrium in the population. The development and application of these methods in human populations has identified thousands of loci associated with a wide array of traits, largely confirming the polygenic view of phenotypic variation [18].
Although the field of human medical genetics has been the largest and most rapid to puruse such approaches, evolutionary geneticists studying non-human model organisms have also carried out GWAS for a wide array of fitness-associated traits, and the development of further resources is ongoing [19]–[21]. In human populations, the cumulative contribution of these loci to the additive variance so far only explain a fraction of the narrow sense heritability for a given trait (usually less than 15%), a phenomenon known as the missing heritability problem [22], [23]. Nonetheless, these GWAS hits represent a rich source of information about the loci underlying phenotypic variation.
Many investigators have begun to test whether the loci uncovered by these studies tend to be enriched for signals of selection, in the hopes of learning more about how adaptation has shaped phenotypic diversity and disease risk [24]–[27]. The tests applied are generally still predicated on the idea of identifying individual loci that look unusual, such that a positive signal of selection is only observed if some subset of the GWAS loci have experienced strong enough selection to make them individually distinguishable from the genomic background. As noted above, it is unlikely that such a signature will exist, or at least be easy to detect, if adaptation is truly polygenic, and thus many selective events will not be identified by this approach.
Here we develop and implement a general method based on simple quantitative and population genetic principals, using allele frequency data at GWAS loci to test for a signal of selection on the phenotypes they underwrite while accounting for the hierarchical structure among populations induced by shared history and genetic drift. Our work is most closely related to the recent work of Turchin et al [28], Fraser [29] and Corona et al [30], who look for co-ordinated shifts in allele frequencies of GWAS alleles for particular traits. Our approach constitutes an improvement over the methods implemented in these studies as it provides a high powered and theoretically grounded approach to investigate selection in an arbitrary number of populations with an arbitrary relatedness structure.
Using the set of GWAS effect size estimates and genome wide allele frequency data, we estimate the mean genetic value [31], [32] for the trait of interest in a diverse array of human populations. These genetic values may often be poor predictors of the actual phenotypes for reasons we address below and in the Discussion. We therefore make no strong claims about their ability to predict present day observed phenotypes. We instead focus on population genetic modeling of the joint distribution of genetic values, which provides a robust way of investigating how selection may have impacted the underlying loci.
We develop a framework to describe how genetic values covary across populations based on a flexible model of genetic drift and population history. In Figure 1 we show a schematic diagram of our approach to aid the reader. Using this null model, we implement simple test statistics based on transformations of the genetic values that remove this covariance among populations. We judge the significance of the departure from neutrality by comparing to a null distribution of test statistics constructed from well matched sets of control SNPs. Specifically, we test for local adaptation by asking whether the transformed genetic values show excessive correlations with environmental or geographic variables. We also develop and implement a less powerful but more general test, which asks whether the genetic values are over-dispersed among populations compared to our null model of drift. We show that this overdispersion test, which is closely related to [33], [34] and a series of approaches from the population genetics literature [35]–[39], gains considerable power to detect selection over single locus tests by looking for unexpected covariance among loci in the deviation they take from neutral expectations. Lastly, we develop an extension of our model that allows us to identify individual populations or groups of populations whose genetic values deviate from their neutral expectations given the values observed for related populations, and thus have likely been impacted by selection. While we develop these methods in the context of GWAS data, we also relate them to recent methodological developments in the quantitative genetics of measured phenotypes (as opposed to allele frequencies) [40], [41], highlighting the useful connection between these approaches. An implementation of the methods described here in the form of a collection of R scripts is available at https://github.com/jjberg2/PolygenicAdaptationCode.
Consider a trait of interest where loci (e.g. biallelic SNPs) have been identified from a genome-wide association study. We arbitrarily label the phenotype increasing allele and the alternate allele at each locus. These loci have additive effect size estimates , where is the average increase in an individual's phenotype from replacing an allele with an allele at locus . We have allele frequency data for populations at our SNPs, and denote by the observed sample frequency of allele at the locus in the population. From these, we estimate the mean genetic value in the population as (1)and we take to be the vector containing the mean genetic values for all populations.
We are chiefly interested in developing a framework for testing the hypothesis that the joint distribution of is driven by neutral processes alone, with rejection of this hypothesis implying a role for selection. We first describe a general model for the expected joint distribution of estimated genetic values () across populations under neutrality, accounting for genetic drift and shared population history.
A simple approximation to a model of genetic drift is that the current frequency of an allele in a population is normally distributed around some ancestral frequency (). Under a Wright-Fisher model of genetic drift, the variance of this distribution is approximately , where is a property of the population shared by all loci, reflecting the compounded effect of many generations of binomially sampling [42]. Note also that for small values, is approximately equal to the inbreeding coefficient of the present day population relative to the defined ancestral population, and thus has an interpretation as the correlation between two randomly chosen alleles relative to the ancestral population [42].
We can expand this framework to describe the joint distribution of allele frequencies across an arbitrary number of populations for an arbitrary demographic history by assuming that the vector of allele frequencies in populations follows a multivariate normal distribution (2)where is an by positive definite matrix describing the correlation structure of allele frequencies across populations relative to the mean/ancestral frequency. Note again that for small values it is also approximately the matrix of inbreeding coefficients (on the diagonal) and kinship coefficients (on the off-diagonals) describing relatedness among populations [38], [43]. This flexible model was introduced, to our knowledge, by [44] (see [45] for a review), and has subsequently been used as a computationally tractable model for population history inference [42], [46], and as a null model for signals of selection [38], [39], [47], [48]. So long as the multivariate normal assumption of drift holds reasonably well, this framework can summarize arbitrary population histories, including tree-like structures with substantial gene flow between populations [46], or even those which lack any coherent tree-like component, such as isolation by distance models [49], [50].
Recall that our estimated genetic values are merely a sum of sample allele frequencies weighted by effect size. If the underlying allele frequencies are well explained by the multivariate normal model described above, then the distribution of is a weighted sum of multivariate normals, such that this distribution is itself multivariate normal (3)where and are respectively the expected genetic value and additive genetic variance of the ancestral (global) population. The covariance matrix describing the distribution of therefore differs from that describing the distribution of frequencies at individual loci only by a scaling factor that can be interpreted as two times the contribution of the associated loci to the additive genetic variance present in a hypothetical population with allele frequencies equal to the grand mean of the sampled populations.
The assumption that the drift of allele frequencies around their shared mean is normally distributed (2) may be problematic if there is substantial drift. However, even if that is the case, the estimated genetic values may still be assumed to follow a multivariate normal distribution by appealing to the central limit theorem, as each estimated genetic value is a sum over many loci. We show in the Results that this assumption often holds in practice.
It is useful here to note that the relationship between the model for drift at the individual locus level, and at the genetic value level, gives an insight into where most of the information and statistical power for our methods will come from. Each locus adds a contribution to the vector of deviations of the genetic values from the global mean. If the allele frequencies are unaffected by selection then the frequency deviation of an allele at locus in population will be uncorrelated in magnitude or sign with both the effect at locus and the allele frequency deviation taken by other unlinked loci. Thus the expected departure of the genetic value of a population from the mean is zero, and the noise around this should be well described by our multivariate normal model.
The tests described below will give positive results when these observations are violated. The effect of selection is to induce a non-independence of the allele frequency deviation () across loci, determined by the sign and magnitude of the effect sizes [10]–[14] and as we demonstrate below, all of our methods rely principally on identifying this non-independence. This observations has important considerations for the false positive profile of our methods. Specifically, false positives will arise only if the GWAS ascertainment procedure induces a correlation between the estimated effect size of an allele () and the deviation that this allele takes across populations . This should not be the case if the GWAS is performed in a single population which is well mixed compared to the populations considered in the test. False positives can occur when a GWAS is performed in a structured population and fails to account for the fact that the phenotype of interest is correlated with ancestry in this population. We address this case in greater depth in the Discussion.
These observation also allows us to exclude certain sources of statistical error as a cause of false positives. For example, simple error in the estimation of , or failing to include all loci affecting a trait cannot cause false positives, because this error has no systematic effect on across loci. Similarly, if the trait of interest truly is neutral, variation in the true effects of an allele across populations or over time or space (which can arise from epistatic interactions among loci, or from gene by environment interactions) will not drive false positives, again because no systematic trends in population deviations will arise. This sort of heterogeneity can, however, reduce statistical power, as well as make straightforward interpretation of positive results difficult, points which we address further below.
As described above, we obtain the vector by summing allele frequencies across loci while weighting by effect size. We do not get to observe the ancestral genetic value of the sample , so we assume that this is simply equal to the mean genetic value across populations . This assumption costs us a degree of freedom, and so we must work with a vector , which is the vector of estimated genetic values for the first populations, centered at the mean of the (see Methods for details). Note that this procedure will be the norm for the rest of this paper, and thus we will always work with vectors of length that are obtained by subtracting the mean of the vector and dropping the last component. The information about the dropped population is retained in the mean of the length vectors, and thus the choice of which population to drop is arbitrary and does not affect the inference.
To estimate the null covariance structure of the populations we sample a large number K random unlinked SNPs. In our procedure, the SNPs are sampled so as to match certain properties of the GWAS SNPs (the specific matching procedure is described in more depth below and in the Methods section). Setting to be the mean sample allele frequency across populations at the SNP, we standardize the sample allele frequency in population as . We then calculate the sample covariance matrix () of these standardized frequencies, accounting for the rank of the matrix (see Methods). We estimate the scaling factor of this matrix as (4)
We now have an estimated genetic value for each population, and a simple null model describing their expected covariance due to shared population history. Under this multivariate normal framework, we can transform the vector of mean centered genetic values () so as to remove this covariance. First, we note that the Cholesky decomposition of the matrix is (5)where is a lower triangular matrix, and is its transpose. Informally, this can be thought of as taking the square root of , and so can loosely be thought of as analogous to the standard deviation matrix.
Using this matrix we can transform our estimated genetic values as: (6)
If then , where is the identity matrix. Therefore, under the assumptions of our model, these standardized genetic values should be independent and identically distributed random variates [39].
It is worth spending a moment to consider what this transformation has done to the allele frequencies at the loci underlying the estimated genetic values. As our original genetic values are written as a weighted sum of allele frequencies, our transformed genetic values can be written as a weighted sum of transformed allele frequencies (which have passed through the same transform). We can write (7)and so we can define the vector of transformed allele frequencies at locus to be
(8)This set of transformed frequencies exist within a set of transformed populations, which by definition have zero covariance with one another under the null, and are related by a star-like population tree with branches of equal length.
As such, we can proceed with simple, straightforward and familiar statistical approaches to test for the impact of spatially varying selection on the estimated genetic values. Below we describe three simple methods for identifying the signature of polygenic adaptation, which arise naturally from this observation.
We first test if the genetic values are unusually correlated with an environmental variable across populations compared to our null model. A significant correlation is consistent with the hypothesis that the populations are locally adapted, via the phenotype, to local conditions that are correlated with the environmental variable. However, the link from correlation to causation must be supported by alternate forms of evidence, and in the lack of such evidence, a positive result from our environmental correlation tests may be consistent with many explanations.
Assume we have a vector , containing measurements of a specific environmental variable of interest in each of the populations. We mean-center this vector and put it through a transform identical to that which we applied to the estimated genetic values in (7). This gives us a vector , which is in the same frame of reference as the transformed genetic values.
There are many possible models to describe the relationship between a trait of interest and a particular environmental variable that may act as a selective agent. We first consider a simple linear model, where we model the distribution of transformed genetic values () as a linear effect of the transformed environmental variables () (9)where under our null is a set of normal, independent and identically distributed random variates (i.e. residuals), and can simply be estimated as . We can also calculate the associated squared Pearson correlation coefficient () as a measure of the fraction of variance explained by our variable of choice, as well as the non-parametric Spearman's rank correlation , which is robust to outliers that can mislead the linear model. We note that we could equivalently pose this linear model as a mixed effects model, with a random effect covariance matrix . However, as we know both and , we would not have to estimate any of the random effect parameters, reducing it to a fixed effect model as in (9) [51].
In the Methods (section “The Linear Model at the Individual Locus Level”) we show that the linear environmental model applied to our transformed genetic values has a natural interpretation in terms of the underlying individual loci. Therefore, exploring the environmental correlations of estimated genetic values nicely summarizes information in a sensible way at the underlying loci identified by the GWAS.
In order to assess the significance of these measures, we implement an empirical null hypothesis testing framework, using , , and as test statistics. We sample many sets of SNPs randomly from the genome, again applying a matching procedure discussed below and in the Methods. With each set of SNPs we construct a vector , which represents a single draw from the genome-wide null distribution for a trait with the given ascertainment profile. We then perform an identical set of transformations and analyses on each , thus obtaining an empirical genome-wide null distribution for all test statistics.
As an alternative to testing the hypothesis of an effect by a specific environmental variable, one might simply test whether the estimated genetic values exhibit more variance among populations than expected due to drift. Here we develop a simple test of this hypothesis.
As is composed of independent, identically distributed standard normal random variables, a natural choice of test statistic is given by (10)
This statistic represents a standardized measure of the among population variance in estimated genetic values that is not explained by drift and shared history. It is also worth noting that by comparing the rightmost form in (10) to the multivariate normal likelihood function, we find that is proportional to the negative log likelihood of the estimated genetic values under the neutral null model, and is thus the natural measurement of the model's ability to explain their distribution. Multivariate normal theory predicts that this statistic should follow a distribution with degrees of freedom under the null hypothesis. Nonetheless, we use a similar approach to that described for the linear model, generating the empirical null distribution by resampling SNPs genome-wide. As discussed below, we find that in practice the empirical null distribution tends to be very closely matched by the theoretically predicted distribution.
Values of this statistic that are in the upper tail correspond to an excess of variance among populations. This excess of variance is consistent with the differential action of natural selection on the phenotype among populations (e.g. due to local adaptation). Values in the lower tail correspond a paucity of variance, and thus potentially to widespread stabilizing selection, with many populations selected for the same optimum. In this paper we report upper tail p-values from the empirical null distribution of both for our power simulations and empirical results. A two tailed test would be appropriate in cases where stabilizing selection is also of interest, however such signals are likely to be difficult to spot with GWAS data because the we are missing the large effect, low frequency alleles most likely to reveal a signal of stabilizing selection.
Having detected a putative signal of selection for a given trait, one may wish to identify individual regions and populations which contribute to the signal. Here we rely on our multivariate normal model of relatedness among populations, along with well understood methods for generating conditional multivariate normal distributions, in order to investigate specific hypotheses about individual populations or groups of populations. Using standard results from multivariate normal theory, we can generate the expected joint conditional distribution of genetic values for an arbitrary set of populations given the observed genetic values in some other set of populations. These conditional distributions allow for a convenient way to ask whether the estimated genetic values observed in certain populations or groups of populations differ significantly from the values we would expect them to take under the neutral model given the values observed in related populations.
Specifically, we exclude a population or set of populations, and then calculate the expected mean and variance of genetic values in these excluded populations given the values observed in the remaining populations, and the covariance matrix relating them. Using this conditional mean and variance, we calculate a Z-score to describe how well fit the estimated genetic values of the excluded populations are by our model of drift, conditional on the values in the remaining populations. In simple terms, the observation of an extreme Z-score for a particular population or group of populations may be seen as evidence that that group has experienced directional selection on the trait of interest (or a correlated one) that was not experienced by the related populations on which we condition the analyses. The approach cannot uniquely determine the target of selection, however. For example, conditioning on populations that have themselves been influenced by directional selection may lead to large Z-scores for the population being tested, even if that population has been evolving neutrally. We refer the reader to the Methods section for a mathematical explication of these approaches.
We conducted power simulations and an empirical application of our methods based on the Human Genome Diversity Panel (HGDP) population genomic dataset [58], and a number of GWAS SNP sets. To ensure that we made the fullest possible use of the information in the HGDP data, we took advantage of a genome wide allele frequency dataset of 3 million SNPs imputed from the Phase II HapMap into the 52 populations of the HGDP. These SNPs were imputed as part of the HGDP phasing procedure in [59]; see our Methods section for a recap of the details. We applied our method to test for signals of selection in six human GWAS datasets identifying SNPs associated with height, skin pigmentation, body mass index (BMI), type 2 diabetes (T2D), Crohn's Disease (CD) and Ulcerative Colitis (UC).
To assess the power of our methods in comparison to other possible approaches, we conducted a series of power simulations. There are two possible approaches to simulate the effect of selection on large scale allele frequency data of the type for which our methods are designed. The first is to simulate under some approximate model of the evolutionary history (e.g. full forward simulation under the Wright-Fisher model with selection). The second is to perturb real data in such a way that approximates the effect of selection. We choose to pursue the latter, both because it is more computationally tractable, and because it allows us to compare the power of our different approaches for populations with evolutionary histories of the same complexity as the real data we analyze. Each of our simulations will thus consist of sampling 1000 sets of SNPs matched to the height dataset (in much the same way we sample SNPs to construct the null distributions of our test statistics), and then adding slight shifts in frequency in various ways to mimic the effect of selection.
Below we first describe the set of alternative statistics to which we compare our methods. We then describe the manner in which we add perturbations to mimic selection, and lastly describe a number of variations on this theme which we pursued in order to better demonstrate how the power of our statistics changes as we vary parameters of the trait of interest, evolutionary process, or the ascertainment.
We estimated genetic values for each of six traits from the subset of GWAS SNPs that were present in the HGDP dataset, as described above. We discuss the analysis of each dataset in detail below, and address general points first. For each dataset, we constructed the covariance matrix from a sample of approximately appropriately matched SNPs, and the null distributions of our test statistics from a sample of sets of null genetic values, which were also constructed according to a similar matching procedure (as described in the Methods).
In an effort to be descriptive and unbiased in our decisions about which environmental variables to test, we tested each trait for an effect of the major climate variables considered by Hancock et al (2008) [63] in their analysis of adaptation to climate at the level of individual SNPs. We followed their general procedure by running principal components (PC) analysis for both seasons on a matrix containing six major climate variables, as well as latitude and longitude (following Hancock et al's rationale that these two geographic variables may capture certain elements of the long term climatic environment experienced by human populations). The percent of the variance explained by these PCs and their weighting (eigenvectors) of the different environmental variables are given in Table 1. We view these analyses largely as a descriptive data exploration enterprise across a relatively small number of phenotypes and distinct environmental variables, and do not impose a multiple testing penalty against our significance measures. A multiple testing penalization or false discovery rate approach may be needed when testing a large number phenotypes and/or environmental variables.
We also applied our test to identify traits whose underlying loci showed consistent patterns of unusual differentiation across populations, with results reported in Table 2. In Figure 3 we show for each GWAS set the observed value of and its empirical null distribution calculated using SNPs matched to the GWAS loci as described above. We also plot the expected null distribution of the statistic (). The expected null distribution closely matches the empirical distribution in all cases, suggesting that our multivariate normal framework provides a good null model for the data (although we will use the empirical null distribution to obtain measures of statistical significance).
For each GWAS SNP set we also separate our statistic into its -like and LD-like terms, as described in (14). In Figure 4 we plot the null distributions of these two components for the height dataset as histograms, with the observed value marked by red arrows (Figures S6–S10 give these plots for the other five traits we examined). In accordance with the expectation from our power simulations, the signal of selection on height is driven entirely by covariance among loci in their deviations from neutrality, and not by the deviations themselves being unusually large.
Lastly, we pursue a number or regionally restricted analyses. For each trait and for each of the seven geographic/genetic clusters described by Rosenberg et al (2002) [62], we compute a region specific statistic to get a sense for the extent to which global signals we detect can be explained by variation among populations with these regions, and to highlight particular populations and traits which may merit further examination as more association data becomes available. The results are reported in Table 3. We also apply our conditional Z-score approach at two levels of population structure: first at the level of Rosenberg's geographic/genetic clusters, testing each cluster in turn for how differentiated it is from the rest of the world, and second at the level of individual populations. The regional level Z-scores are useful for identifying signals of selection acting over broad regional scale or on deeper evolutionary timescales, while the population specific Z-scores are useful for identifying very recent selection that has only impacted a single population. We generally employ these regional statistics as a heuristic tool to localize signatures of selection uncovered in global analyses, or in cases where there is no globally interesting signal, to highlight populations or regions which may merit further examination as more association data becomes available. The result of these analyses are depicted in Figure 5, as well as Tables S3–S14.
In this paper we have developed a powerful framework for identifying the influence of local adaptation on the genetic loci underlying variation in polygenic phenotypes. Below we discuss two major issues related to the application of such methods, namely the effect of the GWAS ascertainment scheme on our inference, and the interpretation of positive results.
Among the most significant potential pitfalls of our analysis (and the most likely cause of a false positive) is the fact that the loci used to test for the effect of selection on a given phenotype have been obtained through a GWAS ascertainment procedure, which can introduce false signals of selection if potential confounds are not properly controlled. We condition on simple features of the ascertainment process via our allele matching procedure, but deeper issues may arise from artifactual associations that result from the effects of population structure in the GWAS ascertainment panel. Given the importance of addressing this issue to the broader GWAS community, a range of well developed methods exist for doing GWAS in structured populations, and we refer the reader to the existing literature for a full discussion [80]–[86]. Here, we focus on two related issues. First, the propensity of population structure in the GWAS ascertainment panel to generate false positives in our selection analysis, and second, the difficulties introduced by the sophisticated statistical approaches employed to deal with this issue when GWAS are done in strongly structured populations.
The problem of population structure arises generally when there is a correlation in the ascertainment panel between phenotype and ancestry such that SNPs that are ancestry informative will appear to be associated with the trait, even when no causal relationship exists [81]. This phenomenon can occur regardless of whether the correlation between ancestry and phenotype is caused by genetic or environmental effects. To make matters worse, multiple false positive associations will tend to line up with same axis of population structure. If the populations being tested with our methods lie at least partially along the same axis of structure present in the GWAS ascertainment panel, then the ascertainment process will serve to generate the very signal of positive covariance among like effect alleles that our methods rely on to detect the signal of selection.
The primary takeaway from this observation is that the more diverse the array of individuals sampled for a given GWAS are with respect to ancestry, the greater the possibility that failing to control for population structure will generate false associations (or bias effect sizes) and hence false positives for our method.
What bearing do these complications have on our empirical results? The GWAS datasets we used can be divided into those conducted within populations of European descent and the skin pigmentation dataset (which used an admixed population). We will first discuss our analysis of the former.
The European GWAS loci we used were found in relatively homogeneous populations, in studies with rigorous standards for replication and control for population structure. Therefore, we are reasonably confident that these loci are true positives. Couple this with the fact that they were ascertained in populations that are fairly homogenous relative to the global scale of our analyses, and it is unlikely that population structure in the ascertainment panels is driving our positive signals. One might worry that we could still generate false signals by including European populations in our analysis, however many of the signals we see are driven by patterns outside of Europe (where the influence of structure within Europe should be much lessened). For height, where we do see a strong signal from within Europe, we use a set of loci that have been independently verified using a family based design that is immune to the effects of population structure [28].
We further note that for a number of GWAS datasets, including some of those analyzed here, studies of non-European populations have replicated many of the loci identified in European populations [87]–[93], and for many diseases, the failure of some SNPs to replicate, as well as discrepancies in effect size estimate, are likely due to simple considerations of statistical power and differences in patterns of LD across populations [94], [95]. This suggests that, at least for GWAS done in relatively homogenous human populations, structure is unlikely to be a major confounding factor.
The issue of population structure may be more profound for our style of approach when GWAS are conducted using individuals from more strongly structured populations. In some cases it is desirable to conduct GWAS in such populations as locally adaptive alleles will be present at intermediate frequencies in these broader samples, whereas they may be nearly fixed in more homogeneous samples. A range of methods have been developed to adjust for population structure in these setting [96]–[98]. While generally effective in their goal, these methods present their own issues for our selection analysis. Consider the extreme case, such as that of Atwell et al (2010) [19], who carried out a GWAS in Arabidopsis thaliana for 107 phenotypes across an array of 183 inbred lines of diverse geographical and ecological origin. Atwell and colleagues used the genome-wide mixed model program EMMA [83], [96], [97] to control for the complex structure present in their ascertainment panel. This practice helps ensure that many of the identified associations are likely to be real, but also means that the loci found are likely to have unusual frequencies patterns across the species range. This follows from the fact that the loci identified as associated with the trait must stand out as being correlated with the trait in a way not predicted by the individual kinship matrix (as used by EMMA and other mixed model approaches). Our approach is predicated on the fact that we can use genome-wide patterns of kinship to adjust for population structure, but this correction is exactly the null model that loci significantly associated with phenotypes by mixed models have overcome. For this reason, both the theoretical distribution of the statistic, as well as the empirical null distributions we construct from resampling, may be inappropriate.
The Cape Verde skin pigmentation data we used may qualify as this second type of study. The Cape Verde population is an admixed population of African/European descent, and has substantial inter-individual variation in admixture proportion. Due to its admixed nature, the population segregates alleles which would not be at intermediate frequency in either parental population, making it an ideal mapping population.
Despite the considerable population structure, the fact that recombination continues to mix genotypes in this population means that much of the LD due to the African/European population structure has been broken up (and the remaining LD is well predicted by an individual's genome-wide admixture coefficient). Population structure seems to have been well controlled for in this study, and a number of the loci have been replicated in independent admixed populations. While we think it unlikely that the four loci we use are false associations, they could in principle suffer from the structured ascertainment issues described above, so it is unclear that the null distributions we use are strictly appropriate. That said, provided that Beleza and colleagues have appropriately controlled for population structure, under neutrality there would be no reason to expect that the correlation among the loci should be strongly positive with respect to the sign of their effect on the phenotype, and thus the pattern observed is at least consistent with a history of selection, especially in light of the multiple alternative lines of evidence for adaptation on the basis of skin pigmentation [68]–[70], [99]–[101].
Further work is needed to determine how best to modify the tests proposed herein to deal with GWAS performed in structured populations.
Our understanding of the genetic basis of variation in complex traits remains very incomplete, and as such the results of these analyses must be interpreted with caution. That said, because our methods are based simply on the rejection of a robust, neutral null model, an incomplete knowledge of the genetic basis of a given trait should only lead to a loss of statistical power, and not to a high false positive rate.
For all traits analyzed here except for skin pigmentation, the within population variance for genetic value is considerably larger than the variance between populations. This suggests that much of what we find is relatively subtle adaptation even on the level of the phenotype, and emphasizes the fact that for most genetic and phenotypic variation in humans, the majority of the variance is within populations rather than between populations (see Figures S14–S19). In many cases, the influence of the environment likely plays a stronger role in the differences between populations for true phenotypes than the subtle differences we find here (as demonstrated by the rapid change in T2D incidence with changing diet, e.g. [102]). That said, an understanding of how adaptation has shaped the genetic basis of a wide variety of phenotypes is clearly of interest, even if environmental differences dominate as the cause of present day population differences, as it informs our understanding of the biology and evolutionary history of these traits.
The larger conceptual issues relate to the interpretation of our positive findings, which we detail below. A number of these issues are inherent to the conceptual interpretation of evidence for local adaptation [103].
Written in matrix notation, the procedure of mean centering the estimated genetic values and dropping one population from the analysis can be expressed as (16)where is an by matrix with on the main diagonal, and elsewhere.
In order to calculate the corresponding expected neutral covariance structure about this mean, we use the following procedure. Let be an by matrix, where each column is a vector of allele frequencies across the populations at a particular SNP, randomly sampled from the genome according to the matching procedure described below. Let and be the mean allele frequency in columns and of respectively, and let be a matrix such that . With these data, we can estimate as (17)
This transformation performs the operation of centering the matrix at the mean value, and rooting the analysis with one population by dropping it from the covariance matrix (the same one we dropped from the vector of estimated genetic values), resulting in a covariance matrix describing the relationship of the remaining populations. This procedure thus escapes the singularity introduced by centering the matrix at the observed mean of the sample.
As we do not get to observe the population allele frequencies, the entries of are the sample frequencies at the randomly chosen loci, and thus the covariance matrix also includes the effect of finite sample size. Because the noise introduced by the sampling of individuals is uncorrelated across populations (in contrast to that introduced by drift and shared history), the primary effect is to inflate the diagonal entries of the matrix by a factor of , where is the number of chromosomes sampled in population (see the supplementary material of [46] for discussion). This means that our population structure adjusted statistics also approximately control for differences in sample size.
As described in the Results, we can use our multivariate normal model of relatedness to obtain the expected distribution of genetic values for an arbitrary set of populations, conditional on the observed values in some other arbitrary set.
We first partition our populations into two groups, those for which we want to obtain the expected distribution of genetic values (group 1), and those on which we condition in order to obtain this distribution (group 2). We then re–estimate the covariance matrix such that it is centered on the mean of group 2. This step is necessary because the amount of divergence between the populations in group 1 and the mean of group 2 will always be greater than the amount of divergence from the global mean, even under the neutral model, and our covariance matrix needs to reflect this fact in order to make accurate predictions. We can obtain this re-parameterized matrix as follows. If is the total number of populations in the sample, then let be the number of populations in group one, and let be the number of populations in group 2. We then define a new matrix such that the columns corresponding the populations in group one have 1 on the diagonal, and 0 elsewhere, while the columns corresponding to group two have on the diagonal, and elsewhere. We can then re–estimate a covariance matrix that is centered at the mean of the populations in group 2. Recalling our matrices and from (17), this matrix is calculated as (19)where we write to indicate that it is a covariance matrix that has been re-centered on the mean of group two.
Once we have calculated this re–centered covariance matrix, we can use well known results from multivariate normal theory to obtain the expected joint distribution of the genetic values for group one, conditional on the values observed in group two.
We partition our vector of genetic values and the re–centered covariance matrix such that (20)
and
(21)where and are vectors of genetic values in group 1 and 2 respectively, and , and are the marginal covariance matrices of populations within group 1, within group 2, and across the two groups, respectively. Letting (i.e. the sum of the elements of ), we wish to obtain the distribution(22)where and give the expected means and covariance structure of the populations in group 1, conditional on the values observed in group 2. These can be calculated as
(23)
and
(24)where the one vectors in line (23) are of length and respectively.
This distribution is itself multivariate normal, and as such this framework is extremely flexible, as it allows us to obtain the expected joint distribution for arbitrary sets of populations (e.g. geographic regions or continents), or for each individual population. Further, (25)
and
(26)where denotes the elements of . In words, the conditional expectation of the mean estimated genetic value across group 1 is equal to the mean of the conditional expectations, and its variance is equal to the mean value of the elements of the conditional covariance matrix. As such we can easily calculate a Z score (and corresponding p value) for group one as a whole as
(27)This Z score is a normal random variable with mean zero, variance one under the null hypothesis, and thus measures the divergence of the genetic values between the two populations relative to the null expectation under drift. Note that the observation of a significant Z score in a given population or region cannot necessarily be taken as evidence that selection has acted in that population or region, as selection in some of the populations on which we condition (especially the closely related ones) could be responsible for such a signal. As such, caution is warranted when interpreting the output of these sort of analyses, and is best done in the context of more explicit information about the demographic history, geography, and ecology of the populations.
As with our excess variance test, explored in the main text, it is natural to ask how our environmental correlation tests can be written in terms of allele frequencies at individual loci.
As noted in (8), we can obtain for each underlying locus a set of transformed allele frequencies, which have passed through the same transformation as the estimated genetic values. We assume that each locus has a regression coefficient (28)where is shared across all loci so that(29)where the are independent and identically distributed residuals. We can find the maximum likelihood estimate by treating as the linear predictor, and taking the regression of the combined vector , across all populations and loci, on the combined vector . As such(30)we can decompose this into a sum across loci such that
(31)As noted in (8), our transformed genetic values can be written as (32)and so the estimated slope () of our regression () is
(33)Comparing these equations, the mean regression coefficient at the individual loci (31) and the regression coefficient of the estimated genetic values (33) are proportional to each other via a constant that is given by one over two times the sum of the effect sizes squared (i.e. ). Our test based on estimating the regression of genetic values on the environmental variable is thus mathematically equivalent to an approach in which we assume that the regression coefficients of individual loci on the environmental variable are proportional to one another via a constant that is a function of the effect sizes. Such a relationship can also be demonstrated for the correlation coefficient () calculated at the genetic value level and at the individual locus level (this is not necessarily true for the rank correlation ), however the algebra is more complicated, and thus we do not show it here.
This is in contrast to the enrichment statistic we compute for the power simulations, in which we assume that the correlations of individual loci with the environmental variable are independent of one another, and then perform a test for whether more loci individually show strong correlations with the environmental variable than we would expect by chance.
We used imputed allele frequency data in the HGDP, where the imputation was performed as part of the phasing procedure of [59], as per the recommendations of [124]. We briefly recap their procedure here:
Phasing and imputation were done using fastPHASE [125], with the settings that allow variation in the switch rate between subpopulations. The populations were grouped into subpopulations corresponding to the clusters identified in [62]. Haplotypes from the HapMap YRI and CEU populations were included as known, as they were phased in trios and are highly accurate. HapMap JPT and CHB genotypes were also included to help with the phasing.
Various components of our procedure involve sampling random sets of SNPs from across the genome. While we control for biases in our test statistics introduced by population structure through our matrix, we are also concerned that subtle ascertainment effects of the GWAS process could lead to biased test statistics, even under neutral conditions. We control for this possibility by sampling null SNPs so as to match the joint distribution of certain properties of the ascertained GWAS SNPs. Specifically, we were concerned that the minor allele frequency (MAF) in the ascertainment population, the imputation status of the allele in the HGDP datasets, and the background selection environment experienced at a given locus, as measured by B value [61], might influence the distribution of allele frequencies across populations in ways that we could not predict.
We partitioned SNPs into a three way contingency table, with 25 bins for MAF (i.e. a bin size of 0.02), 2 bins for imputation (either imputed or not), and 10 bins for B value (B values range from 0 to 1, and thus our bin size was 0.1). For each set of null genetic values, we sampled one null SNP from the same cell in the contingency table as each of the GWAS SNPs, and assigned this null SNP the effect size associated with the GWAS SNP it was sampled to match. While we do not assign effect sizes to sampled SNPs used to estimate the covariance matrix (instead simply scaling by a weighted sum of squared effect sizes, which is mathematically equivalent under our assumption that all SNPs have the same covariance matrix), we follow the same sampling procedure to ensure that describes the expected covariance structure of the GWAS SNPs.
For the skin pigmentation GWAS [67] we do not have a good proxy present in the HGDP population, as the Cape Verdeans are an admixed population. Cape Verdeans are admixed with African ancestry, and European ancestry in the sample obtained by [67] (Beleza, pers. comm., April 8, 2013). As such, we estimated genome wide allele frequencies in Cape Verde by taking a weighted mean of the frequencies in the French and Yoruban populations of the HGDP, such that . We then used these estimated frequencies to assign SNPs to frequency bins.
[67] also used an admixture mapping strategy to map the genetic basis of skin pigmentation. However, if they had only mapped these loci in an admixture mapping setting we would have to condition our null model on having strong enough allele frequency differentiation between Africans and Europeans at the functional loci for admixture mapping to have power [126]. The fact that [67] mapped these loci in a GWAS framework allows us to simply reproduce the strategy, and we ignore the results of the admixture mapping study (although we note that the loci and effect sizes estimated were similar). This highlights the need for a reasonably well defined ascertainment population for our approach, a point which we comment further on in the Discussion.
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10.1371/journal.pgen.1005737 | Seed Dormancy in Arabidopsis Requires Self-Binding Ability of DOG1 Protein and the Presence of Multiple Isoforms Generated by Alternative Splicing | The Arabidopsis protein DELAY OF GERMINATION 1 (DOG1) is a key regulator of seed dormancy, which is a life history trait that determines the timing of seedling emergence. The amount of DOG1 protein in freshly harvested seeds determines their dormancy level. DOG1 has been identified as a major dormancy QTL and variation in DOG1 transcript levels between accessions contributes to natural variation for seed dormancy. The DOG1 gene is alternatively spliced. Alternative splicing increases the transcriptome and proteome diversity in higher eukaryotes by producing transcripts that encode for proteins with altered or lost function. It can also generate tissue specific transcripts or affect mRNA stability. Here we suggest a different role for alternative splicing of the DOG1 gene. DOG1 produces five transcript variants encoding three protein isoforms. Transgenic dog1 mutant seeds expressing single DOG1 transcript variants from the endogenous DOG1 promoter did not complement because they were non-dormant and lacked DOG1 protein. However, transgenic plants overexpressing single DOG1 variants from the 35S promoter could accumulate protein and showed complementation. Simultaneous expression of two or more DOG1 transcript variants from the endogenous DOG1 promoter also led to increased dormancy levels and accumulation of DOG1 protein. This suggests that single isoforms are functional, but require the presence of additional isoforms to prevent protein degradation. Subsequently, we found that the DOG1 protein can bind to itself and that this binding is required for DOG1 function but not for protein accumulation. Natural variation for DOG1 binding efficiency was observed among Arabidopsis accessions and contributes to variation in seed dormancy.
| The Arabidopsis protein DELAY OF GERMINATION 1 (DOG1) is an important regulator of seed dormancy and controls the timing of seedling emergence. The amount of DOG1 protein in mature seeds correlates with their dormancy level. It has been demonstrated that DOG1 is an important contributor to natural variation for seed dormancy and Arabidopsis accessions vary in their DOG1 levels. In this study we showed that the DOG1 gene produces five transcript variants encoding three protein isoforms. Transgenic dog1 mutant seeds expressing single DOG1 transcript variants lack dormancy and do not accumulate DOG1 protein. Simultaneous expression of two or more DOG1 transcript variants encoding different isoforms, however, leads to the accumulation of DOG1 protein and increased seed dormancy. DOG1 protein can bind to itself and Arabidopsis seeds that contain a DOG1 variant unable to bind show reduced seed dormancy. We observed variation for DOG1 binding efficiency between Arabidopsis accessions, indicating a role for DOG1 self-binding in natural variation for seed dormancy.
| Alternative splicing has an important role in the post-transcriptional regulation of higher eukaryotes, but it was long believed to be of minor significance in plants. During the last years consecutive reports demonstrated a steadily increasing percentage of alternatively spliced genes in plants. At the beginning of this century only 1.5% of the Arabidopsis thaliana genes were estimated being alternatively spliced [1]. Within one decade this fraction went up to 61% [2]. Alternative splicing can lead to different outcomes and produces transcripts that code for proteins with altered or lost function. It can also lead to tissue specific transcripts or affect mRNA stability and turnover via nonsense-mediated decay [3,4]. The regulation and function of alternative splicing in plants is still largely unexplored but several examples have demonstrated its functional importance in various processes like photosynthesis, defence responses, the circadian clock, hormone signalling, flowering time, and metabolism [5,6,7,8]. A few examples have shown a role of alternative splicing during seed development and germination. The central regulator of seed maturation, ABSCISIC ACID INSENSITIVE 3 (ABI3), has been cloned in Arabidopsis over 20 years ago [9], but it was only recently found that this gene is alternatively spliced in a developmentally regulated fashion [10]. The PHYTOCHROME INTERACTING FACTOR 6 gene is also alternatively spliced during seed development and one of its two splice forms, PIF6-β influences germination potential [11].
The timing of seed germination determines the seasonal environmental conditions that a plant encounters during its life and thereby its growth and reproductive success. Seed germination is regulated by dormancy, which is defined as the incapacity of a viable seed to germinate under favourable conditions [12]. Seed dormancy is induced during seed maturation and released by dry storage of seeds (after-ripening) or imbibition at low temperatures (stratification) [13], and is regulated by environmental and endogenous factors. Extensive research on seed dormancy in several plant species, including Arabidopsis, has revealed the requirement of the plant hormone abscisic acid (ABA) to induce dormancy during seed maturation, whereas gibberellins (GAs) are required for germination [13,14]. In addition, mutant analyses identified a number of seed dormancy regulators. Apart from factors involved in hormone metabolism and seed maturation, these included several chromatin modifiers and transcriptional regulators [15].
The Arabidopsis gene DELAY OF GERMINATION 1 (DOG1) is a master regulator of seed dormancy acting independent of ABA. DOG1 was first identified as a major Quantitative Trait Locus (QTL) for seed dormancy [16]. Mutations in the DOG1 gene lead to a complete absence of dormancy. DOG1 shows a seed-specific expression pattern and encodes a protein with unknown function. Its transcript and protein abundances in freshly harvested seeds highly correlate with dormancy levels [17,18]. This correlation has been observed under both lab and natural conditions. Environmental conditions that enhance seed dormancy, such as low temperatures during seed maturation, are associated with enhanced DOG1 transcript levels [18,19,20]. Arabidopsis accessions from the south of Europe are in general more dormant and show higher DOG1 transcript levels compared to northern accessions [19]. Interestingly, DOG1 transcript levels also showed the highest correlation among a set of seed dormancy genes with changes in dormancy in buried seeds in the field [21]. During after-ripening DOG1 protein remains stable but loses its activity due to unknown post-translational modifications. Therefore, DOG1 is likely to be part of a timing mechanism for the release of seed dormancy [18]. The DOG1 protein belongs to a small family in Arabidopsis that is conserved in plants. Several DOG1 homologues have been shown to be functionally conserved and are able to enhance seed dormancy in Lepidium sativum [22] and Triticum aestivum [23].
We are only starting to understand the regulation of DOG1 at the protein level and its transcriptional regulation remains to be further investigated. DOG1 transcript levels are enhanced by the TFIIS transcription elongation factor [24,25] and by histone monoubiquitination [26]. DOG1 is alternatively spliced and four splicing variants encoding three different isoforms have been reported [17]. It has recently been shown that the spliceosome disassembly factor NTR1 is required for proper transcript levels and splicing of DOG1 [27].
We have identified a fifth splicing variant of DOG1 that constitutes the majority of its transcripts. Here we show that the accumulation of DOG1 protein requires alternative splicing because single DOG1 protein isoforms are not able to accumulate efficiently in the seed. The DOG1 protein can bind to itself and this self-binding is required for full DOG1 function. Variation in self-binding ability of DOG1 exists in nature and contributes to variation in seed dormancy levels between Arabidopsis accessions.
The Arabidopsis DOG1 gene contains three exons. Its second intron is alternatively spliced and shows both alternative 3’ and alternative 5’ splice site selection, leading to four different transcripts DOG1-α, DOG1-β, DOG1-γ, and DOG1-δ [17]. We quantified the individual transcripts using specific primers. Comparison of total DOG1 transcript levels with the combined levels of the four individual splicing forms indicated that the majority of DOG1 transcript does not exist out of these four forms. Detailed analysis of the DOG1 transcripts by 3’-RACE revealed the presence of a fifth splicing form encoding the same protein as DOG1-β and DOG1-γ, which was designated as DOG1-ε. This splicing form misses the complete third exon (Fig 1A).
The abundance of the five DOG1 splicing variants was followed during seed maturation. As shown previously [17,18], DOG1 expression peaks in the middle of the seed maturation phase and is reduced in fully mature dry seeds. The DOG1-ε transcript represents about 90–95% of the DOG1 transcripts at the measured time-points (Fig 1B and S1A Fig). The ratio between the different splicing forms is fairly constant (Fig 1C and S1B Fig), although DOG1-α is relatively more abundant at the beginning of seed maturation. DOG1-δ is very low abundant, but increases at the end of seed maturation (Fig 1C and S1B Fig).
The alternative splicing of DOG1 only affects the C-terminal part of the protein. DOG1-β is the smallest isoform (consisting of 278 amino acids in the Landsberg erecta (Ler) accession) and shares nearly the complete protein sequence with DOG1-α and DOG1-δ, apart from its last nine amino acids. The DOG1-α and DOG1-δ proteins are longer, respectively 292 and 303 amino acids in Ler, and share their last 24 amino acids with each other (Fig 1A).
The cellular localisation of the three DOG1 isoforms was analysed by transient expression of their N-terminal yellow fluorescent protein (YFP) fusion proteins in Nicotiana benthamiana leaves. All three fusion proteins were mainly detected in the nucleus similar as previously shown in transgenic Arabidopsis seeds containing YFP fused with the DOG1 genomic fragment (Fig 1D) [18]. These results were confirmed by transformation of Arabidopsis protoplasts with C-terminal YFP fusion proteins of the three DOG1 isoforms (S2 Fig). The presence of all three DOG1 protein isoforms in the nucleus suggests that they are able to meet each other.
The Near Isogenic Line DOG1 (NIL DOG1) contains an introgression of a strong DOG1 allele from the Cape Verde Islands (Cvi) accession in the Ler background and has a high level of seed dormancy. In contrast, the dog1-1 mutant (in NIL DOG1 background) does not produce full-length DOG1 protein and is nondormant (Fig 2A) [17]. The function of the three DOG1 protein isoforms was studied by complementation of the dog1-1 mutant with single DOG1 isoforms driven by the native DOG1_Cvi promoter. Transgenic plants with single insertion events were obtained. None of these showed convincing functional complementation as their seed dormancy level was comparable to that of the dog1-1 mutant. Only introduction of DOG1-β caused a very weak restoration of dormancy (Fig 2B). Therefore, single DOG1 isoforms appeared largely non-functional when expressed from a native DOG1 promoter.
Subsequently, transgenic plants with single DOG1 isoforms were mutually crossed and F3 or F4 plants homozygous for two isoforms were selected for all possible combinations. Transgenic lines containing two DOG1 isoforms showed enhanced dormancy compared to lines containing a single DOG1 isoform. Although dormancy levels of these double transformants showed variation, we observed the tendency that plants containing a combination of DOG1-β with DOG1-α or DOG1-δ had higher dormancy levels than plants containing a combination of DOG1-α and DOG1-δ, which germinated already 70–80% directly after harvest (Fig 2C). The dormancy level of double transgenic plants containing DOG1-β as one of the two splicing forms was similar to that of the low dormant Ler accession but much lower than NIL DOG1, which is the wild-type background of the dog1-1 mutant. Finally, we obtained transgenic plants that contain all three DOG1 isoforms after further crossing and selection. Seeds of all these triple transgenic plants showed dormancy restoration and none of them had similar low dormancy levels as the double transgenic lines containing α and δ (Fig 2D).
Seed dormancy highly correlates with DOG1 transcript and protein levels in freshly harvested seeds [18]. Therefore, transcript and protein levels were measured in the single, double and triple transgenic plants. Freshly harvested seeds of the transgenic lines showed some variation in their DOG1 expression but were mostly in a similar range as those of NIL DOG1 (Fig 2E). This was to be expected because DOG1 is transcribed from the same DOG1_Cvi promoter in NIL DOG1 and the transgenic lines. The transcript levels of the transgenes are expected to be slightly lower than the observed DOG1 transcript levels, which include a low amount of transcript from the dog1 mutant gene. Taken this into account, transcript levels in the transgenic lines still did not correlate with dormancy levels. Several of the transgenic lines with single DOG1 isoforms showed high DOG1 expression levels, comparable to NIL DOG1, but were non-dormant. In contrast, dormancy correlated with DOG1 protein accumulation in these transgenic seeds (Fig 2B–2F). DOG1 protein was scarcely detectable in the transgenic lines containing single DOG1 isoforms, in accordance with their lack of seed dormancy. The double and triple transgenic lines accumulated varying levels of DOG1 protein that were mostly in the same range as those found in seeds of the Ler accession, but much lower compared to the wild-type NIL DOG1. Interestingly, the double transgenic lines accumulated slightly higher levels of DOG1 protein than the triple, although they were less dormant. This suggests that the presence of three isoforms gives higher DOG1 activity in comparison to two isoforms. In the double and triple transformants that contain both DOG1-β and DOG1-α or DOG1-δ protein, two bands could be detected. In the non-transgenic controls, however, only the faster migrating band is visible, which corresponds to the DOG1-β protein. This is most likely due to the different ratio of the DOG1 transcript variants between controls and transgenic lines. In the double and triple transformants the different DOG1 transcript variants are expressed at similar levels, whereas in the wild type the transcripts that encode the DOG1-β protein are most abundant (Fig 1C). As DOG1 protein accumulates to higher levels in NIL DOG1 compared to the double and triple transgenic lines despite their comparable transcript levels, the ratio between the isoforms probably influences DOG1 protein accumulation.
We were interested whether an increase in the transcript level of single DOG1 transcripts could lead to complementation of the dog1 mutant. Three constructs with DOG1-α, DOG1-β, and DOG1-δ driven by the constitutive 35S promoter were separately transformed into dog1-1 plants. About 10 to 40% of the obtained independent transformants for all three constructs showed high levels of seed dormancy (Fig 3A). Interestingly, the DOG1-β isoform was most effective in dormancy induction, followed by DOG1-δ. DOG1-α showed relatively lower dormancy levels. Overall, this experiment demonstrated that every DOG1 isoform is biochemically functional to induce seed dormancy.
Both complementing and non-complementing lines for all three constructs were selected for further analysis. Comparison of DOG1 transcript levels between these lines showed a more than 100-fold difference. High levels of DOG1 transcript were only detected in lines with high dormancy levels (Fig 3B). Consistent with the transcript levels, DOG1 protein could only be detected in the dormant transformants where it accumulated to even higher levels than in the NIL DOG1 control (Fig 3C). Taken together with the results from the complementation experiments using the native DOG1 promoter (Fig 2A–2F), these data suggest that all the single DOG1 isoforms are functional but unstable in the cell and can only accumulate when they have very high transcript levels.
Overlapping nuclear localisation and instability of single DOG1 isoforms have prompted us to test mutual binding abilities of the DOG1 isoforms in a yeast two-hybrid experiment. All three DOG1 isoforms were able to bind to themselves and to each other in any combination (Fig 4A). These results were confirmed using the bimolecular fluorescence complementation assay based on split YFP. As shown in Fig 4B, YFP fluorescence could be observed in the nuclei of Arabidopsis embryo cells containing two transgenes consisting of the N- and C- terminal halves of YFP fused to different DOG1 splicing forms. Fluorescence was not detected in the controls.
A series of truncated DOG1 proteins was prepared to identify the region required for self-binding. A yeast two-hybrid assay between these truncated DOG1 proteins and full-length DOG1-δ identified a region of 10 amino acids whose absence makes the protein incapable of binding. Further alanine-scanning experiments in this region revealed that a single replacement, a tyrosine (the 16th amino acid of DOG1_Ler) with alanine, strongly reduced self-binding (Fig 4C), whereas the other seven obtained substitution mutants did not show altered self-binding abilities.
We were interested whether self-binding is necessary for DOG1 function. To answer this question, a complementation experiment was carried out using two constructs. The first contained the genomic region of the DOG1 gene from Ler and the second was identical except for the replacement of tyrosine 16 by alanine (Y16A). Both constructs were transformed into the dog1-1 mutant and transformants with single insertion events were selected. As shown previously [18], the genomic DOG1 clone complemented the dog1 mutant and seeds from the transformants were dormant. In contrast, the Y16A clone showed very weak complementation and 65% of the seeds germinated directly after harvest (Fig 4D and S3 Fig). Therefore, DOG1 protein requires self-binding to induce seed dormancy, although lack of binding ability does not abolish its function completely. A control experiment showed that replacement of glutamic acid at position 13 with alanine, an amino acid that is not affecting self-binding, did not cause a reduced complementation of the dog1 mutant (Fig 4D and S3 Fig).
Several independent Y16A transgenic lines were further analysed for DOG1 protein accumulation. Interestingly, DOG1 protein could be detected in all of these lines at similar levels as in the dormant NIL DOG1 control (Fig 4E). This indicated that the weak complementation of the Y16A lines was not due to reduced accumulation or instability of the Y16A mutant protein. Because the Y16A-DOG1 protein cannot bind to itself, we concluded that self-binding of DOG1 does not influence its protein accumulation but is required for its full function.
DOG1 was originally identified as a major QTL underlying natural variation in dormancy between the Arabidopsis accessions Ler and Cvi. Later on, the DOG1 dormancy QTL was identified in several additional recombinant inbred line populations [28]. This suggests that DOG1 is a major contributor to natural variation for seed dormancy in Arabidopsis. DOG1 alleles from different accessions show sequence polymorphisms in both promoter and coding regions. A correlation was found between dormancy levels and DOG1 transcript levels among different genotypes [17]. Accordingly, it is likely that polymorphisms in the promoters of different accessions cause variation in DOG1 strength between accessions.
Surprisingly, a comparison of DOG1 transcript and dormancy levels in the low dormant accessions Ler and Col, and the dormant line NIL DOG1 (which contains the Cvi allele of DOG1) showed that this correlation was absent in Col, which has relatively high DOG1 transcript levels (Fig 5A and 5B). The DOG1 protein levels of these three genotypes correlated with their transcript levels (Fig 5B) and the Col seeds showed low dormancy despite having high levels of DOG1 protein. This lack of correlation might be caused by a reduced function of the DOG1_Col protein. Therefore, the self-binding ability of DOG1_Col was analysed. As shown in Fig 5C, DOG1_Col showed significantly reduced self-binding in a yeast two-hybrid assay. A sequence comparison of the predicted DOG1 proteins of Ler, Col and Cvi showed a polymorphism within AA13-16 (Fig 5D). In Ler and Cvi this region contains the amino acids ECCY, which are replaced by DSY in Col. Interestingly, the tyrosine at AA16 that is required for self-binding is present in all three accessions. Nevertheless, the observed amino acid changes are likely to affect self-binding because the Col DOG1 protein is not able to bind to itself (Fig 5C). To test the influence of this polymorphism on seed dormancy, two constructs containing different DOG1 alleles were introduced into the dog1-2 mutant (in Col background) for complementation. One of the constructs contained the wild-type DOG1_Col allele, including a 2.2 kb fragment upstream of the START codon and 1.1 kb downstream of the STOP codon. The other construct coded for a modified Col DOG1 protein in which the amino acids DSY at AA13-16 were replaced by ECCY. Transgenic plants containing single introgression events were selected for both constructs and their seed dormancy levels were assessed by following their germination rate during extended seed storage. Similar to the DOG1 complementation lines previously obtained [18], independent transgenic lines with the same construct showed varying degrees of dormancy (S3 Fig). However, plants with the construct encoding the ECCY Col DOG1 protein showed enhanced dormancy levels compared to plants containing the wild-type Col DOG1 construct (Fig 5E and S4 Fig). Therefore, we assume that the polymorphism at AA13-16 contributes to the low seed dormancy of Col by reducing the self-binding ability of the DOG1 protein.
Natural variation for DOG1 self-binding ability was further explored by analysing DOG1 in 58 accessions. A protein sequence comparison identified three main haplotypes for the AA13-16 region, which were named DSY (Col-type), DRY (Sei0-type) and ECCY (Ler/Cvi-type) (S5 Fig). Several accessions from each haplotype were analysed for their DOG1 binding ability using a yeast two-hybrid assay. As expected, the haplotype DSY showed very weak self-binding, which was also the case for the DRY haplotype. In contrast, the ECCY accessions showed strong DOG1 self-binding (Fig 6A). We further studied the DOG1 haplotypes by analysis of their dormancy level and DOG1 protein accumulation in fresh seeds of representative accessions of the three groups (Fig 6B and 6C). By combining DOG1 protein levels and haplotype, we could explain a major part of the dormancy levels of these accessions. The DSY accessions had relatively low dormancy levels except for Sha, Daejoen, Kondara and Kas1 that all showed high DOG1 protein levels. Most of the ECCY accessions had high dormancy levels while having low to medium high DOG1 protein levels. The ECCY accession Ler showed low dormancy, but this accession had very low DOG1 protein levels (Fig 5B). Overall, these data demonstrated that not only expression levels of DOG1 but also differences in self-binding ability affect the strength of DOG1 function in dormancy induction of Arabidopsis natural accessions. We propose that combining the analysis of DOG1 protein levels in freshly harvested seeds with amino acid composition at the 13–16 AA region can lead to an improved prediction of seed dormancy levels in Arabidopsis accessions.
The timing of seed germination determines successful plant establishment. Seed dormancy is a major factor controlling germination potential and has a complex regulation involving several independent pathways [15,28]. The molecular mechanisms that regulate seed dormancy are only beginning to be understood. DOG1 has been identified as a key dormancy gene in Arabidopsis [17,18]. Seed dormancy levels are well correlated with DOG1 protein levels in freshly matured seeds. The DOG1 protein undergoes modification during dry seed storage, which is paralleled by loss of dormancy [18]. A better understanding of seed dormancy requires a thorough knowledge of the regulation of DOG1. Our present study demonstrated that the accumulation of DOG1 protein is influenced by alternative splicing of the DOG1 gene. In addition, we have shown that DOG1 protein function is enhanced by its self-binding.
Alternative splicing is widespread in plants, but relatively few studies have been performed to study its regulatory action in specific plant processes. The main consequences of alternative splicing are alteration or loss of protein function, tissue specificity, and changed mRNA stability and turnover via nonsense mediated decay [3,4]. Here, we demonstrated that the production of several protein isoforms by alternative splicing stimulates the accumulation of DOG1 protein. Five DOG1 splicing variants were identified that are translated into three protein isoforms, DOG1-α, DOG1-β and DOG1-δ. These isoforms could not complement the dog1 mutant when they were expressed from the endogenous DOG1 promoter and DOG1 protein accumulation required the presence of at least two of these isoforms. However, overexpression experiments showed that the isoforms had small differences in their functionality with DOG1-β being the most effective, followed by DOG1-δ and DOG1-α (Fig 3). The individual DOG1 splicing variants and protein isoforms showed a high variation in abundance in wild-type plants. The transcripts encoding the DOG1-β isoform were about 20 times more abundant in mature seeds compared to the transcripts encoding the other two protein isoforms. In accordance, in wild-type seeds DOG1-β showed the highest accumulation and the other isoforms could not be clearly detected on immunoblots. However, our analyses with transgenic plants suggested that the DOG1-β isoform could not sufficiently accumulate in the absence of additional isoforms.
The mechanism that underlies the requirement of multiple isoforms for DOG1 protein to accumulate still needs further characterisation, but we have identified several of its characteristics. First, it is likely that the mechanism depends on active protein degradation because overexpression of single DOG1 isoforms by the 35S promoter can lead to the accumulation of DOG1 protein. We assume that the active DOG1 degradation mechanism is not able to deal with high amounts of single DOG1 isoforms that are continuously translated from abundantly present transcripts. Secondly, the ratio between isoforms appears to be important because equal amounts of splicing forms in the (double and triple) transgenic plants lead to a low level of protein accumulation. The natural ratio between DOG1 isoforms, in which DOG1-β is much more abundant than the other isoforms, leads to high levels of protein accumulation.
DOG1 is an essential gene for seed dormancy and its protein abundance is correlated with dormancy levels. Therefore, the regulation of DOG1 protein accumulation by alternative splicing could be part of a mechanism to fine-tune seed dormancy. We previously showed that DOG1 protein abundance does not decrease during the last part of the seed maturation phase, although DOG1 transcript levels are strongly reduced [18]. Interestingly, the DOG1-δ transcript variant becomes relatively more abundant at the end of seed maturation in comparison to the variants encoding DOG1-β (Fig 1C). This altered ratio might enhance DOG1 protein accumulation and could explain the persistence of DOG1 protein at this time point, despite a general reduction in DOG1 transcript levels. Identification of the factors controlling alternative splicing of DOG1 can lead to a better understanding of its regulation and give new insights in seed dormancy. We have previously identified a splicing factor, SUPPRESSOR OF ABI3 (SUA), which functions during seed maturation and regulates alternative splicing of ABI3 [10]. We analysed DOG1 splicing in the sua mutant, but did not observe any difference compared to wild-type plants. Therefore, other splicing factors than SUA regulate DOG1 alternative splicing. One of these has recently been identified as the spliceosome disassembly factor AtNTR1. The atntr1 mutants have altered transcript levels and splicing of DOG1 as well as reduced dormancy [27].
Our observation that the accumulation of DOG1 protein requires the presence of multiple isoforms inspired us to analyse whether these isoforms can bind to each other. Indeed, we observed self-binding of DOG1 but contrary to our expectation self-binding was not required for protein accumulation because DOG1 still accumulated in a modified version of DOG1 that is unable to bind to itself (Fig 4). However, this modified version of DOG1 had a strongly reduced function as evidenced by the low dormancy level of seeds that were derived from transgenic plants containing DOG1 that is unable to bind to itself. This observation suggests that DOG1 acts in a protein complex that comprises of at least a homodimer.
DOG1 has originally been identified as a seed dormancy QTL and the DOG1 locus shows sequence variation in both promoter and coding region among accessions [17]. It was also shown that variation in DOG1 expression contributes to variation in seed dormancy levels between Arabidopsis accessions [17]. In this work, we have shown additional natural variation at AA13-16 for DOG1 self-binding. Variation in self-binding ability could contribute to variation in DOG1 function and thereby seed dormancy. As DOG1 is a conserved gene, combining the variation in DOG1 expression levels with the variation in DOG1 self-binding has the potential to be developed into a marker to predict dormancy levels of crop seeds. However, these two factors are still not enough to fully explain the variation in seed dormancy levels between Arabidopsis accessions. Although DOG1 is a major dormancy QTL in Arabidopsis, ten other dormancy QTLs have been identified and variation in these QTLs should also be considered. For instance, the high dormancy levels of the Sha and Kondara accessions that have weak non-self-binding DOG1 alleles might be explained by their strong DOG6 alleles [28].
In this and previous studies we have observed a strong negative correlation between DOG1 protein levels and germination potential with two important exceptions. Firstly, after-ripened seeds can germinate in the presence of high levels of DOG1 protein [18]. Secondly, we showed in the present work that seeds containing DOG1 that is not able to bind to itself can germinate. We speculate that the lack of a negative correlation between DOG1 protein accumulation and germination potential could have the same cause in both cases. After-ripening has been shown to be associated with DOG1 protein modifications. These protein modifications might prevent or reduce DOG1 self-binding and thereby its function.
Arabidopsis NIL DOG1_Cvi is a near isogenic line that contains the DOG1 allele from Cvi in a Ler background [18]. The mutant dog1-1 has been obtained in the NIL DOG1 background [17], dog1-2 in Col [18]. Arabidopsis accessions used in this study are listed in S1 Table. The double and triple homozygous transgenic lines were selected after crossings using PCR to confirm their homozygosity. All plants were sown on soil and grown in a growth chamber with 16 h- light/8 h-dark cycle (22°C/16°C), or in a greenhouse where the temperature was maintained close to 23°C, 16 h of light was provided daily. Six weeks vernalisation was applied to the late-flowering accessions to promote flowering. Freshly harvested seeds were immediately used for experiments or stored under constant conditions (21°C, 50% humidity, in the dark) for after-ripening treatment.
About 50 seeds were plated onto a filter paper moistened with demineralized water in Petri dishes and incubated in an alternating condition (12 h light/12 h dark, 25°C/20°C cycle). Radicle emergence was scored after three days, since dog1-1 mutant and after-ripened seeds of other accessions fully germinate within this period. Each germination test was done in at least three replicates from independent plants.
Total RNA was extracted from developing Arabidopsis siliques as described previously [29]. Quantitative RT-PCR was performed as described previously [18], except for the annealing temperature, which was 64°C for splicing variant-specific primer sets. Sequences of the primers used for qRT-PCR are listed in S2 Table. The expression value for each gene was quantified using a standard curve with a serial dilution of plasmid of known concentration, and they were normalised to the value of ACT8 (At1g49240) or HBT (At2g20000) genes. At least three biological replicates were analysed.
To identify additional splicing variants, 3’-RACE was performed using RNA extracted from Ler siliques at 16 days after pollination (DAP) following the standard protocol of 3’-Full RACE Core Set (TAKARA) using the oligo-dT-adapter primer, adapter primer and DOG1 primer 5’-GGATTCTATCTCCGGTACAAGGA- 3’.
Seed protein extraction and immunoblot analysis were performed as described previously using peptide antibody against DOG1 [18].
All the binary constructs were prepared using the Gateway technology (Invitrogen). A 5.07 kb fragment of Col genomic DNA corresponding to the Ler fragment [18] including a 2.22 kb region upstream of the DOG1 start codon, the DOG1 coding region and 1.03 kb downstream of the stop codon, as well as cDNA fragments of each splicing variant from Cvi were cloned into pENTR/D-TOPO vector. Entry clones carrying Y16A and E13A mutations in DOG1_Ler or the ECCY mutation in DOG1_Col were generated by site-directed mutagenesis based on the sequences of DOG1_Ler using the QuickChange II XL site-directed mutagenesis kit (Stratagene). The resultant genomic entry clones were converted into pGWB1 [30] by LR reaction. For isoform specific complementation, each variant cDNA fragment was cloned under the DOG1 promoter_Cvi into the pGreen backbone. For fluorescent protein fusion constructs, each variant cDNA was cloned into 2x Pro 35S: YFP vectors, pENSG-YFP (N-terminal fusion) and pEXSG-YFP (C-terminal fusion), and split YFP vectors, pBatTL-B-sYFPn and pBatTL-B-sYFPc [31] via LR reaction. All the binary constructs were introduced by electroporation into Agrobacterium tumefaciens strains GV3101 or GV3101 carrying the helper plasmid pMP90RK [32] or pSoup [33], which were subsequently used to transform Ler, dog1-1 or dog1-2 plants by floral dipping [34]. All the transgenic lines were first selected based on their antibiotics resistance, their homozygosity was further confirmed by PCR-based genotyping.
Subcellular localisation of each DOG1 isoform was analysed using binary constructs with single variant cDNA from Cvi fused to YFP at their N-terminus or C-terminus and cloned under the CaMV 35S promoter. Transiently expressed fusion proteins were observed in Nicotiana benthamiana leaves as described [35] or in Arabidopsis thaliana protoplasts (from Col) as described [10].
For BiFC assays, embryos from 1 h-imbibed seeds of the double homozygous transformants were dissected from testa/endosperm, and restored YFP fluorescence was analysed. Observations were performed with either Zeiss LSM510 or LSM700 confocal laser scanning microscope system using 514 nm lasers for excitation with 63x oil-immersion objective. The images were analysed using the LSM5 software or ZEN imaging software (Zeiss, Germany).
All three cDNA fragments corresponding to the alpha, beta and delta isoforms from Ler, and the beta isoforms from all other accessions were cloned into pENTR/D-TOPO (Invitrogen), and then recombined in the pACT2-gateway (GAL4 AD fusion) and pAS2-gateway (GAL4 BD fusion) vectors (modified from Clontech). Yeast two-hybrid assays were carried out in yeast strain PJ69-4A [36]. Yeast transformation was performed using a LiAc/SS carrier DNA/PEG method as described [37]. Co-transformed colonies were selected on synthetic dropout medium (SD) lacking Leu (L) and Trp (W). Interaction tests were performed on SD lacking L, W, and His (H) with 5 mM 3-aminotriazole.
DOG1 genomic sequences were collected either by Sanger sequencing of PCR-amplified genomic fragments or re-analysis of the publicly available next generation sequencing reads [38]. The Cao’s dataset covered 80 accessions. Additionally, we included data from Col-0, Ler and Cvi [39].
The polymorphic data are available on Arabidopsis 1001 Genome browser (http://signal.salk.edu/atg1001/3.0/gebrowser.php), however the polymorphisms (SNPs and small INDELs) in the region of interest in Ler and Cvi (we obtained genomic and cDNA sequences by Sanger sequencing) were not correctly shown in the browser. In order to verify or discover new structural variations, the raw reads were assembled using a program (http://mandrake.mpipz.mpg.de:8081/cgi-bin/oscar.pl) and the contigs were constructed for the DOG1 genomic region in each accession. The obtained DOG1 sequences from accessions that successfully assembled into contigs were aligned to the Col-0 reference. The structural variations of those accessions were used for further analysis.
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10.1371/journal.pcbi.1003713 | Network Analysis of Breast Cancer Progression and Reversal Using a Tree-Evolving Network Algorithm | The HMT3522 progression series of human breast cells have been used to discover how tissue architecture, microenvironment and signaling molecules affect breast cell growth and behaviors. However, much remains to be elucidated about malignant and phenotypic reversion behaviors of the HMT3522-T4-2 cells of this series. We employed a “pan-cell-state” strategy, and analyzed jointly microarray profiles obtained from different state-specific cell populations from this progression and reversion model of the breast cells using a tree-lineage multi-network inference algorithm, Treegl. We found that different breast cell states contain distinct gene networks. The network specific to non-malignant HMT3522-S1 cells is dominated by genes involved in normal processes, whereas the T4-2-specific network is enriched with cancer-related genes. The networks specific to various conditions of the reverted T4-2 cells are enriched with pathways suggestive of compensatory effects, consistent with clinical data showing patient resistance to anticancer drugs. We validated the findings using an external dataset, and showed that aberrant expression values of certain hubs in the identified networks are associated with poor clinical outcomes. Thus, analysis of various reversion conditions (including non-reverted) of HMT3522 cells using Treegl can be a good model system to study drug effects on breast cancer.
| The HMT3522 isogenic human breast cancer progression series has been used to study the effect of various drugs on the reversion of the breast cancer cells. Despite significant efforts to delineate key signaling events responsible for phenotypic reversion of the malignant HMT3522-T4-2 (T4-2) breast cells in this series, many questions remain. For example, what is involved in the phenotypic reversion of T4-2 cells at the systems level? In order to answer this question, we analyzed gene expression microarray data obtained from these cells using our recently developed tree-evolving network inference algorithm Treegl. We reconstructed cell-state-specific gene networks using Treegl. Our functional analysis results show that we can not only unravel cell-state specific information characteristic of non-malignant HMT3522-S1 (S1) and malignant T4-2 cells in the series, but can also provide insight into the T4-2 cells reverted by various agents. We found that the networks specific to various conditions of the T4-2 reverted cells are all suggestive of compensatory signaling effects, which, however, are mediated by different signaling pathways to antagonize different drug effects in the reverted cells. Our results demonstrate that the HMT3522 system when analyzed with Treegl may potentially become an effective tool for novel drug-target discovery and identification.
| A major challenge in systems biology is to uncover dynamic changes in cellular pathways that either respond to the changing microenvironment of cells, or drive cellular transformation during various biological processes such as cell cycle, differentiation, and development. These changes may involve rewiring of transcriptional regulatory circuitry or signal transduction pathways that control cellular behaviors. Such information is of particular importance for seeking a deep mechanistic understanding of cellular responses to drug treatments in various diseases, offering a more holistic view of both microscopic and macroscopic changes in the cellular functional machinery than has been available from traditional analyses which usually focus only on finding differential markers or close-up analysis of changes in a handful of molecules constituting parts of some selected pathways of interest.
Network-based differential analysis naturally requires the availability of multiple networks each in principle corresponding to a specific biological condition in question, that are then topologically rewired across conditions [1]. However, most existing computational techniques for reconstructing molecular networks based on high-throughput data cannot capture such dynamic aspects of the network topology; instead, they represent the networks as an invariant graph. For example, it is common to infer a single invariant gene network using microarray data obtained from samples collected over time or multiple conditions. More sophisticated methods such as a trace-back algorithm [1] and DREM [2], [3] do emphasize uncovering the dynamic changes of a network over time using time series data, but limitations in these algorithms allow only certain kinds of dynamic behaviors, such as “active path” [1] or bifurcating sequence of transcriptional activations [2]. Moreover, such methods are heuristic in nature and do not offer statistical guarantees on the asymptotic correctness of the inferred “transient” components in the network, making the results difficult to withstand the harsh standard on stability and robustness when sample quality and size become less ideal, as we face in the analysis to be conducted in this paper.
Indeed, a number of in-depth investigations of disease models have suggested that over the course of cellular transformation in response to microenvironmental changes due to disease progression or drug-induced reversion, there may exist multiple underlying “themes” that determine each molecule's function and relationship with other molecules [4], [5]. As a result, molecular networks at each cellular stage are context-dependent and can undergo systematic rewiring (Figure 1). For example, strong evidence of alterations of various pathways have been reported in the HMT3522 progression series of breast cells when malignant T4-2 cells were phenotypically reverted by various drugs, albeit only manifested by a small number of well-known signaling molecules as discussed below [6]–[8].
In this paper, we conduct an in-depth study of the structural changes in the gene regulatory networks underlying each cell state in both the non-reverted and the reverted HMT3522 progression series of breast cells. The HMT3522 cells have been shown to be an excellent model system for studying the roles of tissue architecture, microenvironment and signaling molecules involved in the nonmalignant and malignant growth and behaviors of breast cells, including the potential of various factors to cause phenotypic reversion of malignant cells to nonmalignant states. These cells originated from a nonmalignant human breast epithelial sample, HMT3522 [9], [10]. HMT3522-S1_LBNL (S1) cells are from early passages which are nonmalignant and dependent on exogenous epidermal growth factor (EGF) to grow. HMT3522-T4-2_LBNL (T4-2) cells were generated from S1 cells by a multi-step process: 238 passages in medium without EGF followed by transplantation into a mouse which generated a tumor, and T4-2 cells were isolated from the serial passage of this tumor; thus T4-2 cells are malignant and tumorigenic [10]. Interestingly, when cultured in three-dimensional (3D) laminin-rich extracellular matrices (lrECM), S1 cells form polarized acinus structures with a central lumen which resemble the terminal milk-secreting alveolar units in normal breasts [6], [11], whereas T4-2 cells form disorganized structures under the same conditions. Signaling molecules such as EGFR, β1-integrin, PI3K, and MAPK are overexpressed in T4-2 cells relative to their levels in S1. Crosstalk between these molecules plays pivotal roles in defining malignant behaviors of T4-2 cells, and downmodulation of them causes phenotypic reversion of T4-2 cells into growth-arrested, normal-looking cells (also called T4R cells later) which form structures resembling S1 acini but often without the lumen [8]. Other molecules, such as TACE or Rap1, have also been shown to be important for reversion of T4-2 cells [12], [13]. NFkappaB was identified as one of the transcriptional regulators involved in disorganization of T4-2 cells [14]. Despite significant efforts to delineate key signaling events responsible for phenotypic reversion of these malignant breast cells, many questions remain. For example, are T4-2 cells reverted by inhibitors of different molecules intrinsically the same? What is involved in the phenotypic reversion of T4-2 cells at the systems level other than a few genes directly related to the signaling molecules mentioned above?
One classical approach to address these questions is to identify genes differentially expressed between different cell states. While this can lead to some information about marginal effects of the genes in a particular stage of cancer progression or reversion, it cannot yield insight into the underlying regulatory mechanisms that govern interaction of genes with one another to carry out complex cellular processes. Instead, we propose a network-based differential analysis, by reverse engineering gene regulatory networks of various conditions of the breast cells to depict a fuller picture of regulatory mechanisms of the cells.
Many methods, as reviewed in [15], [16], have been proposed for reconstructing gene networks using gene expression microarray data. Most of them [17]–[19], however, rely on the statistical assumption that the samples in question were independent and identically distributed (i.i.d), and thus they either lead to estimation of a single network by pooling data from all the samples together, or lead to estimation of a network for each cell state independently. Since the breast cells in this study came from non-reverted HMT3522 cells as well as various conditions of the reverted cells, the regulatory mechanisms in different cell states can be significantly different; therefore, pooling data from different cell states together to estimate one single network does not reveal networks in their full depth. On the other hand, reconstructing a network specific to each cell state independently of the other ones can be statistically inaccurate due to a small sample size for each cell state. Recently, time-varying network detection methods have been proposed that allow information sharing across time and can thus recover a sequence of networks even with small sample sizes [20]–[23]. For example, Song et al. proposed a time-varying dynamic Bayesian network method to estimate a chain of evolving networks over time [22]. However, these methods estimate networks that evolve as a chain of networks over time, not as a series of networks shared by the tree-shaped phenotypic relationships as shown in Figure 1.
Due to the unique challenges we encountered to reconstruct networks that rewire over the tree-shaped phenotypic relationships, we recently proposed Treegl [24], a network reconstruction algorithm that can effectively and jointly recover rewiring regulatory networks present in multiple related cell states. Our approach can not only recover a distinct network for each cell state and reveal sharp differences among networks for different cell states, but also capture and leverage similarities of the networks in the cell states nearby in the phenotypic tree, thereby leading to more accurate estimation of gene interactions in small sample size scenarios. This new angle of estimating networks can reveal information that has not been mined in traditional analysis.
In this paper, we conduct an extensive network analysis of non-reverted HMT3522 cells (normal S1 and malignant T4-2 cells) as well as three different conditions of reverted T4-2 cells using gene expression microarray data obtained from these cells. It is notable that the same set of the gene expression data was first described and used in our previous work published in [24], however, our focus then was to report the novel methodology behind the Treegl algorithm, but not a thorough biological analysis of the HMT3522 series of cells from which the gene expression data was generated. In this current work, we focus more on the biological findings discovered by a more comprehensive network analysis of the data using Treegl and other bioinformatics tools, and aim to provide better biological insights and understandings of the various breast cell states in the HMT3522 series.
In particular, we estimated the network specific to each cell state using Treegl. Our results showed that while the S1-specific network contains predominantly nonmalignant pathways, the T4-specific network contains various cancer-related pathways, both findings consistent with biological evidence [4]. Furthermore, we found that the networks specific to various conditions of the T4-2 reverted cells are enriched with pathways suggestive of compensatory effects. In the T4-2 cells reverted by inhibition of either EGFR or β1-integrin, signaling pathways downstream of EGFR or β1-integrin, mainly via the PI3K-AKT-mTOR axis, are upregulated. Similarly, in the T4-2 cells reverted by either PI3K or MAPKK, we observed upregulation of the pathways both upstream and downstream of PI3K. These results are supported by clinical evidence showing patient resistance to the same anti-breast cancer drugs as we used in the study. Moreover, the compensatory signaling is also observed in the differential network of the T4-2 cells reverted by MMPIs, which involves genes participating in protein catabolic processes. Together, our findings suggest a common resistance mechanism employed by breast cancer cells to antagonize drug effects. Finally, in order to identify potential novel drug targets, we also investigated hubs (i.e., genes with high degrees, see details in Materials and Methods) in the differential networks of the breast cells, and characterized specifically three hubs (NEBL, HBEGF, and PAPD7) whose aberrant expression values are linked with the worst survival outcomes in the breast cancer patients to provide insight into their functional significance on the growth and development of breast cancer cells. Our data suggest that Treegl when applied to an effective disease model system, such as the HMT3522 cells, may potentially become an effective tool for elucidating disease mechanism and discovering novel drug targets, and thus help make personalized medicine possible.
We model a gene network as a Markov network [25], which is a graph where is the set of vertices (genes), and is the set of edges. Genes and do not have an edge between them if and only if they are conditionally independent given the values of all other genes. We contrast this with a correlation network (a common approach for modeling gene networks), in which and are connected if their marginal pairwise correlation is greater than a certain threshold. Correlation can be effective when analyzing a pair of genes in isolation. However, when studying the dependence between two genes in the context of other genes, correlation can confound direct/indirect relationships, thus producing undesirable results (Figure 2).
Specifically, we model the gene network for each cell state as a Gaussian Markov network. Gaussian distributions are special in that the inverse of the covariance matrix, called the precision matrix , completely encodes the structure of the Markov network. In particular, an edge exists in the Markov network if and only if the corresponding precision matrix element is non-zero.
Thus, under our model, the problem of learning the structure of a gene network reduces to estimating . This is straightforward when the number of genes is smaller than that of microarray samples : one can compute the sample covariance matrix and simply get its inverse. However, in high dimensional settings, when (as is our case), the sample covariance matrix is not invertible, and thus the problem becomes substantially more challenging. A statistically principled solution is the graphical lasso [26], which estimates the neighborhood of gene by using regularized regression. The regression coefficients can be interpreted as estimates of the precision matrix elements up to a proportionality constant (see Materials and Methods for more details). After the neighborhood of each gene is estimated independently, the results are combined to form a network.
However, our goal is not estimate a single network, but rather a collection of networks, one for each cell state. One simple solution is to estimate the network for each state independently of the others using the graphical lasso. However, this approach can result in poor quality of the networks due to the small sample size per cell state. To overcome this challenge, our recently proposed algorithm, Treegl [24], utilizes the following strategy. Similar to the graphical lasso [26], [27], Treegl estimates the neighborhood of each gene independently of those of other genes using regularized regression. However, unlike previous methods learning only a single network, Treegl simultaneously estimates neighborhoods of a gene in multiple networks each corresponding to a unique state in the phenotypic tree of the breast cells. It is unique in that Treegl makes use of a total variation regularizer based on the progression and reversal relationships between pairs of cell states in question to bias the amount of topological differences between networks underlying the related states, and to allow information regarding probabilistic independencies between genes to propagate across all states either directly or indirectly related by phenotypes. Such a strategy can lead to highly statistically confident estimation of a gene Markov network [28], even under small sample size scenarios. In the Materials and Methods section, we will offer details of a novel statistical regularization technique that makes this possible.
From the theoretical standpoint, Treegl is an instance of the general varying coefficient varying structure (VCVS) formalism analyzed in [29]. The VCVS model encodes changing structures of gene networks in different cell states as a function of regression coefficients in regularized regression problems. Estimating these regression coefficients, and thus the associated network structures then reduces to solving a convex optimization problem jointly over all cell states. The global optimal solution for such a problem can be found using standard convex solvers.
Moreover, the VCVS formalism allows one to theoretically examine and prove the statistical conditions under which changes in structures can be correctly estimated even in the high dimensional setting when . This distinguishes our approach from other methods [17], [30] that are highly non-convex and therefore rely on local search heuristics that only find local optima. These existing methods also do not offer sound statistical machinery for addressing difficult conditions such as nonstationarity (e.g., time-evolving) and high-dimensionality under small sample size as we encountered in our study.
Having a theoretical framework allows us to trade-off model expressivity and learning complexity in a principled manner. For example, if we allow a complex and arbitrary network model (i.e., a dense network), then there would be no guarantees on the quality of the recovered network structure in small-sample size scenarios. Instead, by enforcing a restricted model (i.e., a sparse network), its likelihood function is by definition convex and an optimal solution may be found. Thus, the quality of the resulting solution can be theoretically characterized, and it can be determined under which conditions the correct underlying parameters (network structure in this case) are discovered. Fortunately, sparsity is also biologically justifiable. For example, it is common to find a transcription factor regulating a limited number of genes under specific conditions [31].
We first evaluate Treegl's performance on simulated microarray data. In order to find out how effectively Treegl can detect change points of multiple networks while sharing information among related cell states at the same time, we design the simulated networks as illustrated in Figure S1. In particular, for each experiment, an artificial collection of 70 networks related by a tree-shaped lineage are generated, in which a sequence of 10 identical networks is connected to a network of different topology via a change/branching point (see details in Materials and Methods). Then, a small number of samples are generated from each of the networks. It is important to note that Treegl does not know a priori which of the networks are identical and which are different and thus has to discover this based on the samples.
In order to evaluate how well Treegl can recover the underlying network structures for the samples in the simulation data, we compare Treegl with the static method estimating a single network and the method estimating each network independently by plotting the precision-recall curves which show the recall for different values of precision based on the network estimated by the three methods. As illustrated in Figures 3 & S2, Treegl performs favorably to the other two methods. It should also be noted that compared to the static method which produces only one network, Treegl can produce different networks related by the tree lineage. The independent method also produces different networks but it performs poorly compared to Treegl.
In order to reverse engineer gene networks of the breast cells, we first used a phenotypic tree to represent the relationships of the cells (Figure 1). Due to the small sample size of the microarray data and imbalance of the sample abundance for different cell states (see Materials and Methods for details) — both of the problems pose significant challenges to network reconstruction — we used what is known of the interrelatedness of signaling pathways affecting phenotypic reversion to pool data derived from various samples in order to increase the power of the network inference. In particular, since EGFR and β1-integrin are cross-modulated in the HMT3522 cells [8], we assumed that the gene networks in the T4-2 cells reverted by inhibiting either of the molecules share reasonable similarity, and hence we grouped data from these reverted cells together to form the EGFR/ITGB1-T4R group. Likewise, we grouped together data from T4-2 cells reverted by either a PI3K inhibitor, a MAPK inhibitor, or a dominant-negative Rap1 to form the PI3K/MAPKK-T4R group, because PI3K and MAPK are also cross-modulated in the breast cells and Rap1 signals through PI3K. A tree diagram illustrating the relationships of the cells are shown in Figure 1. Based on these relationships, we reverse engineered gene networks for the HMT3522 cells using Treegl.
It is important to point out that it would be nearly statistically impossible to reconstruct a cell-state-specific gene network using existing methodology based on three microarray samples per group as in the dataset we used here. Note that we will also refer to different groups of the cells in the phenotypic tree in Figure 1 as different cell conditions or states.
The reconstructed networks for non-reverted and various conditions of the reverted HMT3522 cells are illustrated in Figure S3. They share many topological similarities as well as differences. About 60% of the network edges are common to all cell conditions represented in the phenotypic tree diagram, consistent with underlying biological similarities shared between them. In the following, we concentrate on only the edges specific to each cell state, which we call the differential network for each cell state.
In order to validate our network reconstruction results, we used an external microarray dataset. Since previous evidence suggests that there is a high correlation between the degrees and essentiality of genes in yeast networks, we hypothesize that i) if a gene is indeed involved in the networks of the breast cells, its abnormal expression would have higher impact on the survival of breast cancer patients than those genes which are not in the networks; and ii) if a gene is a hub (with a high degree) in the differential networks of breast cells, its abnormal expression would have higher impact on the survival of breast cancer patients than those with low degrees. We therefore investigated the effect of the aberrant expression values of the hubs in the differential networks of the HMT3522 cells on the survival of human breast cancer patients. The external dataset we used is a gene expression microarray dataset obtained from 295 primary human breast tumors [39], employed previously to identify gene expression signatures which may be predictive of patient clinical outcomes. The same dataset was also used previously [12] to demonstrate the impact of abnormal expression of TACE, TGFA, and AREG, which were shown to play important roles in the HMT3522 series of the breast cancer cells grown in the 3D culture, on the survival of the same cohort of the breast cancer patients.
In order to define hubs, we examined the distribution of genes with varying degrees in the differential networks. Figure S7 shows that while a majority of the genes have degrees of 1–3, much fewer genes have a degree greater than 5, and therefore, we designated hubs to be genes with degree greater than 5 in the differential networks. Note that the same criterion was also used to define hubs in [40].
Indeed, we found that 18% of the genes in the networks of the five breast cell states affect patient survival significantly, whereas that only 6% of the genes which are not in the breast cell networks but are present in the external dataset affect patient survival significantly. Our results also showed that 22% of the hubs in the differential networks of the breast cells affect patient survival significantly. GO analysis revealed that these significant hubs are enriched with genes involved in regulation of cell migration, mobility, growth, and proliferation (see Table S4 for a list of the hubs), and all of these biological activities are known to be essential for cancer cell development and progression. Similarly, 23% of the genes with degree >10 and also with degree >20 affect patient survival significantly. However, for genes with degrees equal to one to five in the differential networks, the percentage of them affecting patient survival drops to 17%. Together, these results indicate that i) genes in the breast cell networks indeed have higher tendency of influencing patient survival significantly than those not in the networks, and also that ii) hubs in the differential networks are more likely to affect patient survival significantly than those with low degrees, suggesting the structures of the reconstructed networks are valid.
In order to identify potential novel drug targets, we examined three hubs, NEBL, HBEGF, and PAPD7, whose extreme (i.e., either lower or higher) expression values are correlated with the lowest 15-year patient survival rates (35%, 30% and 34%, respectively) and also with low 10-year survival rates (60%, 42% and 56%, respectively) in the examined dataset (Figure 6). Previous evidence has shown that abnormal expression of TACE, TGFA, and AREG are associated with 62%, 61% and 54% of the 10-year survival rates respectively, and associated with 57%, 50% and 54% of the 15-year survival rates respectively, in the same cohort of the patients [12]. Our results, therefore, suggest that NEBL, HBEGF, and PAPD7, similar to TACE, TGFA, and AREG, also play important roles in breast cells.
Since little is known about NEBL, HBEGF, and PAPD7, we examined their neighbors in the corresponding differential networks (which we call neighborhood analysis) to shed light on their functions in breast cancer.
Figure 7A shows the NEBL subnetwork in the S1 differential network. NEBL encodes a member of the nebulin family of proteins, which bind actin and are components of focal adhesion complex. Our data showed that decreased expression of NEBL is associated with 36% of 15-year survival rate for breast cancer, suggesting a protective role of this protein when overexpressed. Genes interacting with NEBL in the NEBL subnetwork are mainly involved in energy production by oxidation of organic compounds, actin and cytoskeletal protein binding, regulation of growth, and anatomical structure morphogenesis, all of which are consistent with the biological evidence suggesting involvement of nebulin in migratory cells [41].
Figure 7B shows the HBEGF subnetwork in the T4-2 differential network. HBEGF encodes a heparin-binding EGF-like growth factor, which is an EGFR ligand [42]. We found that higher expression of HBEGF is correlated with 34% of 15-year survival rate (Figure 6B), and that the neighbors of HBEGF in the HBEGF subnetwork are involved in diverse biological processes and functions, such as glycolysis, apoptosis, participating in laminin-5 complex, notch signaling pathway, and angiogenesis, all of which are in line with previous findings showing the high expression level of HBEGF is positively related to the aggressiveness of the breast tumors [43] and that HBEGF plays key roles in tumorigenicity and invasiveness of ovarian cancer [44].
Finally, we examined the PAPD7 subnetwork in the MMP-T4R differential network (Figure 7C). PAPD7 encodes DNA polymerase sigma. Our data indicated that overexpression of PAPD7 is associated with 30% of 15-year survival rate and 8.5 years of median survival time (Figure 6C), both of which are the worst patient outcomes correlated with all the hubs in the differential networks, suggesting that PAPD7 plays significant roles in breast cancer cells. Previous evidence showed that a homolog of PAPD7 in Saccharomyces cerevisiae Trf4 plays a key role in RNA quality control by degrading aberrant or unwanted RNAs in the nucleus [45]. Interestingly, our functional analysis revealed that genes interacting with PAPD7 in the MMP-T4R differential network are significantly enriched with those involved in RNA degradation and metabolic process, as well as regulation of Ras and small GTPase mediated signal transduction, and phosphatidylinositol signaling system (Figure 7C), which implicates that similar to Trf4, PAPD7 also participates in crucial functions such as RNA quality control in human cells.
Taken together, these findings suggest that the three hubs, NEBL, HBEGF, and PAPD7, in the differential networks play important roles in growth and development of breast cancer cells, and may thus become potential novel therapeutic targets. More important, these results also suggest that our reconstructed networks can not only reveal genes which have high impact on patient survival in specific cell conditions, but also can provide insight into their functions by neighborhood analysis, and thus facilitate personalized drug target discovery and identification, and help make personalized breast cancer therapy possible.
The problem of estimating rewiring networks simultaneously from multiple cell states in the phenotypic tree, as solved by Treegl, is fundamentally different from either estimating a single “average” network from the samples pooled from all states and subsequently “trace-out” active subnetworks corresponding to each state [1], or estimating multiple networks independently. The latter strategies are common practices in the system biology community, which either directly or indirectly assume the network in question is static, and samples of the nodal states in the phenotypic tree are i.i.d. across (when pooled) or within cell states. In reality, such an assumption is biologically invalid as well as statistically unsubstantiated. The Treegl algorithm elegantly couples all the inference problems pertained to each network in the tree of multiple conditions, and achieves a globally optimal and statistically well behaving solution based on a principled VCVS model and a convex optimization formulation.
In our analysis of the HMT3522 breast cancer cell lines, we reverse engineered 5 different gene networks specific to each cell state represented in the phenotypic tree. The S1 differential network contains genes predominantly involved in normal cellular activities, while the T4-2 differential network is enriched with pathways playing active roles in cancers. Interestingly, compensatory signaling appears to be a recurring theme of the T4-2 cells phenotypically reverted by different agents. In the T4-2 cells reverted by inhibition of either EGFR or β1-integrin (i.e., the EGFR/ITGB1-T4R group), despite the absence of the ErbB pathway, signaling events downstream of EGFR or β1-integrin, mainly via the PI3K-AKT-mTOR axis, seem to be upregulated. These results are supported by clinical evidence showing that some breast cancer patients exhibit drug resistance after being treated with EGFR inhibitors. Similarly, in the PI3K/MAPKK-T4R cells, their differential network is enriched with genes closely connected to PI3K, suggesting they are upmodulated to make up for the loss of PI3K signaling, also agreeing with clinical findings showing patient resistance to PI3K inhibitors. Likewise, the compensatory effect is observed in the differential network of the T4-2 cells reverted by MMPIs, which involves genes participating in protein catabolic processes presumably to make up for the loss of the MMP function. The effect of MMPIs for treating breast cancer patients was disappointing in clinical trials, but no conclusive evidence for ineffectiveness has been put forward [38]. Our results suggest that the failure of treating breast cancer patients by MMPIs involves upmodulation of the catabolic processes in the treated patients due to compensatory effect. Together, these results suggest despite phenotypic similarities, T4-2 cells reverted by various drugs are intrinsically different from one another; similar compensatory mechanisms, however, appear to be utilized by the T4-2 cells to antagonize effects of the different drugs.
In order to compare our network-based approach with traditional statistical test-based approach, we also analyzed the gene expression data using ANOVA, and identified 1432 genes significantly differentially expressed (FDR p-value<0.05) across different cell states; then we used pairwise t-tests to further identify significant differences between cell states. We found that due to small sample size problems, these traditional approaches are too stringent to reveal interesting signals. For example, we examined the genes differentially expressed between the T4-2 cells reverted by MMP inhibitors (MMP-T4R) and other cell states, in particularly between MMP-T4R and S1, as well as between MMP-T4R and T4. Our results show that there are 473 genes significantly differentially expressed in MMP-T4R, comparing to S1, and the only two GO functional groups significantly enriched (FDR p-value<0.05) among these genes are “mitotic cell cycle” and “sterol biosynthesis process.” Comparing to T4, there are 375 genes differentially expressed in MMP-T4R, and there are no GO groups significantly enriched among these genes.
Moreover, we examined genes in the differential network of MMP-T4R which are involved in some of the significantly enriched GO groups, e.g., “proteasome complex” and “cellular catabolic process”, both of which suggest compensatory signaling in the MMP-T4R cells. We found that among 12 genes in the differential network of MMP-T4R (“PSME3, PSMA4, PSMB8, PSMD10, PSMA3, PSMB9, PSME2, PSMD7, PSMA6, PSMC2, PSMA2, PSMD6”) which are involved in “proteasome complex”, only two of them (PSMA3, PSMB9) significantly differ between MMP-T4R and S1 as identified by ANOVA, and two (PSMC2, PSMB9) significantly differ between MMP-T4R and T4. Likewise, among 33 genes in the differential network of MMP-T4R which are involved in “cellular catabolic process”, only 5 genes (“PSMB9, ANAPC5, USP18, IDH1, PSMA3”) significantly differ between MMP-T4R and S1 as identified by ANOVA, and 3 genes (“PSMB9, USP18, IDH1”) differ between MMP-T4R and T4.
Furthermore, we looked into the 22 hubs in the differential networks which significantly affect patient survival, and found that only 8 (36%) of them are differentially expressed across 5 cell states as identified by ANOVA and a majority (64%) of them are not differentially expressed. Similarly, among 99 hubs in the differential networks, 43% are differentially expressed, while 57% are not. These results suggest that under small-sample-size scenarios, traditional statistical tests are too stringent to capture interesting signals, while our network-based differential analysis can leverage on similarities among different samples while revealing key differences which set them apart.
In order to identify potential novel drug targets, we also investigated hubs in the breast cells whose aberrant expression values are significantly associated with survival outcomes of breast cancer patients. We found that genes in the networks of the breast cells have 2 times higher tendency than those not in the networks to affect patient survival in the cohort we studied. Also, hubs in the breast networks appear more likely to influence patient survival than genes with low degrees. Indeed, the proportion of the hubs with high degrees which are significant survival genes (22% for hubs with degree >5, and 23% for hubs with degree >10 and also for those with degree >20) is not much higher than that (17%) of the genes with low degrees. The reasons for this can be explained as follows.
When previous evidence suggests that in yeast networks, a gene with a higher degree is more likely to be an essential gene [46], [47], an essential gene is defined as “the cell is unviable when the gene is knocked off” [47]. However, it is difficult to know/determine which genes are essential in humans. Nevertheless, in light of the definition of ‘essentiality’ in yeast, we think it is plausible to believe that the actual percentage of the hubs (with degree >5) in the differential networks of the breast cells, which can affect patients significantly, is 22%+x%, rather than 22%, and the reasons why we cannot see the phenotypic effect of the x% of the hubs on patient survival may include: i) these hubs are so essential to humans that any abnormality would lead to death, even before breast tumors were formed or diagnosed; and/or ii) there are some redundant genes which can make up for the loss/gain of functions of these essential hubs.
Despite the fact that our results suggest that the genes in the breast cell networks are more likely to affect patient survival than those which are not, and also that hubs in the differential networks tend to affect patient survival more than genes with low degrees, our data show that the distributions of the patient survival rates (5-year, 10-year or 15-years) associated with these different groups of genes are not significantly different, suggesting that the patient survival rates are not only affected by degrees of genes in the breast cell networks, but also affected by the functionalities of the genes.
We have also characterized the three hubs in the cell-state-specific differential networks whose aberrant expression values are linked with the worst survival outcomes in the breast cancer patients: NEBL in S1 cells, HBEGF in T4-2 cells, and PAPD7 in the MMP-T4R group of the reverted cells. Our results are not only in line with existing information known about these genes, but also provide insight into their functional significance on the growth and development of breast cancer cells. These hubs are promising to serve as potential drug targets for personalized breast cancer therapy.
The major challenge of this work is the small sample size of the microarray data we have used for the network inference. The data was from 15 microarrays in total, and the T4-2 cells reverted by different agents had to be pooled together in order to increase the power of the network inference. Even though the sample grouping strategy is biologically justifiable (see details in the Results section), our abilities to find differences between T4-2 cells reverted by different agents are limited due to mixed samples in the EGFR/ITGB1-T4R and PI3K/MAPKK-T4R groups of the reversion cells. For example, it is difficult to dissect which specific pathways are abnormally regulated (compared to S1 cells) in which reversion cell state: T4-2 reverted by EGFR inhibitors or by ITGB1 inhibitors. Likewise, it is also difficult to reveal differences in the T4-2 cells reverted by different agents in the PI3K/MAPKK-T4R group. Moreover, mixed samples can reduce power to detect interesting signals in the data. Despite suggesting compensatory events in the reversion cells, the enriched pathways in the EGFR/ITGB1-T4R and the PI3K/MAPKK-T4R cells are not significant (unadjusted p-values<0.05, but FDR p-values>0.1). However, since our data agree well with clinical evidence, they may facilitate clinicians to identify specific molecules which lead to resistance in the drug-treated breast cancer patients. In order to overcome the limitations of the mixed samples, we also focus on finding similarities of the different T4-2 reversion cells. Our results show that we were able to discover a significant amount of information that agrees with the facts and evidence previously known in the literature. Moreover, we were also able to delineate a mechanistic framework at the systems level that can facilitate further elucidation of the mechanisms underlying different states of the breast cells in the progression and reversion model. Experimental validations are nevertheless needed to further verify our findings.
In summary, this work demonstrates our recently developed Treegl algorithm can not only provide a holistic view (i.e., the so-called “pan-cell-state” view that echoes the emerging “pan-cancer” or “pan-disease” approach nowadays to biomedical analysis) of the progression and reversion model of the breast cells worthy of further exploration, but also allows us to gain a deeper and systems-level understanding about the behaviors of nonmalignant and malignant breast cells, which may help novel drug target discovery and make personalized breast cancer therapy possible.
HMT3522 S1 and T4-2 cells were grown in 3D lrECM as previously described [6], [48]. The T4-2 cells were reverted using each of the following reverting agents as described previously: an EGFR inhibitor Tyrphostin AG 1478 and a human EGFR-blocking monoclonal antibody mAb225 [7], a β1-integrin inhibitor AIIB2 [6], a MAPK inhibitor PD98059 [7], a PI3K inhibitor LY294002 [8], dominant-negative Rap1 [13]; an MMP inhibitor GM6001 [49], and a broad-range inhibitor of MMPs and ADAMs, TNF protease inhibitor–2 (TAPI-2) [12].
S1, T4-2 and reverted T4-2 cells were isolated from 3D cultures with PBS/EDTA as previously described [50]. Total cellular RNA was extracted using RNeasy Mini Kit with on column DNase digestion (Qiagen). RNA was quantified by measuring optical density at A260 and quality was verified by agarose gel electrophoresis. Purified total cellular RNA was biotin labeled and hybridized to the Affymetrix GeneChip human genome HG-U133A arrays as previously described [51].
Gene expression microarray data was obtained from 15 total RNA samples prepared from the HMT3522 breast cells grown in 3D lrECM and treated with various reverting agents or vehicle controls as mentioned above. Unfortunately, T4-2 cells reverted by some agents have only one sample per each reversion cell state. Even though our method, Treegl, is designed for small sample size scenarios, having only one sample per state is not enough for network inference — as it is known that it takes at least two samples to measure even a simple quantity like correlation. Thus, in order to increase the power of the network inference, we grouped the arrays into the following five categories with each having 3 samples: (i) S1 cells (3 arrays); (ii) T4-2 cells (3 arrays); (iii) the EGFR/ITGB1-T4R group, which contains two arrays of the T4-2 cells reverted by the EGFR inhibitor Tyrphostin AG 1478 and the human EGFR-blocking monoclonal antibody mAb225, respectively, and one array of the T4-2 cells reverted by a β1-integrin inhibitor AIIB2; vi) the PI3K/MAPKK-T4R group, which contains one array of the T4-2 cells reverted by a MAPK inhibitor PD98059, one array of the T4-2 cells reverted by a PI3K inhibitor LY294002, and one array of the T4-2 cells reverted by dominant-negative Rap1; and (v) the MMP-T4R group, which contains two arrays of the T4-2 cells reverted by an MMP inhibitor GM6001, and one array of the T4-2 cells reverted by a broad-range inhibitor of MMPs and ADAMs, TAPI-2. The biological justification on this grouping strategy is provided in the Results section.
In order to identify networks specific to each state of the breast cells, we utilized a phenotypic tree model to represent the relationships of different states of the breast cells (Figure 1). In particular, since the HMT3522 series were originated from S1 cells, we positioned S1 cells as the root of the phenotypic tree. Then we made S1 cells the parent of T4-2 cells, since T4-2 cells were derived from S1 cells. Finally, we made T4-2 cells the parent of the three conditions of the T4-2 cells reverted by various agents (the EGFR/ITGB1-T4R group, the PI3K/MAPKK-T4R group, and the MMP-T4R group).
Raw gene expression data was preprocessed using the following procedure. The data from the perfect match (PM) probes on the Affymetrix arrays was first log2-transformed, and normalized using the CyclicLoess normalization method to minimize unwanted noise in the data [52]. We did not use the difference between the values from the PM probes and those from the mismatch (MM) probes (i.e., PM – MM) to represent values of the probes for each gene, because it has been shown that the MM values can pick up both non-specific and specific signal of the probes, and thus PM-MM values may attenuate real signal values from the PM probes [53]. The normalized PM values were then summarized into gene expression values using the median polish technique [54]. For some transcripts, multiple probes on an array target the same transcript; the values of the probes were combined by taking the median of the values to represent the expression level of the corresponding transcript. There are 12,977 unique genes on the arrays. The complete microarray dataset is available at the Gene Expression Omnibus (GEO) database (http://www.ncbi.nlm.nih.gov/geo - GSE42125).
To reduce biological noise in the data, we removed genes whose expression values showed low variability across different groups of the breast cells. In particular, for each gene, we calculated its median expression values in five different groups of the breast cell states. If the fold change value of a gene between any of the two groups was larger than 1.3, we included the gene for the downstream analysis. The reasons why we used the fold change of 1.3 as the threshold value to filter genes are as follows: i) based on our previous experience with human lung disease studies [55], we found that a fold change of 1.2–1.3 is enough to elicit significant biological changes in humans; and ii) When using the threshold value of 1.3, 5,440 genes passed the filter, which we consider is a reasonable number for the downstream network analysis by Treegl. Then we applied Treegl to reconstruct gene networks in the five breast cell states using expression values of the qualified genes.
We now give a mathematical formulation of representing the gene networks in order to introduce our algorithm. Consider the problem of modeling different gene networks, each corresponding to a unique cell state . Each cell state has i.i.d. microarray replicates. All the arrays in the dataset have the same set of genes. In our case, we have 5 different conditions of the breast cells in the phenotypic tree: S1, T4, EGFR/ITGB1-T4R, PI3K/MAPKK-T4R, and MMP-T4R.
As commonly done, we model each gene network as a weighted undirected graph, where the vertices represent genes and the edges represent interactions in the network. Let represent a network in cell state , where denotes the set of genes that is fixed for all cell states and denotes the set of edges specific to the network for cell state . Let where be the vector of expression values of genes on array in cell state . We assume , i.e. that the vector of expression values follows a multivariate Gaussian distribution.
We are interested in reconstructing a set of networks that are related by the phenotypic tree as shown in Figure 1. For each cell state , let be the parent of the cell state in the tree; alternatively, we can also view as a descendant of . In our case, , , . We generally let correspond to S1, correspond to T4, and correspond to the EGFR/ITGB1-T4R group, the PI3K/MAPKK-T4R group, and the MMP-T4R group, respectively. Thus, in our formulation, recovering the structures of the gene regulatory networks in different breast cell states corresponds to estimating the network structure for each cell state.
Consider first estimating the edge set of a single network from the data. As described in the Results section, we model the gene network for each cell state as a Gaussian Markov network. Therefore the inverse of the covariance matrix, called the precision matrix, , completely encodes the structure of the Markov network. An edge exists in the Markov network if and only if the corresponding precision matrix element is non-zero.
A Gaussian Markov network, encoded via the precision matrix, allows us to model more sophisticated dependencies than a correlation network, which is encoded by the covariance matrix. In particular, the precision matrix elements are related to the partial correlation between and (denoted as , see below for details). Formally, partial correlation between a pair of random variables given a set of controlling variables is defined as follows. Let and denote the residuals from performing linear regression of with and with , respectively. The partial correlation is then defined as the correlation between and . Unlike correlation, which simply measures the association between a pair of random variables, partial correlation intuitively measures the association between a pair of variables with a set of controlling variables removed (where here is all the other genes). The partial correlation, due to its close relationship with the elements of the precision matrix, makes the latter much more suitable than the covariance matrix for distinguishing between indirect and direct relationships as shown in Figure 2.
Since our goal is to learn the structure of the Markov network, we are only concerned with estimating which precision matrix elements are zero and which are not (rather than the exact precision matrix values). Therefore it suffices to estimate the partial correlation coefficients, which are proportional to the precision matrix elements by the equation . An estimation algorithm can be constructed by exploiting the relationships between the partial correlation coefficients and a linear regression model [56]. Specifically, consider a linear regression model where gene is treated as the response variable and all the other genes are covariates. The regression coefficient of covariate is then proportional to the partial correlation .
The above facts enable us to use regression-based methods to estimate the elements of the precision matrix (up to a proportionality constant), and thus the underlying network structure. In particular, our method is based on an efficient neighborhood selection algorithm [26] based on -norm regularized regression that works well in practice and has strong theoretical guarantees. In this approach, the neighborhood of each gene (the set of edges incident to ) is estimated independently of the neighborhoods of other genes. After estimating each neighborhood, the results are then combined to produce the estimated network. In every neighborhood estimation step, gene is treated as a response variable, and all the other genes are the covariates. An penalized linear regression (also known as the lasso [57]) is performed to give an estimate of the regression coefficients .
Then by leveraging the relationship between the regression coefficients and the partial correlation, the estimated gene network is constructed by adding an edge to if either or is non-zero (max-symmetrization).
Obviously, networks for each cell state can be estimated independently by using the method described above. However, this can lead to very poor estimates of the edge sets, because in common laboratory settings only a few replicates of gene expression data can be obtained. To overcome this limitation, we estimated the networks by assuming that the networks share similarities due to their relationships as suggested by the phenotypic tree, but also have some sharp differences. For example, for S1 and T4-2 cells, we assume they have considerable differences as the former is nonmalignant while the latter is tumorigenic; however, since T4-2 were derived from S1, we also assume that these cells share substantial similarity. This is the motivation behind Treegl, the algorithm that we first presented in [24].
Treegl is unique in that it makes use of a total variation regularizer, which allows information to be shared across different cell states, and thus encourages the resulting networks to be similar while allowing differences in the networks to be revealed. More specifically, Treegl adopts the idea of neighborhood selection and additionally penalizes the differences between the neighborhoods of adjacent states in the breast cell phenotypic tree. This makes Treegl more effective in small-sample-size settings than existing approaches since it can estimate a collection of networks more robustly by leveraging the similarities among them.
In summary, Treegl proposes the following optimization problem for jointly recovering the neighborhoods of genes for all the cell states in the phenotypic tree of the breast cells:
In the equation above, the first term corresponds to the residual sum of squares as in normal linear regression. indicates the vector of the expression values of all genes except , and similarly, . is defined as . The second term (corresponding to ) is a penalty on the edge weights (similar to [26]), where denotes the norm of vector , which is the sum of the absolute values of the components of . This penalty promotes sparsity in the edge weights by enforcing most of the edge weights to be zero. The assumption of sparsity is biologically justifiable. For example, it is common to find a transcription factor regulating a limited number of genes under specific conditions [31]. The details of the regularization can be seen in [57]. The third term (also called the total variation penalty) associated with enforces sparsity of differences between S1 and T4-2 as well as between T4-2 and each of the T4R groups (as illustrated in the tree structure in Figure 1), but not between T4Rs and S1. This encourages many (but not all) of the elements of to be identical to those of . The fourth term (also associated with ) additionally penalizes the differences between each of the T4R groups and S1, while allowing for sharp differences to be revealed between the two groups. Note that if the fourth term was not used, the T4R networks would be biased to be more similar to the T4-2 network than the S1 network. This would be undesirable, since it is unknown a priori whether each of the T4R states are more similar to T4-2 or S1 cells.
and are regularization parameters that control the amount of penalization (see below for details on how we selected these parameters). Because the minimization problem is convex, we solved it using the CVX solver [58], as we described in [24].
In this work, we focus on genes linked by positive edges, because interaction of these genes is easier to interpret. For example, suppose genes X and Y are linked by positive edges, and genes Y and Z are also linked by positive edges. Intuitively, this suggests that genes X and Y are regulated in the same direction, that is, when gene X is up (or down)-regulated, gene Y is also up (or down)-regulated. Same is true for genes Y and Z which are also regulated in the same direction. As a result, we can also decide that genes X and Z are regulated in the same direction.
On the other hand, interpreting interaction of genes linked by negative edges is more complicated. For example, suppose genes A and B are linked by a negative edge and genes B and C also linked by a negative edge. This intuitively means that A and B are regulated in the opposite direction, and that B and C are also regulated in the opposite direction; it is, however, unclear what is the relationship of A and C, which may be regulated in either the same or the opposite direction.
Due to the reasons stated above, we chose to limit the scope of this work by focusing only on the positive edges to simplify our interpretation of the results.
Choosing the regularization parameters and is a challenging problem in high dimensional statistics. Kolar and Xing proposed to use the Bayesian information criterion (BIC) score to select these parameters [59]. This approach can be useful in low dimensional settings; however, it does not perform well in high dimensional settings [60]. In this work, since we have a good knowledge of the biological properties of the S1 and T4-2 cells in the HTM3522 system, we employed a knowledge-based approach to tune and , namely, we tuned these parameters based on our prior knowledge about S1 and T4-2 cells, which turned out to be highly effective in the high dimensional, small sample size setting as we encountered in this work. Specifically, we first varied and in the set {4, 4.5, 5, 5.5, 6, 6.5, 7} and the set {0.5, 1, 1.5, 2, 2.5}, respectively, and generated cell-state-specific networks for each possible pair of and . These sets of and were chosen because the networks can be generated with reasonable sparsity. Then we examined the biological pathways significantly enriched in the differential network of the S1 and T4-2 cells, and found that when and , almost all of the enriched pathways in the T4-2 network make the best biological sense in that they are either well described in previous studies or are known pathways active in cancers. Since we used S1 and T4-2 cells to help tune the regularization parameters, we present and discuss mainly biological findings we made from the networks of the T4-2 reversion cells to avoid circular reasoning.
We describe below how the networks in our simulation experiments were generated. Consider the following artificial collection of 70 networks, related by a tree:
Treegl does not know a priori which networks are identical and which are not. The number () of samples are then generated for each network under the Gaussian Graphical Model assumption. We vary the values of in the simulation experiments, and the results presented in Figures 3 are based on the values indicated in the figure. In each scenario, the number of edges is twice as much as the number of nodes.
To evaluate Treegl, we conduct a total of 10 simulation experiments, and plot the precision-recall curves showing the recall for different values of precision based on the networks reconstructed by Treegl. The error bars in the curves indicate the first and third quartiles of the results. Details on how we generated the precision-recall curves and selected the regularization parameters can be found in [24].
To identify pathways significantly enriched in the gene networks of the 5 breast cell states estimated by Treegl, we performed pathway analysis on the list of the genes involved in each network using the Category Bioconductor package with minor modification (http://www.bioconductor.org). The Category package uses hypergeometric tests to assess overrepresentation of the KEGG pathways among genes of interest. A list of 12,977 unique genes on the Affymetrix GeneChip Human Genome U133A was used as the reference gene list for the pathway analysis. A pathway is considered to be significant if p<0.1 with the FDR controlling procedure of Benjamini & Hochberg [61].
To find out genes significantly associated with certain diseases in the differential networks of the breast cell states, we performed pathway analysis as described above. For each differential network, pathways related to diseases and significantly enriched in the network were singled out; genes in the network that are involved in the enriched disease-related pathways were reported as the genes significantly associated with the diseases in the network.
To identify functional groups of genes significantly enriched in the gene networks of the breast cells estimated by Treegl, we performed GO analysis on the list of the genes involved in each network using the GOstat program [62]. The GOstat program finds the enriched functional groups using Fisher's exact tests. The GOstat program was also used to identify functional groups of genes enriched among the neighborhoods (or the subnetworks) of the hubs significantly affecting patient survival. A functional group is considered to be significant if p<0.05 with the FDR controlling procedure of Benjamini & Hochberg. A list of 12,977 unique genes on the Affymetrix GeneChip Human Genome U133A was used as the reference gene list for the GOstat program.
We define hubs as genes with positive degree greater than 5 in the differential networks of the breast cell states. Survival analysis was performed using microarray expression values of the hubs extracted from a gene expression microarray data set obtained from 295 primary human breast tumors [39]. For each hub, its expression values across all patients were divided into three groups: lower quartile, interquartile, and upper quartile groups. Kaplan–Meier curves were used to estimate the association of expression values of the hubs in the three groups with patient survival. The log-rank test was used to calculate p-values of the survival curves. A hub was considered as significant if the p value of its associated survival curve <0.05 after controlling for multiple testing using the Bonferroni procedure.
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10.1371/journal.pntd.0007080 | Detection of Zika virus in mouse mammary gland and breast milk | Clinical reports of Zika Virus (ZIKV) RNA detection in breast milk have been described, but evidence conflicts as to whether this RNA represents infectious virus. We infected post-parturient AG129 murine dams deficient in type I and II interferon receptors with ZIKV. ZIKV RNA was detected in pup stomach milk clots (SMC) as early as 1 day post maternal infection (dpi) and persisted as late as 7 dpi. In mammary tissues, ZIKV replication was demonstrated by immunohistochemistry in multiple cell types including cells morphologically consistent with myoepithelial cells. No mastitis was seen histopathologically. In the SMC and tissues of the nursing pups, no infectious virus was detected via focus forming assay. However, serial passages of fresh milk supernatant yielded infectious virus, and immunohistochemistry showed ZIKV replication protein associated with degraded cells in SMC. These results suggest that breast milk may contain infectious ZIKV. However, breast milk transmission (BMT) does not occur in this mouse strain that is highly sensitive to ZIKV infection. These results suggest a low risk for breast milk transmission of ZIKV, and provide a platform for investigating ZIKV entry into milk and mechanisms which may prevent or permit BMT.
| Can Zika virus be transmitted from nursing mothers to their children via breast milk? Only 4 years have passed since the Zika virus outbreak in Brazil, and much remains to be understood about the transmission and health consequences of Zika infection. To date, some case reports have detected Zika virus RNA in the breast milk of infected mothers, but the presence of a virus’ RNA does not mean that intact virus is present. Milk also contains many natural defense components against infection, so even intact virus carried in breast milk may not be infectious to a child. Here we used a mouse that is genetically engineered to be highly susceptible to Zika infection, and tested whether 1) we could find intact virus in mouse breast milk and 2) infection was passed from mother to pups. We found very low levels of intact Zika virus in mouse breast milk, and found none of the nursing pups to be infected. The model of Zika virus breast milk infection developed in this study establishes a system by which we may learn whether Zika RNA in human breast milk is truly infectious to children, and how Zika virus may enter the milk.
| Zika virus (ZIKV) is an enveloped virus with a positive-sense, single-stranded RNA genome [1]. For over half a century, this flavivirus was regarded as an arbovirus leading to self-limiting, febrile disease. However, confirmation of or association with new syndromes, including teratogenesis, adult Guillain Barre Syndrome, genital persistence, and sexual transmission, have begun to emerge since the 2015–2016 Brazil ZIKV outbreak. Due to devastating outcomes associated with infection of the developing brain and ZIKV’s apparent ability to cross intact mucosae [2–4], a key question arises: can ZIKV be transmitted by breast milk?
Reports of ZIKV RNA detection in breast milk are accumulating [5–10]. Although no epidemiologic data regarding ZIKV in lactating women are currently available, ZIKV RNA has been reported in breast milk from 3 [5, 9] to 33 [6] days after maternal onset of fever. Reports conflict as to whether isolated ZIKV RNA represents infectious virus [7]. In one study, cytopathic effect (CPE) could not be demonstrated in cells cultured with either of the breast milk samples from two mothers who nursed infected infants [9]. In two separate reports, CPE was seen upon culturing of cells with breast milk of mothers with uninfected nursing children [8, 10]. In another study, CPE was demonstrated in cells cultured with milk from a ZIKV-infected mother, and the nursing child was infected with an isolate with ZIKV genome identity of more than 99% between the infected mother and child [5].
Historically, the epidemiology and mechanisms of flavivirus breast milk transmission (BMT) have posed somewhat of a scientific enigma. Hepatitis C virus or Japanese encephalitis virus BMT has not been documented, whereas West Nile virus [11] and yellow fever vaccine strain [12] BMT have been reported. Dengue virus (DENV) infects approximately 390 million people annually and DENV RNA has been detected in breast milk [13], but reports of BMT are rare. Furthermore, in the 1970s, two studies also demonstrated that DENV and Japanese encephalitis virus were neutralized by the lipid fraction of breast milk [14, 15].
In this study, we explored a mouse model for BMT of ZIKV using AG129 mice that are deficient in both type I and II interferon (IFN) receptors, and represent a highly sensitive animal model of ZIKV challenge [16]. Following infection of AG129 dams with ZIKV on the date of parturition, viral RNA was detected in pup stomach milk clots (SMC) as early as 1 day post maternal infection (dpi) and as late as 7 dpi. In contrast, ZIKV NS2B immunofluorescent immunohistochemistry (IHC) and examination for CPE of inoculated Vero cells and focus forming assay did not demonstrate infectious virus in fresh milk or in nursing pups. Enzyme IHC provided evidence of intracellular viral replication (i.e. ZIKV NS2B expression) in cells morphologically consistent with epithelial cells, myoepithelial cells, and macrophages within the mammary gland. ZIKV NS2B expression was observed also in the SMC, and infectious particles were observed in fresh milk samples after 3 serial passages in Vero cells. The detection of potentially infectious ZIKV in the milk of this mouse model suggest that infectious virus may be present in human breast milk. However, BMT did not occur in this highly stringent ZIKV challenge system. These results suggest a low risk for human BMT of ZIKV, and set the stage for investigating ZIKV entry into milk and mechanisms by which BMT are prevented or permitted.
129/Sv mice deficient in type I and type II IFN receptors (AG129) were bred and maintained at the La Jolla Institute for Allergy & Immunology (LJI) under standard pathogen free conditions. LJI has established an animal care and use program in compliance with The Public Health Service Policy on the Humane Care and Use of Laboratory Animals and maintains an animal welfare assurance with the Office of Laboratory Animal Welfare (OLAW). The animal care and use program is guided by the US Government Principles for the Utilization and Care of Vertebrate Animals Used in Testing, Research and Training and by the 8th edition of the Guide for the Care and Use of Laboratory Animals. As such, all research involving animals is reviewed and approved by the IACUC in accordance with The PHS policy on the Humane Care and Use of Animals and the 8th edition of The Guide. In addition, LJI’s animal care and use program is accredited by AAALAC International. All experiments involving these mice were approved by the Institutional Animal Care and Use Committee under protocol no. AP028-SS1-0615. Samples sizes: Fig 1 (1A to 1D: 3 pups per group from 3 separate mothers, 1E: 3 pups per group from 3 other separate mothers), Fig 2 (3 mothers per group), Fig 3 (3 mothers per group), Fig 4 (4A and 4B: 6 pups per group from 3 separate mothers, 4C and 4D: 3 pups per group from 3 separate mothers, 4E to 4F: other 3 pups per group from 3 separate mothers). Fig 5 (5A to 5C: images representative from 3 independent experiments, 5D: 3 mothers per group, 5E: 10 pups per group from 3 separate mothers). Animal experiments were not randomized or blinded.
ZIKV strain FSS13025 (Cambodia, 2010) was obtained from the World Reference Center for Emerging Viruses and Arboviruses (WRCEVA). This strain was isolated from a pediatric case [17]. ZIKV was cultured using C6/36 Aedes albopictus mosquito cells as described previously [18]. Viral titers were determined by using baby hamster kidney (BHK)-21 cell-based focus forming assay (FFA) [19]. Eight-week-old female mice were infected retro-orbitally (r.o.) with 1 x 102 focus forming units (FFU) of ZIKV FSS13025 in 200 μl 10% FBS/PBS. African green monkey kidney-derived Vero E6 cells were purchased from ATCC. Vero cells were grown in Dulbecco's modified Eagle's medium (DMEM, GIBCO) supplemented with 1% HEPES, 1% penicillin/streptomycin (GIBCO) and 10% fetal bovine serum (FBS, Gemini's BenchMark) at 37°C, 5% CO2.
Mouse organs were collected in 800 μl RNA later (Ambion) and the tissues were transferred to 1% BMe/RLT buffer. Maternal mammary gland, brain, spleen, and the pup body minus the head and stomach were each placed in 800 μl. Before analysis, the skin of the head and rest of the body tissues were removed to avoid contamination from the mother’s saliva. SMC were removed from the pup’s stomach for separate analysis. The pups heads and stomachs tissues were processed in 250 μl (stomach was washed twice with PBS in order to remove remnant milk). The tissues were next homogenized for 3 minutes using Tissuelyser II (Qiagen Inc.) and centrifuged for 1 minute at 6,000 g. Tissue samples, SMC, and serum from ZIKV-infected mice were extracted with the RNeasy Mini Kit (tissues) or Viral RNA Mini Kit (serum and SMC) (Qiagen Inc.). Real-time qRT-PCR was performed using the qScript One-Step qRT-PCR Kit (Quanta BioSciences) and CFX96 TouchTM real-time PCR detection system (Bio-Rad CFX Manager 3.1). A published primer set was used to detect ZIKV RNA (Lanciotti, 2008). Fwd, 5’-TTGGTCATGATACTGCTGATTGC-3’; Rev, 5’-CCTTCCACAAAGTCCCTATTGC-3’ and Probe, 5’-6-FAM-CGGCATACAGCATCAGGTGCATAGGAG-Tamra-Q-3’. Cycling conditions were as follows: 45°C for 15 min, 95°C for 15 min, followed by 50 cycles of 95°C for 15 sec and 60°C for 15 sec and a final extension of 72°C for 30 min. Viral RNA concentration was determined based on an internal standard curve composed of five 100-fold serial dilutions of an in vitro transcribed RNA based on ZIKV FSS13025.
The mammary gland was collected at 5 days post-infection (dpi) and was fixed in PFA for 24 hr at 4°C. ZIKV-infected tissues and mock-infected tissues were obtained. Tissues were processed and stained according to standard Visikol HISTO process (protocol.visikol.com) for antibody labeling. Tissues were immersed in Visikol Permeabilization Buffer at room temperature overnight. The following day, 2 mm thick tissue sections were processed through a series of washing steps of increasing methanol concentrations (50%, 80%, 100%), followed by permeation with 20% DMSO in methanol, and subsequently decreasing concentrations of methanol and back into PBS 1X. Tissues were then incubated in Visikol Penetration Buffer for 12 hr, washed with PBS, and incubated at 37°C in Visikol Blocking Buffer™ for 12 hr. Tissues were then transferred to microcentrifuge tubes for antibody labeling. The primary antibodies Smooth Muscle Actin (αSMA) (Invitrogen; goat polyclonal) and anti-ZIKV NS2B (GeneTex; rabbit polyclonal) were diluted at 1:100 in Visikol Antibody Buffer, and tissues were incubated at 37°C for 7 days. Tissue sections were washed in 1X Visikol Washing Buffer and then transferred to the secondary labeling solution. Secondary antibodies (DyLight 488 conjugated anti-goat and Alexa 594 conjugated anti-rabbit IgG-Invitrogen) were diluted at 1:200 in Antibody Buffer and the samples were incubated for another 3 days along with DAPI counterstain at 1:1000 dilution). Tissues were washed and cleared for imaging using (LSCM). For clearing, both control and infected tissues were dehydrated with sequentially increasing concentrations of methanol (i.e. 50% in PBS, 80% in H2O, 100%) for 30 min in each step, followed by incubation in Visikol HISTO-1 for 12 hours, and then into Visikol HISTO-2. Tissues were mounted in Visikol HISTO-2 and imaged using a Leica SP5 LSCM (laser scanning confocal microscope) using DAPI, Argon-488, and 594 nm lasers with 10X and 20X magnification objectives.
The mammary gland was collected at 5, 7, 9 and 11 dpi; SMC were collected at 5 dpi; and mock-infected samples were prepared. Tissues were fixed in zinc formalin for 24 hr at room temperature. Tissues were processed for paraffin embedding, and sections for slides were cut at 4 μm thickness. For histopathologic evaluation, slides were stained with hematoxylin and eosin. For IHC, slides were microwaved in Antigen Unmasking Solution (Vector Laboratories), endogenous peroxidase activity was blocked by incubation in Bloxall (Vector Laboratories), and nonspecific protein binding was blocked by incubation in 10% goat serum. Slides were then incubated in rabbit anti-ZIKV NS2B antibody (Genetex) diluted at 1:100. ZIKV NS2B protein is a cofactor of the NS2B-NS3 protease which cleaves the viral polyprotein and is thus present during viral replication. Therefore, detection of NS2B serves as a marker of replicating virus as opposed to incomplete, phagocytosed, or degraded virions. Slides were then incubated sequentially in ImmPRESS HRP anti-rabbit IgG (Vector Laboratories), and NovaRED HRP Substrate (Vector Laboratories). IHC slides were also counterstained with hematoxylin. For each slide, the anti-ZIKV NS2B antibody staining was controlled with a slide using nonspecific Rabbit IgG (Vector Laboratories) substituted for the anti-ZIKV NS2B antibody, and control tissues from known infected and uninfected mice were included for each batch. A board-certified veterinary pathologist, who was blinded to each slide’s experimental conditions, read and scored each slide for immunoreactivity. Mammary gland slides were examined for mastitis by a pathologist. Bright field imaging was performed with a Zeiss Axio Scan.Z1 microscope and the images were acquired using Zen 2 software (Carl Zeiss). SMC were frozen on dry ice and sent to the Texas A&M Veterinary Medical Diagnostic Laboratory for transmission electron microscopy.
To detect viral NS2B protein expression, Vero E6 cells were grown to 70% confluency on glass coverslips. Cells were either mock-infected or inoculated with ZIKV FSS13025 at a MOI of 0.001 or with SMC supernatant. The SMC was collected from the pup’s stomach on d3 after birth from AG129 dams that had been previously infected retro-orbitally with 1 x 102 FFU of ZIKV FSS13025. The SMC was collected at day 3 post infection because this time point was the peak RNA viral burden in the SMC. At day 5 after SMC treatment, Vero cells were fixed in 4°C methanol and permeabilized with 0.1% Triton X-100. Protein blocking was performed with 10% goat serum, followed by incubation with anti-ZIKV NS2B antibody (Genetex) at 1:400 dilution. Coverslips were incubated with Alexa Fluor 594 (Invitrogen) at 1:300 dilution and then inverted onto glass slides for mounting. Imaging was performed by confocal microscopy.
All data were analyzed with Prism software, version 7.0 (GraphPad Software) and expressed as means ± SEM. For viral burden and focus forming assay data, Krustal-Wallis test was used to compare more than two groups. This test was performed only in ZIKV-infected samples. Mock was not considered in the analysis. p<0.05 was considered a significant difference.
To begin evaluating whether ZIKV could infect the mammary gland and be transmitted to breastfed infants, 8-week-old female AG129 mice were infected with ZIKV strain FSS13025. Viral burdens in several tissues were first assessed at 5, 7, 9 and 11 dpi via qRT-PCR. ZIKV RNA levels in the mammary glands were similar at the four time points (Fig 1A). As expected, high levels of ZIKV RNA were present in the brain, spleen, and serum with no significant difference among the four time points. With the exception of serum, there was a slight reduction on 5 dpi compared with 11 dpi (Fig 1B–1D), indicating ZIKV dissemination into tissues. To test for the presence of infectious virus in the mammary gland, we measured viral titers by FFA (Fig 1E). High levels of infectious ZIKV were present in the mammary gland at 5 dpi with a slight reduction in the subsequent days analyzed, demonstrating that ZIKV establishes productive infection in the AG129 mouse mammary glands.
To localize ZIKV replication within the mammary gland of AG129 mice, 8-week-old female AG129 mice were mock-infected or infected with ZIKV strain FSS13025, followed by visualization of ZIKV infection via laser scanning confocal microscopy. After clearing with Visikol HISTO, 800–1000 μm thick portions of the mammary gland were imaged under laser scanning confocal microscopy. Immunofluoresence staining was performed to assess expression of ZIKV NS2B, a marker for viral replication [22], and alpha smooth muscle actin (αSMA), present in myoepithelial cells. At 5 dpi, strong expression of NS2B and αSMA was detected in the mammary gland of ZIKV-infected AG129 mice (Fig 2A and 2B). The 3D images from this tissue (S1 and S2 Figs) and staining for ZIKV NS2B and DAPI (Fig 2C) showed similar results. Thus, ZIKV NS2B colocalizes with αSMA-expressing cells within the mammary glands of AG129 mice, suggesting myoepithelial cells as one of the cellular hosts of ZIKV in the mammary gland.
To confirm ZIKV replication within the mammary gland of AG129 mice, tissues were fixed in Zinc formalin and then stained for expression of ZIKV NS2B at 5, 7, 9 and 11 dpi. No difference was observed among all times points, and we show 5 and 9 dpi as representative (Fig 3). NS2B expression was detected in cells morphologically consistent with mammary epithelial cells, myoepithelial cells, and interstitial macrophages (Fig 3A). NS2B expression in cells in the stroma surrounding the teat canal on a nipple cross section (Fig 3B) and teat Langerhans cells was also observed (Fig 3C). Additionally, histopathologic evaluation of these tissues revealed an absence of mastitis. Thus, ZIKV replicates locally in the mammary gland, and these enzyme IHC results in combination with z-projection images suggest that myoepithelial cells are major cellular hosts of ZIKV.
Having established the presence of ZIKV RNA and infectious viral particles in the mammary gland, we proceeded to examine whether ZIKV was transmitted from infected mothers to neonates through breastfeeding. Neonatal heads, stomach tissues, SMC, and the rest of the bodies (without skin to avoid contamination from the mother’s saliva) were examined for the presence of ZIKV RNA by qRT-PCR. No ZIKV RNA was detected in the head and the rest of body in the neonates 1 to 7 days after birth (Fig 4A and 4B). However, viral RNA was present in SMC and stomach tissues at almost all time-points tested from 1 to 7 days after birth (Fig 4C and 4D). As ZIKV RNA does not necessarily indicate production of infectious virus, we next assessed for the presence of infectious ZIKV in SMC and stomach tissues via FFA. No infectious ZIKV particles were detected in SMC and stomach (Fig 4E and 4F). Thus, breastfeeding does not appear to be a significant route of ZIKV transmission into neonates in this mouse model.
To further assess the lack of infectious ZIKV in SMC, we inoculated SMC supernatant onto Vero cells. Infectivity of the SMC supernatant was assessed by immunofluorescence staining for ZIKV NS2B expression and CPE in the Vero cells, and plaque assay of the Vero culture supernatants. ZIKV NS2B expression and CPE were observed in the positive control cells infected with ZIKV. However, Vero cells inoculated with SMC supernatant did not show any NS2B protein expression or CPE (Fig 5A and 5B), and plaque assay confirmed the absence of infectious virus in the culture supernatant of SMC supernatant-treated Vero cells (Fig 5C). To assess whether ZIKV NS2B expression is observed in the breast milk were present in the SMC and might also infect the stomach tissue, 8-week-old female AG129 mice were infected with ZIKV strain FSS13025, followed by sacrificing of pups on d5 after postpartum and examination for the expression of ZIKV NS2B on the pup SMC and stomach tissue by IHC. Of 10 sampled pup stomachs, ZIKV NS2B expression surrounded nuclear material in SMC from 3 pup stomachs (Fig 5D). However, no ZIKV NS2B expression was detected in the full thickness of the gastric walls. These results suggest that replicating ZIKV may be passed in milk and is likely cell-associated; however, breast milk does not contain sufficient replication-competent ZIKV to initiate infection in cell culture and in IFN receptor-deficient mice.
Finally, we determined whether there is infectious virus present in fresh breast milk by FFA. To increase the sensitivity of infection in these samples, we performed serial passages in Vero cells of fresh breast milk collected at 5 and 7 days postpartum. Only one fresh milk sample collected at d5 showed a low infectivity in the first passage. However, we observed an increase of infectious particles at the second and third passage in both times points (Fig 5E).
In this study, we were able to detect ZIKV RNA in pup stomach milk clots and maternal mammary glands, and within the latter, ZIKV NS2B antigen localized to cells morphologically consistent with glandular epithelial cells, myoepithelial cells, and macrophages. ZIKV-permissive cells were also identified in the teat stroma and epidermis. Further, low levels of replicating virus were detected in fresh milk and ZIKV NS2B expression was detected in SMC samples. These results provide a framework for investigating ZIKV entry into the milk and raise the additional question of whether normal nursing-associated ingestion of maternal epidermal cells and blood may also play roles in ZIKV transmission.
We propose that infectious ZIKV may enter human breast milk but may be subsequently inactivated by endogenous or exogenous factors such as lipid, antimicrobial proteins, or gastric acid. Several studies have shown that the acidic pH and the digestive enzymes present in the stomach inactivate virus [23–25], and combine with mucus to form a chemical barrier to infection. Because dams were infected on the day of parturition, milk in this experiment should not have contained any ZIKV-neutralizing antibody. Recently in a rhesus macaques model, ZIKV RNA was present in saliva, another potential route of mucosal exposure [3], but no infectious virus was detected. Another study demonstrated that human breast milk inactivates ZIKV after prolonged storage [26]. Additionally, human breast milk has been reported to reduce the infectivity of HIV, HCV, and dengue virus. Thus, antiviral properties of breast milk may reduce BMT [21, 27].
Human viruses with known clinically relevant risk of BMT are cytomegalovirus (CMV) [28] and HIV-1 [27, 29–31]. Although mastitis is a risk factor for BMT of both viruses, most cases of BMT occur in the absence of mastitis. Further, most cases of CMV and HIV BMT involve a seroconverted mother rather than infection of a naïve mother in the nursing period. Infectious CMV has been isolated from up to 80% of infected breast milk samples, whereas infectious HIV has been extremely difficult to isolate from breast milk. DNA and RNA from other human viruses including herpesviruses, parvovirus, rubella virus, arboviral flaviviruses, and hepatitis viruses A, B and C have been detected in milk [32]. However, perhaps owing to low clinical relevance of BMT of these viruses, it is largely unknown whether detected nucleic acids were non-infectious viral genetic material or derived from neutralized virions.
After over two decades of research, the pathogenesis of HIV BMT remains poorly understood. It is estimated that BMT causes approximately 40% of mother-to-child transmission case of HIV. However, isolation of infectious virus from breast milk is rarely successful. HIV RNA, and rarely infectious virus, have been isolated from whey and cellular fractions of milk [33]. In contrast to CMV, viral loads in cellular fractions of milk correlate to transmission whereas loads in cell-free fractions do not. These findings have suggested that an intracellular location shields HIV from immune defenses such as lactoferrin, tenascin-C, defensins, and mucin [34]. Meanwhile, factors such as antibodies and HIV-gag-specific cytotoxic T lymphocytes may reduce cell-associated virus loads. Our early findings with ZIKV showed nursing mouse pups were not infected following ingestion of milk from infected dams. Therefore, the data are not sufficient to conclude that ZIKV infection can be passed via breastfeeding, and support early data suggesting the same for humans [8].
High ZIKV susceptibility of AG129 mice, which globally lack type I and type II IFN receptors, is often cited as a pitfall for many virology studies. However, in the current state of ZIKV science, in which it is unknown whether BMT is a clinical reality and there are no animal models of ZIKV entry into milk, a highly susceptible dam represents an excellent starting point to begin mechanistic manipulations which may reduce entry of viral RNA into milk. Furthermore, neonates, which are also deficient in IFN receptors, are a highly sensitive detection system for arranging conditions that may enable BMT. Indeed, the absence of infection in neonates in this study provides an early suggestion that infectious ZIKV is not easily transmitted through breast milk or other maternal-neonatal contact. It should also be noted that in humans, the tonsil is one of the first potential entry sites for orally ingested ZIKV [3], whereas mice do not have tonsils.
Because ZIKV is already known to have devastating consequences on the developing brain and there are both benefits to and substitutes for breastfeeding [35] it is imperative to fully understand the mechanisms which enable or prevent BMT. The results of this study provide a mouse model for investigating entry of ZIKV RNA into breast milk, and the pups provide a sensitive system for testing modulations which might permit BMT.
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10.1371/journal.pgen.1004059 | DNA Methylation Changes Separate Allergic Patients from Healthy Controls and May Reflect Altered CD4+ T-Cell Population Structure | Altered DNA methylation patterns in CD4+ T-cells indicate the importance of epigenetic mechanisms in inflammatory diseases. However, the identification of these alterations is complicated by the heterogeneity of most inflammatory diseases. Seasonal allergic rhinitis (SAR) is an optimal disease model for the study of DNA methylation because of its well-defined phenotype and etiology. We generated genome-wide DNA methylation (Npatients = 8, Ncontrols = 8) and gene expression (Npatients = 9, Ncontrols = 10) profiles of CD4+ T-cells from SAR patients and healthy controls using Illumina's HumanMethylation450 and HT-12 microarrays, respectively. DNA methylation profiles clearly and robustly distinguished SAR patients from controls, during and outside the pollen season. In agreement with previously published studies, gene expression profiles of the same samples failed to separate patients and controls. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs from patients and healthy controls were challenged with allergen. We observed changes in the proportions of memory T-cell populations between patients (Npatients = 35) and controls (Ncontrols = 12), which could explain the observed difference in DNA methylation. Our data highlight the potential of epigenomics in the stratification of immune disease and represents the first successful molecular classification of SAR using CD4+ T cells.
| T-cells, a type of white blood cell, are an important part of the immune-system in humans. T-cells allow us to adapt our immune-response to the various infectious agents we encounter during life. However, T-cells can also cause disease when they target the body's own cells, e.g. Psoriasis, or when they react to a harmless particle or ‘antigen’, i.e. allergy. Much evidence supports an environmental, or ‘epigenetic’, component to allergy. Surprisingly, although allergy is viewed as a T-cell disease with an epigenetic component, no studies have identified epigenetic differences between healthy individuals and allergic individuals. Using a state-of-the-art genome-wide approach, we found that we could clearly and robustly separate allergic patients from healthy controls. It is often assumed that these changes reflect changes in DNA methylation in a given type of cell; however such differences can also result from different mixtures of T-cell subtypes in the samples. Indeed, we found that allergic patients had different proportions of T-cell sub-types compared to healthy controls. These changes in T-cell proportions may explain the difference in DNA methylation profile we observed between patients and controls. Our study is the first successful molecular classification of allergy using CD4+ T cells.
| The modest effects of genetic variants in inflammatory diseases indicate the importance of epigenetic mechanisms like DNA methylation to disease pathology. However, studies of inflammatory diseases have shown conflicting results. In monozygotic twins discordant for multiple sclerosis (MS), no significant differences in DNA methylation profile were found [1]. A more recent study of monozygotic twins discordant for psoriasis identified widespread differences between siblings [2]. Other studies of autoimmune diseases have reported varying findings [3]. Discordant monozygotic twin studies benefit from a constant genetic background on which to identify disease-associated epigenetic changes. However, intrinsically, such studies tend to involve small samples sizes, and may thus lack the power to detect small, rare, disease-associated changes in DNA methylation. The variation between studies may be due to disease heterogeneity, variations in disease course and the confounding effects of treatment, and as the disease causing agent is unknown, it is difficult to experimentally model disease pathogenesis.
By contrast, seasonal allergic rhinitis (SAR) occurs at defined time points each year and the disease causing agent, pollen, is known. These unique features of SAR permit analysis of CD4+ T-cells from SAR patients during and after the pollen season in vivo, and by allergen-challenge in vitro [4]. An epigenetic component in SAR is supported by its increasing prevalence in the developing world, failure of genome-wide association studies to identify a consistent genetic component to the disease, and frequent discordance for SAR between monozygotic twins [5], [6]. DNA methylation changes at numerous loci are required for appropriate differentiation of naïve CD4+ T-cells into CD4+ T effector cell subtypes [7].
We generated genome-wide expression (Npatients = 9, Ncontrols = 10) and DNA methylation (Npatients = 8, Ncontrols = 8) profiles for CD4+ T-cells from untreated SAR patients and healthy controls, both during and outside the pollen season. Consistent with previous studies, we found that CD4+ T-cell gene expression profiles were poor classifiers of SAR. However, we observed clear and robust separation of patients and controls by DNA methylation signature, both during and outside the pollen season. Separation by methylation (Npatients = 12, Ncontrols = 12), but not by gene expression (Npatients = 21, Ncontrols = 21) was also observed in an in vitro model system in which purified PBMCs were challenged with allergen in culture. Moreover, we found that these methylation profiles were significantly associated with disease severity in patients during season, and may be due to differing proportions of central memory CD4+ T-cells (Npatients = 35, Ncontrols = 12). This, to our knowledge, is the first successful molecular classification of SAR using CD4+ T-cells, and highlights the potential of genome-wide epigenetic technologies in the stratification of immune disease.
Seasonal allergic rhinitis (SAR) is a powerful disease model because, (1) SAR's clear clinical manifestations make it easy to assess disease severity, (2) the affected cell type, CD4+ T-cells, can be obtained from patients when they are symptom-free (outside the pollen season) and compared to the same cell type in the same individual when symptomatic (during the pollen season). We aimed to test the ability of CD4+ T-cell DNA methylation to separate SAR patients from healthy controls. The cohorts used in the study are outlined in Supplemental Figure S1. In a previous study, we obtained PBMCs from adult SAR patients (N = 21; age = 25.4 years±7.8 SD) and healthy controls (N = 21; age = 25.7 years±10.1 SD) outside the pollen season, and challenged these cells with either grass pollen extract or diluent (PBS) (Figure 1A). Seven days post-challenge, total CD4+ T-cells were isolated by magnetic-activated cell sorting (MACS, positive-selection), and the mRNA expression profile determined by gene expression microarray (GEO: GSE50223). Consistent with several previous studies, we were unable to separate patients and controls after challenge with allergen by CD4+ T-cell mRNA expression profile [6], [8], [9] (Figure 1B).
Here, PBMCs from a new cohort of SAR patients (N = 12; age = 28.3 years±12.1 SD) and healthy controls (N = 12; age = 27.3 years±10.7 SD) were allergen-challenged, after which purified CD4+ T-cell DNA was analysed using Illumina HumanMethylation27 DNA methylation microarrays. Unsupervised hierarchical clustering of samples by genome-wide DNA methylation profiles resulted in two groups, ‘H’ (healthy controls) and ‘P’ (patients), clearly separating samples by disease-state (Figure 1C, left panel). Consensus clustering, whereby the data are repeatedly re-sampled and re-clustered, found that clusters ‘H’ and ‘P’ were reproducibly and stably identified (P<0.05). We confirmed this separation in the data using principal component analysis (PCA), which also revealed a clear separation between allergen-challenged CD4+ T-cells from patients versus controls (Figure 1C, right panel). A leave-one out (LOO) cross-validation found that methylation data accurately classified all samples as patient or healthy control (χ2;P<0.0001). DNA methylation and gene expression signatures did not separate patients and healthy controls after diluent challenge (Figure S2).
Though striking, the results obtained from in vitro allergen-challenge of PBMCs may be confounded by cell culture effects. To verify the observations made after in vitro allergen-challenge, the mRNA expression and DNA methylation profiles of in vivo CD4+ T-cells were determined in a new cohort of SAR patients and healthy controls (GEO: GSE50387). In this experiment we used Illumina HumanMethylation450k methylation arrays, which quantitatively assess 450,000 CpG sites across the genome, with over half the probes targeting CpGs outside gene promoters and a quarter of all probes targeting CpGs in non-genic regions. CD4+ T-cells were purified from fresh blood collected from SAR patients and healthy controls by negative magnetic cell sorting both during and outside the pollen season in the same calendar year. Symptom scores for patients were recorded at the time of collection (Table S1). DNA and total RNA were harvested simultaneously from each sample. Subsequently, cDNA and bisulfite converted DNA was applied to Illumina HT12 expression microarrays (Npatients = 9, Ncontrols = 10; both during and outside the pollen season) and HumanMethylation450k microarrays microarrays (Npatients = 8, Ncontrols = 8; both during and outside the pollen season), respectively.
Unsupervised hierarchical clustering of samples by mRNA expression profile did not result in separation of samples by disease state either during or outside the pollen season (Figure 2A, left panel). No effect of array batch or sex was evident in the observed clusters (Figure 2A, left panel). These findings are in line with those reported here (Figure 1B) and in numerous previous studies of CD4+ T-cells in allergy and other autoimmune diseases [6], [8], [9]. PCA of the gene expression data also failed to distinguish between SAR patients and healthy controls (Figure 2A, right panel). Differentially expressed genes were identified as those showing differential expression between patients and controls (Mann-Whitney U-test; P<0.05, unadjusted) both during and outside the pollen season (357 genes identified). The relative changes observed across all genes were small (mean absolute fold change = 0.39, SEM ± 0.01). Gene Ontology (GO) term analysis failed to identify any significantly enriched gene annotation clusters after correction for multiple correction. However, four of the top 10 annotations related to lymphocyte activation (Figure S3A). All four clusters consisted of mixtures of the same seven genes, namely; HSPD1, TGFBR2, CD134 (TNFRSF4, OX40), CD45 (PTPRC), SPN (CD43), IL2RA, IL13RA1. This group of genes is highly relevant; CD43−/− mice show a pronounced Th2 phenotype and exhibit increased in inflammation in two allergic mouse models [10] and association studies have identified both IL2RA and TGFBR2 as susceptibility loci for allergy and asthma [11]. CD143 is a regulator of CD4+ T-cell memory and CD45 isoforms are key markers of memory CD4+ T-cells, though it is not possible to determine the exact isoform mis-expressed due to the positioning of the gene expression probe in the 5′ UTR of CD45 [12]. To determine the relationship between the observed changes in gene expression and DNA methylation we determined the average promoter methylation for all genes represented on the 450K methylation array. Changes in gene expression and associated promoter DNA methylation between patients and controls were not significantly negatively correlated (Pearson's r = 0.14, P = 0.07), as would be expected if DNA methylation were acting to silence transcription (Figure S3B).Thus, although it was not possible to separate patients from controls by gene expression profile, a small set-of disease-relevant miss-expressed genes were identified.
In contrast, unsupervised hierarchical clustering of all probes by DNA methylation profile resulted in clear separation of the samples by disease state, regardless of season (during or outside pollen season); seven of eight healthy controls grouped in cluster ‘H’, and six of eight patient samples grouped in cluster ‘P’ (Figure 2B, left panel). Consensus clustering identified clusters ‘H’ and ‘P’ with high statistical confidence (Multiscale Bootstrap Resampling; P<0.05). Interestingly, the paired measurements (during and outside season) of each sample also clustered neatly together; again implicating disease status as the major modifier of DNA methylation profile between patients and controls samples, while also highlighting the reproducibility and accuracy of the data (paired samples were collected and processed at least five months apart) (Figure 2, left panel). PCA resulted in even clearer separation of samples along the main principle component, with only one healthy sample clustering among the patient samples (Figure 2, right panel). LOO analysis correctly classified samples as patient or healthy control in all samples collected outside season (χ2;P<0.0001), and all but two healthy samples collected during season (χ2;P = 0.01). PCA1 explained approximately 14% of the variation in the data and 3-times the variation explained by PCA2 (5%). Although our findings indicated that disease status was the major mediator of DNA methylation differences between patients and healthy controls an effect of pollen season was also evident. With the exception of one outlier, healthy samples clustered tightly together along PCA1, whereas patient samples showed greater spread along PCA1 (Figure 2B, right panel). Given this observation, we tested if the position along PCA1 of patient samples was associated with patient symptom score during season. Symptom scores for each patient are listed in Supplemental Table S1. Significantly, we found that symptom score explained 74% of the variation in patient sample variation along PCA1, a strong and highly significant correlation (Spearman's rho = 0.86, P = 0.011) (Figure 2C). To our knowledge, such a strong association between individual or genome-scale markers and symptoms has not been previously been described in allergy, nor in other inflammatory diseases. However, given the small sample size (Npatients = 8) of the study presented here, the use of DNA methylation as a marker of disease severity needs to be tested in a much larger cohort. The association with disease severity may also explain the difficulty in identifying an epigenetic component to other immune-related diseases in which the disease course is more variable and complex to assess. However, as patient symptoms can vary dramatically during the pollen season it is important that the observed correlation between DNA methylation and symptom severity is tested at several time-points during a pollen season to validate the robustness of the preliminary observation reported here. If our findings are applicable to other inflammatory diseases, an important implication is that DNA methylation may help to stratify such diseases.
The observed differences in DNA methylation between patients and controls were small (mean absolute change = 1.2%±2.3 SD), bi-directional, and genome-wide, 12,000 probes (3.5% of all probes) were found to have changed significantly (Mann-Whitney U-test; P<0.01; unadjusted) (Figure S4A). Indeed, the 1,000 most significantly altered probes changed by only ±10% (Figure S4B). Given the small size of the observed methylation differences and the known technical variation associated with 450K methylation arrays [13], we selected five CpG loci for validation by pyrosequencing in 4 SAR patients and 4 healthy controls. We selected three CpGs that showed significant methylation differences between patients and healthy controls (among top 50 altered probes) which were also located in annotated gene promoters (PIEZO1 promoter CpG, RPP21 promoter CpG, HLA-DMA promoter CpG) and 2 control CpG loci (unmethylated CpG; GAPDH promoter & methylated CpG; CD74 promoter). Pyrosequencing primers are listed in Table S2. Array methylation was highly significantly correlated with pyrosequencing methylation for all CpG loci (Spearman's rho = 0.98, P<10−6) (Figure S4C), and the absolute difference in methylation measurements between the array and pyrosequencing across all CpGs was small (median difference = 2.41%±3.2 MAD; mean difference = 4.1%±4 SD). The methylated (Figure S4D) and unmethylated (Figure S4E) control CpGs were also validated by pyrosequencing, although agreement between array and pyrosequencing was slightly better for the unmethylated CpG site. Critically, pyrosequencing confirmed the direction and scale of DNA methylation change observed between patients and controls by 450K methylation array at the three disease-associated test loci (Figure S4F–S4H).
Our findings agree with a recent quantitative study of genome-wide methylation in CD4+ T-cells from monozygotic twins discordant for the autoimmune disease, psoriasis, in which affected and unaffected siblings were distinguished by numerous, small, bi-directional changes in DNA methylation, with no one CpG exhibiting a significant change in DNA methylation level [2]. Moreover, and very recent study of DNA methylation in CD4+ T-cells from patients with the autoimmune disease Systemic Lupus Erythematosus (SLE) also reported widespread small changes in DNA methylation [14]. Interestingly, we found that the genic location of significantly altered probes was enriched for gene-bodies and non-genic regions (χ2; P<0.0001) (Figure S5A). This highlights the importance of using unbiased genomic technologies; as assays targeted towards detection of large changes in DNA methylation in promoter regions may miss subtle but informative changes in other genomic compartments. To further dissect the genomic compartmentalization of the observed changes in DNA methylation we focused on regulatory elements whose function is known to be modified by DNA methylation, namely promoters, DNaseI hypersensitive sites (DHS) and enhancer elements. Interestingly, differentially methylated probes appeared enriched in annotated enhancers compared to those located in DHSs and promoters (χ2; P<10−5) (Figure S5B). This enrichment was observed both during and outside the pollen season (Figure S5B). Enhancer probes differentially methylated during and outside the pollen season showed a significant overlap (Fisher's exact test; P<0.001) (Figure S5C). Significantly, those enhancer probes differentially methylated both during and outside the pollen season (N = 960) showed a clear tendency towards loss of methylation in patients in contrast to the bi-directional changes observed for all differentially methylated probes (Figure S5D). As the activity of many enhancer elements and consequently their associated genes, is affected by DNA methylation, this finding may reflect the epigenetic re-modeling of enhancer elements in CD4+ T-cells. However, as most enhancers are only active in a small number of tissues we cross-referenced the observed differentially methylated enhancers with a recently published experimentally determined set of Th1-, Th2- and Th0-specific enhancers [15], [16]. Only 1 of the differentially methylated enhancer probes (N = 960) mapped to the Th1/Th2/Th0 enhancers. Thus, is seems unlikely that the observed changes in enhancer methylation have function gene expression consequences in Th2 cells, the key pathogenic cell-type in allergy. However, as Th2 cells constitute <3% of the total CD4+ T-cell population, we cannot exclude large functionally relevant changes in DNA methylation at enhancers in other CD4+ T-cell subsets.
Differences in DNA methylation are often assumed to reflect changes in DNA methylation in a given type of cell; however such differences can also result from changes in the proportions of cell types in samples. Indeed, a recent study reported that a significant proportion of the DNA methylation differences observed between breast tumours (N = 248) reflected the number of infiltrating T-cells, and was significantly associated with prognosis [17]. Thus, the observed, small, genome-wide and bi-directional changes in DNA methylation between SAR patients and controls may reflect changes in the proportions of CD4+ T-cell sub-populations between patients and controls. Given that DNA methylation profile clearly separates samples by disease-state both during and outside the pollen season we hypothesized that the observed differences may have been due to differences in memory CD4+ T-cell subsets. Indeed, we have previously observed a reduction in CD4+ central memory T-cells (TCM) in a small cohort of allergic patients [18]. Here we extended this study, using FACS analysis to quantify CD4+ T-cell subsets in an additional 26 subjects (Npatients = 35, Ncontrols = 12) (Figure S6). SAR patients were significantly depleted for CD4+ central memory T-cells (TCM) (11% of total CD4+ T-cells) relative to healthy individuals (18.7% of total CD4+ T cells), a reduction of 41% in the TCM population in patients relative to controls (Mann-Whitney; P = 0.001) (Figure 3). The values for TCM cells in healthy individuals reported here are similar to those reported in independent studies [19], affirming the accuracy of our methodology. Altered TCM proportions are being implicated in a growing number of immune-related diseases [19]–[21]. This finding was also consistent with enrichment for the Gene Ontology (GO) term, ‘negative regulation of memory T-cell differentiation’, found when genes containing the 100 most significantly changed probes were analyzed (Figure S5E, right panel). However, given the enrichment for altered probes in non-genic and non-promoter regions of genes, the results of such GO term analyses must be viewed with caution. As the quantification of CD4+ T-cell subsets and DNA methylation profiling were performed on different cohorts of patients and controls, conclusions drawn from these independent observations must be cautious. Determination of DNA methylation profile, CD4+ T-cell subset structure and symptom severity in a large cohort of patients and controls at multiple time-points during and outside the pollen season would allow a more robust analysis of the inter-dependence of these variables. Although our results suggest that differences in T-cell sub-populations may contribute to the observed differences in DNA methylation between SAR patients and controls, they do not exclude a direct role for DNA methylation changes within CD4+ T-cell subsets. Analysis of gene expression and DNA methylation in each memory CD4+ T-cell subset in a large cohort of patients and healthy controls is required to directly test the association between CD4+ T-cell subset changes and observed changes in total CD4+ T-cell methylation. It is technically challenging to obtain sufficient numbers of each subtype of CD4+ T-cell from standard (40 mL) blood samples. An alternate future strategy may be to determine reference genomes for each CD4+ T-cell subtype and use these subtype specific methylomes to estimate sub-type proportions from a total CD4+ T-cell methylome [22]. This approach has recently been successfully applied to the study of rheumatoid arthritis [23].
The ability to separate patients and healthy controls by quantitative DNA methylation array, but not by gene expression array is interesting. Assuming little or no change in DNA methylation or gene expression profiles within CD4+ T-cell subtypes, small changes in the proportions of subtypes should result in very subtle changes in both the DNA methylation and gene expression profiles of total CD4+ T-cells. These subtle changes can be detected by the DNA arrays, given 1) their sensitivity – they are quantitative and accurate, and 2) their power – they assess 450,000 CpG sites. Gene expression arrays are not quantitative and have a restricted dynamic range of values which is often determined by probe design, not gene transcript levels. Moreover, whereas gene expression arrays typically have probes targeting ∼40,000 transcripts, only a small proportion of these are actually expressed (informative) in any given cell type, further reducing the power of the assay. Moreover, DNA methylation profiles are very stable in normal somatic cells, showing less inter-individual variation than RNA levels. Thus, whereas detecting small changes (<5%) in CD4+ T-cell substructure by gene expression microarray is theoretically possible, it would require a very large sample size; much greater than that used in this study (Npatients = 9, Ncontrols = 10; both during and outside the pollen season). Our results support the use of both DNA methylation arrays and gene expression arrays simultaneously, particularly, where cell-type composition may contribute to the molecular signature of the disease.
Epigenetic regulation plays a key role in Th differentiation. Pronounced changes in DNA methylation patterns are observed at several key loci during helper T cell differentiation [24]. Up-regulation of Il4, IL5, and IL13 gene expression in Th2 cells is accompanied by a pronounced loss of DNA methylation and gain of permissive histone marks across the locus [25], [26]. Similarly, the promoter of the Th1 gene, Ifng, is unmethylated in Th1 cells, but hypermethylated in Th2 cells, reinforcing helper T-cell identity. DNA demethylation also occurs at regulatory regions of the FOXP3 gene in Treg cells [27], and at the IL17A promoter in Th17 cells [28]. Allergy involves an inappropriate Th2 response to a benign allergen such as pollen [29], and several observations point to a key role for epigenetics in the pathogenesis of SAR. Murine studies have established that a diet rich in methyl donors, such as folic acid enhances allergic airway disease in progeny [30], and knock-out studies of the DNA methyltransferase, Dnmt3a, resulted in dysregulation of important Th2 cytokines, including Il13, and increased inflammation in a mouse model of asthma [31]. In humans, large meta-analysis of GWAS identified few loci associated with SAR [32], and those loci identified did not contain genes encoding Th2 genes or other genes of known relevance for SAR. A recent study identified stable and functional DNA demethylation at the key regulatory gene FOXP3 in patients cured by specific immunotherapy (SIT) [33], directly linking disease reversal with DNA demethylation. Recent studies have reported differences in DNA methylation in airway epithelial cells between asthmatic children and healthy controls [34] and reported that methylation levels at the key cytokine gene, IL2, in cord blood was associated with asthma exacerbations in childhood [35]. However, systematic clinical studies of the genome-wide distribution and role of DNA modifications in Th differentiation in SAR are lacking.
Analysis of the transcriptome of CD4+ T-cell subsets have failed to identify clear and reproducible differences between patients and healthy controls in several immune-diseases [6], [8], [9]. The circulating total CD4+ T-cell population is complex, comprised of several subsets that vary markedly between individuals. This complexity renders identification of subtle allergy-specific transcriptional signals challenging with current approaches. Unlike gene expression microarrays which typically assay 20,000–40,000 transcripts and have a small dynamic range, the DNA methylation microarrays employed here assay ∼450,000 individual CpG sites with quantitative accuracy. We suggest that quantitative DNA methylation microarrays can act as a proxy measure of the cell-population structure of samples, and as such, may be powerful analytical tools for diseases in which the proportions of different cell sub-types is likely to be of pathogenic significance such as immune-diseases and cancer [2], [17], [19]. Indeed, a very recent publication of genome-wide methylation in CD4+ T-cells from patients with Systemic Lupus Erythematosus (SLE), identified similar, small widespread changes in DNA methylation and associated these epigenetic changes with changes to CD4+ T-cell populations in SLE patients [14]. This exciting finding is completely consistent with the preliminary results reported here and suggests that alterations in CD4+ T-cell populations may be a general feature of many immune diseases. As CD4+ T-cell population structure and DNA methylation profiles reported here were determined in different cohorts, simultaneous analysis of the DNA methylation and gene expression profiles of CD4+ T-cells in the same cohort of patients and controls is required to directly determine the contribution of changes in subtype structure between patients and controls to the observed difference in total CD4+ T-cell methylation. Our findings highlight the potential to stratify immune diseases with DNA methylation.
In vitro and in vivo array studies (cohorts 1–3, Figure S1) were approved by the ethics board of University of Gothenburg and all participants provided written consent for participation. The quantification of CD4+ T-cell subtypes study (cohort 4, Figure S1), was approved by the ethics board of Linkoping University and all participants provided written consent for participation.
We recruited patients with SAR and matched healthy controls of Swedish origin at The Queen Silvia Children's Hospital, Gothenburg (cohorts 1–3, Figure S1). We recruited patients with SAR and matched healthy controls of Swedish origin at Linkoping University Hospital, Linkoping (cohort 4, Figure S1). SAR was defined by a positive seasonal history and a positive skin prick test or by a positive ImmunoCap Rapid (Phadia, Uppsala, Sweden) to birch and/or grass pollen. Patients with perennial symptoms or asthma were not included. The healthy subjects did not have any history for SAR and had negative ImmunoCap Rapid tests. Supplemental Figure S1 provides an overview of the experiments performed on each cohort used in the research presented here. Severity of patient symptoms (itchiness of the eyes, block sinus, running nose) during season were self-assessed using a visual analogue scale (1–10). The score for each symptom was summed to give a single symptom severity score for each patient (Supplemental Table S1).
A detailed description of all methods employed in this study can be found in Supplemental Text S1.
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10.1371/journal.ppat.1007883 | HIV and HCV augments inflammatory responses through increased TREM-1 expression and signaling in Kupffer and Myeloid cells | Chronic infection with human immunodeficiency virus (HIV) and hepatitis C virus (HCV) affects an estimated 35 million and 75 million individuals worldwide, respectively. These viruses induce persistent inflammation which often drives the development or progression of organ-specific diseases and even cancer including Hepatocellular Carcinoma (HCC). In this study, we sought to examine inflammatory responses following HIV or HCV stimulation of macrophages or Kupffer cells (KCs), that may contribute to virus mediated inflammation and subsequent liver disease. KCs are liver-resident macrophages and reports have provided evidence that HIV can stimulate and infect them. In order to characterize HIV-intrinsic innate immune responses that may occur in the liver, we performed microarray analyses on KCs following HIV stimulation. Our data demonstrate that KCs upregulate several innate immune signaling pathways involved in inflammation, myeloid cell maturation, stellate cell activation, and Triggering Receptor Expressed on Myeloid cells 1 (TREM1) signaling. TREM1 is a member of the immunoglobulin superfamily of receptors and it is reported to be involved in systemic inflammatory responses due to its ability to amplify activation of host defense signaling pathways. Our data demonstrate that stimulation of KCs with HIV or HCV induces the upregulation of TREM1. Additionally, HIV viral proteins can upregulate expression of TREM1 mRNA through NF-кB signaling. Furthermore, activation of the TREM1 signaling pathway, with a targeted agonist, increased HIV or HCV-mediated inflammatory responses in macrophages due to enhanced activation of the ERK1/2 signaling cascade. Silencing TREM1 dampened inflammatory immune responses elicited by HIV or HCV stimulation. Finally, HIV and HCV infected patients exhibit higher expression and frequency of TREM1 and CD68 positive cells. Taken together, TREM1 induction by HIV contributes to chronic inflammation in the liver and targeting TREM1 signaling may be a therapeutic option to minimize HIV induced chronic inflammation.
| Although HIV antiviral therapy has limited the progression to AIDS in infected patients, there is still significant morbidity and mortality from HIV-driven diseases due to sustained inflammation. In this study, we sought to elucidate how HIV and HCV could impact inflammation in the liver and cause progressive liver disease that can eventually lead to cirrhosis and liver cancer. We found that HIV upregulates the inflammatory response amplifier, TREM1, in primary Kupffer Cells (KCs) that are liver-resident macrophages. Enhanced TREM1 expression subsequently is involved in augmented immune responses triggered by HIV or HCV. Additionally, our data demonstrates that blocking TREM1 expression reduces inflammatory responses mediated by HIV or HCV stimulation. Ultimately, our understanding of this mechanism may yield additional therapeutic strategies to help infected patients and give insight into inflammation driven liver cancer.
| Cell-intrinsic innate immune responses provide the first line of defense against invading viral pathogens. These early innate immune responses not only blunt the initial spread of infection but also activate the adaptive immune system and secondary host defense mechanisms. However, the immune reaction must be controlled and balanced in order to maintain immune homeostasis and to prevent subsequent tissue injury arising from chronic inflammation [1–3]. Human immunodeficiency virus (HIV) and hepatitis C virus (HCV) are RNA viruses capable of inducing and sustaining systemic inflammation [4,5]. HCV infects hepatocytes and subsequently stimulates robust hepatic antiviral immune responses by stimulating the secretion of type III interferons, cytokines, and chemokines [6–8]. During HCV infection, cytokines and chemokines drive hepatic inflammation by recruiting mononuclear immune cells, including T cells and monocytes, into the liver. These inflammatory responses are thought to cause hepatocyte damage through several mechanisms that ultimately lead to liver fibrosis, cirrhosis and hepatocellular carcinoma (HCC) [9,10]. Distinct from HCV, HIV establishes infection within CD4 expressing immune cells such as CD4+ T lymphocytes and macrophages [5,11]. Infection with HIV causes significant perturbations within the immune system including CD4 T cell depletion and the induction of systemic inflammation through bacterial translocation across the gut mucosa [12–14]. Since the increased level of microbial products and metabolites can penetrate the liver, HIV infection may indirectly impact hepatic parenchymal and nonparenchymal cells. It has recently been reported that microbial pathogen-associated molecular patterns (PAMPs) from the leaky gut are sensed by hepatocytes, hepatic stellate cells (HSCs) and Kupffer cells (KCs) to trigger proinflammatory and profibrotic signaling [15]. Several studies have also demonstrated that HIV directly infects nonparenchymal liver cells, including HSCs and KCs, which constitute 4–8% of the total liver cell population [16]. Infection of KCs with HIV has been demonstrated by in vitro viral replication experiments and by detection of viremia in HIV infected patients or SIV infected macaques where KCs where specifically analyzed [17–20]. Subsequent interaction with HIV not only results in stimulating proinflammatory responses but also in altering KC phenotype and function [20,21]. In this and related inflammatory processes, pathogen recognition receptors (PRRs) including Toll like receptors (TLRs), Retinoic acid-inducible gene-I (RIG-I)-like receptors (RLRs), and Nucleotide oligomerization domain (NOD)-like receptors (NLRs) all have been shown to play an essential role in hepatic innate immune responses triggered by HIV PAMPs [22]. These receptors are expressed at a low level to maintain the tolerogenic state within the liver. However, upregulation of PRRs has been reported to increase hepatic inflammation and is linked to the progression of chronic liver disease [23,24]. This suggests that augmented hepatic innate immune responses may play a critical role in liver pathology in patients with HIV and HCV infection.
Similar to pathogen recognition receptors (PRRs), TREM1 is involved in innate immune and inflammatory responses. TREM1 is a 30KDa V-type IgG orphan immunoreceptor extensively expressed on the surface of neutrophils, monocytes, and macrophages. Activation of TREM1 signaling by crosslinking of the ligand results in production of tumor necrosis factor alpha (TNFα), IL-18 and C-C Motif Chemokine Ligand 2 (CCL2) through the adaptor DNAX-Activation Protein 12 (DAP12) [25,26]. This activation amplifies proinflammatory responses induced by TLR or NLR ligands [27]. Accordingly, TREM1 expression and activation has been reported to be linked to pathological conditions including rheumatoid arthritis, inflammatory bowel disease, and liver diseases including hepatocellular carcinoma [28–30].
Therefore, the current study sought to investigate the mechanisms through which HIV or HCV infection impacts innate immune responses in KCs and macrophages that express several distinct PRRs and orchestrate innate and adaptive immune responses. Our data demonstrate that stimulation with HIV or HCV induces robust inflammatory immune responses in KCs and monocyte derived macrophages (MDMs) independent of viral infection. We also show augmented inflammatory responses through TREM1 upregulation by HIV or HCV exposure. Furthermore, we confirm that increased TREM1 signaling stimulates inflammatory cytokine production from macrophages. These findings demonstrate that TREM1 contributes to inflammatory responses observed in KCs and macrophages during viral infection and that suppression of TREM1 signaling may be used as strategy to attenuate virus-induced liver disease progression and the subsequent development of HCC.
To characterize intrinsic innate immune responses in primary KCs, we first compared the basal expression of genes that are essential for intrinsic innate immunity in macrophages. We used THP1 monocytes for comparison as this cell line is representative of macrophage immune responses. Importantly, human primary cells were also used for comparison and included MDMs and non-parenchymal liver cells (NPCs). Quantitative PCR (qPCR) analysis revealed that KCs, NPCs and MDMs express higher endogenous mRNA levels of (RIG-I), melanoma differentiation-associated gene 5 (MDA-5), Interferon Gamma Inducible Protein 16 (IFI16), TLR3, and Myeloid Differentiation Primary Response 88 (MYD88) when compared to THP1 monocytes and hepatocytes (primary human hepatocytes and HepaRG). Conversely, the levels of cyclic GMP-AMP synthase (cGAS) and Stimulator of interferon genes (STING) were higher in the THP1 monocytes, but the expression of these cytosolic DNA sensors was lower in hepatocytes. (S1A–S1C Fig). We next examined the basal protein expression levels of several PRRs and their adaptor proteins. Since some genes were undetectable at baseline, we used THP1, MDMs, NPCs, and KCs that were first stimulated with interferon (IFN)α. Treatment with IFNα increased protein expression of RIG-I, MDA-5, IFI16, and MYD88 in NPCs and MDMs. In KCs, we observed the upregulation of MDA-5 following stimulation with IFNα. However, STING and MAVS levels did not increase in THP1, MDMs, NPCs, and KCs following stimulation with IFNα (Fig 1A and S1D Fig).
To further investigate innate immune responses in KCs, the cells were transfected (t) with poly(I:C) or interferon stimulatory DNA (ISD) for 24 hours. Then, qPCR analysis was performed in order to examine mRNA expression of genes involved innate immune responses, including C-X-C Motif Chemokine Ligand 10 (CXCL10), C-C Motif Chemokine Ligand 5 (CCL5), IFNβ, IL-28, Interferon Induced Protein With Tetratricopeptide Repeats 2 (IFIT2), and radical S-adenosyl methionine domain-containing protein 2 (RSAD2). Transfection of viral mimetics significantly increased innate immune signaling mRNA expression, whereas direct addition (d) of poly(I:C) or ISD to the culture media elicited marginal increases in mRNA expression (Fig 1B). Next, to identify secreted chemokines from KCs, the supernatants from cells transfected with poly(I:C) or ISD were analyzed via enzyme-linked immunosorbent assay (ELISA) analyses. Interestingly, stimulation with both RNA and DNA viral mimetics induced secretion of the chemokine CXCL10 from KCs (Fig 1C). Collectively, these data indicate that KCs possess functional innate immune signaling pathways, as do other macrophage cell types, and that they are capable of mounting antiviral immune responses following stimulation with diverse viral mimetics.
Although HIV has been shown to directly infect KCs in vitro and viral HIV RNA has been detected in the intracellular compartment of KCs isolated from HIV infected patients, the innate immune response to HIV remains to be fully clarified [17,18,21,31]. To evaluate whether HIV stimulates inflammatory responses in KCs, in an unbiased analysis, we exposed KCs to HIV-IIIB [multiplicity of infection (MOI) = 2] for 24 hours and performed microarray analyses. When comparing HIV-stimulated KCs to untreated controls, we identified HIV regulated genes utilizing a cutoff of P<0.05 and a fold-change greater than 2.0 with a false discovery rate (FDR) cutoff of 0.05.
Our microarray results demonstrate that HIV stimulation altered gene expression of various inflammatory and antiviral genes at the transcriptional level (Fig 2A and 2B). As shown in Fig 2C, upregulation of chemokines and interferon-stimulated genes were observed with the top 10 genes being induced greater than 15 fold. In addition, pathway analysis was performed using the Reactome Pathway Knowledgebase and verified using Qaigen’s Ingenuity Pathway Analysis software. Our data demonstrated that several general innate immune response pathways were upregulated in HIV-treated KCs, including immune signaling, inflammation, myeloid maturation, stellate cell activation and TREM1 signaling (Fig 2D, 2E and 2F). Overall, our microarray data suggest that HIV induces an inflammatory gene signature in KCs that may contribute to liver disease progression.
To validate our microarray data, we treated KCs with increasing MOI of HIV, for 24 hours, and assessed changes in expression of inflammatory genes by qPCR analysis. Several proinflammatory cytokines and chemokines including IL-1β, IL-6, CXCL10 and CCL5 were upregulated consistent with the microarray data (Fig 3A). We also validated protein expression levels of these genes by ELISA with supernatants obtained from HIV treated KCs (Fig 3B). Importantly, TREM1 protein upregulation was confirmed (Fig 3C and 3D) in MDMs and KCs following stimulation with HIV by flow cytometry or ELISA analysis. Additionally, we observed the upregulation of several interferon stimulated genes (S2A Fig). Finally, we confirmed that the viral particle was critical for stimulation since media obtained directly from the isolated viral stock, by filtration, did not induce CXCL10 or TREM1 gene expression (S2B Fig). To further confirm upregulation of this inflammatory gene signature in other macrophage/monocyte cell types, we stimulated human MDMs with HIV. qPCR analysis demonstrated that HIV stimulation in MDMs elicited the upregulation of similar inflammatory cytokines and chemokines (S3A Fig) as observed in the stimulated KCs. We also verified the purity of the macrophages and addressed the possibility of contaminating dendritic cells and neutrophils in our MDM preparations by quantitating the levels of CD68, CD15, and CD209 expression (S2C Fig). Next, we examined the expression of other key inflammatory proteins through a cytokine multiplex ELISA array. The levels of sixteen protein targets were measured in the supernatants from HIV treated KCs. These results demonstrated that HIV stimulation promotes the secretion of cytokines, such as TNF-α and IL-10 in KCs compared to untreated controls (Fig 3E). Overall, these data suggest that HIV simulation may drive inflammatory responses through the stimulation of liver and other tissue resident macrophages.
Next, we evaluated inflammatory responses to HCV in KCs and MDMs. Although KCs do not support HCV replication, several studies have reported that HCV stimulation induces proinflammatory cytokine and chemokine secretion, including IL-1β [7,32,33]. We subsequently assessed changes in gene expression by qPCR analysis utilizing KCs or MDMs stimulated with HCV. Similar to HIV, HCV increased the transcription of IL-1β, IL-6, and CCL5 (S4A Fig). We also detected upregulation of the anti-inflammatory cytokine IL-10. In addition, we confirmed upregulation of these genes following HCV stimulation in MDMs (S5A Fig). Next, we analyzed the production of proinflammatory cytokines, including IL-1β, IL-6 and the chemokine CCL5, through ELISA on supernatants from HCV stimulated KCs (S4B Fig). Cell surface expression of TREM1 was also quantitated on MDMs following stimulation with HCV using flow cytometry (S4C Fig). Together, our results demonstrate that HCV also induces a proinflammatory response in hepatic and non-hepatic macrophages.
Since we observed broad inflammatory responses in virus stimulated macrophages, the kinetics of gene induction following HIV or HCV stimulation was investigated. Several genes were upregulated in KCs following 3 hours of viral exposure. mRNA expression of inflammatory cytokines such as IL-1β and IL6 were elevated at 24 hours post stimulation; however, CCL5 peaked at 8 hours and decreased 24 hours after the addition of virus. In contrast, TREM1 gradually increased its expression and peaked at 24 hours after HIV or HCV stimulation (S3B and S5B Figs). Next, to determine if the inflammatory signature was dependent on viral infection, anti-CD4 or anti-CD81 receptor targeted neutralizing antibodies were applied to MDMs prior to HIV or HCV exposure and the treated cells were then tested for gene upregulation. Regardless of treatment with a non-specific control IgG or the specific receptor IgG antagonist, viral stimulation was able to upregulate inflammatory genes (S3C and S5C Figs). This suggests that viral infection and replication was not necessary to trigger the inflammatory response. To further determine whether viral replication was required for the observed inflammatory responses, we stimulated MDMs with UV inactivated virus (S3F and S5E Figs). We also examined virus endocytosis using p24 immunostaining and observed that UV irradiated viral particles were engulfed by macrophages (S3E Fig). Our qPCR analysis revealed that there were no differences in inflammatory gene induction between UV treated or untreated HIV or HCV (S3D and S5D Figs). These data suggest that macrophage responses to HIV or HCV are independent of replication during the early stages of viral exposure.
Recently, several studies have demonstrated that endocytosis is involved in HIV triggered inflammatory responses [33–35]. To investigate the influence of the endocytosis of virions that may subsequently trigger changes in genes expression, we utilized the small molecule Dynasore that inhibits caveolar- and clathrin-mediated pathways. The induction of several genes were unaffected, in MDMs, following treatment with Dynasore and HIV (Fig 4A). However, the induction of TREM1 gene expression was significantly decreased with Dynasore administration. Involvement of the endocytic process, in TREM1 upregulation, was specific to HIV and HCV stimulation since Dynasore administration had no effect on LPS-mediated TREM1 upregulation (Fig 4B and S6A Fig). We also tested the possibility that autocrine cytokine signaling may result in the upregulation of TREM1 in MDMs. MDMs were treated with supernatant filtrates of HIV stimulated MDMs at different time points. Importantly, the filtration step removed residual virions from the media while secretary cytokines would remain. Our results revealed that the media filtrate from HIV or HCV stimulated MDMs did not upregulate TREM1 (S6B Fig). Moreover, incubation with exogenous cytokines including TNF-α, IL-6 and IFNα did not upregulate TREM1 expression (S6C Fig).
To evaluate which viral components activate signaling that leads to TREM1 upregulation, we transfected MDMs with viral mimetics including poly(I:C), ISD, or 2’3’cGAMP. We also applied HIV genomic RNA directly to MDMs. Neither HIV genomic RNA, nor these viral mimetics stimulated the upregulation of TREM1 (S6D and S6E Fig). Since, HIV genomic RNA or these viral mimetics had no impact on TREM1 upregulation, we next determined whether HIV viral proteins could induce TREM1 gene expression. It has been reported that the transmembrane HIV envelope protein gp41 stimulates TREM1 upregulation in peripheral blood mononuclear cells (PBMCs) [36]. In addition, incubation with the recombinant HIV envelope protein gp160 and accessory protein, Tat, was shown to upregulate TREM1 in RAW 264.7 cells [37]. To determine if HIV viral proteins are capable of upregulating TREM1 gene expression in human macrophages, we stimulated MDMs with several HIV recombinant viral proteins and measured TREM1 protein expression on the cell surface by flow cytometry. In accordance with other studies, we confirmed upregulation of TREM1 by the HIV envelope protein gp120. Moreover, we observed that accessory proteins including Pro and Rev can enhance the surface expression of TREM1 (Fig 4C). However, HIV core protein p24 demonstrated no effect on cell surface expression of TREM1.
To determine which signaling pathways are responsible for TREM1 gene induction in human macrophages during HIV or HCV stimulation, we used pathway-specific inhibitors. TREM1 gene expression is regulated by several transcription factors, including nuclear factor-kappa B (NF-κB), Activator protein 1 (AP-1), and cAMP response element binding protein (CREB). Accordingly, we first confirmed that HIV stimulation induces NF-κB, activation in KCs by staining for p65 nuclear localization and then utilized the corresponding antagonists of these signaling pathways (Fig 4D). Interestingly, TREM1 gene induction was significantly decreased by the IKK complex selective inhibitor BMS345541 (Fig 4E and S6F Fig). Additionally, treatment with the MAP kinase inhibitor SB203580 partially suppressed TREM1 gene induction, suggesting that activation of AP-1 is required. However, treatment with a TANK-binding kinase 1 (TBK1) inhibitor BX795 was unable to abolish upregulation of TREM1 demonstrating the involvement of signaling pathways that are distinct from those involved in IFN production. We confirmed that cell viability was unaffected following treatment with these small molecules using the alamarBlue assay (S6G Fig). Next, we examined expression of the TREM1 protein, by flow cytometry, after treating with the HIV viral proteins Pro and Rev in the presence of the NF-KB inhibitor. Fig 4F demonstrates that TREM1 upregulation by these viral proteins was significantly decreased in cells that were treated with BMS345541. Taken together, these findings demonstrate that TREM1 gene expression is modulated by the endocytosis of virions and that HIV viral proteins induce TREM1 through NF-κB signaling.
Given that TREM1 plays a significant role in inflammatory responses, we generated a stable cell line overexpressing TREM1 using lentiviral transduction of THP1 cells to evaluate the function of TREM1 signaling during viral exposure. We confirmed TREM1 mRNA expression by qPCR analysis and cell surface expression by flow cytometry (S7A Fig). To determine if exogenous expression of TREM1 had an effect on baseline expression of genes involved in inflammatory signaling, the endogenous mRNA expression of NFKB1A, IKBKB, MYD88 and Toll Like Receptor Adaptor Molecule (TRIF) were compared. qPCR analysis demonstrated that the exogenous expression of TREM1 did not alter the gene expression of NF-κB or TLR4 signaling adaptors (S7B Fig). It has been reported that TREM1 amplifies innate immune responses through receptor mediated activation by binding its cognate ligand. Accordingly, we used an antibody agonist to stimulate TREM1 signaling that induces known target genes including Inhibin Beta A (INHBA). We treated the parental THP1 and our TREM1 overexpressing THP1 cells with the agonist to verify specificity. Following treatment with the TREM1 agonist, TREM1 expressing THP1 cells robustly upregulated expression of INHBA, while the agonist minimally affected INHBA gene induction in the parental THP1 cells (S7C Fig). Next, we investigated the role of TREM1 signaling in HIV and HCV induced immune responses. We incubated these viruses with control IgG or the TREM1 agonist in KCs or MDMs. qPCR analysis demonstrated that stimulation with HIV or HCV in combination with the TREM1 agonist enhanced the upregulation of IL-1β, IL-6, TNFα, and TREM1 itself (Fig 5A and 5B and S7D Fig). Consistent with our qPCR results, ELISA analysis demonstrated that the TREM1 agonist alone increased IL-6 production. When compared to either virus or TREM1 agonist alone, incubation with both HIV or HCV and the TREM1 agonist resulted in 10-fold higher secretion of proinflammatory cytokines (Fig 5C and 5D and S7E and S7F Fig). However, this effect was limited to inflammatory cytokines. Collectively, our data demonstrate that activation of TREM1 results in significant upregulation of IL-1β, TNFα, and IL-6 and that activation of TREM1 signaling augments inflammatory cytokine production in macrophages.
After ligand binding, activation of TREM1 signaling is mediated by homotypic interactions between the immunoreceptor tyrosine-based activation motifs (ITAM) between TREM1 and the adaptor DNAX-Activation Protein 12 (DAP12). This interaction results in phosphorylation of DAP12 which facilitates the recruitment of Src family kinases to activate downstream signaling involving p38 mitogen-activated protein kinase (p38MAPK), c-Jun amino-terminal kinases (JNK) and the extracellular signal-regulated kinases1 and 2 (ERK1/2) pathways [28]. To determine which downstream signaling pathways are stimulated by TREM1 activation, selective inhibitors targeting ERK1/2, p38MAPK, or JNK were utilized after stimulating the TREM1 expressing THP1 cells with the TREM1 agonist. INHBA gene induction was assessed by qPCR analysis. Among them, U0126, an ERK1/2 inhibitor, significantly suppressed gene upregulation of INHBA (S8A Fig). We also performed Western blot analysis on MDMs following stimulation with HIV or HCV in the presence or absence of the TREM1 agonist. Fig 6A demonstrates that TREM1 activation resulted in increased levels of phosphorylated ERK1/2 following stimulation with HIV or HCV (Fig 6A and S8B Fig). Involvement of ERK signaling was further confirmed through studies utilizing U0126. In the presence of the inhibitor, the TREM1 agonist failed to enhance the expression of IL-1β, IL-6 and TNFα (Fig 6B and 6C, S8C and S8D Fig). In addition, we utilized ERK2 siRNA to knockdown and subsequently confirm the role of ERK in TREM1 signaling. In accordance with ERK1/2 inhibitor, silencing of ERK2 decreased inflammatory responses which were elicited by the TREM1 agonist (S9A and S9B Fig). Taken together, these findings support the conclusion that the robust activation of ERK signaling, following TREM1 stimulation, augments HIV or HCV driven inflammatory responses in macrophages.
To demonstrate that TREM1 signaling modulates HIV-induced inflammatory responses, we downregulated endogenous TREM1 expression using targeted siRNAs and confirmed siRNA knockdown of TREM1 protein expression by ELISA (Fig 7A and 7B). TREM1 was downregulated in MDMs by approximately 70% and the ability of HIV or HCV to upregulate inflammatory response was diminished in siRNA treated cells. In addition, HIV stimulation of IL-1β, IL-6, and TNFα was abrogated following downregulation of TREM1 in MDMs. Similarly, downregulation of TREM1 decreased IL-1β and IL-6 protein production in HCV stimulated MDMs (Fig 7A and 7B, and S10A and S10B Fig). The effect of TREM1 down-regulation on stimulation with the TREM1 agonist was also assessed. Our qPCR results demonstrated that TREM1 agonist mediated gene upregulation was decreased in cells treated with TREM1 siRNA (Fig 7C). Collectively, our data suggest that upregulation and subsequent activation of TREM1 modulates HIV or HCV induced inflammatory responses.
To validate our in vitro results, we obtained PBMC samples from HCV infected (n = 14), HIV infected (n = 10) and healthy individuals (n = 5) and the expression of TREM1 and CD68 was assessed by flow cytometry. We observed that the percentage of TREM1 positive macrophages in HCV-infected patients was not significantly different than the healthy control population. Importantly, HIV infected patients had a significantly higher number of TREM1 positive macrophages when compared to the uninfected controls (Fig 8A and 8B). Next, we further examined TREM1 expression in PBMCs and plasma from healthy controls (n = 3) and HIV infected individuals (n = 10). qPCR analysis demonstrated that PBMCs from HIV infected patients have significantly higher TREM1 expression levels when compared to healthy controls (Fig 8C). Similarly, plasma collected from HIV patients contained significantly higher levels of soluble (s)TREM1 (Fig 8D). Therefore, these results indicate that high TREM1 expression on myeloid cells, in the blood, may be a novel characteristic of HIV infected individuals and that targeting TREM1 signaling might provide a therapeutic strategy to ameliorate virus-induced chronic inflammation.
Despite effective control of AIDS by antiretroviral therapy (ART), the HIV pandemic is still a major health concern worldwide. Infected individuals frequently manifest chronic systemic inflammation that can result in cardiovascular, kidney and liver disease [38–41]. Similarly, HCV infection also causes chronic inflammation in the liver and ultimately the development of cirrhosis and HCC [7,9]. In this study, we characterized the innate immune response in KCs and other macrophages and elucidated a molecular mechanisms contributing to inflammatory responses observed in HIV or HCV infected individuals. We demonstrated that stimulation with HIV or HCV upregulates inflammatory cytokines, chemokines, and TREM1 expression which subsequently can amplify inflammatory responses. These inflammatory responses do not require viral infection or viral replication but are driven by exposure to viral proteins on intact virions. Similar to our studies, several papers have already demonstrated that HIV or HCV are capable of activating the inflammasome [32,33,42,43] which is another important driver of systemic inflammation. Specifically, our KC microarray data demonstrates that HIV activates antiviral and inflammatory responses and that these immune responses are elicited without viral infection and replication. The most upregulated subset of genes in KCs following HIV-1 exposure were antiviral genes. We observed increased levels of various interferon stimulated genes (ISGs) including IFI44L, ISG15, DDX58, RSAD2, IFITM and the IFIT family of proteins. Supporting this observation, previous reports have demonstrated that HIV stimulation leads to robust ISG and inflammatory gene upregulation in MDMs. Recently, Decalf et al. demonstrated that HIV fusion to the cell membrane of target cells induces low level IFN protein expression as determined by sensitive bead-based assays and secreted IFN ultimately contributes to the upregulation of ISGs [34]. However, in our studies, we demonstrated that endocytosis of HIV can also elicit inflammatory responses in macrophages but we were unable to demonstrate the production of IFN. Nasr et al. also investigated the mechanism of ISG induction by HIV in macrophages [44]. Their results demonstrated that at early time points, ISGs are upregulated by extracellular vesicles (EVs) within the viral inoculum that was used to stimulate macrophages. Our filtration experiments demonstrated that intact virions can also directly stimulate inflammatory responses in macrophages. In addition, they demonstrated that at later time points, newly synthesized mRNA from HIV can also stimulate inflammatory responses. However, the inflammatory response that we report here are independent of viral nucleic acids. Collectively, data from this report and others demonstrate that HIV stimulation leads to antiviral responses in macrophages and we have extended these studies to include specific regulation of TREM1 in KCs.
Importantly, our KC microarray analysis highlighted the activation of TREM1 signaling during HIV stimulation. TREM1 is exclusively expressed in the myeloid cell lineage and is involved in the amplification of production of inflammatory cytokines and chemokines during viral infection. We verified TREM1 upregulation by HIV and HCV in human macrophages including KCs and MDMs. We also demonstrated that cell surface recognition of the HIV particle through endocytosis stimulates TREM1 gene upregulation. Indeed, HIV viral proteins including gp120, pro, and rev, but not the HIV genome, trigger the upregulation of TREM1 gene expression. Moreover, experiments conducted with distinct cell signaling inhibitors revealed that the activation of NF-KappaB signaling is responsible for HIV mediated TREM1 gene upregulation. Importantly, we also observed significant upregulation of TREM1 in HIV infected patient samples. Supporting our observation, two recent studies have demonstrated that TREM1 upregulation occurs following stimulation with HIV viral proteins. Denner et al. examined the effect of the HIV-1 transmembrane envelope protein gp41 on PBMCs and characterized changes in gene expression of both proinflammatory cytokines and TREM1 through microarray analysis [36]. The authors also found increased levels of soluble TREM1 (sTREM1) in the supernatant from stimulated cells. sTREM1 is either a splice variant or it is a shed form of TREM1 and it has been considered a negative regulator of TREM1 [45]. We were able to detect it in HIV infected patient blood (n = 13, Fig 8D). Since PBMCs contain various cell types, it is possible that sTREM1 is produced by cells other than macrophages in response to gp41 stimulation [36]. Yuan et al. demonstrated that TREM1 is upregulated in MDMs following HIV stimulation. They also demonstrated that stimulation of Raw264.7 cells with recombinant gp120 and Tat proteins upregulated TREM1 expression and prevented cells from undergoing apoptosis. In our studies, we also found that gp160 and other HIV proteins upregulate TREM1, indicating that more than one HIV protein may induce TREM1 gene expression in vivo [37].
TREM1 was also upregulated on CD68 positive cells in the blood of HCV infected patients (Fig 8B) but the results were not significant. This may be due to the fact that unlike HIV, HCV is a hepatotrophic virus that does not cause significant hematologic pathology. Indeed, statistically significant results were obtained from the blood of HIV infected patients that demonstrated much greater expression on TREM1 (Fig 8). Future studies on hepatic expression of TREM1, using liver biopsies from infected patients, will be carried out to confirm our results obtained using KCs.
Studies focusing on other distinct viruses have also demonstrated that viral infection results in upregulation of TREM1 and that expression can be organ specific. Elevated expression of TREM1 was observed in the brains from mice infected with pathogenic West Nile virus (WNV) NY99 [46]. Marburg virus (MARV) and Ebola virus (EBOV) have also been reported to induce TREM1 gene expression in neutrophils [47]. sTREM1 is found in the sera of patients infected with Crimean Congo Haemorrhagic Fever (CCHF) virus, DENV, HBV, and HCV [48–51]. Accordingly, upregulation of TREM1 may modulate virus mediated inflammation as a general host defense mechanism and subsequent driver of disease progression in chronic viral infection. Kozik et al. has addressed the function of TREM1 by utilizing lymphocytic choriomeningitis virus (LCMV) that causes murine viral hepatitis in WT or TREM1 deficient mice. Their study revealed that LCMV infection upregulates TREM1 in neutrophils and that TREM1 deficiency decreased viral mediated liver damage. This indicates that TREM1 signaling can augment virus driven liver pathology as we propose for patients with HIV and HCV coinfection. Weber et al. demonstrated that TREM1 did not affect viral clearance but TREM1 deficiency increased the survival of mice during influenza virus infection [52]. Moreover, using an in vitro model, treatment with an antagonist peptide, LP17, dampened inflammatory responses and abrogated the production of TNF-α and IL-1β in MARV and EBOV stimulated neutrophils [47]. Our studies also demonstrated that activation of TREM1 signaling significantly enhanced HIV or HCV mediated inflammatory responses through the ERK signaling pathway. Given that TREM1 amplifies PRR signaling, it is likely that virus induced activation of PRRs in the liver [8,53] may also contribute to TREM1 associated hepatic inflammatory responses during HIV or HCV infection.
In the liver, TREM1 expression has been implicated in multiple hepatic diseases including liver cancer, fatty liver disease, alcoholic liver disease and other drivers of liver injury and fibrosis [54–57]. Duan et al. found that TREM1 not only modulates inflammation in liver tissue, but promotes the proliferation and spread of cancer cells. The authors demonstrated that higher expression of TREM1 in HCC patients contributed to mortality and increased recurrence [57,58]. Wu et al. has specifically demonstrated that TREM1 expression on KCs modulates inflammatory responses and drives the development of HCC using a diethylnitrosamine (DEN) induced HCC mouse model [30]. In addition, Nguyen-Lefebvre et al, from the same group, reported that KCs argument chronic liver inflammatory responses and promotes hepatic fibrogenesis through Notch and Oncostatin M (OSM) signaling using a carbon tetrachloride mouse model and the data was supported by analyzing samples from patients with hepatic fibrosis [57]. Perugorria et al reported that TREM2, a negative regulator of TREM1, is also upregulated on non-parenchymal liver cells in cirrhotic livers from humans and mice. The authors also reported that TREM2 deficiency heightened carbon tetrachloride and acetaminophen induced liver injury and that TREM2 carries out its protective functions by suppressing ROS production and lipid peroxidation [59]. These studies implicate TREM1, and associated regulatory molecules, in liver-specific inflammation and our data demonstrate that TREM1 upregulation by HIV and HCV, in macrophages, amplifies virus induced inflammatory responses. Additional studies have also reported that endogenous ligands for TREM1, HMGB1 and HSP70, are also upregulated in various liver diseases and may further drive hepatic inflammation through TREM1 [60–64]. This suggests that pharmacologic inhibition of TREM1, in patients with HIV and viral hepatitis coinfection, may be an attractive strategy to limit subsequent systemic and hepatic inflammation and the subsequent development of HCC.
The human monocytic cell line THP-1 (ATCC) and H9 cells (ATCC) were maintained in RPMI 1640 medium (Invitrogen) containing 10% Fetal Bovine Serum (FBS) (45000–736, VWR) 1% penicillin/streptomycin (15140–122, Life Technologies), and 50mM β-Mercaptoethanol (21985023, ThermoFisher Scientific). Primary human monocytes were isolated by negative selection using the human monocyte enrichment kit (19059, Stem cell Technologies) from PBMCs which were isolated from the buffy coat by Ficoll-Paque Plus (17-1440-02, GE Healthcare) Then, monocytes were maintained Dulbecco’s Modified Eagle Medium containing 10% human serum (1830–0002, SeraCare), 1% L-glutamine (35050–061, Life Technologies), and 1% penicillin/streptomycin. Macrophage differentiation was conducted by culturing with 6 ng/ml M-CSF (300–25, Peprotech) for 4 days and maintained in fresh media without M-CSF for 2 or 3 days. Primary human Kupffer cells (KCs) were obtained from Life Technologies (HUKCCS) and maintained in Advanced DMEM (D5796, Sigma), 1% ITSx (41400–045, Thermo Fisher), 10% FBS, 15mM HEPES (15630–080, Life Technologies), 1% GlutaMax, and 1% penicillin/Streptomycin (Life Technologies). All cells were maintained at 37°C in 5% carbon dioxide.
All participants were recruited from University of Miami, Jackson Memorial Hospital in Miami, FL. The three patient populations were volunteers in this study (a) 11 HIV infected, (b) 16 HCV infected, and (c) 5 healthy control. HIV or HCV infection was determined by OraQuick (1001–0079 and 1001–0181, OraSure) HIV or HCV antibody test. Peripheral blood mononuclear cells (PBMCs) were isolated and used for experiments. This study was approved by the Institutional Review Board at the University of Miami and Jackson Health System’s Office of Research and Grants.
This study was conducted using approved IRB by Coordinating Committee from University of Miami and University of Pittsburgh. All participants were adult and written informed consent was provided by the donors/ patients.
Alpha IFN 2α (IFN-α) was purchased from PBL (11105–1) and used to treat cells for 24 hours at 1000 U/ml unless otherwise indicated. Recombinant human IL-6 (200–06) and TNFα (300-01a) were purchased from Peprotech. polyinosinic-polycytidylic acid (poly(I:C)(tlrl-picw), 2’3’-cGAMP(tlrl-nacga23), LPS-B5 (tlrl-pb5lps) were purchased from Invivogen. IFN stimulatory DNA (ISD), which is a 90-bp non-CpG oligomer, was synthesized using following primers: 5’-TACAGATCTACTAGTGATCTATGACTGATCTGTACATGATCTACATACAGATCTACTAGTGATCTATGACTGATCTGTACATGATCTACA-3’ and 5’-TGTAGATCATGTACAGATCAGTCATAGATC ACTAGTAGATCTGTATGTAGATCATGTACAGATCAGTCATAGATCACTAGTAGATCTGTA-3’.
Signaling inhibitors used as following: Dynasore (ab120192, Abcam), BMS345541 (B9935, Sigma), BX795 (tlrl-bx7, Invivogen), SB203580 (S8307, Sigma), U0126 (Cell signaling), SB203580 (Sigma), and SP600125 (Sigma). All drugs were prepared in DMSO or water according to the company instruction. The following reagents were obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: p96ZM651gp160-opt and p96ZM651gag-opt from Drs. Yingying Li, Feng Gao, and Beatrice H. Hahn (PMID: 14585212). Western blot analysis was performed using the following antibodies: anti-MDA5 (AT113, Enzo Life Sciences), anti-RIG-I (AT111, Enzo Life Sciences), anti-IFI16 (HPA002134, Sigma), anti-MAVS (ab31334, abcam), anti-IкBα (CL-21, Santa Cruz). All other antibodies are obtained from Cell Signaling: anti-MYD88 (4283), anti-STING (13647), anti-phospho ERK1/2(4370), anti-ERK1/2(4695). The following antibodies or agonists were used for neutralizing assay: anti-human TREM1 (Mab1278, R&D System), anti-human CD81 (555675, BD Biosciences), anti-human CD4 (344602, Biolegend), and Isotype Mouse IgG1 (400101, Biolegend). The following reagents were obtained through the NIH AIDS Reagent Program, Division of AIDS, NIAID, NIH: HIV-1 BaL gp120 Recombinant Protein (4961), HIV-1 HXB2 Rev Recombinant Protein (12707), HIV-1 HXB2 Pro Recombinant Protein (1178), and HIV-1 IIIB p24 Recombinant Protein (12028).
Human embryonic kidney 293 cells (293T HEK cells, CRL-3216, ATCC) were transfected with HIV Bal encoding plasmid (provided by Dr. Stone) using Lipofectamine 3000(L3000-008, Life Technologies), and supernatant of the cells was collected and filtered through a 0.45-μM filter. Filtered supernatant was further concentrated using a 50KDa cut-off filter (VS15T32, Sartorius) after washing with PBS [65]. HIV IIIB strain were obtained from Dr. Dykxhoorn. HIV p24 amount was measured by ELISA (NEK050001KT, Perkin elmer) and capacity of viral replication was tested in TZM-BL reporter cells (NIH AIDS Reagent Program from Dr. John C. Kappes, Dr. Xiaoyun Wu and Tranzyme Inc) (100 ng p24 equivalents of HIV was calculated to be a MOI of 0.1. Unless otherwise stated, a MOI = 1 was used in the experiment. HCV JFH-1 was obtained from Dr. Jake Liang (NIH) and propagated as previously described [66]. For UV inactivation, HCV were UV irradiated in a UVC 500 UV Crosslinker (Hoefer) for a total dose of 0.6 J/cm2 and HIV was irradiated in a six-well cell culture dish at an intensity of 1.5 J/cm2 [67]. In order to isolate viral genome, virus containing supernatant was filtered through a 0.45-μM filter unit and precipitated using PEG-it Virus Concentration Solution (LV810A-1, System Biosciences). From the pellet, viral RNA isolation was performed using Trizol (15596–018, Life Technologies).
After isolating the total RNA from MDMs with the RNeasy kit (74106, Qiagen), the isolated RNA was quantified with a NanoDrop (ThermoFisher) and was analyzed with an Agilent bioanalyzer (Agilent Technologies, Palo Alto, CA) for RNA quality. RNA was amplified with an Agilent Enzo kit and amplified complimentary RNA was subject to perform an Affymetrix Human 133 Plus 2.0 microarray chip containing 54,675 gene transcripts. Obtained data were normalized using the robust multiarray average (RMA) algorithm and analyzed by Partek Prosoftware (Partek, St. Charles, MO). To identify genes in the gene ontology analysis, we used the commercial gene pathway analysis web tool (https://portal.genego.com/). The signal values of each probe set ID from the selected gene lists were plotted by the commercial software Partek to generate the heat map.
Further pathway analysis was performed using Reactome Pathway Knowledgebase open-source software, a curated and peer-reviewed aggregate pathway database [68,69]. Results were supported by verification of Qiagen Ingenuity Pathway Analysis software. Gene set analysis was performed, and top ranked pathways by–log (p-value) were selected and visualized using Graphpad Prism 7 Software. The microarray data can be found using the Gene Expression Omnibus accession number GSE69589 (https://www.ncbi.nlm.nih.gov/genbank/).
Total RNA from cells were isolated with Trizol reagent (Invitrogen, USA) or RNAeasy kit (74106, Qiagen), and 1 μg of RNA was used for reverse-transcription to cDNA using qScript cDNA Supermix (101414–106, Quanta Bio-Sciences) according to manufacturer’s instructions. Real-time RT-PCR was performed using PerfeCTa FastMix II (97065–990, Quanta Bio-Sciences). Fluorescence real-time PCR reactions were run using C100 Touch Thermal Cycler (Bio-Rad) instrument. All primers and probes for quantitation of mRNA for target genes were purchased from IDT or ABI (Applied Biosystems) and FAM-Labeled TaqMan Probe, were normalized to endogenous control eukaryotic 18S ribosomal RNA. (4319413E, Life Technologies)
Monocytes were cultured and differentiated in Upcell dish (Z688789, Sigma) and cells were detached cells with Accutase (561527, BD Bioscience). siControl NonTargeting siRNA (D-001210–02), Smart Pool siTREM1 (M-017974-00-005), Smart Pool siERK2 (M-003555-04-0005) were purchased from GE life science (Dharmacon). 0.25×106 monocytes were differentiated to macrophages in a 48 well plate and transfected with small interfering RNA (siRNA) at a final concentration of 200nM/well using RNAiMAX (13778–030, Invitrogen). After 48 hours, cells were challenged with HIV or HCV and subjected to qPCR analysis.
Differentiated Monocyte derived macrophages were treated with Accutase for 20 min and detached with gentle pipetting. Cells were washed with FACS buffer (5%FBS, 2mM EDTA, 0.09% sodium azide in PBS) and incubated with FcR binding inhibitor (14-9161-73, ebioscience) for 20min on ice. Cells were then incubated with human TREM-1 phycoerythrin conjugated monoclonal antibody (FAB1278P, R&D System), or mouse IgG1 phycoerythrin Isotype Control (IC002P, R&D System), 30min at 4°C. BD Cytoperm kit (BDB554714, BD Bioscience) was used for intracellular staining of CD68 using human CD68 BV421(BD564943, BD Bioscience) and data was acquired on a Becton Dickinson LSRF Fortessa (Beckman, USA) and analyzed with FlowJo (Tree Star, USA).
Supernatants from cells were harvested and ELISA was performed to detect Human CCL5 (DY278-05), IL-1β (DY1270-05), IL-6 (DY206-05), TREM1 (DTRM10C) according to the manufacture’s protocol. All ELISA kits were purchased from R&D System except VeriPlex Human Cytokine 16-Plex ELISA Kit (51510–1, PBL).
Human TREM1 encoding lentiviral plasmid (LV343336) and lentiviral packaging mix (LV003) were purchased from Applied Biological Materials Inc. 293T HEK cells were transfected with TREM1 expressing lentivirus production as it described in manufacture’s guideline. 1×106 THP1 cells were used for lentiviral transduction with 1 μg/ml polybrene (H9268, Sigma). Twenty four hours post transduction, cells were washed with fresh media and maintained in selection media containing 2 μg/ml puromycin (A11138-03, Life Technologies). Expression level of TREM1 in THP1 cells (THP1-TREM1) was measured by ELISA and Flow cytometry.
Statistics significance was determined by the unpaired student’s t-test. The asterisks indicate *p < 0.05, **p < 0.01, and ***p < 0.001.
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10.1371/journal.pmed.1002736 | The effect of a programme to improve men’s sedentary time and physical activity: The European Fans in Training (EuroFIT) randomised controlled trial | Reducing sitting time as well as increasing physical activity in inactive people is beneficial for their health. This paper investigates the effectiveness of the European Fans in Training (EuroFIT) programme to improve physical activity and sedentary time in male football fans, delivered through the professional football setting.
A total of 1,113 men aged 30–65 with self-reported body mass index (BMI) ≥27 kg/m2 took part in a randomised controlled trial in 15 professional football clubs in England, the Netherlands, Norway, and Portugal. Recruitment was between September 19, 2015, and February 2, 2016. Participants consented to study procedures and provided usable activity monitor baseline data. They were randomised, stratified by club, to either the EuroFIT intervention or a 12-month waiting list comparison group. Follow-up measurement was post-programme and 12 months after baseline. EuroFIT is a 12-week, group-based programme delivered by coaches in football club stadia in 12 weekly 90-minute sessions. Weekly sessions aimed to improve physical activity, sedentary time, and diet and maintain changes long term. A pocket-worn device (SitFIT) allowed self-monitoring of sedentary time and daily steps, and a game-based app (MatchFIT) encouraged between-session social support. Primary outcome (objectively measured sedentary time and physical activity) measurements were obtained for 83% and 85% of intervention and comparison participants. Intention-to-treat analyses showed a baseline-adjusted mean difference in sedentary time at 12 months of −1.6 minutes/day (97.5% confidence interval [CI], −14.3–11.0; p = 0.77) and in step counts of 678 steps/day (97.5% CI, 309–1.048; p < 0.001) in favor of the intervention. There were significant improvements in diet, weight, well-being, self-esteem, vitality, and biomarkers of cardiometabolic health in favor of the intervention group, but not in quality of life. There was a 0.95 probability of EuroFIT being cost-effective compared with the comparison group if society is willing to pay £1.50 per extra step/day, a maximum probability of 0.61 if society is willing to pay £1,800 per minute less sedentary time/day, and 0.13 probability if society is willing to pay £30,000 per quality-adjusted life-year (QALY). It was not possible to blind participants to group allocation. Men attracted to the programme already had quite high levels of physical activity at baseline (8,372 steps/day), which may have limited room for improvement. Although participants came from across the socioeconomic spectrum, a majority were well educated and in paid work. There was an increase in recent injuries and in upper and lower joint pain scores post-programme. In addition, although the five-level EuroQoL questionnaire (EQ-5D-5L) is now the preferred measure for cost-effectiveness analyses across Europe, baseline scores were high (0.93), suggesting a ceiling effect for QALYs.
Participation in EuroFIT led to improvements in physical activity, diet, body weight, and biomarkers of cardiometabolic health, but not in sedentary time at 12 months. Within-trial analysis suggests it is not cost-effective in the short term for QALYs due to a ceiling effect in quality of life. Nevertheless, decision-makers may consider the incremental cost for increase in steps worth the investment.
International Standard Randomised Controlled Trials, ISRCTN-81935608.
| Gender-sensitised lifestyle change programmes in a professional sport setting are an exciting development in men’s health promotion, with the potential to engage men who are underserved by most programmes.
A healthy lifestyle and weight management programme delivered in professional sporting settings (Football Fans in Training [FFIT]) has been shown to be effective and cost-effective in delivering long-term weight loss in overweight and obese Scottish football fans.
We drew on the success of FFIT to develop and evaluate the EuroFIT programme in four European countries. Whereas FFIT introduced physical activity and dietary change for weight loss, EuroFIT focused on increasing physical activity and reducing sedentary time as desirable outcomes in their own right.
We conducted a randomised controlled trial (n = 1,113) in 15 football clubs in four countries and showed that the EuroFIT programme was effective in increasing objectively measured physical activity but not sedentary time 12 months after baseline.
EuroFIT participants also showed improvements in diet, body weight, indicators of cardiometabolic health, well-being, and other secondary outcomes.
EuroFIT was not cost-effective in the short term because there were no differences in quality of life because, on the measure we used, participants already had high levels of quality of life at baseline.
Gender-sensitised lifestyle programmes delivered in professional football clubs have shown great promise in Europe and could play an important public health role in engaging underserved men.
Changing time spent sedentary proved difficult. Future lifestyle intervention studies should attempt to ensure that participants understand the distinction between being more physically active and spending more time upright.
| Physical activity is important in preventing chronic diseases, including cardiovascular disease, type 2 diabetes, and several cancers [1,2]. Global recommendations from the World Health Organisation (WHO) advise at least 150 minutes per week in moderate-to-vigorous physical activity. Recent estimates show that nearly one third of adults worldwide do not meet these recommendations and around 9% of premature deaths worldwide in 2008 can be attributed to lack of physical activity [2]. Not meeting the WHO physical activity recommendations costs healthcare systems globally 53.8 billion international dollars (INT$), with an additional indirect cost of INT$13.7 billion [3].
Sedentary behaviour has recently been shown to be associated with all-cause and cardiovascular mortality, independently of physical activity [4]. Sedentary behaviour is defined as any waking behaviour in a sitting, reclining, or lying posture with energy expenditure ≤1.5 metabolic equivalent tasks (METs) [5]. A meta-analysis has shown that interventions focusing primarily on physical activity have little effect on sedentary behaviour [6], and a specific focus on sedentary behaviour is needed to achieve substantial improvements in sedentary behaviour. Combining such a specific focus on sedentary behaviour in a lifestyle intervention programme with a focus on both physical activity and diet is novel, and given the contribution of all three behaviours to the burden of the world’s leading noncommunicable diseases, such a programme could have a substantial public health impact.
Men are often underrepresented in behavioural lifestyle interventions and are considered a hard-to-reach and underserved group [7]. However, many men lead an unhealthy lifestyle and are at high risk for developing noncommunicable diseases. It has been suggested that of all facets of health promotion, physical activity might be the most likely behaviour to engage men with their health. A systematic review has identified gender-sensitised physical activity programmes as a key development in men’s health promotion, with the potential to engage hard-to-reach men. The review also reported that all four identified studies that involved men engaging in physical activity with other men through professional sports resulted in increased physical activity [8]. Gender-sensitised physical activity programmes for men may also provide useful strategies in promoting other areas of men’s health. Another systematic review concluded that weight loss and maintenance for men is best achieved with interventions increasing physical activity and improving diet while using behaviour change techniques [7].
Achieving sustainable health behaviour change is challenging, and at-risk population groups, including overweight and/or inactive men, are difficult to engage and underserved. The Scottish Football Fans in Training (FFIT) lifestyle programme was designed to attract overweight men and enable them to lose weight through improvements in physical activity and diet. FFIT was shown to be cost-effective in supporting clinically significant weight loss. It also significantly improved self-reported physical activity and diet at 12 months [9], and improvements were partially maintained 42 months after baseline [10]. The multi-country European Fans in Training (EuroFIT) programme shifted the focus from weight loss to improving physical activity and sedentary time [11]. Like FFIT, EuroFIT uses the allegiance many fans have to their football club to attract at-risk men to a group-based lifestyle change programme delivered in their clubs.
This paper describes the results from the randomised controlled trial that aimed to evaluate the effectiveness of the EuroFIT lifestyle programme. The primary aim of the trial is to determine whether EuroFIT can help men aged 30–65 years with a self-reported body mass index (BMI) ≥27 kg/m2 to increase objectively assessed physical activity and decrease objectively assessed sedentary time over a 12-month period. Secondary outcomes of the trial include cost-effectiveness, food intake, body weight, BMI, waist circumference, resting systolic and diastolic blood pressure, cardiometabolic blood biomarkers, well-being, self-esteem, vitality, and quality of life.
We undertook a pragmatic two-arm randomised controlled trial in 15 professional football clubs from leagues in England (five clubs), the Netherlands (four clubs), Norway (three clubs), and Portugal (three clubs). Study participants were randomised to receive the intervention or a waiting list comparator (1:1), stratified by club. The study protocol is published [11].
Football clubs were selected by contacting clubs known by the study team to be likely to be interested in taking part. We sought a minimum of three and a maximum of five in each country, and the first 15 clubs that signed up were included. Clubs were Arsenal, Everton, Newcastle, Manchester City, and Stoke (England); Ado den Haag, Groningen, Philips Sport Vereniging (PSV), and Vitesse (the Netherlands); Rosenborg, Strømsgodset, and Vålerenga (Norway); and Benfica, Porto, and Sporting (Portugal).
Football clubs led recruitment of participants using emailed invitations to fans, the club website, social media posts, features in local press, and match-day recruitment.
Participants registered interest online, providing contact details, age, self-reported height and weight, and preferred football club. A follow-up telephone call included the adapted Physical Activity Readiness Questionnaire-Plus questionnaire (PAR-Q+) [12], previous participation in health promotion programmes at the club, and asking if men were willing to consent to randomisation and to wearing an activity monitor for 1 week at baseline and again at both follow-up assessments. On the consent form, men had the opportunity to opt into providing blood samples at the baseline and the 12-month follow-up measurements.
Men were eligible if they were aged 30–65, had a self-reported BMI of ≥27 kg/m2, and consented to study procedures. Men were excluded if they reported a contraindication to moderate intensity physical activity in the PARQ+ or participation in an existing health promotion programme at the club, or did not provide at least 4 days of usable activity monitor data at baseline.
Participants were randomly allocated to intervention or comparison groups following baseline measurement. The allocation sequence for each football club was generated by a computer programme written by a statistician not involved in the final analysis. The sequence was generated using randomised permuted blocks, stratified by club, with block lengths of 4 and 6, at random. The sequence was securely stored, with access restricted to those responsible for maintaining the randomisation system.
Trial coordinators accessed randomisation allocation via a secure online portal. They informed participants by telephone and email whether they had been allocated to start the EuroFIT programme immediately (the intervention group) or to undertake the programme 12 months later (the waiting list comparison group). It was not possible to mask participants or the fieldwork team to allocation, but the primary outcome measurements could not be accessed by either, and allocation was not known by study statisticians until after database lock.
EuroFIT was primarily designed to support men to become more physically active, reduce their sedentary time, and maintain these changes to at least 12 months after baseline. Dietary change was also introduced for those who wanted to lose weight. The programme was delivered at club stadia to groups of 15–20 men over 12 weekly, 90-minute sessions that combined interactive learning of behaviour change techniques with graded group-based physical activity. A reunion meeting was scheduled 6–9 months after the start of the programme. To facilitate group bonding and team spirit, the same group of 15–20 men were expected to attend at the same time each week.
Details of the EuroFIT programme are published, including a description of the programme in the template for intervention description and replication (TIDieR) [13]. In brief, we developed detailed manuals for coaches and participants, and trained club coaches over 2 days to deliver programme content in an appropriate and accessible style. This included encouraging positive banter, making sessions enjoyable, promoting a ‘team’ environment, and using interactional styles congruent with other (predominantly) male contexts [14]. The programme aimed to work with rather than against predominant constructions of masculinity [9,14] whilst supporting lifestyle change. Some elements (e.g., tips to change diet or increase physical activity) were adapted to country-specific norms. Coaches were taught about the importance of warm-up activities to prevent injuries, and the programme included the Fédération Internationale de Football Association (FIFA) 11+ programme [15]. Coaches taught participants to choose from a ‘toolbox’ of behaviour change techniques (including setting and reviewing goals for behaviours and outcomes, action planning, self-monitoring, and information about health and emotional consequences of change) and to emphasise personally relevant benefits of behaviour change (e.g., being better able to fulfil valued activities and roles). These behaviour change techniques were offered as tools for men to use for however long they found them useful and to encourage men to develop internalised and self-relevant motivation for becoming more active, sitting less, and eating a healthier diet [16].
We developed a novel pocket-worn, validated device (SitFIT) [17] to allow self-monitoring of sedentary and nonsedentary time (time spent upright [18]), in addition to daily steps (S1 Appendix). In the first week of the programme, men were taught how to measure the time they spent upright and the number of steps they take each week as a baseline. In the second and subsequent weeks, they were encouraged to follow an incremental programme to set weekly goals to slowly increase the number of steps and time spent upright each week, and to use the SitFIT to monitor their progress to these goals. Evidence on the use of self-monitoring devices for physical activity after participation in the FFIT programme suggests that although some continued to find them useful in the long term, others do not, as walking and other physical activity was embedded in everyday life without self-monitoring being necessary [19].
EuroFIT also explicitly encouraged between-session and post-programme peer support for changing behaviour through interacting with each other using a social media platform most of them were familiar with (e.g., WhatsApp, Facebook Groups). They were not given specific instructions on the content of interaction; coaches could decide whether or not they participated in the interactions. Between-session group social support was also encouraged using game-based social interaction with the MatchFIT app (S1 Appendix). MatchFIT allowed participants to contribute their weekly steps to their group’s collective average step count and compare it with that of a virtual competitor team. Coaches encouraged the use of MatchFIT as a means for participants to support one another as they pursued increases in their step counts, but did not themselves participate. Programme materials are available through request at http://eurofitfp7.eu/impact/eurofit-programme/.
A fieldwork team collected outcome data at baseline, post-programme, and after 12 months in club stadia. They scheduled separate measurement sessions for intervention and comparison groups post-programme to minimise contamination. For participants who consented to biomarker assessment, we took a venous blood sample at baseline and 12 months, after 6 hours fasting.
To maximise attendance and retention, we made appointments by telephone, confirmed by email or letter, and sent short message service (SMS) reminders. We scheduled additional measurements either in stadia or at home as needed, but almost all men attended the regular measurement sessions. We recorded sociodemographic characteristics (age, ethnicity, education, marital status, current employment status, and income) at baseline.
In thanks for their participation in the research, we offered a club store voucher for the equivalent of €25 at post-programme and €75 at the 12-month measurements.
With two primary outcomes, sample size calculations were based on achieving 90% power at a 2.5% significance level. In order to detect an effect size of 0.25 standard deviation (SD) units, a sample size of 399 per group was required. For physical activity (SD approximately 4,000 steps per day), this equates to an average increase of 1,000 steps/day. For sedentary time (SD almost 100 minutes/day [32]), this equates to an average decrease in sitting time of 25 minutes/day. To achieve almost 800 men with outcome data at 12 months, we estimated we would need to randomise 1,000 participants.
Continuous data are summarised as mean and SD, median and interquartile range (IQR), or mean and standard error (SE) for multiply-imputed data in the cost-effectiveness analyses. Categorical data are summarised as frequencies and percentages. Outcomes post-programme and at 12 months were analyzed using linear mixed-effects regression models, adjusted for randomised group and baseline value of the outcome measure as fixed effects, and football club and country as random effects. Model residual distributions were examined graphically, and data were transformed as necessary. All analyses were intention-to-treat.
Baseline data were summarised by randomised group and for those who did or did not provide outcome activPAL data at the post-programme and 12-month assessment points, to assess the representativeness of those who provided outcome data for analysis.
Sensitivity analyses were carried out for analyses of the two primary outcomes and for body weight: (a) multiple imputation of missing baseline data, (b) repeated measures analysis, using data from all three time points in the same model, and (c) analyses to account for waking wear time (the duration for which the activPAL device was worn whilst the participant was awake).
For repeated measures analyses, data from all three time points (baseline, post-programme, and 12 months) were included as outcomes; fixed effects were included for randomised group, time point, football club, and a randomised group-by-time interaction. A random participant effect was included, and a general (unstructured) covariance structure was allowed for model residuals across the three time points. Intervention effects at post-programme and 12 months were estimated using the interaction terms from these models.
Two methods were used to account for waking wear time. First, the primary analysis models were repeated using the mean number of steps per hour and the percentage of waking time spent sedentary as outcome variables. Second, the repeated measures analyses described above were repeated with waking wear time included as a fixed effect.
For the primary outcomes and weight at 12 months, intervention effect heterogeneity was assessed by extending the regression models to include group-by-moderator interaction terms. Moderating factors considered were age, marital status, years of education, employment status, income, club, country, baseline BMI, long-standing illness, and pain in upper and lower joints.
All p-values are two-sided. For the primary outcomes, p-values <0.025 are considered statistically significant. For all other analyses, no adjustment has been made for multiple comparisons, and p-values <0.05 are considered suggestive of true associations. The statistical analysis plan is provided in S1 Analysis Plan.
We used multiple imputation, using predictive mean matching to account for the skewed distribution of costs to impute missing costs and effects. We constructed 20 imputed data sets (loss of efficiency, <5%). Mixed-effects regression models estimated effect differences, and linear regression models estimated cost differences. We calculated incremental cost-effectiveness ratios (ICERs) by dividing the cost difference between the intervention and comparison groups by the effect difference. Statistical uncertainty was estimated using bias-corrected and accelerated bootstrapping with 5,000 replications and plotted on cost-effectiveness planes. Cost-effectiveness analysis (CEA) curves show the probability that the EuroFIT programme was cost-effective compared with the comparison group for a range of different ceiling ratios. The ceiling ratio is the amount of money society is willing to pay for one unit of effect extra. This ceiling ratio is set at a maximum of £30,000 per QALY by the National Institute for Health and Care Excellence (NICE). However, for other effect measures (such as steps per day), such predefined ceiling ratios are not available.
A sensitivity analysis considered cost-effectiveness from the healthcare provider’s perspective (i.e., excluding absenteeism costs). We also performed a complete case analysis to examine if imputation influenced our results.
Members of the public who had experience of similar programmes were members of our Strategic Partners Advisory Board and Trial Steering Committee and shaped the development of the protocol. Others, who had no previous involvement in similar programmes and were recruited through participating football clubs, advised on the development of the EuroFIT programme, specifically in commenting on prototypes of the SitFIT device and MatchFIT app. They also commented on trial procedures in a test of our measurement procedures undertaken before baseline measurement.
The study was approved in each country by local ethics committees before the start of the EuroFIT study (ethics committee of the VU University Medical Center [2015.184]; Regional committees for medical and health research ethics, Norway [2015/1862]; Ethics Council of the Faculty of Human Kinetics, University of Lisbon [CEFMH 36/2015]; and Ethics Committee at the University of Glasgow College of Medicine, Veterinary and Life Sciences [UK] [200140174]). Written informed consent to participate in the study was be obtained from all participants.
SW, CB, EA, MNS, FvN, SK, JJ, SK, PMcS, ØR, GCR, AMcC, HvdP had full access to the data. All other authors contributed to data interpretation.
The lead author (SW) affirms that this manuscript is an honest, accurate, and transparent account of the study being reported; that no important aspects of the study have been omitted; and there were no deviations from protocol.
The trial is registered in the International Standard Randomised Controlled Trials registry as ISRCTN32677491.
Participants were recruited between September 19, 2015, and February 2, 2016. Participant flow through the trial is shown in Fig 1. Main reasons for exclusion for men who showed interest in the trial were BMI <27 kg/m2 (42.4%), inability to reach men after they expressed interest, men not being approached because the study had reached the maximum number of participants at a club (39.3%). Participants spanned all sociodemographic groups, but a majority were ‘native’ to the study country (meaning each of the participant, their mother, and their father was born there), had at least 12 years of education, were in full-time work, and were married or living with a partner (Table 1). At baseline, participants’ mean daily step count was 8,372 steps/day, sedentary time was 625 minutes/day, and BMI was 33.2 kg/m2.
Those who provided outcome data (i.e., those who returned activPAL monitors with at least 4 valid days of measurements) were, on average, approximately 2 years older than those who did not, and slightly more likely to be married (S1 Tables, Table A). There was no clear difference in income in those who provided outcome data, nor in ethnicity, education, employment, or prevalent long-standing illness. In terms of baseline measures of study outcomes (S1 Tables, Table B), those who provided outcome were generally more active and less obese at baseline, compared with those who did not provide outcome data. This is a common feature of lifestyle intervention studies, in which those with the poorest lifestyle are hardest to engage in research.
We observed deliveries of the fourth session in 14/15 clubs. In these, coaches delivered 221 of 252 (88%) key tasks. Coaches in each of the 15 clubs provided attendance records for 553 programme participants: of these, 473 men (85.6%) attended at least 6 of the 12 sessions; 296 (53.5%) attended 10 or more sessions; and 85 (15.3%) attended all 12 sessions. Intervention participants rated their overall experience of the EuroFIT programme positively, producing a median score of 9 on a 10-point scale (IQR 8, 10; 70 missing). Asked to report their use of the SitFIT and MatchFIT, 65.1% of intervention participants reported they used the SitFIT ‘a great deal’ (score 4 on a scale of 0–4) and 36.8% reported they used MatchFIT ‘a great deal’.
The intervention group had a higher mean daily step count at 12 months than the comparison group (estimated difference: 678 steps/day [97.5% confidence interval (CI), 309–1,048], p < 0.001). There was no evidence of a difference between groups in sedentary time (estimated difference: −1.6 minutes/day [97.5% CI, −14.3–11.0], p = 0.77) (Table 2). In post-programme measurement, larger between-group differences in step counts (estimated difference: 1,208 steps/day [95% CI, 869–1,546]) and sedentary time (estimated difference: −14.4 minutes/day [95% CI, −25.1 to −3.8]) were observed (Fig 2). Sensitivity analyses using multiple imputations, adjusting for activPAL wear time and repeated measures analysis, showed broadly similar results (S1 Tables, Tables C, D and E).
Data summaries for participants who provided data at both baseline and post-programme, or baseline and 12 months, are provided in S1 Tables, Tables F and G.
There was no evidence that improvement in physical activity at 12 months varied by age, marital status, years of education, employment status, income, club, country, baseline BMI, long-standing illness, or pain in upper and lower joints. There was a significant interaction between the effect of the programme on sedentary time at 12 months and limiting long-standing illness (p = 0.034), so that those with limiting long-standing illness increased their sedentary time. There was no evidence of any other intervention effect differences between subgroups (S1 Fig, Figure A and B).
Mean body weight, BMI, waist circumference, and the proportion of participants with BMI ≥30 kg/m2 all improved significantly in favor of the intervention group (Table 3). The intervention effect on body weight varied by baseline BMI (interaction p < 0.001), with greater effects seen in those with larger BMI at baseline (S1 Fig, Figure C).
All self-reported behaviours, including diet, improved post-programme and at 12 months in favor of the intervention, except alcohol intake, which improved only at 12 months (Table 4). In contrast to objective measurements, self-reported sitting time at 12 months significantly decreased in the intervention group compared with comparison.
The intervention also improved several cardiovascular risk biomarkers at 12 months. Systolic and diastolic blood pressure were both improved; fasting insulin and HOMAIR were reduced by 15%; and fasting triglycerides, and ALT and GGT concentrations were reduced by 7%–8% (Table 5).
The intervention significantly improved self-reported well-being, self-esteem, and vitality, but not quality of life, as measured by the EQ-5D-5L at 12 months (Table 6).
The intervention group reported more recent injuries and higher upper and lower joint pain scores post-programme, and a higher lower joint pain score at 12 months (Table 7).
Prices per cost item and unadjusted mean differences in costs between the two groups are presented in Table 8. Costs of the EuroFIT programme differed between countries, ranging from £189.5 to £267.5 per participant. There were no significant differences in any other cost categories between intervention and comparison groups except for visits to physiotherapists. There was no statistically significant difference in total societal costs.
The mean difference in QALYs between the intervention and comparison group was small and not statistically significant (Table 9). One QALY lost in the intervention group was associated with an incremental cost of £126,119 compared with the comparison group. The probability of EuroFIT being cost-effective compared with the comparison group was at most 0.13 for ceiling ratios up to 30,000 £/QALY (S2 Fig).
One additional step/day in the intervention group was associated with an incremental cost of £0.41 compared with the comparison group (equating to £410 per 1,000 extra steps/day). There was a 0.95 probability of EuroFIT being cost-effective compared with the comparison group at a ceiling ratio of £1.50 per extra step/day. One minute less sedentary time in the intervention group was associated with an incremental cost of £172 compared with the comparison group. The maximum probability of cost-effectiveness for sedentary time was 0.61 at a ceiling ratio of £1,800 per minute less sedentary time. The incremental cost of EuroFIT for an additional participant achieving a decrease in weight of at least 5% was £2,228. There was a 0.95 probability of EuroFIT being cost-effective compared with the comparison group at a ceiling ratio of £6,000 per additional participant achieving a decrease in weight of at least 5%, £1 per additional minute of physical activity, and £6,000 per additional participant meeting the physical activity guidelines.
The results of the cost-effectiveness analysis from a healthcare provider perspective (Table 8) and using complete cases only were comparable to the main analysis.
Seven SAEs were reported, six in the intervention group (diagnosis of heart disease, fractured wrist, fractured rib, two anterior cruciate ligament ruptures, and a torn meniscus) and one death in the comparison group. Five were deemed likely to be associated with EuroFIT (the fractured rib occurred during a warm-up at a EuroFIT session; the other injuries occurred during football matches organised by participants after the programme had finished, but still indirectly linked to participation in the programme).
A large number of men expressed interest in the EuroFIT programme in each of the 15 football clubs. The programme helped participants to achieve increases in objectively measured physical activity but did not result in a lasting decrease in objectively measured sedentary time 12 months after baseline. The EuroFIT programme also helped men to improve secondary outcomes including weight, waist circumference, diet, well-being, self-esteem, and vitality. However, in the within-trial analysis the programme did not improve quality of life as measured by EQ-5D-5L and hence was not cost-effective based on QALYs.
The EuroFIT programme was based on the successful weight loss and healthy living programme, FFIT and, like FFIT, had wide inclusion criteria. It had a sound theory base and logic model [13], the behaviour change technique ‘toolbox’ included those known to initiate and sustain behaviour change [33,34], and the programme drew on sociological understanding of masculinities to attract and retain participants [9,14]. EuroFIT was well regarded by participants, over 80% of whom attended at least half of the sessions. Post-programme, 65% of men in the intervention group reported using the SitFIT device to self-monitor steps and sitting time ‘a great deal’; 37% reported using the game-based MatchFIT app to encourage interaction between sessions and after the programme ended ‘a great deal’. Although EuroFIT attracted men from across the socioeconomic spectrum, the majority who took part were well educated and in paid work. With no obvious denominator population, we have no way of knowing if those attracted are representative of all men from local fan bases who support particular clubs.
There was an increase in recent injuries and in upper and lower joint pain scores post-programme, which might also explain higher physiotherapist costs observed in the intervention group. Although observations to assess overall fidelity showed that coaches delivered 88% of tasks as intended, preliminary analyses of other process evaluation data suggest that coaches sometimes delivered physical activity sessions that were more vigorous than specified and did not sufficiently emphasise warm-up and cooldown exercises. A focus during the 2-day coach training may be needed to avoid too many injuries.
The EuroFIT evaluation spanned four European countries and 15 professional football clubs, used objective measurement of physical activity and sedentary time, and retained over 80% of participants to objective 12-month outcome measurement. This suggests that the results are likely to be generalisable to other football clubs within Europe. It was not possible to blind participants to which group they were in, although physical activity and sedentary time were objectively assessed.
The men attracted to the programme already had quite high levels of physical activity at baseline (8,372 steps/day). This may have limited the room for improvement and led to underestimation of the potential effects of the programme if less active participants were recruited. It has been known for some time that recruitment of those most in need of physical activity interventions is more challenging than recruiting those who are already reasonably active [35]. It is possible that even more active, personalised approaches to recruitment [36] and limiting eligibility to those who do not achieve the recommended levels of physical activity would help to avoid an overrepresentation of more active men and would provide more opportunity for less active men to join the programme.
Another limitation is the potential for possible reactivity effects, in which participants change their physical activity and sedentary behaviours during the measurement week. Due to the unblinded nature of the study, the effectiveness of the intervention might have been overestimated if the intervention group did increase their activity levels more than the comparison group as a result of social desirability. However, no studies to date have reported on substantial reactivity effects in studies using 7-day accelerometer assessments.
EuroFIT showed above average improvements in physical activity compared with systematic reviews and meta-analysis of other physical activity intervention programmes [37–39]. The findings from the EuroFIT trial reinforce those from a recent systematic review suggesting that gender-sensitised physical activity interventions in professional sports settings are a promising route for promoting men’s health [8]. The review identified several physical activity interventions in this setting; the FFIT study, designed to help overweight men lose weight through improvements in physical activity and diet, was the only large randomised controlled trial [40]. Recent long-term follow-up of participants in the FFIT study showed that improvements in weight loss and in self-reported physical activity were maintained 3.5 years after baseline [10]. The FFIT programme has been adapted for delivery Canada (in ice hockey) [41] and Australia (in Aussierules football) [42]. FFIT formed the basis for the development of EuroFIT; the success of the EuroFIT programme offers further evidence of the long-term public health potential of this approach.
The FFIT trial reported greater weight loss (4.94 kg; 95% CI, 4.0–5.9) than we found in EuroFIT (2.4 kg; 95% CI, 1.7–3.1), although improvements in self-reported physical activity were broadly comparable. These differences may be because dietary choice was introduced later in EuroFIT than in FFIT and weight loss emphasised only for those who wanted to do so. In FFIT, dietary and physical activity changes were both emphasised as ways of achieving and maintaining a healthier weight.
The focus of EuroFIT on reducing sedentary time was only successful in the short term. A systematic review showed similarly short-lived reductions in sedentary time [43], although some interventions showed effects up to 12 months. Workplace interventions have achieved larger reductions in sedentary time, although consistent long-term change has not yet been reported [44]. There are no clear, publicly known guidelines for reducing sedentary time, knowledge of the association between high levels of sedentary time and health is still not widespread, and sedentary time is often confused with physical inactivity [45]. Preliminary analyses of qualitative data from EuroFIT’s process evaluation suggest that both participants and coaches were confused by the combined messages of increasing physical activity and simultaneously increasing time spent upright. For example, the SitFIT device was liked by participants but mostly used to self-monitor stepping; few participants reported self-monitoring time spent upright. Future lifestyle intervention studies should attempt to ensure that participants understand the distinction and appreciate the benefits of decreasing sedentary time, as well as increasing physical activity.
Although the EuroFIT programme was not expensive to deliver (between £180 and £268 per participant), the lack of improvement in quality of life (as measured by the EQ-5D-5L) meant that the probability of it being cost-effective at ceiling ratios up to £30,000 per QALY was only 0.13 over a 12-month time frame. The equivalent probability for the FFIT programme, which estimated QALYs via the Short Form-12 (SF12) questionnaire rather than EQ-5D-5L, was 0.89 at the same ceiling ratio over the same time frame [40]. Although the EQ-5D-5L is now the preferred measure for cost-effectiveness analyses across Europe, baseline EQ-5D-5L utility scores were relatively high in EuroFIT (0.93), suggesting a ceiling effect that limits room for improvement in EQ-5D-5L utility scores. Whether the EuroFIT programme is considered cost-effective for physical activity and body weight at 12 months and shorter-term improvement in sedentary time depends on decision-makers’ willingness to pay for the observed improvements in these outcomes. We are in the process of developing a model of longer-term cost-effectiveness over a 5-year horizon to represent the benefits of physical activity in reducing the incidence of four chronic health conditions (colorectal cancer, type 2 diabetes, coronary heart disease, and stroke) and mortality.
We have added to previous evidence [8,40] that suggests engaging men in physical activity through programmes that work with existing constructs of masculinity is a promising route for promoting men’s health. We have shown that, while participation in the EuroFIT programme did not result in improvement in sedentary time, it did result in improvements in physical activity, body weight, waist circumference, diet, well-being, vitality, and self-esteem and also to cardiovascular risk biomarkers.
A 678 steps/day increase in objectively measured physical activity is substantial. Objectively measured levels of physical activity are always lower than self-reported levels [46], and global physical activity recommendations are based on self-report. The association between objectively measured physical activity and health biomarkers is substantially stronger than the association with self-reported physical activity [47]. Given the observed improvements in cardiovascular risk biomarkers, EuroFIT is likely to result in important reduction in the risk of ill health if the improvement in physical activity is maintained.
Combining lessons learned from EuroFIT and its predecessor, FFIT, will allow the further refinement of evidence- and theory-based lifestyle change programmes delivered in professional sports settings.
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10.1371/journal.pgen.1002025 | Eight Common Genetic Variants Associated with Serum DHEAS Levels Suggest a Key Role in Ageing Mechanisms | Dehydroepiandrosterone sulphate (DHEAS) is the most abundant circulating steroid secreted by adrenal glands—yet its function is unknown. Its serum concentration declines significantly with increasing age, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. We conducted a meta-analysis of genome-wide association data with 14,846 individuals and identified eight independent common SNPs associated with serum DHEAS concentrations. Genes at or near the identified loci include ZKSCAN5 (rs11761528; p = 3.15×10−36), SULT2A1 (rs2637125; p = 2.61×10−19), ARPC1A (rs740160; p = 1.56×10−16), TRIM4 (rs17277546; p = 4.50×10−11), BMF (rs7181230; p = 5.44×10−11), HHEX (rs2497306; p = 4.64×10−9), BCL2L11 (rs6738028; p = 1.72×10−8), and CYP2C9 (rs2185570; p = 2.29×10−8). These genes are associated with type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins. Several SNPs were associated with changes in gene expression levels, and the related genes are connected to biological pathways linking DHEAS with ageing. This study provides much needed insight into the function of DHEAS.
| Dehydroepiandrosterone sulphate (DHEAS), mainly secreted by the adrenal gland, is the most abundant circulating steroid in humans. It shows a significant physiological decline after the age of 25 and diminishes about 95% by the age of 85 years, which has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity. Twin- and family-based studies have shown that there is a substantial genetic effect with heritability estimate of 60%, but no specific genes regulating serum DHEAS concentration have been identified to date. Here we take advantage of recent technical and methodological advances to examine the effects of common genetic variants on serum DHEAS concentrations. By examining 14,846 Caucasian individuals, we show that eight common genetic variants are associated with serum DHEAS concentrations. Genes at or near these genetic variants include BCL2L11, ARPC1A, ZKSCAN5, TRIM4, HHEX, CYP2C9, BMF, and SULT2A1. These genes have various associations with steroid hormone metabolism—co-morbidities of ageing including type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc finger proteins—suggesting a wider functional role for DHEAS than previously thought.
| Dehydroepiandrosterone sulphate (DHEAS), mainly secreted by the adrenal gland, is the most abundant circulating steroid in humans. It acts as an inactive precursor which is converted initially into DHEA and thereafter into active androgens and estrogens in peripheral target tissues [1]. In humans the serum concentration of circulating DHEAS is 100- to 500-fold or 1000 to 10,000 higher than that of testosterone and estradiol respectively. Unlike DHEA, which is swiftly cleared from the circulation and shows diurnal variation, serum DHEAS concentrations are stable and facilitate accurate measurement and diagnosis of pathology [2].
DHEAS is distinct from the other major adrenal steroids (cortisol and aldosterone) in showing a significant physiological decline after the age of 25 and diminishes about 95% by the age of 85 years [3]. This age-related decline has led to speculation that a relative DHEAS deficiency may contribute to the development of common age-related diseases or diminished longevity [4], [5]. Low DHEAS concentrations are possibly associated with increased insulin resistance [6], [7] and hypertension [8], but not with incident metabolic syndrome [9]. It is strongly associated with osteoporosis in women [10], [11] but not in men [12]. Concurrent change in DHEAS tracks with declines in gait speed, modified mini-mental state examination score (3MSE), and digit symbol substitution test (DSST) in very old women but not in men [13]. Low circulating DHEAS is also strongly associated with cardiovascular disease and mortality in men [14]–[18] but not in women [19]. A recent 15-year follow-up study showed that DHEAS was negatively related to all-cause, all cancers, and other medical mortality, whereas high DHEAS concentrations were protective [20]. This has led to its widespread and uncontrolled use as a controversial anti-ageing and sexual performance supplement in the USA and other western countries without any clear data about efficacy, potential risks or benefits [21].
Despite these observations, the physiological function of DHEAS and its importance in maintaining health are poorly understood. Although previous twin [22], [23] and family-based studies [24], [25] have shown that there is a substantial genetic effect with a heritability estimate of 60% [22], no specific genes regulating serum DHEAS concentration in healthy individuals have been identified to date. Therefore, the current study meta-analyzed the results of genome-wide association studies (GWAS) performed in a total of 14,846 individuals from seven cohorts to identify common genetic variants associated with serum DHEAS concentrations. The findings not only advance understanding of how serum DHEAS concentration is regulated by genes but also provide clues as to its mechanism of action as well as Mendelian randomisation principles [26].
We carried out a meta-analysis of 8,565 women and 6,281 men of European origin from collaborating studies: TwinsUK (n = 4,906), Framingham Heart Study (FHS) (n = 3,183), SHIP (n = 1,832), Rotterdam Study (RS1) (n = 1,597), InCHIANTI (n = 1,182), Health ABC (n = 1,222), and GOOD (n = 924). Serum samples were collected either after overnight fasting or non-fasting in each cohort and DHEAS was measured by either immunoassay or liquid chromatography tandem mass spectrometry (LC-MS/MS) methods (Table 1). Mean age differed across the cohorts from 19 to 74 years in men and 50 to 74 years in women and corresponding mean DHEAS concentrations varied from 1.20 to 7.05 µmol/L (Table 1).
Each cohort performed GWA tests for log transformed DHEAS on ∼2.5 million imputed single nucleotide polymorphisms (SNPs) in men and women separately with adjustment for age, and additionally for age and sex for those cohorts who had data in both men and women. Then Z-scores from each cohort were pooled for the meta-analysis at each SNP.
In all our individual GWAS, λGC, which is defined as the median χ2 (1 degree of freedom) association statistic across SNPs divided by its theoretical median under the null distribution [27], ranged from 0.984 to 1.023, indicating that there was no population stratification or it was very minor. Further, we corrected for population stratification by applying the genomic control method [27]; the λGC in the meta-analysis is 1.017. In addition, the effect direction was consistent across all the cohorts and there is no between-study heterogeneity as indicated by I2 ranging between 0 and 0.12 (Table 2).
We found 44 SNPs were associated with serum DHEAS concentrations in men at conventional genome-wide significance (p<5×10−8), which are all located on chromosome 7q22.1 (Figure 1B; Table S1). All these SNPs except for three were significant in women (Figure 1A; Table S1). In addition, 19 SNPs located on chromosome 19q13.3 were found in women to be associated with serum DHEAS concentrations with p<5×10−8. In the sex-combined meta-analysis, the significance became stronger for all these SNPs (Figure 1C; Table S1). Further, we found 8 SNPs located on chromosome 10q23.33 which represents two regions more than 2 MB apart, 12 SNPs on chromosome 15q15.1, and in addition, 4 SNPs on chromosome 19q13.3 were associated with serum DHEAS concentrations with p<5×10−8. Together we found a total of 87 SNPs associated with serum DHEAS concentrations with p<5×10−8, representing five chromosomal regions of less than 1 Mb each (Table S1).
The most significantly associated SNPs in each of these five regions are presented in Table 2. The minor allele of rs11761528 (p = 3.15×10−36) on chromosome 7q22.1, rs2637125 (p = 2.61×10−19) on chromosome 19q13.3, and rs2497306 (p = 4.6×10−9) and rs2185570 (p = 2.29×10−8) on chromosome 10q22.33 (more than 2 Mb apart), were negatively associated with DHEAS concentrations. In comparison, the minor allele of rs7181230 (p = 5.44×10−11) on chromosome 15q15.1 was positively associated with serum DHEAS concentrations. Based on the HapMap3 release2 CEU data, the significant 87 SNPs from within the five regions have low pair-wise r2, indicating potentially multiple independent signals. To verify this, we performed a conditional meta-analysis with adjustment for the five most significant SNPs plus age and sex in each cohort.
After this adjustment, all other SNPs on chromosome 10, 15, and 19 became non-significant (Figure 1D). However, on chromosome 7, we found two independent signals; one defined by rs11761528 and a second located 370 kb upstream in the 3′ UTR of the TRIM4 and CYP3A43 genes (rs17277546, p = 4.50×10−11). Furthermore, we identified two additional significant loci associated with DHEAS, one on chromosome 2q13 (rs6738028, p = 1.72×10−8), and another on chromosome 7 within the ARPC1A gene (rs740160 located 161 kb downstream of rs11761528, p = 1.56×10−16) (Table 2; Figure 1D). In total, we found eight independent SNPs associated with serum DHEAS concentrations at conventional genome-wide significant level (p<5×10−8) (Table 2). The effect was consistently in the same direction across all cohorts (Table 2). No heterogeneity among cohorts was observed (Table 2). These SNPs together explained ∼4% of the total and ∼7% of genetic variance of serum DHEAS concentrations (based on TwinsUK data). To further look at whether the magnitude of these genetic association varies with age, we carried out an interaction analysis between age and each of these 8 SNPs on serum DHEAS concentrations by including an interaction term of age×SNP in the linear regression model in each cohort and then meta-analyzed the results. We found that there was no significant interaction between age and each of these SNPs (all p values≥0.05).
The genes at, or near the identified SNPs, include BCL2L11 on chromosome 2, ZKSCAN5, ARPC1A, TRIM4 and CYP3A43 on chromosome 7, HHEX and CYP2C9 on chromosome 10, BMF on chromosome 15, and SULT2A1 on chromosome 19 (Figure 2). To explore the potentially functional impacts and likely genetic mechanisms, we used two resources: Genome-wide expression data from the Multiple Tissue Human Expression Resource (MuTHER) [28] (http://www.muther.ac.uk/) based on ∼777 unselected UK twins sampled for skin, adipose tissue, and lymphoblastoid cell lines (LCLs) (more details in Text S1); and published gene expression data in human liver [29]. We found that 3 DHEAS-associated SNPs were clearly associated with the related gene expression levels in at least one tissue after accounting for multiple testing (Table 3). These specific transcript associations provide further evidence for the likely functional gene at each locus.
Further, we carried out gene ontology and pathway analyses using a gene set enrichment analysis (GSEA) approach in MAGENTA [30] which consists of four main steps: First, DNA variants, e.g. SNP, are mapped onto genes. Second, each gene is assigned a gene association score that is a function of its regional SNP association p-values. Third, confounding effects on gene association scores are identified and corrected for, without requiring genotype data. Fourth, a GSEA-like statistical test is applied to predefined biologically relevant gene sets to determine whether any of the gene sets are enriched for highly ranked gene association scores compared to randomly sampled gene sets of identical size from the genome. More details of these four steps are described in the method section. In this analysis, we identified three pathways which passed our significance threshold (false discovery rate (FDR)<0.05); xenobiotic metabolism with FDR = 0.001 (pathway database: KEGG and Ingenuity), retinoid X receptor (RXR) function with FDR = 0.003 (pathway database: Ingenuity), and linoleic acid metabolism with FDR = 0.02 (pathway database: KEGG) (Figure S1). The top significant genes with p<5.0×10−8 include CYP3A4, CYP3A43, CYP3A5, and CYP3A7 on chromosome 7, and CYP2C8 and CYP2C9 on chromosome 10 for all three pathways, and SULT2A1 for RXR pathway. The best index SNPs are rs17277546 for CYP3A4 and CYP3A43, rs4646450 for CYP3A5 and CYP3A7, rs2185570 for CYP2C9, rs11572169 for CYP2C8, and rs2637125 for SULT2A1. The full list of the genes in each of the three pathways and the best index SNPs for each gene are listed in Table S2. Three SNPs – rs17277546, rs2185570, and rs2637125 are the DHEAS-associated SNPs found in our meta-analysis. Both rs4646450 and rs11572169 were associated with DHEAS with p values of 8.8×10−17 and 4.8×10−8, respectively, but become non-significant in the conditional meta-analysis because rs4646450 is in linkage disequilibrium (LD, r2 = 0.429) with rs11761528 which is the most significant DHEAS-associated SNP while rs11572169 is in high LD (r2 = 0.778) with rs2185570. Intriguingly, two pathways - xenobiotic metabolism and linoleic acid metabolism, have been linked to ageing in model organisms [31]–[36].
This is the first meta-analysis of GWA studies on serum DHEAS in 14,846 Caucasian subjects. We found 8 common SNPs that implicate nearby genes that are independently associated with serum DHEAS concentrations and provide clues to its role in ageing.
Among the genes identified, SULT2A1, a specialized sulpho-transferase which converts DHEA to DHEAS in the adrenal cortex, is an obvious candidate gene [3]. SULT2A1 has a broad substrate specificity, which includes conversion of pregnenolone, 17α-hydroxypregnenolone, and DHEA to their respective sulphated products [37]. Once sulphated by SULT2A1, pregnenolone and 17α-hydroxypregnenolone are no longer available as substrates for HSD3B2. Therefore, SULT2A1 sulphation of pregnenolone and 17α-hydroxypregnenolone removes these substrates from the mineralocorticoid and glucocorticoid biosynthetic pathways. This suggests that high levels of SULT2A1 would ensure the formation of DHEAS [3].
Variation in SULT2A1 expression has previously been associated with variation of DHEAS concentration [38]. The SULT2A1 gene is predominantly expressed in the adrenal cortex and to a lesser extent in the liver. We found that rs2547231 (p = 1.76×10−17), located 12 kb downstream of SULT2A1, was strongly associated with expression levels of SULT2A1 in human liver tissues. Although this SNP is not the most strongly associated with serum DHEAS, it is itself in strong LD with the most significant SNP rs2637125 (r2 = 0.658). However, we did not find a significant association with SULT2A1 expression levels in LCL, skin, and adipose tissues, suggesting a tissue specific effect. The SULT2B1b is also reported to play a role in sulphation of DHEA, but in comparison, the strongest signal from that genomic region was rs10417472 with a p = 0.06. In contrast, enzymatic removal of the sulphate group from DHEAS to form DHEA is performed by steroid sulphatase gene (STS), but that gene is on the X chromosome and so was not assessed in this meta-analysis.
CYP2C9 is an important cytochrome P450 enzyme, accounts for approximately 17–20% of the total P450 content in human liver, and catalyzes many reactions involved in drug metabolism as well as synthesis of cholesterol, steroids and other lipids [39]. We found that rs2185570 located in the CYP2C9 gene region is associated with serum DHEAS concentrations. This SNP is in strong LD with rs4086116 and rs4917639 (r2 = 0.67 for both) which were found to be associated with acenocoumarol [40] and warfarin maintenance dosage [41] respectively in recent GWAS.
Two other cytochrome P450 enzymes – CYP11A1 and CYP17A1, are two important enzymes which are required in the synthesis of DHEAS in the adrenal gland [3], however, the strongest signals in the genomic region were rs2930306 with p = 0.29 for CYP11A1 and rs4919686 with p = 0.04 for CYP17A1.
The decline in serum DHEAS concentrations with increasing age has been proposed as a putative biomarker of ageing [21]. We found that two putative ageing genes – BCL2L11 and BMF [42] are associated with serum DHEAS concentrations. Both of them encode proteins which belong to the BCL2 family and act as anti- or pro-apoptotic regulators that are involved in a wide variety of cellular activities. BCL2L11 has been implicated in chronic lymphocytic leukaemia (rs17483466, P = 2.36×10−10) [43] and follicular lymphoma (rs3789068, P for trend = 0.0004) [44]. The DHEAS-associated SNP rs6738028 is not however one of the same SNPs associated with lymphocytic leukaemia and follicular lymphoma nor is it in LD with them. Nevertheless, rs6738028 is strongly associated with BCL2L11 gene expression levels in both LCL and adipose tissues, suggesting its putative regulatory role. There is relatively little data on the human BMF gene or the protein product, but Bmf−/− mice show altered immune and hematopoietic phenotypes as well as defects in uterovaginal development. However, we were not able to detect any association between rs7181230 and the expression levels of BMF in the tissues we studied.
HHEX encodes a member of the homeobox family of transcription factors, many of which are involved in developmental processes. This gene has been found to be associated with type 2 diabetes by several recent GWAS [45]–[51]. The risk alleles of the diabetes-associated SNPs rs1111875 and rs5015480 are associated with low serum DHEAS concentrations although the p values (p = 0.0009 for both SNPs) didn't reach to the GWAS significance level. This is consistent with the observation in which the low serum DHEAS concentrations were associated with insulin resistance [6], [7]. The identified DHEAS-associated SNP rs2497306 is in moderate LD with rs1111875 and rs5015480 (r2 = 0.38). And the major allele of rs2497306 is associated with increasing serum DHEAS concentrations. The reason for the observed association is unknown. Studies showed that insulin infusion increases the metabolic clearance of DHEA and DHEAS [52], [53], resulting in decreased DHEA and DHEAS concentrations, and DHEA administration significantly enhances insulin sensitivity attenuating the age-related decline in glucose tolerance [54], partly explaining why the diabetes-associated gene is also associated with DHEAS. Interestingly, HHEX null mice show cardiovascular, endocrine, liver, muscle, nervous system, and metabolic phenotypes, suggesting extensive multisystem roles for the protein product of this gene. The findings could help dissect causal pathways for the observed associations between DHEAS, insulin resistance, age-related decline in glucose tolerance [54], and other age related phenotypes [55].
Three identified DHEAS-associated SNPs on chromosome 7 (Figure S2), which were independent, and 161 kb downstream (rs740160) and 370 kb upstream (rs17277546) apart from rs11761528 which is located in the middle of the region, are located in four genes - ZKSCAN5, ARPC1A, and TRIM4/CYP3A43. ZKSCAN5 encodes a zinc finger protein of the Kruppel family and is expressed ubiquitously in adult and fetal tissues with the strongest expression in testis [56]. rs11761528 is located in the intron of the ZKSCAN5 gene. It is the strongest signal we found and explains 1% of the total variance of serum DHEAS concentration alone. ARPC1A encodes one of seven subunits of the human Arp2/3 protein complex which has been implicated in actin polymerization and filament assembly in cells [57]. TRIM4 encodes a member of the tripartite motif (TRIM) family whereas CYP3A43 is another cytochrome P450 enzyme. The potential mechanisms for the association are unknown, but we found that rs17277546 is strongly associated with expression levels of TRIM4 not CYP3A43, suggesting TRIM4 is the possible candidate for DHEAS. However, rs17277546 is the best index SNP for both CYP3A43 and CYP3A4 genes in the pathway analysis, indicating a fine mapping in this region is needed to reveal the potential mechanism for the association. Further, the region harbours many other genes including CYP3A7 which has been reported to increase the clearance of DHEA and DHEAS [58] and a common haplotype polymorphism in the gene has been associated with DHEAS [59], [60]. However, none of the DHEAS-associated SNPs are associated with its expression levels in the tissues we studied, and the best index SNP rs4646450 for CYP3A7 found in our pathway analysis is in LD with rs11761528 and become non-significant in the conditional analysis.
In the pathway analysis, two DHEAS-associated SNPs (rs2185570 and rs17277546) were contained in all three pathways we found and one SNP (rs2637125) was contained in the RXR function pathway. Intriguingly, components of the xenobiotic metabolism pathway have been linked to ageing in model organisms, for example, age-associated changes in expression of genes involved in xenobiotic metabolism have been identified in rats [31], [32], up-regulation of xenobiotic detoxification genes has been observed in long-lived mutant mice [33], and adrenal xenobiotic-metabolizing activities increase with ageing in guinea pigs [34]. Furthermore, linoleic acid metabolism has also been linked to changes with ageing in rat cardiac muscle [35] and in human skin fibroblasts [36]. Taken together, these findings suggest that molecular pathways involved in ageing and longevity may also underlie DHEAS regulation, suggesting shared genetic components in both processes and corroborating a role for DHEAS as a marker of biological ageing.
In summary, this first GWAS identified eight independent SNPs associated with serum DHEAS concentrations. The related genes have various associations with steroid hormone metabolism, co-morbidities of ageing including type 2 diabetes, lymphoma, actin filament assembly, drug and xenobiotic metabolism, and zinc fingers - suggesting a wider functional role for DHEAS than previously thought.
Seven study samples contributed to this meta-analysis of GWA studies on serum DHEAS concentrations, comprising a total of 14,846 men and women of Caucasian origin. The consortium was made up of populations from TwinsUK (n = 4,906), Framingham Heart Study (FHS) (n = 3,183), SHIP (n = 1,832), Rotterdam Study (RS1) (n = 1,597), InCHIANTI (n = 1,182), Health ABC (n = 1,222), and GOOD (n = 924). Full details can be found in Text S1.
Blood samples were collected from each of the study participants either after overnight fasting or non-fasting and the serum levels of DHEAS were measured by either immunoassay or liquid chromatography tandem mass spectrometry (LC-MS/MS) methods (Text S1). Because the distribution of the serum DHEAS levels was skewed, we log transformed the concentrations and the transformed data used in the subsequent analysis.
Seven study populations were genotyped using a variety of genotyping platforms including Illumina (HumanHap 317k, 550k, 610k, 1M-Duo BeadChip) and Affymetrix (array 500K, 6.0). Each cohort followed a strict quality control on the genotyping data. More details on the quality control and filtering criteria can be found in Text S1. In order to increase genomic coverage and allow the evaluation of the same SNPs across as many study populations as possible, each study imputed genotype data based on the HapMap CEU Build 36. Algorithms were used to infer unobserved genotypes in a probabilistic manner in either MACH (http://www.sph.umich.edu/csg/abecasis/MACH), or IMPUTE [61]. We exclude non-genotyped SNPs with an imputation quality score <0.2 and SNPs with allele frequency <0.01 from meta-analysis.
Each study performed genome-wide association testing for serum concentrations of DHEAS across approximately 2.5 million SNPs under an additive genetic model separately in men and women (Text S1). The analyses were adjusted for age. In addition, the association testing was performed in the combined men and women data with adjustment for age and sex. Studies used PLINK, GenABEL, SNPTEST, QUICKTEST, or MERLIN for the association testing. The summary results from each cohort were meta-analyzed by Z-score pooling method implemented in Metal (http://www.sph.umich.edu/csg/abecasis/metal/). We chose this method to minimize the impact of the different assays used for serum DHEAS measurements. Specifically, for each study, we converted the two-sided P value after adjustment for population stratification by the genomic control method to a Z statistic that was signed to reflect the direction of the association given the reference allele. Each Z score was then weighted; the squared weights were chosen to sum to 1, and each sample-specific weight was proportional to the square root of the effective number of individuals in the sample. We summed the weighted Z statistics across studies and converted the summary Z score to a two-sided P value. We also used I2 index to assess between-study heterogeneity and the inverse variance weighted method to estimate the effect size. Genome-wide significance was defined as p<5×10−8. The association between the DHEAS-associated SNPs and the related gene expression levels in MuTHER data were examined by mixed linear regression modelling which takes both family structure and batch effects into account. The significance was defined as p<0.006 after accounting for multiple testing (Bonferroni method, correcting 9 independent tests).
All studies were approved by local ethics committees and all participants provided written informed consent as stated in Text S1.
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10.1371/journal.pgen.1002579 | Promoter Nucleosome Organization Shapes the Evolution of Gene Expression | Understanding why genes evolve at different rates is fundamental to evolutionary thinking. In species of the budding yeast, the rate at which genes diverge in expression correlates with the organization of their promoter nucleosomes: genes lacking a nucleosome-free region (denoted OPN for “Occupied Proximal Nucleosomes”) vary widely between the species, while the expression of those containing NFR (denoted DPN for “Depleted Proximal Nucleosomes”) remains largely conserved. To examine if early evolutionary dynamics contributes to this difference in divergence, we artificially selected for high expression of GFP–fused proteins. Surprisingly, selection was equally successful for OPN and DPN genes, with ∼80% of genes in each group stably increasing in expression by a similar amount. Notably, the two groups adapted by distinct mechanisms: DPN–selected strains duplicated large genomic regions, while OPN–selected strains favored trans mutations not involving duplications. When selection was removed, DPN (but not OPN) genes reverted rapidly to wild-type expression levels, consistent with their lower diversity between species. Our results suggest that promoter organization constrains the early evolutionary dynamics and in this way biases the path of long-term evolution.
| Species diverge by mutations that change protein structure or protein regulation. While the evolution of protein sequence was studied extensively, much less is known about the divergence of gene expression. To better understand the process of gene expression evolution, we characterized the early genomic response of yeast cells to selection for high gene expression. Notably, the response to selection was strongly dependent on the organization of the gene promoter: genes whose promoters had a pronounced nucleosome-free region (NFR) primarily duplicated the chromosome containing the gene of interest, while genes whose promoters lacked a pronounced NFR adapted by trans mutations not involving duplications. Further, when selection was removed, the former (but not the later) evolved strains reverted rapidly to wild-type expression levels, consistent with their lower diversity between species. Together, our study provides strong support to the idea that physiological regulation impacts the evolutionary path and suggests that, by regulating promoter nucleosomes, cells can regulate the response to selection and control the long-term stability of the selected changes.
| The plasticity of biological traits is manifested on multiple time scales. Regulatory mechanisms govern the physiological adaptation of an individual to changing conditions. On evolutionary time scales, phenotypes are modulated by genetic mutations. Although operating on very different time scales, regulatory variance and evolvability were proposed to be linked [1]–[8]. For example, a trait that needs to be buffered against environmental or stochastic variations will show a limited regulatory variance, and will be harder to perturb by genetic mutations. A related idea is that regulatory variance directs the evolutionary dynamics by marking the directions most susceptible to changes. Experimental evidences supporting these ideas are still sparse, but recent studies in yeast provided genome-wide support to this linkage in the context of gene expression.
Adaptation of cells to different environmental conditions depends largely on changes in expression levels, whereas evolution depends on changes in both expression and function. While most studies of evolutionary changes focused on changes in gene function, the role of gene expression in generating phenotypic diversity was emphasized by experiments that traced phenotypic and morphological differences to variations in gene expression [9]–[12] and by genome-wide mapping of gene expression profiles which demonstrated rapid divergence even between closely related species [13]–[19].
In yeast, the divergence of gene expression was linked to the organization of promoter nucleosomes, thereby connecting evolutionary divergence with physiological regulation [20]–[22]. Genes whose expression diverged rapidly typically lack an NFR proximal to the transcription start site (OPN genes), while the expression of genes with a pronounced proximal NFR (DPN genes) remained largely conserved. OPN genes are additionally more responsive to environmental changes, display a higher cell-to-cell variability (noise) and tend to have a TATA box in their promoters [20].
Multiple, not mutually exclusive, processes can explain the increased divergence of OPN genes. First, the highly responsive OPN promoters may be more sensitive to mutations accumulating by random drift. This promoter organization may enhance sensitivity to cis mutation in the promoter itself due, for example to non-linear interactions between promoter nucleosomes and transcription factors. Similarly, the spectrum of effective trans mutations may be larger for OPN genes [22]. Indeed, OPN promoters integrate a larger number of signals, are more responsive to regulatory factors and diverge more in mutation-accumulation experiments where selection is eliminated [23].
A second possibility is the two classes of genes are subject to distinct selection forces. Increased expression of low-responsive (DPN) genes may be more deleterious and will therefore be eliminated more efficiently by purifying selection. Similarly, mutations in high-responsive genes may contribute more to evolutionary adaptation leading to their rapid fixation. Alternatively, DPN and OPN genes may be subject to similar selection forces for changing expression, but selection is more easily satisfied by OPN genes due to their flexible promoter structure. This last possibility would provide a direct support to the idea that flexible promoter organization can direct the dynamic path taken by evolution.
To examine the hypothesis that OPN and DPN genes differ in the way by which they respond to identical selection forces, we artificially selected for high expression of GFP-fused yeast proteins and examined the genomic response to this selection. The expression of dozens of GFP-fused proteins was successfully increased, irrespectively of their promoter class. Notably, promoter class did influence the genetic change leading to the increased expression: Selection for high expression of DPN genes resulted in duplication of large genomic region (mostly full chromosomes) containing the gene of interest. In contrast, large-scale duplications were much less prevalent in OPN genes, which changed their expression primarily through trans mutations not involving duplications. When selection was removed, DPN (but not OPN) strains reverted back to wild-type expression levels, consistent with their lower diversity between species. Our results suggest that promoter organization impacts on the early evolutionary dynamics and by this biases the path of long-term evolution.
We chose forty-one yeast proteins that span a range of mid-to-high expression levels, with no preferences towards specific functions or positions along the chromosome (Table S1 and Figure S1). Genes were distributed between the DPN and OPN classes, as quantified by the relative nucleosome occupancy of their proximal promoter (‘OPN-measure’) [20]. The OPN-measure is defined as the ratio between nucleosome occupancy of the proximal versus distal region of the promoter, thereby quantifying the extent by which the proximal promoter region is depleted of nucleosomes. This measure strongly correlate with the flexibility of gene expression [20]. For each selected gene, we obtained the corresponding GFP-fusion protein [24] and used its fluorescence to select for high-expressing cells. Specifically, the distribution of fluorescence within the cell population was monitored using fluorescence activated cell sorter (FACS), and the top 1.5% (20,000) cells displaying the highest fluorescence levels (normalized by FSC-A which is an indicator of cell size) were collected (Figure 1B, Materials and Methods). The selected cells were grown, and the selection procedure was repeated until a clear shift in mean expression was observed, or up to a maximum of eleven cycles. No shift was observed in the FSC-A distribution, indicating that selection did not increase cell size.
Selection for high expression was successful in the majority of cases (35/41) (see Figure S3 caption for definition of success). Most genes increased expression after 3–6 rounds of selection. In some cases, expression increased gradually over subsequent cycles, perhaps reflecting the co-existence of multiple mutations with similar effects. Notably, expression increased by a typical 1.5 to 3 fold, and the extent of this change was not correlated with the promoter type (Figure 1C and 1D and Figures S2 and S3). The evolved high expression was stable for many generations. No significant change in mean expression was observed in control experiments in which the identical procedure was used but cells were FACS collected without selection.
To further understand the genetic mechanisms leading to increased expression, we first asked if the driving mutations are dominant or recessive. If the mutation causing the increased expression is dominant, high GFP expression will be maintained also when crossing the haploid evolved strain with a wild-type strain. In contrast, if the mutation is recessive, the level of GFP fluorescence will be reduced or even lost after crossing with a wild-type strain. Any mutation which is linked to the GFP locus (e.g. mutation in the promoter or gene duplication) will show a dominant effect in our assay. In contrast, mutations in trans regulators can be either dominant or recessive, depending on whether their impact is maintained or reduced when combined with the wild-type allele.
Strikingly, genes of the DPN class evolved almost exclusively by dominant mutations, whereas OPN genes were mostly associated with recessive mutations that either eliminated or significantly reduced the expression of the evolved GFP allele in the diploid background (Figure 2 and Figure S3). Thus, of the fifteen DPN genes that evolved higher expression, 13 were classified as dominant (all single-colonies isolated from the evolved population maintained their high expression upon mating with a wild-type strain), one was recessive (all single-colonies showed reduced expression in a heterozygote background), and one population contained a mixture of dominant and recessive colonies, indicating two modes of evolution. In sharp contrast, of the 20 OPN genes, 13 were classified as recessive, four were a mixture of dominant and recessive colonies, and only three were dominant.
When directly comparing the nucleosome organization pattern (OPN score) of the genes evolving by dominant versus recessive mutations, we found the two distributions to be distinct with a p-value of 1.63*10−5. To further verify the reproducibility of the results, we repeated the selection procedure for 26 of the strains. Of the ten DPN genes evolved in this second round of validation only one gene changed its classification from dominant to a mixture of dominant and recessive. For the OPN genes, of the 16 genes examined in the second round twelve retained the same mode of evolution, one changed its classification from recessive to dominant, 2 changed from a mixture of dominant and recessive to dominant only and one did not evolve (Table S1). Combining the two experiments together, the hypothesis that the frequency of dominant versus recessive mutations depends on nucleosome organization is supported with a p-value of 1.26*10−6.
Next, we asked whether the mutations driving GFP expression change occurred in cis or in trans. Cis mutations are linked to the gene itself, and can be generated for example by mutations in the gene promoter or by gene duplication. As mentioned above, such mutations are necessarily dominant in our assay. In contrast, trans mutations may be either dominant or recessive. Mutations can be classified as cis or trans by examining the expression of the wild-type allele of the evolved gene within the evolved cells. Mutations in cis will have no effect on this second (wild-type) copy, while trans mutations are expected to increase also the expression of the second copy.
We therefore generated heterozygote diploids by mating the evolved colonies with wild-type cells in which the allele corresponding to the evolved gene was fused to mCherry. Notably, all the dominant mutations were classified as cis, showing no increase in mCherry expression (Figure S4). In contrast, coordinate elevation of GFP and mCherry levels were observed in all recessive cases where the evolved expression levels were only partially compensated in the diploid background. Taken together, we conclude that DPNs evolved primarily by dominant cis mutations while OPNs typically evolved by recessive trans mutations. We observed no correlation between the mode of evolution and the initial expression level, the presence of a TATA box [23] or repeats in the promoter sequence [25], initial noise or chromosomal position (Table S1).
Many of the dominant mutations increased expression by about two fold. To examine whether they present duplication of the associated gene, we measured the GFP DNA copy-number using real time PCR. With the exception of two cases, dominant mutations all involved gene duplication (Figure 3A). To define the duplicated region, we used an array-based comparative-genomic hybridization (CGH). Notably, large-scale duplications were identified in 11/13 dominant cases we assayed. Typically full chromosomes were duplicated (9/11), and the duplications invariably spanned the gene subject to selection (Figure 3B and 3C, and Figure S5). In principle, trans mutations could also result from duplications of regulatory genes. Yet, in only one of the sixteen recessive cases we examined we observed a duplication of a small chromosome fragment.
We measured the competitive fitness of the evolved strains. The majority of strains displayed some growth defect, and there was no apparent distinction between the recessive and the dominant mutations (Figure S7). Still, since missegregation of chromosomes during cell division is a relatively common event [26], [27], we hypothesized that strains evolving by large scale duplications will revert faster than these evolving by other means once selection for GFP expression is removed. To examine that, we grew nineteen of the evolved strains for ∼130 generations and monitored GFP levels temporally. Out of seven strains with duplicated chromosomes tested, six reverted to their pre-selected expression level and this reversion was caused by the loss of the duplicated chromosome (Figure 4). In contrast, all twelve strains without such duplication maintained the evolved high expression (Figure 4 and Figure S6). It is likely that those strains improved their growth rate through alternative, compensating, mutations and not by reverting the original mutation leading to the increase GFP expression. Together, our results suggest that although both DPN and OPN gene groups evolve initially at the same rate, the solutions found by DPN genes is less likely to be maintained in the long term. This effectively results in DPN strains having fewer evolutionary solutions available for increasing gene expression in evolutionary time scale.
Genetic changes leading to increased gene expression can be classified as regulatory cis-effects, regulatory trans-effects and segmental duplications that increase gene's copy number. By regulatory cis-effects we refer to mutations in the close vicinity of the gene promoters, altering, for example, the binding of regulatory factors. Trans-effects refer to mutations that occur elsewhere in the genome, for example modulating the activity of an upstream transcription factor or signaling protein. Such mutations are expected to have a wider influence on gene expression compared to cis-effects as they will modulate the expression of many (or all) targets of the associated trans factor. Finally, gene expression can also be increased by duplication, consisting of either a full chromosome or a chromosomal region containing the gene of interest. Chromosome duplications will modulate the expression of hundreds of unrelated genes.
Studies that compared gene expression between related organisms revealed that most expression changes result from regulatory cis and trans mutations, with cis-effects dominating the divergence between species, while trans-effects dominate the divergence between different strains of the same species [18], [28], [29]. At least in yeast, trans-effects preferentially modulate the expression of genes of the OPN promoter type, but are less significant at genes of the DPN class [18]. Large scale chromosomal duplications are typically not observed when comparing yeast strains and species. This distribution of effects could reflect the frequency of initial mutations arising in the population, the interplay between their selective advantages versus possible deleterious outcomes, or the probability of reverting back the original mutations.
Our results provide a complementary view on the early response to selection for high expression. We find that the genetic changes dominating this initial adaptation differ from those dominating the long-term evolution. First, regulatory cis effects were not observed. Rather, expression was modulated either by large-scale duplications or by regulatory trans mutations. Most notably, the choice between trans mutations and large-scale duplications was essentially dictated by the gene promoter class: genes of the DPN class changed expression almost exclusively through duplications, whereas genes of the OPN class did so primarily through trans-effects not involving duplications.
This difference between early and later evolutionary mechanisms reflects transition from ‘general’ to more specific solutions: for the DPN class, the general solution of large-scale duplications appears to be the most easily accessible. It arises easily, and is indeed frequently observed during initial selections [30]–[33]. This solution, however, is more difficult to maintain due to pleiotropic effects and the high frequency by which the additional chromosome can be lost, which may explain its absence in species or strains.
For the OPN class, trans-effects appear to be the more accessible solution, while chromosome duplications are observed at significantly lower frequency. OPN genes occupy the same chromosomes as DPN genes and thus should have the same likelihood of being duplicated. The preferential modulation of OPN genes by trans effects may thus indicate the larger spectrum of trans factors that influence the expression of those genes which increases the number of potential trans mutations.
Notably, we find that although trans mutations arising in our strains did decrease the competitive growth fitness to the same extent as did chromosome duplications, they were easier to maintain. We hypothesize that this reflects the large spectrum of mutations that can compensate for the reduction in cell growth, making it unlikely that the cells precisely revert the mutation leading to the increased expression. This is in contrast to the case of chromosomal duplication, where reversion of the original mutation (loss of the duplicated chromosome) is most likely. Notably, this difference in reversion strategy may explain the prevalence of trans-dependent divergence of OPN genes [18]. These trans mutations may have arisen during transient selection for high expression, but were maintained even after selection was removed, possibly due to a compensatory mutation. Consistent with this possibility, we recently demonstrated extensive trans-dependent expression variability of OPN genes that is buffered by the activity of chromatin regulators [34].
Finally, the most specific solutions (e.g. cis regulatory mutations) require more time to emerge compared to the other more general processes, yet their specificity to the gene of interest allows their long-term maintenance in the population which may explain their dominance in the divergence between species [18], [28], [35]. It may also be that those cis effects are mostly neutral and do not arise in response to selection, at least not in response to a strong selection. Indeed, if expression can be easily increased through trans mutations or duplication, those will arise quickly and will reduce further pressure for the emergence of cis mutation.
It should be noted that our study was performed in a haploid background, which favors the emergence of recessive mutations. In contrast, in nature yeast cells exist primarily as diploids. Many of the trans mutations we identified will have no effect in a diploid background. Yet, a large fraction of them were only partially recessive and thereby manifested also in the diploid background. It is likely that those mutations will dominate the initial evolution of OPN genes also in a diploid background.
In conclusion, we find that gene expression readily evolves in response to strong selection. Furthermore, we propose that the genetic mechanisms by which expression evolves, and hence the stability of this genetic change, depends on the organization of gene promoter. Together, our study supports the idea that regulatory variance shapes evolutionary path by biasing long-term evolutionary changes to genes with flexible OPN promoter organization.
To estimate the degree to which each promoter is consistent with the OPN and DPN general classes, we divided the average nucleosome occupancy at the transcription start site (TSS)-proximal region (0–150 bp upstream of TSS) by the average nucleosome occupancy at the TSS-distal region (200–400 bp upstream of the TSS). This measure was averaged over three independent datasets of nucleosome occupancy, including Lee et al. [36], Kaplan et al. [37], and Tsui et al. [38].
Selection experiments were performed using the GFP-fusion library strains [24]. BY4741 (MATa his3Δ1 leu2Δ0 met15Δ0 ura3Δ0), was used as a control strain for the CGH analysis. BY4742 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0) was used to create diploids in the dominant-recessive experiment as well as to create the mCherry-fusion strains in the cis-trans experiment. BY4741 MATa YDL227cΔ::TEF2pr-mCherry- kanMX4 was used in the competitive growth experiment. Yeast strains were grown on synthetic complete (SC) media for FACS sorting, flow cytometry analysis and competitive growth experiments. Strains were maintained on YPD-agar plates. Selection for diploids was done on SC-Lys-Met agar plates.
Single colonies of each parental strain were grown to logarithmic phase. Each individual cell in the population was monitored using FACSAriaII cell sorter (Becton Dickinson) using a Coherent Sapphire Solid state 488 nm 20 mW laser. Gating was first done for small, single cell population based on the FSC-A, FSC-W and SSC-A counts, which are associated with cell size and geometry. GFP level for each gated cells was then monitored versus its FSC-A level and cells having the highest GFP versus FSC-A value were collected. In total, 20,000 cells of the top 1.5% (normalized) GFP were collected into 5 ml of SC medium. The selected cells were grown overnight. The population divides for 7–10 times between selections. The population did not reach stationary phase under this conditions so no further dilutions were needed before the next round of selection. Cells were subject to the same selection procedure until a clear shift of the mean expression was observed, or up to a maximum of eleven cycles. The control population went through exactly the same procedure, but 20,000 of the total gated population rather the top 1.5% GFP were collected by FACS. Single colonies were isolated for each evolved strain on YPD-agar plates. Eleven single colonies were picked for each gene and were subject to further analysis.
The mCherry protein was amplified together with the hygromycinB phosphotransferase (hph), a gene conferring resistance to the antibiotic hygromycin B, from the pBS35 plasmid using the primers F2 and R1 used in the construction of the GFP library (http://yeastgfp.yeastgenome.org/yeastGFPOligoSequence.txt). The amplified fragments were transformed into the yeast strain BY4742 using the LiAc/SS-Carrier/PEG transformation method [39]. After overnight recovery, the yeast cells were plated on synthetic complete (SC) medium+hygromycin B (0.3 mg/ml, Calbiochem). Correct integration was verified by PCR using cherry reverse primer (5′-tgaactccttgatgatggcc-3′) and gene specific check primer (GFP library). The expression level of each fusion protein was measured prior to mating with its GFP homologue.
Fluorescence was measured by flow cytometery on the BD LSRII system (BD Biosciences) with a High Throughput Sampler extension (HTS), With Excitation wavelength of 488 nm for GFP and of 594 nm for Cherry. The FACS fcs files were imported into Matlab using an available script [40]. The FACS data was processed by gating based on FSC and SSC, removal of outliers from the GFP population and calculation of mean and standard deviation.
DNA from 3 individual colonies of each evolved strain analyzed as well as from its parental strain at two replicates. DNA was extracted using Masterpure Yeast DNA Purification Kit (Epicentre Biotechnologies). Real time PCR was performed in Lightcycler 480 (Roche). Reactions were done using LightCycler 480 Probes Master. GFP DNA content was detected using primers: 5′-cacatggtccttcttgagtttg-3′ and 5′-atagttcatccatgccatgtgta-3′ together with probe no. 3 (Universal ProbeLibrary, Roche) Act1 was used as a reference gene and was detected using primers: 5′-tccgtctggattggtggtand-3′ and 5′-tgagatccacatttgttggaag-3′ together with probe no 139 (Universal ProbeLibrary, Roche). GFP gene content of each strain was normalized to its parental strain. Analysis of the results was done using LightCycler 480 software. DNA content of the reverted strains was monitored in two individual colonies together with the corresponding evolved strain. Real time reaction were done using Absolute Blue SYBR Green mix (Thermo Scientific), analyzed and normalized as above. Primer used were: YBR197C (chr 2) 5′-aggtgaaagtaagcgacgcg-3′; 5′-tgaaccagctgagggtttcct-3′ YCR047C (chr 3) 5′-tatgtcgtccacctggtcgtcg-3′; 5′tcctaaacagcggttgatgagg3′ ERG1 (chr 7) 5′- cagtcataccaccaccagtcaatg-3′; 5′-gccaaactcctacttgccagc-3′ URA1 (chr 11) 5′- tccaagatagcgaattcaacg-3′; 5′-tttcccaggcacattaggac-3′ SMA2 (chr 13) 5′- acctaccgtttggcattgac-3′; 5′-atagggcatttcctgtgtgc-3′ GFP 5′- gtggagagggtgaaggtga-3′; 5′- gttggccatggaacaggtag-3′ ACT1 5′- tcgttccaatttacgctggtt-3′; 5′ –cgattctcaaaatggcgtg-3′.
DNA was extracted using Masterpure Yeast DNA Purification Kit (Epicentre Biotechnologies). After RNAse treatment and EtOH precipitation, DNA was digested using AluI and RsaI restriction enzymes (Promega) and purified with QIAquick PCR purification kit (QIAGEN). DNA was then labeled and Hybridized to microarrays following Agilent Oligonucleotide Array-Based CGH for Genomic DNA Analysis protocol. Arrays were scanned using Agilent microarray scanner and quantified using the Spotreader software (Niles Scientific).
A 180K custom Agilent CGH microarray was defined by selecting 60,000 high-quality probes (with average spacing of 140 bp) from the Agilent-014741 Yeast Whole Genome 244K microarray design. Three repeats of each selected probes were dispersed at different random positions in the microarray.
Probes with CV>40% or median<3000 were removed. The signal was calculated as log of (median intensity – median background intensity). Negative values were removed. The repeats were averaged. We have noted that the averaged signal was negatively correlated with distance from telomeres (r = −0.24), and therefore subtracted the lowess curve of signal as a function of distance (matlab malowess function span of 10%). The signal was further normalized by subtracting lowess curve (span = 1%) of the reference (Cy3 against Cy3 signal of WT, and Cy5 against Cy5 signal of WT). The signal shown in both unsmoothed and smoothed (lowess span = 0.5%) forms.
To characterize the competitive growth of each of the strains, we utilize a high throughput flow cytometry assay. For each strain tested two evolved colonies as well as two colonies of the corresponding parental strain were analyzed. Each colony was grown together with a wild type strain (BY4741) marked with mCherry expressed under the constitutive TEF promoter in the same well of a 96-well plate. The strains were inoculated in the well in equal concentrations and diluted repeatedly in 24 hour intervals for 4 days. GFP and mCherry cells frequencies were measured by FACS at the initial inoculation and at each dilution point. Each experiment was repeated 3 times. The differences in the strains growth rate can be derived from the frequencies measured by FACS. The fitness advantage of one competing strain over the other is calculated as follows: Denote ni as the number of cells of type i, gi as the growth rate of cell type i, fi as the frequency of cells type i out of the whole population, and p as the number of types of cells. If the frequencies of any two types of cells are divided, log2 transformed, and plotted against time, we get a curve whose slope is the difference in their growth rate, and this is what we refer to as fitness advantage:
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10.1371/journal.pgen.1007058 | Gli3 is a negative regulator of Tas1r3-expressing taste cells | Mouse taste receptor cells survive from 3–24 days, necessitating their regeneration throughout adulthood. In anterior tongue, sonic hedgehog (SHH), released by a subpopulation of basal taste cells, regulates transcription factors Gli2 and Gli3 in stem cells to control taste cell regeneration. Using single-cell RNA-Seq we found that Gli3 is highly expressed in Tas1r3-expressing taste receptor cells and Lgr5+ taste stem cells in posterior tongue. By PCR and immunohistochemistry we found that Gli3 was expressed in taste buds in all taste fields. Conditional knockout mice lacking Gli3 in the posterior tongue (Gli3CKO) had larger taste buds containing more taste cells than did control wild-type (Gli3WT) mice. In comparison to wild-type mice, Gli3CKO mice had more Lgr5+ and Tas1r3+ cells, but fewer type III cells. Similar changes were observed ex vivo in Gli3CKO taste organoids cultured from Lgr5+ taste stem cells. Further, the expression of several taste marker and Gli3 target genes was altered in Gli3CKO mice and/or organoids. Mirroring these changes, Gli3CKO mice had increased lick responses to sweet and umami stimuli, decreased lick responses to bitter and sour taste stimuli, and increased glossopharyngeal taste nerve responses to sweet and bitter compounds. Our results indicate that Gli3 is a suppressor of stem cell proliferation that affects the number and function of mature taste cells, especially Tas1r3+ cells, in adult posterior tongue. Our findings shed light on the role of the Shh pathway in adult taste cell regeneration and may help devise strategies for treating taste distortions from chemotherapy and aging.
| Adult taste cell regeneration is essential for maintaining peripheral taste cells throughout life. The Shh pathway is an important regulator of taste bud development and regeneration in both embryonic and adult stages. We show that the transcription factor Gli3, an important effector of the Shh pathway, is expressed in Tas1r3-expressing sweet/umami taste receptor cells and Lgr5-expressing taste stem cells. Conditional deletion of the Gli3 gene led to increased numbers of Tas1r3-expressing taste cells and Lgr5-expressing stem cells along with altered responses to bitter and sweet tastants. Our findings shed light on adult taste cell regeneration and may lead to new treatments of taste disorders associated with aging and chemotherapy.
| In mouse tongue taste buds are found in three types of papillae: anterior fungiform (FF), lateral foliate (FO), and posterior circumvallate (CV). The numerous FF papillae each contain a single taste bud, while the two FO and single CV papillae each contain hundreds of taste buds [1, 2]. Each taste bud contains ~50–100 mature receptor cells classified as type I, type II, or type III cells based on morphology and markers. These cells are further classified into functional subtypes that respond to basic taste qualities of sweet, bitter, umami, sour, and salt [3–5].
Embryonic taste papillae development, especially for anterior tongue, has been well-studied [6–8]. Canonical developmental pathways such as Wnt, sonic hedgehog (Shh), Notch, and fibroblast growth factor (Fgf) pathways drive embryonic taste papillae development [reviewed in: 2, 6, 9]. In adult mice, taste cells survive only 3–24 days, necessitating their regeneration throughout life [10]. However, much less is known about the regulation of adult taste cell regeneration, even though it is essential for maintaining the sense of taste throughout life. Adult taste cell regeneration is affected by aging, radiation treatment and chemotherapy, infection, and autoimmune diseases [11–18].
The role of Shh signaling in regulating taste papillae development and taste cell differentiation at the embryonic and adult stages has been well studied [19–27]. In embryos, SHH is a suppressor of taste placode formation in the FF papillae [17, 28] while it promotes development of taste buds in CV papillae [29]. Yet, SHH overexpression in adult taste epithelium induces numerous ectopic FF taste buds but has no major effect in other taste fields [1, 27]. SHH signals through the membrane-bound receptors PTCH1 and SMO to regulate the bi-functional transcription factors GLI2 and GLI3, the principal effectors of the pathway in adults [30–34]. In the absence of SHH signaling, GLI2 and GLI3 are C-terminally truncated to generate transcriptional repressors that are mostly sequestered in the cytoplasm [30, 33, 34]. SHH signaling prevents the proteolysis of GLI2 and GLI3 and promotes their localization to the nucleus, where they regulate the expression of numerous target genes [35–37].
The role of GLI3 in adult taste cell turnover has not been investigated to date. We generated single-cell RNA-Seq data from multiple taste cell subtypes from mouse CV papillae and found that Gli3 is highly expressed in green fluorescent protein (GFP) marked cells positive for Lgr5 (Lgr5-GFP marked taste stem cells) and in Tas1r3-GFP marked type II taste cells. These results were confirmed using reverse transcription PCR (RT-PCR), in situ hybridization, and immunohistochemistry. Conditional knockout of Gli3 (Gli3CKO) in CV and FO papillae increased taste bud size and the numbers of Tas1r3- and Lgr5-expressing taste cells relative to wild-type animals (Gli3WT). Similar changes were observed in Gli3CKO taste organoids derived from Lgr5-GFP+ taste stem cells. In posterior tongue in vivo and in organoids these alterations were accompanied by changes in the expression of Tas1r3, Trpm5, Gnat3, and multiple Gli3 target genes. In line with changes in taste cell number and gene expression, Gli3CKO mice showed increased lick and glossopharyngeal (GL) nerve responses to sweet and umami taste stimuli and decreased lick responses to bitter and sour taste stimuli. Our data indicate that Gli3 is a negative regulator of differentiation and/or survival of taste stem cells and Tas1r3+ type II taste cells that influences taste receptor cell composition and function.
To identify Gli-family transcription factors selectively expressed in subsets of adult taste cells, we analyzed single-cell RNA-Seq data generated from Lgr5-GFP+ stem, Tas1r3-GFP+ type II and Gad1-GFP+ type III taste cells isolated from respective GFP-transgenic mouse strains. The transcription factors Gli1 and Gli2 were expressed in all three types of cells, while Gli3 (and its upstream regulators Ptch1 and Smo) was highly expressed in both Lgr5-GFP+ and Tas1r3-GFP+ but not in Gad1-GFP+ taste cells (S1 Table). RT-PCR showed that Gli3 was expressed in FF, FO and CV papillae taste tissue, as well as in lingual epithelium devoid of taste buds (Fig 1A). Using GFP+ taste cells purified by fluorescence-activated cell sorting (FACS) Gli3 was found in Lgr5-GFP+ and Tas1r3-GFP+ but not Gad1-GFP+ taste cells (Fig 1A). Quantification of Gli3 mRNA expression by quantitative PCR (qPCR) showed that it is expressed at high levels in Lgr5-GFP+ and Tas1r3-GFP+, but at only low levels in Gad1-GFP+ taste cells (Fig 1B). In situ hybridization using Gli3 antisense probes confirmed that it is expressed in taste cells in all three taste papillae (Fig 1C–1E), while the control sense probe produced only minimal background signal in taste cells (Fig 1F–1H). Indirect immunohistochemistry using an antibody generated against the N-terminus of GLI3 capable of detecting both the truncated and full-length forms of the protein revealed that it too is expressed in taste cells in all three taste papillae (Fig 1I–1K). The specificity of RNA probes (S1B and S1C Fig) and antibody (S1E Fig) were validated with the positive control jejunum tissue. Further, both reagents produced weak or no signal in CV papillae from Skn-1a knockout mice that lack all type II taste cells (S1D and S1F Fig); pre-incubation of GLI3 antibody with its immunogenic peptide confirmed specificity of the reagent (S1G Fig).
To confirm these results and identify other taste cell subtypes that express GLI3 we double-labeled taste cells with the GLI3 antibody along with other antibodies or with GFP transgenes that mark specific types of taste cells. In both anterior and posterior tongue fields GLI3 was frequently co-expressed with Tas1r3-GFP, a marker for sweet and umami receptor-expressing type II cells (Fig 2A, 2D and 2G); less frequently with TRPM5, a marker for all type II cells (Fig 2B, 2E and 2H); and at even lower frequency with Gnat3-GFP, a marker for another subset of type II cells (Fig 2C, 2F and 2I). Double-labeled immunohistochemistry with anti-GLI3 antibody plus an antibody against the type III taste cell markers CAR4 (S2A–S2C Fig) and 5-hydroxytryptamine (5-HT) (S2D–S2F Fig) or with intrinsic GFP fluorescence of the type I marker Glast1-GFP (S2G–S2I Fig) revealed that GLI3 is generally not expressed in type I or III taste cells.
Quantification showed that GLI3 was frequently expressed with TAS1R3 and TRPM5, but rarely or not at all with CAR4, 5HT or GLAST (S2 Table). Among TAS1R3+ cells, ~92% expressed GLI3 in either CV or FO papillae. With TRPM5+ cells, 45–64% expressed GLI3, with a higher percentage in the CV papillae. For GNAT3+ cells 31–51% expressed GLI3, with a lower percentage in the FO vs. CV papillae taste cells. Only about 3% of CAR4+ and 1% of 5HT+ type III cells also expressed GLI3. While for GLAST+ type I cells, no GLI3+ cells were found among 88 CV and 64 FO papillae taste cells examined. Among the GLI3+ cells, nearly all (97–99%) also expressed TAS1R3 and/or TRPM5, but only 35–63% expressed GNAT3. In sum, GLI3 is expressed in type II cells, most often in the Tas1r3-GFP+ subset and less frequently in TRPM5+ or GNAT3+ subsets.
As a key mediator of the Shh pathway that regulates the expression of a large number of genes [35, 37], Gli3 may play a significant role in taste cell regeneration and survival. To test this, we generated a double-knockin mouse strain homozygous for the floxed Gli3 allele and also carrying the Lgr5-EGFP-IRES-CreERT2 allele. In this strain, administering the ERT2 ligand tamoxifen would ablate Gli3 in Lgr5+ stem cells in posterior tongue. Immunostaining with an antibody for KCNQ1, a marker for all taste cells, showed that in Gli3 conditional knockout (Gli3CKO) mice, the CV papillae taste buds were larger in size and contained more taste cells than did those of Gli3WT mice (Fig 3A, 3B and 3I). Most of this may be accounted for by an increase in the number of TAS1R3+ type II cells, as the numbers of TRPM5+ and TAS1R3+ but not GNAT3+ and PKD2L1+ (a type III cell marker) cells increased dramatically in Gli3CKO mice (Figs 3C–3H, 3J, S3A, S3E and S3I). Conversely, the numbers of CAR4+ type III cells decreased significantly in Gli3CKO mice (S3B, S3F and S3I Fig). In agreement with this, qPCR showed that mRNAs expressing Tas1r3, Gna14 and Trpm5, but not Gnat3, Pkd2l1 or Snap25, increased in Gli3CKO mice (Figs 3K and S3J). As expected, the number of GLI3+ cells and the amount of Gli3 mRNA decreased drastically, indicating that Gli3 deletion was successful. At the same time, GFP expression from the Gli3 locus was turned on in CV papillae tissue from Gli3CKO mice, which supports this conclusion (S3C, S3D, S3G–S3J Fig). Lgr5-GFP is also expressed in FO papillae (S4A Fig) and changes similar to that in CV papillae were observed in taste buds from FO papillae from Gli3CKO mice; the number of taste cells per taste bud and the size of taste buds were higher, as were the numbers of TRPM5- and T1R3-expressing type II taste cells (S4B–S4G, S4P & S4Q Fig). Conversely, the number of GNAT3-expressing type II cells and PKD2L1- and CAR4-expressing type III cells did not change; as expected, the number of GLI3-expressing cells were reduced drastically (S4H–S4O, S4P & S4Q Fig). Analysis of FACS-purified cell populations revealed that the proportion of Lgr5+ taste stem cells increased in Gli3CKO mice (S5A Fig). Consistent with this, qPCR showed that expression of Lgr5 mRNA in FACS-purified Lgr5-GFP+ cells (S5B Fig) and in CV papillae (S5C Fig) increased in Gli3CKO mice. As expected, the level of Gli3 mRNA in Lgr5-GFP+ cells from Gli3CKO mice was markedly reduced (S5B Fig). Examining CV papillae taste cells from Gli3CKO mice showed that the expression of mRNA encoding the Gli3 target gene Jag2 decreased, while that for its upstream regulator Ptch1 increased (S5D Fig). Based on results from selectively ablating Gli3 from Lgr5+ cells in posterior tongue, we infer that Gli3 suppresses the generation or survival of certain subsets of taste cells, in particular the Lgr5+ stem and Tas1r3+ type II cells in CV and FO papillae, but also the CAR4+ type III cells in CV papillae in vivo.
Taste organoids cultured from single Lgr5-GFP+ cells faithfully recapitulate many features of taste cell development and function [38]. The effect of Gli3 ablation on the regenerative potential of Lgr5-GFP+ taste stem cells was tested in taste organoids derived from the double-knockin (floxed Gli3 Lgr5-EGFP-IRES-CreERT2) mice by adding tamoxifen to the culture medium. Immunostaining of Gli3CKO organoids showed that the proportion of TAS1R3+ cells increased significantly while that of CAR4+ cells decreased (the proportion of GNAT3+ cells remained unchanged) (Figs 4A–4E, S6A, S6B and S6I). Although not quantified, the proportion of NTPDase-expressing cells appeared largely unchanged (S6C–S6D Fig). Consistent with these results, qPCR of Gli3CKO organoids showed that mRNAs expressing Tas1r3, Lgr5, Gna14 and Gnat3 increased, while those of Pkd2l1 and Snap25 decreased, and that of Trpm5 and NTPDase2 remained unchanged (Figs 4F and S6J). Further, the expression of Shh target genes Gli1, Mycn, Jag2, and Ccnd2 decreased in Gli3CKO organoids, while that of the upstream regulator Ptch1 increased dramatically (S6K Fig). As expected, in tamoxifen-treated Gli3CKO organoids the numbers of GLI3-immunoreactive cells and the level of Gli3 mRNA decreased, while GFP expression from the Gli3 locus, indicative of successful Gli3 deletion, was turned on (S6E–S6J Fig). Collectively, these data suggest that Gli3 ablation in Lgr5+ taste stem/progenitor cells promote expansion or survival of Tas1r3+ cells and suppresses the differentiation of CAR4+ type III taste cells.
In light of the profound changes in the proportion of taste cell subtypes and taste gene expression in tamoxifen-treated Gli3CKO mice, we investigated the effect of Gli3 ablation on taste responses. In brief-access taste tests, Gli3CKO mice showed altered behavioral responses to multiple taste qualities. Compared to Gli3WT, the Gli3CKO mice displayed increased preference for sucrose, sucralose and monosodium glutamate (Fig 5A–5C), increased aversion to denatonium benzoate and citric acid (Fig 5D and 5F), but no change in response to salt (Fig 5E). Glossopharyngeal (GL) nerve recording revealed that compared to Gli3WT the Gli3CKO mice had increased nerve responses to sucrose, sucralose, and denatonium (Figs 6A, 6B, 6D and S7). However, the GL nerve responses to monosodium glutamate, citric acid, and NaCl were unchanged in Gli3CKO vs. Gli3WT mice (Figs 6E and S7). Furthermore, there were no significant differences in chorda tympani (CT) nerve responses of Gli3CKO vs. Gli3WT mice to most of the taste stimuli tested (S8 Fig), consistent with the mosaic expression of Lgr5-Cre in the FF papillae taste buds that are innervated by the CT nerve [39].
We used single cell transcriptomics to identify transcription factors selectively expressed in Tas1r3+ taste receptor cells, reasoning that they might play a role in the development and/or maintenance of these taste cells. Although the transcription factor Skn-1a is critical for the development of Tas1r3+ taste receptor cells, it plays this role in all type II taste cells [40]. We anticipated that other transcription factors would be expressed selectively in particular subsets of type II cells, e.g. Tas1r3+ sweet/umami cells or Tas2r+ bitter cells. By single cell transcriptomics we found that the transcription factor Gli3 and its upstream regulators Ptch1 and Smo were more highly expressed in Tas1r3+ taste cells and Lgr5+ stem cells than in Gad1+ type III cells. Conventional expression studies using PCR, in situ hybridization and immunohistochemistry confirmed that Gli3 was indeed expressed in taste cells. Using Skn-1a null mice lacking all type II cells we showed that Gli3 was expressed selectively in type II cells. By double immunohistochemistry we found that Gli3 was most highly expressed in Tas1r3+ taste cells vs. other types of type II cells (e.g. Trpm5+ or Gnat3+ type II cells), and not expressed in type I or III taste cells.
Gli1,2,3 are zinc finger-containing transcription factors that act via the Shh pathway to regulate organogenesis and self-renewal [41, 42]. Gli2 and Gli3 are the main effectors of the Shh pathway in adults, with Gli2 acting mainly as a transcriptional activator and Gli3 as a repressor [33, 34]. Overexpression of Gli2 leads to malformation of taste buds in FF papillae, while overexpression of a dominant negative Gli2 transgene or deletion of Gli2 in taste cell precursors results in loss of taste buds in both FF and CV papillae [19, 43]. However, prior to our work the effects of manipulating Gli3 on adult taste cell regeneration were not known. To determine what role Gli3 might play in taste cells we turned to knockout mice. Conventional Gli3 null mice are embryonic/perinatal lethal [44, 45]; therefore we generated conditional null mice in which Gli3 was selectively eliminated from taste stem cells using a transgene in which CRE-ERT2 was driven from the Lgr5 promoter. Conditional ablation of Gli3 from taste stem cells and their progeny in the posterior taste field led to altered taste bud morphology with numbers of Tas1r3+ taste cells, but not of Gnat3+ cells. These changes may be cell-autonomous and cause only an increase in taste bud size at the expense of the epithelial tissue within the taste papillae or cause an overall increase in the size of the taste papillae by affecting the fate of the neighboring non-taste epithelium by non-cell-autonomous mechanisms. We have not tested which of these two possibilities account for the changes in Gli3CKO mice. The Gli3CKO mice showed altered short-term lick test responses to sweet, umami, and bitter tastants and diminished glossopharyngeal nerve responses to sweet and bitter. Although Lgr5-CRE-ERT2 is only expressed in a weak, mosaic pattern in FF papillae, we observed modest changes in CT nerve responses to sucralose and citric acid, indicating that Gli3 could play a role in the anterior taste field also. Definitively determining this will require experiments using a Cre driver that is strongly expressed in FF papillae.
The Shh pathway is active in all taste papillae [19, 24, 46, 47], but its effect is context dependent. In the embryonic stage, Shh signaling suppresses the development of FF papillae, while it promotes taste bud development in CV papillae [20, 28, 29, 48]. In adults, Shh expressing cells give rise to all subtypes of taste cells; pharmacological inhibition of Shh signaling inhibits taste cell turnover [21, 24, 47, 49]. Further, overexpression of Shh in the lingual epithelium triggers the development of multiple ectopic FF taste buds [27]. SHH is secreted by a subpopulation of post-mitotic cells in the base of the taste buds, and SHH-responsive, putative stem cells are located around and outside the base of taste buds. Indeed, current evidence suggests that the Shh pathway is active in stem cells [19, 23]; and is critical for the development of taste cells in all taste fields, as noted above. Lgr5 is a marker for posterior taste stem cells, but also a co-receptor in the Wnt signaling pathway [38, 39]. Because we ablated Gli3 in posterior tongue using the Lgr5-CreERT2 driver and because the Shh pathway is downstream of Wnt [28, 50] it is likely that we ablated Gli3 in taste stem cells before Shh signaling was turned on. Using the Lgr5-CreERT2 driver and tamoxifen, Gli3 was ablated from most but not all posterior field taste cells. The remaining Gli3+ cells may be progeny of the Lgr5+ cells where Gli3 deletion failed or long-lived taste cells generated prior to tamoxifen treatment.
In Gli3CKO mice the numbers of type III cells did not change overall, but the Car4+ subset of type III cells decreased markedly. Notably, Gli3 is not expressed in type III cells, including Car4+ cells, so its effect on this cell type is most likely a consequence of Gli3 activity in the Lgr5+ stem cells themselves or in lineage-specific precursor cells that gave rise to Car4+ cells. It is possible that the lack of Gli3 inhibits the differentiation of Car4+ cells as it does not affect the expression of Car4.
CAR4 is thought to be necessary for amiloride-insensitive salt taste perception [51], but Gli3CKO mice retained normal salt taste sensitivity. Conversely, Gli3CKO mice had heightened sensitivity to all other primary taste qualities in brief-access tests and to bitter and sweet tastants in taste nerve responses. The magnitude of the changes in lick responses in particular are somewhat surprising because the anterior taste field is not affected in Gli3CKO mice, and may mask the effect of changes in the posterior taste field. But it is possible that taste buds in the soft palate, which also are endoderm-derived and in the pharynx could show changes similar to those in CV and FO papillae in Gli3CKO mice, although we have not tested this. Consistent with these observations, only those taste qualities that elicit stronger responses in the posterior taste field, namely sweet and bitter, show robust changes in Gli3CKO mice. On the other hand, the behavioral and GL nerve responses to umami tastants did not change dramatically, although Gli3CKO mice had a higher number of Tas1r3+ cells and expressed more Tas1r3 mRNA than did wild-type mice in CV papillae. This may reflect the low baseline umami taste sensitivity and expression level of the Tas1r1 subunit of the umami taste receptor in CV papillae [52, 53]. In Gli3CKO mice the number of bitter (Gnat3+) and sour (Pkd2l1+) receptor cells did not change, but the sensitivity to these tastants, especially to bitter, increased. This may be attributed at least in part to changes in innervation density or selective innervation of particular types of taste cell types, although we have not tested this. Another possibility is that the expression level of taste receptors or their downstream signaling/regulatory machinery changed in Gli3CKO mice. Indeed, the expression of many taste marker genes, such as Tas1r3, Trpm5, Gnat3, Gna14, Snap25, Pkd2l1 and NTPDase2, is affected in Gli3CKO mice and/or organoids.
In CV papillae and/or organoids derived from Gli3CKO mice, we observed changes in mRNA expression of the Gli3 target genes Ccnd2, Mycn, and Jag2 and of the upstream regulator Ptch1. While these changes confirm that Gli3 deletion had the expected effects, they represent only a small subset of the thousands of Gli3 target genes. RNA-Seq analysis of Gli3CKO taste cells may help identify many more genes affected by Gli3 deletion and help delineate the developmental pathways regulated by Gli3 in taste cells.
In this study we demonstrate the utility of organoids cultured from purified taste stem cells for studying taste system development. Being an ex vivo system, taste organoids are not influenced by signals from other tissues. Hence, the results of genetic or other manipulations can be interpreted in a more straightforward manner. Also, the role in the taste system of key genes and pathways can be readily studied in taste organoids without concern for lethal effects from knockouts in vivo. Further, large numbers of cultured taste cells can be obtained from organoids which will be useful for protein expression and biochemical studies. Indeed, the effect of Gli3 knockout in taste organoids largely parallels that observed in vivo, underlining the utility of this system.
What could be responsible for the increases in Lgr5+ and Tas1r3+ cells in Gli3CKO mice? In other tissues, Shh signaling can drive either differentiation or maintenance of stem cells [54, 55]. It is possible that Gli3 enhances taste stem cell maintenance and acts as a negative regulator of taste cell differentiation. Another possibility is that Gli3 promotes apoptosis of Tas1r3+ and/or Lgr5+ cells. In either case, our data support a critical role of Gli3 activity in both stem and type II sweet taste receptor cells. The continued expression of Gli3 in Tas1r3+ cells and the profound changes in the number of these cells and in sweet and bitter taste sensitivity in Gli3 conditional knockout mice are evidence for an additional role for Shh signaling and Gli3 in these mature taste cells. One way to tease apart the role of Shh signaling in stem and Tas1r3+ cells is by conditional ablation of Gli3 or other Shh pathway components using Tas1r3- or type II-specific Cre drivers (e.g. Skn-1a [40]).
The role of other signaling pathways in taste development can also be context dependent. The Wnt and Bmp signaling pathways are critical for the development of embryonic FF papillae, but play relatively minor roles in the CV papillae [17, 28, 56–58], while the Fgf signaling pathway, much like the Shh pathway, plays opposite roles in embryonic CV and FF papillae development [2]. Such differences are not surprising given that developmentally the FF papillae originate from the ectoderm while the CV and FO papillae are derived from the endoderm [59]. Many of these pathways may play relatively subtle but significant roles in taste fields where they seem dispensable (similar to what we have shown for Gli3, and by extension the Shh pathway).
In summary, our results indicate that Gli3 is a suppressor of taste stem cell proliferation and affects the number and function of mature taste cells, especially of the Tas1r3+ subtype in posterior tongue. Our findings shed more light on adult taste cell regeneration and may help devise strategies for treating taste distortions caused by conditions such as chemotherapy and aging.
All animal experiments were performed in accordance with the National Institutes of Health guidelines for the care and use of animals in research and approved by the Institutional Animal Care and Use Committee at Monell Chemical Senses Center (protocols: #1163, #1151). 6-12-week old were used for all experiments. Animals were housed with a 12-h light/dark cycle and ad libitum access to food and water. The double-knockin mouse strain carrying a floxed Gli3 allele was a kind gift from Dr. Rolf Zeller, University of Basel (Basel, Switzerland) [60]. Lgr5-EGFP-IRES-CreERT2 knockin mice and Tas1r3-GFP and Gnat3-GFP transgenic mice were as previously described [61, 62]. Glast1-EMTB-GFP was a kind gift from Dr. Eva Anton, University of North Carolina School of Medicine (Chapel Hill, NC)[63]. Skn-1a knockout mice were a kind gift from Dr. Ichiro Matsumoto, Monell Chemical Senses Center (Philadelphia, PA) [40]. For Cre activation, tamoxifen (Sigma-Aldrich, St. Louis, MO; cat. no. T-5648) was dissolved in corn oil (Sigma-Aldrich cat. no. C8267) to a stock concentration of 20 mg/ml and administrated by oral gavage for three weeks at a dose of 2 mg/20 g body weight. Mice were given 2-day breaks each week during treatment to recover from the drug. Tissue was harvested 4 weeks after completion of tamoxifen treatment.
Mice were sacrificed by CO2 asphyxiation, and the tongues excised. An enzyme mixture (0.5 ml) consisting of dispase II (2 mg/ml; Roche, Mannheim, Germany; cat. no. 04942078001) and collagenase A (1 mg/ml; Roche cat. no. 10103578001) in Ca2+-free Tyrode’s solution (145 mM NaCl, 5 mM KCl, 10 mM HEPES, 5 mM NaHCO3, 10 mM pyruvate, 10 mM glucose) was injected under the lingual epithelium, which was then incubated for 15 min at 37°C. Lingual epithelia were peeled gently from the underlying muscle tissue and used for single-cell RNA-Seq, FACS sorting, or RNA isolation.
Single cell RNA-Seq was done as described [64]. GFP-expressing cells that were not physically attached to any other cell or cell fragment were picked irrespective of their shape individually from single cell preparations of CV papillae of Tas1r3-GFP (type II, sweet and umami receptor cells, n = 9), Lgr5-GFP (stem cells, n = 5), and Gad1-GFP type III, sour and high salt receptor cells, n = 11) transgenic mice. Two rounds of single-cell mRNA amplification were done using the TargetAmp 2-Round aRNA Amplification Kit 2.0 (Epicentre, Madison, WI). The antisense RNA generated from single cells was converted to Illumina sequencing libraries using the NEBNext Ultra Directional RNA Library Prep Kit for Illumina (New England Biolabs, Ipswitch, MA) and sequenced using the Illumina HiSeq 2000 platform. Sequencing reads were mapped to the mouse genome (version mm10, p4) using the STAR aligner [65] using Gencode M7 as splice junction database (https://www.gencodegenes.org/mouse_releases/7.html). The reads mapping to genes were counted using the featureCounts package [66] with Gencode M7 as reference. Data normalization and differential expression analysis were done using the DESeq2 package in R [67]. We obtained 30–70 million reads per library, of which 70–90% could be aligned to the mouse genome. On average, 10,184 genes were expressed per cell above an arbitrary cutoff of 10 reads per gene after normalization.
GFP-fluorescent Tas1r3+, Gad1+, and Lgr5+ taste cells were isolated by FACS from male mice of these respective genotypes. The region of the lingual epithelium containing the CV papillae from four to five mice was excised and pooled, minced into small pieces, incubated with trypsin (0.25% in PBS) for 10–25 min at 37°C, and mechanically dissociated into single cells using heat-pulled Pasteur pipettes. Cell suspensions were filtered using 70-μm cell strainers (BD Biosciences, Bedford, MA; cat. no. 352350) and then with 35-μm cell strainers (BD Biosciences cat. no. 352235). Cells were sorted into culture medium for organoid culture or Trizol LS (Thermo Fisher cat. no. 10296010) for RNA isolation using a BD FACS Aria II SORP FACS machine (Flow Cytometry and Cells Sorting Resource Laboratory, University of Pennsylvania), according to the enhanced green fluorescent GFP (EGFP) or GFP signal (excitation, 488 nm; emission, 530 nm).
Total RNA was isolated from freshly dissected taste papillae, nontaste control epithelium from the ventral surface of the tongue, and taste organoids using the PureLink mini kit with on-column DNA digestion using PureLink DNase (Thermo Fisher cat. no. 12185010) and converted into cDNA using Super Script VILO kit (Thermo Fisher cat. no. 11755050). RNA from FACS-sorted cells was isolated using the Trizol LS kit, and cDNA was synthesized using Ovation qPCR System (NuGEN, San Carlos, CA; cat. no. 2210–24). End-point PCR and qPCR were done as described [68]. Initially the expression of Gli3 was plotted as the logarithm of the ratio between its cycle threshold value and that of Gapdh. Subsequently, all qPCR results were normalized using the ΔΔCt method with Bact as reference.
Taste organoids were prepared as described [38]. Briefly, GFP fluorescent cells sorted from double-knockin mice were mixed with 4% chilled Matrigel (v/v; BD Biosciences, San Jose, CA; cat. no. 354234) and cultured in DMEM/F12 (Thermo Fisher cat. no. 11320–033) supplemented with Wnt3a-conditioned medium (50%, v/v), R-spondin-conditioned medium (20%, v/v), Noggin-conditioned medium (10%, v/v), N2 (1%, v/v; Thermo Fisher cat. no. 17502–048), B27 (2%, v/v; Thermo Fisher cat. no. 12587–010), Y27632 (10 μM; Sigma-Aldrich cat. no. Y0503), and epidermal growth factor (50 ng/mL; Thermo Fisher). Wnt3a- and R-spondin-conditioned medium are generated from Wnt3a and R-spondin stable cell lines as described [69]. Noggin conditioned medium was made in house. The culture medium was changed first at day 5–7 and once every 2–3 days thereafter. For passage, single-cell preparations were made from taste organoids by digestion with 0.25% trypsin for 10 min at 37°Cat day 14 before seeding again onto culture plates. 4-Hydroxytamoxifen (10 μg/ml; Sigma-Aldrich cat. no. H7904) was added into the fresh sorted cells for 5 consecutive days for Cre activation.
Adult male mice were euthanized by CO2 asphyxiation, and taste-papillae-containing portions of the tongue were quickly removed and briefly rinsed in ice-cold PBS. For in situ hybridization, tissues were freshly frozen in Tissue-Tek O.C.T. mounting media (Sakura Finetek, Torrance, CA; cat no. 4583) using a 100% ethanol dry ice bath and sectioned within 1 h after dissection. For immunohistochemistry, tissues were fixed for 1 h at 4°C in 4% paraformaldehyde in 1× PBS and cryoprotected in 20% sucrose in 1× PBS overnight at 4°C before embedding in O.C.T. Sections (10 μm thickness, coronal for FF and CV papillae, horizontal for FOL papillae) were prepared using a CM3050S cryostat (Leica Microsystems) and applied on precoated Fisherbrand Superfrost microscope slides (Fisher Scientific, Hampton, NH, Cat no 12-550-123). Sections were dried at 40°C for 20 min and immediately used for in situ hybridization or stored at −80°C for immunostaining.
Standard in situ hybridization methods were used as described. Fresh tissue sections with taste papillae were incubated with hybridization of 0.3 μg/ml Gli3 probe (GenBank NM_00813, 1809–2427 bp). Antisense and sense RNA probes were used at equivalent concentrations and run in parallel in the same experiment to ensure equivalent conditions. For each experiment, a positive control hybridization using Tas1r3 probe was done. In addition, in situ hybridization experiments were done on positive control tissues to confirm the quality and specificity of the RNA probes.
Immunostaining of taste buds was done as described. The antibodies used in this study and their concentrations are listed in S4 Table. For serotonin detection, mice were injected with 5-HT (Sigma-Aldrich cat. no. H9523) and sacrificed after 2 h. Species-specific secondary antibodies (S4 Table) were used to visualize specific taste cell markers and GLI3.
For antibody staining of organoids, cultured organoids were collected in 1.5 mL Eppendorf tubes and fixed for 15 min in fresh 4% paraformaldehyde in 1× PBS supplemented with MgCl2 (5 mM), EGTA (10 mM), and sucrose (4%, wt/v), washed three times for 5 min with 1× PBS, and blocked for 45 min with SuperBlock blocking buffer (Thermo Fisher cat. no. 37515) supplemented with 0.3% (v/v) Triton X-100 and 2% (v/v) donkey serum. They were then incubated at 4°C overnight with the desired primary antibodies (S4 Table). They were washed 3× for 5 min with 1× PBS and incubated for 1 h with species-specific secondary antibodies (1:500). 6-Diamidino-2-phenylindole (DAPI, 1:1000) in deionized water was used to visualize the nuclei following secondary antibody.
Bright-field images were generated using a Nikon DXM 1200C digital camera attached to a Nikon Eclipse 80i microscope and captured using Nikon NIS-Element F 3.00 software. Acquisition parameters were held constant for images with both antisense and sense probes. Fluorescent images were captured with the TCS SP2 Spectral Confocal Microscope (Leica Microsystems Wetzlar, Germany) using UV, Ar, GeNe, and HeNe lasers and appropriate excitation spectra. Scanware software (Leica Microsystems) was used to acquire z-series stacks captured at a step size of 2–3 μm. Acquisition parameters (i.e., gain, offset, PMT settings) were held constant for experiments with antibodies and for controls without antibodies. Digital images were cropped and arranged using Photoshop CS (Adobe Systems). Fluorescence images within a figure were adjusted for brightness and contrast for background standardization.
Quantitative measurements were carried out to determine the percentage of singly and doubly labeled type II and type III taste cells that co-expressed GLI3 and taste marker proteins. Confocal images from two to four sections from CV and FO papillae in each mouse were used for counting. To avoid counting the same cells more than once, sections separated from each other by at least 40 μm were chosen. Nuclear staining with DAPI was used to help distinguish individual taste cells. Only cells with entire cell bodies and nuclei visible were used for counting. GLI3-positive and taste-marker-labeled taste cells were counted in respective single-channel images, and the double-positive cells were counted using overlaid images.
KCNQ1 antibody staining was used to visualize taste buds for determination of taste bud size and taste cell number. Measurement of taste bud size was conducted as previously described [70]. Five Gli3CKO and Gli3WT mice were used for counting taste cell number. We found, on average, 10 taste buds per trench and 20 cells in each taste bud section. Only taste buds with typical morphology (with clear taste pore and the base of taste bud reaching the basement membrane) were used for analysis. Serial sections from similar regions of the tissue from each mouse were used to minimize location difference in taste bud number and size. The average number of nuclei in each taste bud was used as a proxy for the number of taste cells. For measuring the number of taste buds, all KCNQ1+ taste buds were counted, regardless of the morphology of taste bud. Double immunostaining was conducted using KCNQ1 antibody and respective taste cell marker antibodies to quantify the number of T1R3+, TRPM5+, GNAT3+, PKD2L1+, and CAR4+ taste cells per total taste cells per section. To quantify the percentage of taste cell subtypes in taste organoids with a clear single organoid profile from Gli3CKO mice, single and double immunostaining was performed using specific taste cell markers or Gli3 antibody. Nuclear staining with DAPI was used to help distinguish individual taste cells. Only cells with entire cell bodies and nuclei visible were used for counting.
Brief access tests were conducted using the Davis MS-160 mouse gustometer (Dilog Instruments, Tallahassee, FL) as described[71]. The following taste compounds were tested: sucrose (30, 100, 300, 1000 mM), sucralose (1, 3, 10, 30 mM), monosodium glutamate (MSG; 30, 100, 300, 100 mM), denatonium (0.3, 1, 3, 10 mM), citric acid (1, 3, 10, 30 mM), and NaCl (30, 100, 300, 600 mM). Mice were water- and food-restricted (1 g food and 1.5 mL water) for 23.5 h before test sessions for appetitive taste compounds (sucrose, sucralose, and MSG). For the aversive taste compounds (citric acid, denatonium, and NaCl), mice were water-deprived for 22.5 h before testing. In each test session, four different concentrations of each taste compound and water control were presented in a random order for 5 s after first lick, and the shutter reopened after a 7.5-s interval. The total test session time was 20 min. An additional 1-s “washout period” with water was interposed between each trial in sessions testing aversive tastants. Gli3CKO and Gli3WT mice were tested at the same time in parallel. Each mouse was tested with all the compounds. After each session mice were allowed to recover for 48 h with free access to food and water. Body weight of the mice was monitored daily, and only mice at or over 85% their initial body weight were used. The ratio of taste stimulus to water licks was calculated by dividing the number of licks for taste compounds by the number of licks for water presented in the parallel test session. Lick ratios > 1 indicate preference behavior to the taste compound, and lick ratios < 1 indicate avoidance behavior to the taste compound. With bitter and sour stimuli it appears that lick responses show a ceiling effect (maximum aversion) at higher concentrations. Thus, strain differences could only be seen at the lower concentrations.
The same sets of mice used for behavioral tests were used for electrophysiological recording of taste responses. Whole-nerve responses to tastants were recorded from the chorda tympani (CT) or the glossopharyngeal (GL) nerves as described [72]. Mice were anesthetized by an intraperitoneal injection (10 ml/kg, with 2.5 ml/kg further doses as necessary) of a mixture of ketamine (4.28 mg/ml), xylazine (0.86 mg/ml), and acepromazine (0.14 mg/ml). Under anesthesia, the trachea of each mouse was cannulated, and the mouse was then fixed in the supine position with a head holder to allow dissection of the CT or the GL nerve. The right CT nerve was dissected free from surrounding tissues after removal of the pterygoid muscle and cut at the point of its entry to the tympanic bulla. The right GL nerve from a different animal was exposed, dissected free from underlying tissues and cut near its entrance to the posterior lacerated foramen. All chemicals were used at ~24°C. The entire nerve was placed on the Ag-AgCl electrode. An indifferent electrode was placed in nearby tissue. For taste stimulation of fungiform papillae (FP), the anterior half of the tongue was enclosed in a flow chamber made of silicone rubber. For taste stimulation of the CV, an incision was made on each side of the animal's face from the corner of the mouth to just above the angle of the jaw, and the papillae were exposed and their trenches opened by slight tension applied through a small suture sewn in the tip of the tongue. For taste stimulation of fungiform papillae (FP), the anterior half of the tongue was enclosed in a flow chamber made of silicone rubber. Taste solutions were delivered to each part of the tongue by gravity flow for 30 s (CT) or 60 s (GL) at the same flow rate as the distilled water used for rinse (~0.1 ml/s). The following taste compounds were tested: sucrose (100, 300, 1000 mM), sucralose (3, 10, 30 mM), MSG (30, 100, 300 mM), denatonium (1, 3, 10 mM), citric acid (3, 10, 30 mM), and NaCl (30, 100, 300 mM). Neural responses resulting from chemical stimulations of the tongue were fed into an amplifier (K-1; Iyodenshikagaku, Nagoya, Japan) and monitored on an oscilloscope and an audio monitor. The whole-nerve responses were integrated with a time constant of 1.0 s, recorded using software (PowerLab 4/30; AD Instruments, Bella Vista, Australia), and analyzed using LabChart Pro software (AD Instruments). Nerve response magnitudes were measured at 5, 10, 15, 20, and 25 s after stimulus onset for the CT nerve and at 5, 10, 20, 30, and 40 s for the GL nerve. The stability of each preparation was monitored by the periodic application of 0.1 M NH4Cl. A recording was considered to be stable when the 0.1 M NH4Cl response magnitudes at the beginning and end of each stimulation series deviated by no more than 15%. Only responses from stable recordings were used for data analysis. At the end of the experiment, animals were killed by injecting an overdose of the anesthetic. The response values were averaged and normalized to responses to 100 mM NH4Cl to account for mouse-to-mouse variations in absolute responses. In Glossopharyngeal nerve recordings (S7 Fig), the responses appear not to return to baseline for some of the highest concentrations of stimuli because it is difficult and takes much time to wash them out completely from the CV cleft. Subsequent recordings were only done after repeated washings to make sure the previous response return to baseline. All data were compared as normalized units.
Prism (GraphPad Software) was used for statistical analyses, including calculation of mean values, standard errors, and unpaired t-tests of cell counts and qPCR data. Data from taste behavioral tests and gustatory nerve recording were compiled using Microsoft Excel. For statistical analyses of behavioral and nerve responses, two-way ANOVA and post hoc t-tests were used to evaluate the difference between genotype (Gli3CKO and Gli3WT mice) and concentration using OriginPro (OriginLab). p-Values < 0.05 were considered significant.
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10.1371/journal.ppat.1002263 | A Trigger Enzyme in Mycoplasma pneumoniae: Impact of the Glycerophosphodiesterase GlpQ on Virulence and Gene Expression | Mycoplasma pneumoniae is a causative agent of atypical pneumonia. The formation of hydrogen peroxide, a product of glycerol metabolism, is essential for host cell cytotoxicity. Phosphatidylcholine is the major carbon source available on lung epithelia, and its utilization requires the cleavage of deacylated phospholipids to glycerol-3-phosphate and choline. M. pneumoniae possesses two potential glycerophosphodiesterases, MPN420 (GlpQ) and MPN566. In this work, the function of these proteins was analyzed by biochemical, genetic, and physiological studies. The results indicate that only GlpQ is an active glycerophosphodiesterase. MPN566 has no enzymatic activity as glycerophosphodiesterase and the inactivation of the gene did not result in any detectable phenotype. Inactivation of the glpQ gene resulted in reduced growth in medium with glucose as the carbon source, in loss of hydrogen peroxide production when phosphatidylcholine was present, and in a complete loss of cytotoxicity towards HeLa cells. All these phenotypes were reverted upon complementation of the mutant. Moreover, the glpQ mutant strain exhibited a reduced gliding velocity. A comparison of the proteomes of the wild type strain and the glpQ mutant revealed that this enzyme is also implicated in the control of gene expression. Several proteins were present in higher or lower amounts in the mutant. This apparent regulation by GlpQ is exerted at the level of transcription as determined by mRNA slot blot analyses. All genes subject to GlpQ-dependent control have a conserved potential cis-acting element upstream of the coding region. This element overlaps the promoter in the case of the genes that are repressed in a GlpQ-dependent manner and it is located upstream of the promoter for GlpQ-activated genes. We may suggest that GlpQ acts as a trigger enzyme that measures the availability of its product glycerol-3-phosphate and uses this information to differentially control gene expression.
| Mycoplasma pneumoniae serves as a model organism for bacteria with very small genomes that are nonetheless independently viable. These bacteria infect the human lung and cause an atypical pneumonia. The major virulence determinant of M. pneumoniae is hydrogen peroxide that is generated during the utilization of glycerol-3-phosphate, which might be derived from free glycerol or from the degradation of phospholipids. Indeed, lecithin is the by far most abundant carbon source on lung epithelia. In this study, we made use of the recent availability of methods to isolate mutants of M. pneumoniae and characterized the enzyme that generates glycerol-3-phosphate from deacylated lecithin (glycerophosphocholine). This enzyme, called GlpQ, is essential for the formation of hydrogen peroxide when the bacteria are incubated with glycerophosphocholine. Moreover, M. pneumoniae is unable to cause any detectable damage to the host cells in the absence of GlpQ. This underlines the important role of phospholipid metabolism for the virulence of M. pneumoniae. We observed that GlpQ in addition to its enzymatic activity is also involved in the control of expression of several genes, among them the glycerol transporter. Thus, GlpQ is central to the normal physiology and to pathogenicity of the minimal pathogen M. pneumoniae.
| Pathogenic bacteria have developed a large battery of enzymes and mechanisms for extracting nutrients from their hosts, and the requirement for nutrient acquisition can be regarded as one of the driving forces for virulence [1]–[3]. In consequence, the metabolic capabilities of a pathogen reflect its adaptation to a particular niche in a particular host.
Mycoplasma pneumoniae is a causative agent of atypical pneumonia, however implication of this bacterium in several additional infections including encephalitis, aseptic meningitis, acute transverses myelitis, stroke, and polyradiculopathy has been reported [4]–[7]. These bacteria are members of the taxonomic class Mollicutes that are characterized by an extreme reductive evolution that results in the smallest genomes that allow independent life. Moreover, the mollicutes have lost the cell wall and most metabolic pathways, since they obtain the building blocks for their cellular macromolecules from the host tissue. However, even in these minimal pathogens, there is a close relation between metabolism and virulence (for a review, see [8]). M. pneumoniae thrives at the apical surface of lung epithelia. Thus, these bacteria must have evolved to utilize the carbon sources present in this niche. The pulmonary surfactant is composed of about 90% phospholipids and 10% proteins [9]. This suggests that phospholipids play a major role in the nutrition of M. pneumoniae.
Glycerophospholipids, the major building blocks of the cell membrane in bacteria and eukaryotes, are degraded in several steps. First, the fatty acids are cleaved from the phospholipids resulting in the formation of glycerophosphodiesters. In these molecules, the phosphate group of glycerol-3-phosphate is linked to another compound, called the head group. In eukaryotes, choline is by far the most abundant head group, and lecithin, the choline-containing phospholipid accounts for about 80% of all phospholipids in human lung cells [9]. In the second step, the choline head group is cleaved due to the activity of a glycerophosphodiesterase resulting in the formation of glycerol-3-phosphate that can feed into glycolysis after oxidation to dihydroxyacetone phosphate (see Figure 1). In M. pneumoniae, the latter reaction is catalyzed by the glycerol-3-phosphate oxidase GlpD [10]. GlpD transfers the electrons to water resulting in the formation of hydrogen peroxide, the major virulence factor of M. pneumoniae [11]. In consequence, the virulence of M. pneumoniae glpD mutant cells is severely attenuated [10].
While the metabolism of glycerol has been well studied in M. pneumoniae and other mollicutes such as M. mycoides [10], [12], [13], only little is known about the glycerophosphodiesterases required for lipid utilization. Many bacteria encode multiple glycerophosphodiesterases. In E. coli, both enzymes are enzymatically active in lipid degradation; however, they are differentially regulated, with GlpQ and UgpQ being induced in the presence of glycerol-3-phosphate and under conditions of phosphate starvation, respectively [14], [15]. In B. subtilis, out of three putative glycerophosphodiesterases, only GlpQ has been studied. The corresponding gene is under dual control and its expression is induced when phosphate becomes limiting and glycerol is available [16], [17]. Moreover, glpQ expression is repressed if more favourable carbon sources such as glucose are present [18]. In Haemophilus influenzae, another bacterium thriving in the respiratory tract, the glycerophosphodiesterase is involved in pathogenicity. The enzyme generates choline, which in turn is used for the biosynthesis of the bacterial lipopolysaccharide layer - a major virulence determinant of Gram-negative bacteria [19], [20]. Similarly, glycerophosphodiesterase activity is implicated in virulence of different Borrelia species. The enzyme is only present in the relapsing fever group and may help the bacteria to reach a higher cell density in the blood of the host as compared to Lyme disease spirochaetes [21].
In many bacteria, central enzymes of metabolism do not only fulfil their catalytic function, but in addition, they are also involved in signal transduction. In this way, the information on the availability of important metabolites can be directly determined by the enzyme in charge of their conversion, and this information is then often transferred to the transcription machinery. Collectively, such enzymes have been termed trigger enzymes [22]. They can control gene expression by directly acting as DNA- or RNA-binding transcription factors as the E. coli proline dehydrogenase and the aconitase or by controlling the activity of transcription factors by covalent modification or a regulatory protein-protein interaction as observed for several sugar permeases of the bacterial phosphotransferase system and the B. subtilis glutamate dehydrogenase, respectively [23]–[26].
In this work, we have analyzed the role of the two potential glycerophosphodiesterases encoded in the genome of M. pneumoniae. Biochemical and physiological studies demonstrate that one of the two proteins, GlpQ, is a functional glycerophosphodiesterase. GlpQ is essential for hydrogen peroxide formation in the presence of deacylated phospholipids as the carbon source and, in consequence, for cytotoxicity. Moreover, GlpQ may act as a trigger enzyme by controlling the expression of a set of genes encoding lipoproteins, the glycerol facilitator, and a metal ion ABC transporter.
Since phospholipids are the most abundant potential carbon sources for M. pneumoniae living at lung epithelial surfaces, we considered the possibility that these bacteria synthesize enzymes that cleave the polar head groups from the glycerophosphodiesters to produce glycerol-3-phosphate that can be utilized by the enzymes of glycerol metabolism [10] (Figure 1). Two genes that potentially encode such enzymes are present in the genome of M. pneumoniae, i.e. mpn420 (renamed to glpQ) and mpn566. An alignment of the corresponding proteins to glycerophosphodiesterases from other bacteria is shown in the supporting information (Figure S1).
In order to assess the biochemical properties and physiological relevance of the putative glycerophosphodiesterases, their corresponding genes, glpQ and mpn566, were cloned into the expression vector pGP172, thus allowing a fusion of the proteins to an N-terminal Strep-tag facilitating purification. The recombinant proteins were purified and the activities were first determined using glycerophosphocholine (GPC) as the substrate and a set of divalent cations. As shown in Figure 2, purified GlpQ was active against GPC, and the activity was highest in the presence of magnesium ions (10 mM). Manganese and zinc ions did also support activity, although to a lesser extent (Figure 2). In contrast, the enzyme was inactive in the presence of calcium and cobalt ions (data not shown). The activity assay with purified MPN566 revealed no activity with GPC, irrespective of the cation present in the assay (data not shown). We did also test the activity of both proteins with glycerophosphoethanolamine and glycerophosphoglycerol. However, neither protein was active with any of these substrates. Thus, our data demonstrate that GlpQ is active as a glycerophosphodiesterase, whereas MPN566 does not exhibit such an activity.
The two proteins GlpQ and MPN566 share ∼58% identical residues. Thus, it seems surprising that MPN566 was inactive in the enzymatic assay. However, several residues that are known to be important for the activity of glycerophosphodiesterases are conserved in GlpQ but not in MPN566 (Figure S1). These residues include Trp-36 and Glu-38 as well as the conserved HD motif (Asn-51 and Leu-52 in MPN566) and Phe-110. Interestingly, a similar arrangement with two GlpQ-like proteins is also observed in M. genitalium, and as in M. pneumoniae, one protein has all the conserved residues characteristic for glycerophosphodiesterases, whereas the second protein has similar deviations from the consensus as MPN566 (Figure S1). In order to test whether a restoration of the conserved residues would also convert MPN566 to a biologically active glycerophosphodiesterase, we replaced the five amino acids that differ from the consensus by those residues present in GlpQ. The resulting mutant allele was cloned into pGP172, and purification attempted. Unfortunately, this protein was highly unstable and purification was impossible.
The analysis of mutants is one of the most powerful tools for studying gene functions and bacterial physiology. The isolation of desired M. pneumoniae mutants became possible only recently by the introduction of the “Haystack mutagenesis” [27]. To get more insights into the physiological role of the glycerophosphodiesterases GlpQ and its paralog MPN566, we attempted to isolate mutants affected in the corresponding genes.
The strategy of “Haystack mutagenesis” is based on an ordered collection of pooled random transposon insertion mutants that can be screened for junctions between the transposon and the gene of interest due to transposon insertion. The 64 pools were used in a PCR to detect junctions between the glpQ or mpn566 genes and the mini-transposon using the oligonucleotides SS35 and SS40 (for the respective genes) and SH30 (for the mini-transposon) [28] (Figure 3). Positive signals were obtained for both genes. From pools that gave a positive signal, colony PCR with the 50 individual mutants resulted in the identification of the desired glpQ and mpn566 mutants. The presence of the transposon insertion in both genes was verified by Southern blot analysis (Figure 3). To test whether these strains contained only unique transposon insertions, we did another Southern blot using a probe specific for the aac-aphD resistance gene present on the mini-transposon. As shown in Figure 3, only one band hybridizing with this probe was detected in each strain, moreover, this fragment had the same size as the AgeI or PstI/SacI fragment hybridizing to the glpQ and mpn566 probe, respectively (Figure 3). The isolated glpQ and mpn566 mutant strains were designated GPM81 and GPM82. The position of the transposon insertion in the two genes was determined by DNA sequencing. The glpQ gene was disrupted between nucleotides 517 and 518, resulting in a truncated protein of 172 amino acids with one additional amino acid and the following stop codon encoded by the inserted mini-transposon. The disruption of the mpn566 gene was located between nucleotides 157 and 158, resulting in a truncated protein of 52 amino acids with one additional amino acid and the following stop codon.
First, we compared the ability of the wild type strain and the two mutant strains to utilize glucose and glycerol as the single carbon sources (Figure 4). As an additional control, we used the glpD mutant strain GPM52. This strain is defective in glycerol-3-phosphate oxidase and therefore unable to utilize glycerol as the only carbon source [10]. As shown in Figure 4A, the wild type and the glpD and mpn566 mutant strains grew well with glucose. In contrast, the glpQ mutant GPM81 grew more slowly and did not reach the final biomass as compared to the other strains. As reported previously, the wild type strain exhibited very slow growth with glycerol as the only carbon source [29]. In this respect, the glpQ and mpn566 mutants were indistinguishable from the wild type. As reported previously, the glpD mutant strain did not grow at all in glycerol-containing medium [10]. In conclusion, the active glycerophosphodiesterase GlpQ is required for maximal growth in the presence of glucose, whereas its absence does not interfere with the slow growth in the presence of glycerol.
Since the disruption of glpQ affected the growth properties of the bacteria, we wondered whether this might reflect changes in cell morphology and in the movement of the bacteria. The morphology of the wild type and mutant bacteria was analyzed by scanning electron microscopy, and no differences were detected (Figure S2). An analysis of the gliding velocities of the three strains revealed that the wild type strain glided with a velocity of 0.32±0.09 µm/s, whereas the glpQ and mpn566 mutants exhibited velocities of 0.2±0.08 µm/s and 0.3±0.1 µm/s, respectively (Videos S1, S2, and S3). Thus, the active glycerophosphodiesterase GlpQ is required for full gliding velocity of the bacteria.
The utilization of glycerol or glycerophosphodiesters results in the generation of hydrogen peroxide, the major cytotoxic product of M. pneumoniae. We asked therefore whether the glpQ and mpn566 disruptions would affect hydrogen peroxide formation and if so, whether it also affects cytotoxicity. Hydrogen peroxide formation was assayed in M. pneumoniae cultures that contained glucose, glycerol, GPC, glycerol-3-phosphate or no carbon source. In the absence of an added carbon source, neither the wild type strain nor the mutants formed substantial amounts of hydrogen peroxide (Figure 5). It is interesting to note that the wild type and the mpn566 mutant formed some hydrogen peroxide even in the absence of any added carbon source. This might result from the presence of low concentrations of phospholipids in the medium. Similarly, essentially no hydrogen peroxide was produced in the presence of glucose. If glycerol was available, maximal hydrogen peroxide formation (9.5 mg/l) was observed in the wild type strain. In the glpD mutant that served as a control, no hydrogen peroxide was formed. This is in good agreement with previous reports on the increase of hydrogen peroxide generation in the presence of glycerol and its dependence on a functional glycerol-3-phosphate oxidase [10]. The hydrogen peroxide production in the glpQ and mpn566 mutants was similar to that observed in the wild type strain. This result reflects that the metabolite glycerol is downstream from the glycerophosphodiesterase activity. In the presence of GPC, the wild type strain produced similar amounts of hydrogen peroxide (9 mg/l) as in the presence of glycerol. In contrast, no hydrogen peroxide formation was detected for the glpQ mutant GPM81, whereas the disruption of mpn566 did not have any effect on the production of hydrogen peroxide (Figure 5). This result is in good agreement with the enzymatic activities of the two proteins: GlpQ is the only active glycerophosphodiesterase in M. pneumoniae, and no glycerol-3-phosphate, the substrate of GlpD, can be formed in its absence, whereas MPN566 is dispensable for the utilization of GPC. We also tested the ability of the M. pneumoniae strains to form hydrogen peroxide in the presence of glycerophosphoethanolamine and glycerophosphoglycerol. These compounds did not stimulate hydrogen peroxide in any of the strains tested (data not shown). This is in excellent agreement with the result of the enzyme assay that suggested that neither GlpQ nor MPN566 is able to degrade these substances. Finally, we tested whether hydrogen peroxide was formed in the presence of glycerol-3-phosphate. As shown in Figure 5, no significant formation of hydrogen peroxide was observed in any of the strains tested. This suggests that the uptake of glycerol-3-phosphate is rather inefficient.
To assess the cytotoxicity of the different M. pneumoniae strains, we infected confluently grown HeLa cell cultures with M. pneumoniae cells (multiplicity of infection: 2). The cytotoxicity of the mutants was compared to that of the wild type strain and M. pneumoniae GPM52 that is affected in glpD. As shown in Figure 6, the HeLa cells had undergone nearly complete lysis after four days upon infection with wild type M. pneumoniae (cytotoxicity of 89%). As observed previously, the glpD mutant GPM52 has a reduced cytotoxicity (51%) resulting in a large portion of viable cells after infection [10]. For the glpQ mutant GPM81, nearly all HeLa cells had survived the infection suggesting that GlpQ is essential for cytotoxicity. In contrast, cytotoxicity induced by the mpn566 mutant strain GPM82 was equivalent to that of the wild type strain (Figure 6). These data clearly demonstrate that the active glycerophosphodiesterase GlpQ is required for host cell damage, whereas MPN566 is not. Moreover, they support the assumption that hydrogen peroxide formation is the major factor that contributes to host cell damage.
In order to exclude the possibility that the phenotypes observed with the glpQ mutant are due to a polar effect on the downstream alaS gene, we compared the alaS transcript levels in the wild type strain and the glpQ mutant. We observed fourfold increased amounts of alaS mRNA levels in the glpQ mutant, most likely due to the presence of a strong promoter in the transposon (data not shown). The expression of the alaS gene in the glpQ mutant strongly suggests that the observed phenotypes are the result of the glpQ disruption rather than of a polar effect. However, to provide unequivocal evidence for the implication of GlpQ in the growth phenotype as well as in hydrogen peroxide production and cytotoxicity, we performed a complementation assay. For this purpose, the M. pneumoniae glpQ gene with its own promoter was cloned into the integrative vector pMTnTetM438 and introduced into the chromosome of the glpQ mutant GPM81 (for details, see “Materials and Methods”). The resulting complementation strain GPM92 and the isogenic glpQ mutant GPM93 carrying the empty vector integrated into the chromosome were analysed for growth in the presence of glucose and glycerol, for hydrogen peroxide formation and for cytotoxicity. As shown in Figure 4A, the complemented mutant grew in the presence of glucose as the wild type strain. In contrast, the control strain carrying the empty vector grew slowly in the presence of glucose and was in this respect indistinguishable from the original glpQ mutant. These data clearly establish that the growth defect of the glpQ mutant in Hayflick medium containing glucose is a specific result of the glpQ inactivation. Similar results were observed for hydrogen peroxide production. Ectopic expression of the glpQ gene in the mutant strain restored the wild type phenotype, i.e. strong hydrogen peroxide formation in the presence of GPC. Again, the empty vector did not alter the phenotype of the glpQ mutant (see Figure 5). Finally, we assessed the cytotoxicity of the complemented strain towards HeLa cells. As one would expect from the restoration of hydrogen peroxide production upon complementation, the complemented mutant GPM92 was toxic for the HeLa cells, whereas the mutant carrying the control vector was not (Figure 6). In conclusion, ectopic expression of glpQ complemented all mutant phenotypes thus demonstrating, that the active glycerophosphodiesterase GlpQ is indeed essential for hydrogen peroxide production in the presence of the major substrate glycerophosphocholine and for cytotoxicity of M. pneumoniae.
As reported above, the glpQ mutant exhibits multiple phenotypes related to motility, metabolism, and pathogenicity. We asked therefore whether some of the effects are due to changes in the proteome of the glpQ mutant GPM81. To answer this question, we compared the total protein profiles of the wild type strain and the glpQ and mpn566 mutants, GPM81 and GPM82, respectively, after growth in glucose and glycerol. While the protein patterns in the mpn566 mutant were indistinguishable from the wild type strain under both conditions, several differences were noted for the glpQ mutant.
To identify those proteins that exhibit altered accumulation in the glpQ mutant, the total proteins of the wild type and the glpQ mutant strains were identified by mass spectrometry. For the protein extracts from glucose-grown cells, 532 different proteins were identified. This corresponds to about 77% of the theoretical proteome of M. pneumoniae. In the presence of glycerol, 473 proteins corresponding to 69% of the theoretical proteome were identified. The differences in protein expression between glucose- and glycerol-grown cells as well as proteins that could not be detected at all are summarized in Tables S1 and S2. A detailed list of the differences of the protein profiles between the wild type strain and the glpQ mutant is presented in Tables S3 and S4. As expected, the GlpQ protein was detected in the protein extracts of the wild type strain but not in those of the glpQ mutant strain. In glucose-grown cells, 33 and 21 proteins were in elevated and reduced amounts, respectively, in the glpQ mutant. The strongest increase was observed for the glycerol facilitator GlpF and the uncharacterized lipoprotein MPN162. A strongly reduced accumulation was observed for the lipoprotein MPN506. In the presence of glycerol, five induced and five repressed proteins were detected (see Table S4). Five proteins were subject to a identical regulation under both conditions (Table 1).
It has been shown before that changes at the proteome level may result from altered gene expression or from changes in protein stability [30], [31]. Therefore, we studied the expression of the genes corresponding to the most prominently regulated proteins and of genes encoding potential regulators, transport systems and potential pathogenicity factors. For this purpose, we isolated RNA from cultures grown in modified Hayflick medium supplemented with glucose and performed slot blot analyses (Figures 7 and S3).
These studies demonstrated that the regulation of the glycerol facilitator GlpF and the lipoproteins MPN162 and MPN506 occurs at the level of transcription (Table 1). Moreover, our results confirmed the higher expression of glpF and mpn162 and the repression of mpn506 in the glpQ mutant. For the other proteins that were induced in the presence of glucose, with exception of plsC and mpn566 (nearly two-fold higher transcript levels), similar accumulation of the mRNAs compared to the protein amount was observed (Table S3 and Figure S3). In contrast, for the proteins that were present in reduced amounts in glucose-grown cells, no changes of the corresponding mRNAs were observed for all transport proteins. Interestingly, the lipoprotein MPN083 showed a similar pattern at the level of transcription as the induced proteins and the ribonucleoside-diphosphate reductase (encoded by nrdFIE) was the only protein with reduced mRNA amounts, however changes in transcript level were not significant (Table S3 and Figure S3).
The proteome and transcription analyses identified three genes that are significantly regulated - either induced or repressed - in a GlpQ-dependent manner. An inspection of the upstream region of these genes revealed the presence of a common palindromic DNA motif (Figure 8). To exclude the possibility that this motif is randomly distributed in the genome of M. pneumoniae because of the extremely AT-rich consensus sequence, we tested its presence in the genome using the GLAM2SCAN algorithm [32]. In nine cases (matching score cut-off ≥30), this potential motif was located upstream of open reading frames, among them the three genes mentioned above. Therefore, the expression of the remaining six genes was tested by slot blot analysis, and for two of these genes, cbiO and mpn284, a significant accumulation and reduction of the mRNA, respectively, was observed (data not shown; Figure 7). Interestingly, the corresponding proteins, a subunit of a putative metal ion ABC transporter CbiO and the uncharacterized lipoprotein MPN284 were found to be present in higher or lower amounts in the glpQ mutant in glycerol-grown cell. Thus, there is a very good agreement between the regulatory effect of GlpQ at the proteome level, the regulation at the level of transcription, and the presence of the cis-acting element.
Two cytotoxicity factors are known in M. pneumoniae: The formation of hydrogen peroxide and the CARDS toxin that possesses ADP-ribosyltransferase activity [11], [33]. This work establishes that the glycerophosphodiesterase GlpQ of M. pneumoniae is essential for cytotoxicity of these bacteria. This is in excellent agreement with previous reports that carbon metabolism is intimately linked to virulence in pathogenic bacteria, including M. pneumoniae and other mollicutes [1], [2], [8]. The utilization of glycerol and phospholipids plays a particularly important role in the virulence of Mycoplasma species: Hydrogen peroxide, the major cytotoxic substance produced by these bacteria, is generated as a product of glycerol metabolism, and both glpD and glpQ mutants are severely affected in pathogenicity [10, this work]. In M. mycoides, pathogenicity is associated with the presence of a highly efficient ABC transporter for glycerol. Non-pathogenic strains of M. mycoides rely on the less efficient glycerol facilitator for glycerol uptake [34].
In M. pneumoniae, GlpQ is not only important for virulence but also for growth in the commonly used medium in the laboratory, i.e. Hayflick medium with glucose as the added carbon source (see Figure 4A). This observation is in good agreement with a recent analysis of the M. pneumoniae metabolism that suggested that glycerol is essential for growth of M. pneumoniae [35]. Accordingly, no difference between the wild type strain and the glpQ mutant was observed during growth in the presence of glycerol (see Figure 4B). Therefore, it is tempting to speculate that some glycerophosphodiesters in the Hayflick medium support growth.
In addition to GlpQ, M. pneumoniae encodes a second paralogous protein. However, as shown in this work, this protein does not exhibit enzymatic activity nor does the inactivation of the corresponding gene (mpn566) cause any detectable phenotype. This lack of detectable activity of MPN566 is easily explained by the lack of conservation of amino acid residues that are essential for the activity as a glycerophosphodiesterase. Interestingly, a very similar arrangement with two glpQ-like genes is also present in M. genitalium and Mycoplasma alligatoris. Based on the conservation of the catalytically important residues (see Figure S1), there is an active and an inactive enzyme in M. genitalium, as observed here for M. pneumoniae. In M. alligatoris, both potential glycerophosphodiesterases contain all the important amino acids suggesting that both proteins are enzymatically active. It is tempting to speculate that the possession of two active glycerophosphodiesterases is related to the fact that M. alligatoris is the only mollicute that obligatorily causes fatal infections [36]. In the syphilis spirochaete, Treponema pallidum, one glpQ-like gene is present; however, the encoded protein is not active as a glycerophosphodiesterase. Again, the inactivity is most likely caused by the lack of conservation of functionally important amino acids [37], [38]. The presence of inactive GlpQ-like proteins in several pathogens, including a spirochaete and M. genitalium, the bacterium with the smallest genome, suggests that these proteins have other functions that have yet to be identified. Unfortunately, the experiments reported in this study did not give any hints as to a putative function of MPN566.
Many proteins have activities in addition to their primary functions. On one hand, this allows gene duplication and specialization to non-related functions of similar proteins. On the other hand, a protein may acquire a second useful activity and act as a so-called moonlighting protein [39]. The former is very common and might apply to the putative functional specialization of GlpQ and MPN566. In contrast, the latter phenomenon is true for all trigger enzymes that measure the availability of their respective metabolites and transduce this information to the regulatory machinery of the cell. In mammals, a glycerophosphodiesterase controls the development of skeletal muscles independent from its enzymatic activity [40]. Our results suggest that GlpQ might also have such a second activity. Indeed, the expression of the glycerol facilitator GlpF, a lipoprotein, and the ATP-binding subunit of a metal ion ABC transporter are strongly overexpressed in the glpQ mutant, whereas two uncharacterized lipoproteins are less expressed in the mutant. Interestingly, the genes that appear to be repressed by GlpQ are more strongly transcribed in the presence of glycerol as the carbon source (as compared to glucose). In contrast, the two lipoprotein genes mpn284 and mpn506 that require GlpQ for expression are only weakly expressed in the presence of glycerol, but they are strongly induced if glucose is used as the carbon source. These observations might be explained as follows: In the presence of glucose, only little glycerol or glycerol-3-phosphate (the product of the reaction catalyzed by GlpQ) is present in the cell. Free GlpQ might then directly bind DNA or trigger the DNA-binding activity of another, yet unknown transcription factor, resulting in repression or activation of the two sets of genes. In the presence of glycerol, glycerol-3-phosphate would be formed due to the activity of glycerol kinase, and this metabolite might then prevent GlpQ from its regulatory activity. As a result, those genes that are subject to GlpQ-dependent repression (glpF, mpn162, and cbiO) are stronger expressed than in the presence of glucose, whereas the GlpQ-activated genes (mpn284, mpn506) would be less expressed. Finally, in the glpQ mutant, the former set of GlpQ-repressed genes is highly constitutively expressed, and only a very low level of transcription can be detected for the two GlpQ-dependent lipoprotein genes. Since glycerol-3-phosphate is the product of the glycerophosphodiesterase reaction, this metabolite is an excellent candidate for detection by GlpQ. Moreover, the glpQ gene is constitutively expressed and the GlpQ protein was detected in M. pneumoniae cells irrespective of the carbon source used in similar amounts in this study [41, this study]. Thus, GlpQ is available in the cell under all conditions to cause regulation. In a recent study on the phosphoproteome of M. pneumoniae, phosphorylation of GlpQ was observed [42]; however, no precise phosphorylation site could be detected and predicted, respectively. Therefore, the functional relevance of this modification remains unknown so far.
As observed for several other transcription regulators and trigger enzymes, GlpQ exerts both an activating and repressing effect on gene expression. The location of the putative cis-acting element correlates perfectly with the regulatory effect: Those genes that seem to be repressed by GlpQ-dependent manner have this element overlapping or in the very close vicinity of the -10 region of the promoters. This element is the only conserved promoter element in M. pneumoniae and it is sufficient for transcription initiation [30], [41], [43]. Binding of GlpQ or of a transcription factor that is controlled by GlpQ would prevent a productive interaction with RNA polymerase and therefore cause transcription repression. On the other hand, the cis-acting elements that may be involved in the regulation of the GlpQ-activated genes are located upstream of the promoters. This is usually the case for binding sites of transcription activators and fits perfect with the observed regulation.
Our future work will focus on the elucidation of the mechanism(s) by which GlpQ controls gene expression. Moreover, we will address the functions of the lipoproteins that are subject to glycerol- and GlpQ-dependent regulation.
The M. pneumoniae strains used in this study were M. pneumoniae M129 (ATCC 29342) in the 32nd broth passage, and its isogenic mutant derivatives GPM52 (glpD::mini-Tn, GmR) [10], GPM81 (glpQ::mini-Tn, GmR), and GPM82 (mpn566::mini-Tn, GmR). M. pneumoniae was grown at 37°C in 150 cm2 tissue culture flasks containing 100 ml of modified Hayflick medium as described previously [29]. Carbon sources were added to a final concentration of 1% (w/v). Growth curves were obtained by determining the wet weight of M. pneumoniae cultures as described previously [29]. Strains harboring transposon insertions were cultivated in the presence of 80 µg/ml gentamicin and/or 2 µg/ml tetracycline as required. Escherichia coli DH5α and BL21(DE3)/pLysS [44] were used as host for cloning and recombinant protein expression, respectively.
The sequences of the oligonucleotides used in this study are listed in Table S5.
To achieve complementation of the glpQ mutant, we constructed strain GPM92 as follows: The M. pneumoniae glpQ gene including its own promoter was amplified using the primer pair SS245/SS267. The PCR product was digested with EcoRI and XhoI and cloned between the EcoRI/SalI sites of the integrative plasmid pMTnTetM438 [45]. The resulting plasmid, pGP695, was introduced by electroporation into the genome of the M. pneumoniae glpQ mutant GPM81. As a control, we transformed GPM81 with the empty vector pMTnTetM438. The resulting strain was M. pneumoniae GPM93. To exclude multiple insertions of the integrative plasmids in the two constructed strains, we performed Southern blot analyses with both mutants using a probe specific for the tetracycline resistance gene. In both cases, unique insertion events were detected.
M. pneumoniae chromosomal DNA was prepared as described previously [28]. Finally, digests of chromosomal DNA were separated using 1% agarose gels and transferred onto a positively charged nylon membrane (Roche Diagnostics) [44] and probed with Digoxigenin labeled riboprobes obtained by in vitro transcription with T7 RNA polymerase (Roche Diagnostics) using PCR-generated fragments as templates. Primer pairs for the amplification of glpQ, mpn566, aac-ahpD, and tet gene fragments were SS42/SS43, SS44/SS45, SH62/SH63, and SS272/SS273, respectively (Table S5). The reverse primers contained a T7 RNA polymerase recognition sequence. In vitro RNA labeling, hybridisation and signal detection were carried out according to the manufacturer’s instructions (DIG RNA labeling Kit and detection chemicals; Roche Diagnostics).
The M. pneumoniae genes encoding proteins similar to glycerophosphodiesterases (glpQ and mpn566) were amplified with chromosomal DNA as the template and the primer pairs SS34/SS35 and SS39/SS40, respectively. The PCR products were digested with SacI and BamHI and cloned into the expression vector pGP172 that allows the fusion of the target proteins to a Strep-tag at their N-terminus [46]. The resulting plasmids were pGP1018 and pGP1020. Since the glpQ gene contains three TGA codons that are recognized as stop codons in E. coli, these codons were replaced by TGG specifying tryptophan as in M. pneumoniae. For this purpose we applied the multiple mutation reaction [47] using the phosphorylated mutagenesis primers SS36, SS37, and SS38 and the external primers SS34 and SS35. The PCR product was digested and cloned into pGP172 as described above. The resulting expression vector was pGP1019. The plasmids pGP1019 and pGP1020 allowed the purification of the putative M. pneumoniae glycerophosphodiesterases (GlpQ and MPN566) carrying an N-terminal Strep-tag.
A mutant variant of MPN566 was obtained by the multiple mutation reaction using pGP1020 as the template and the phosphorylated mutagenesis primers SS192, SS193, and SS194 and the external primers SS39 and SS40. The PCR product was cloned into pGP172 as described above and the resulting plasmid was pGP661.
The putative glycerophosphodiesterases were overexpressed in E. coli BL21(DE3)/pLysS. Expression was induced by the addition of IPTG (final concentration 1 mM) to exponentially growing cultures (OD600 of 0.8). Cells were lysed using a french press (20.000 p.s.i., 138,000 kPa, two passes, Spectronic Instruments, UK). After lysis the crude extracts were centrifuged at 15,000 g for 60 min. The crude extract was passed over a Streptactin column (IBA, Göttingen, Germany). The recombinant proteins were eluted with desthiobiotin (IBA, final concentration 2.5 mM).
After elution the fractions were tested for the desired protein using 12% SDS-PAGE. Only fractions that contained the desired protein in apparent homogeneity (content of the specific protein >95%) were used for further experiments. The relevant fractions were combined and dialyzed overnight. Protein concentration was determined according to the method of Bradford using the Bio-Rad dye-binding assay where Bovine serum albumin served as the standard.
Glycerophosphodiesterase activity was measured in a coupled spectrophotometric assay as described previously [48]. The enzyme assay is based on the formation of glycerol-3-phosphate and the subsequent oxidation by the glycerol-3-phosphate dehydrogenase and the formation of NADH. Briefly, 5 µg of glycerophosphodiesterase were incubated with 20 U of rabbit muscle glycerol-3-phosphate dehydrogenase (Sigma) in a 0.9 M glycine-hydrazine buffer containing 0.5 mM glycerophosphodiester and 0.5 mM NAD+ in a volume of 1 ml. Divalent cations were added as indicated. NADH formation was determined photospectrometrically at 340 nm.
The hydrogen peroxide production in M. pneumoniae was determined using the Merckoquant peroxide test (Merck, Darmstadt, Germany) as previously described [10]. Briefly, growing cells were resuspended in assay buffer and after incubation for 1 h at 37°C, glucose, glycerol, glycerol-3-phosphate or glycerophosphodiesters (final concentration 100 µM) were added to one aliquot. An aliquot without any added carbon source served as the control. The test strips were dipped into the suspensions for 1 s and subsequently read.
Whole cell extracts of the different M. pneumoniae strains were prepared as described previously [31]. In order to analyze the complete proteome, 15 µg of the cell extracts were separated by one-dimensional 12% SDS-PAGE and the gels subsequently stained with Coomassie Brillant Blue R250 dye (Serva). For protein identification, each running lane was cut out into 15 pieces followed by a separate analysis by mass spectrometry. The proteome analyses were performed in triplicate.
Gel pieces were washed twice with 200 µl 20 mM NH4HCO3/30% (v/v) acetonitrile for 30 min, at 37°C and dried in a vacuum centrifuge (Concentrator 5301, Eppendorf). Trypsin solution (10 ng/µl trypsin in 20 mM ammonium bicarbonate) was added until gel pieces stopped swelling and digestion was allowed to proceed for 16 to 18 hours at 37°C. Peptides were extracted from gel pieces by incubation in an ultrasonic bath for 15 min in 20 µl HPLC grade water and transferred into micro vials for mass spectrometric analysis.
The tryptic digested proteins obtained from the one-dimensional SDS PAGE gel pieces were subjected to a reversed phase column chromatography (Waters BEH 1.7 µm, 100-µm i.d.×100 mm, Waters Corporation, Milford, Mass., USA) operated on a nanoACQUITY UPLC (Waters Corporation, Milford, Mass., USA). Peptides were first concentrated and desalted on a trapping column (Waters nanoACQUITY UPLC column, Symmetry C18, 5 µm, 180 µm × 20 mm, Waters Corporation, Milford, Mass., USA) for 3 min at a flow rate of 1 ml/min with 0.1% acetic acid. Subsequently the peptides were eluted and separated with a non-linear 80-min gradient from 5–60% acetonitrile in 0.1% acetic acid at a constant flow rate of 400 nl/min. MS and MS/MS data were acquired with the LTQ Orbitrap mass spectrometer (Thermo Fisher, Bremen, Germany) equipped with a nanoelectrospray ion source. After a survey scan in the Orbitrap (r = 30,000), MS/MS data were recorded for the five most intensive precursor ions in the linear ion trap. Singly charged ions were not taken into account for MS/MS analysis.
Tandem mass spectra were extracted using Sorcerer v3.5 (Sage-N Research). All MS/MS samples were analyzed using SEQUEST (Thermo Fisher Scientific, San Jose, CA, USA; version 2.7, revision 11). Database searching was performed against a target decoy database of M. pneumoniae with added common laboratory contaminant proteins. Cleavage specificity for full tryptic cleavage and a maximum of 2 missed cleavages was assumed. SEQUEST was run with a fragment ion mass tolerance of 1.00 Da and a parent ion tolerance of 10 ppm. Oxidation of methionine (+15.99492 Da) and phosphorylation of serine/threonine/tyrosine (+79.966331 Da) were specified in SEQUEST as variable modifications. Proteins were identified by at least two peptides applying a stringent SEQUEST filter (Xcorr vs. charge state: 1.8 for singly, 2.2 for doubly, 3.3 for triply, and 3.5 for higher charged ions). To address protein amount differences between the M. pneumoniae wild type and mutant strains, fold-changes were calculated by comparing number of assigned spectra for each protein (mutant vs. wild type strain).
Preparation of total M. pneumoniae RNA was done as previously described [29]. For slot blot analysis, serial twofold dilutions of the RNA extract in 10x SSC (2 µg–0.25 µg) were blotted onto a positively charged nylon membrane using a PR 648 Slot Blot Manifold (Amersham Biosciences). Equal amounts of yeast tRNA (Roche) and M. pneumoniae chromosomal DNA served as controls. DIG-labelled riboprobes were obtained by in vitro transcription from PCR products that cover ORF internal sequences using T7 RNA polymerase (Roche). The reverse primers used to generate the PCR products contained a T7 promoter sequence (Table S5). The quantification was performed using the Image J software v1.44c [49].
Infection of HeLa cell cultures with M. pneumoniae cells was done as described previously [10], [31]. After four days upon infection, HeLa cells cultures were stained with crystal violet and photographed. Additionally, lactate dehydrogenase (LDH) release of HeLa cell cultures after 2 h of infection was used as an index of cytotoxicity. LDH release was measured with the CytoTox 96 Non-Radioactive Cytotoxicity Assay (Promega) according to the manufacturer’s instructions. Results are expressed as cytotoxicity calculated as the percentage of total LDH release after cell lysis with the lysis buffer provided in the kit. The cytotoxicity assays were performed in triplicate.
After growing of M. pneumoniae cultures in 5 ml volumes to mid-log, the cells were scraped off and passed ten times through a syringe. Then, 20 µl of this cell suspension were inoculated to 2 ml of Hayflick medium in a Lab-Tek chamber slide (Nunc). After growing cells to mid-log phase, the medium was removed and the cells were washed three times with PBS and fixed with 1% glutaraldehyde for 1 h. The samples were washed three times with PBS and then dehydrated sequentially with 30, 50, 70, 90, and 100% ethanol for 10 min each. Immediately, the critical point dried of samples was performed (K850 critical point drier; Emitech Ashfort, United Kingdom) and sputter coated with 20 nm of gold. Samples were observed using a Hitachi S-570 (Tokyo, Japan) scanning electron microscope.
After passing through a syringe cells grown in a 5 ml culture, 20 µl of disaggregated cells were inoculated to 2 ml of modified Hayflick medium including 3% gelatine in 14 mm glass bottom culture dishes plates (MatTek). Cell movement was examined at 37°C using a Nikon Eclipse TE 2000-E microscope, and images were captured at intervals of 2 s for a total of 2 min with a digital sight DS-SMC Nikon camera controlled by NIS-Elements BR software. Tracks from 50 individual motile cells corresponding to 2 min of observation and 2 separated experiments were analyzed to determine the gliding velocity and gliding motile patterns.
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10.1371/journal.pcbi.1002028 | Disentangling the Roles of Approach, Activation and Valence in Instrumental and Pavlovian Responding | Hard-wired, Pavlovian, responses elicited by predictions of rewards and punishments exert significant benevolent and malevolent influences over instrumentally-appropriate actions. These influences come in two main groups, defined along anatomical, pharmacological, behavioural and functional lines. Investigations of the influences have so far concentrated on the groups as a whole; here we take the critical step of looking inside each group, using a detailed reinforcement learning model to distinguish effects to do with value, specific actions, and general activation or inhibition. We show a high degree of sophistication in Pavlovian influences, with appetitive Pavlovian stimuli specifically promoting approach and inhibiting withdrawal, and aversive Pavlovian stimuli promoting withdrawal and inhibiting approach. These influences account for differences in the instrumental performance of approach and withdrawal behaviours. Finally, although losses are as informative as gains, we find that subjects neglect losses in their instrumental learning. Our findings argue for a view of the Pavlovian system as a constraint or prior, facilitating learning by alleviating computational costs that come with increased flexibility.
| Beautiful background music in a shop may well tempt us to buy something we neither need nor want. Valenced stimuli have broad and profound influences on ongoing choice behaviour. After replicating known findings whereby approach is enhanced by appetitive Pavlovian stimuli and inhibited by aversive ones, we extend this to withdrawal behaviours, but critically controlling for the valence of the withdrawal behaviours themselves. We find that even when withdrawal is appetitively motivated, it is still inhibited by appetitive Pavlovian stimuli and enhanced by aversive ones. This shows, for the first time, that the effect of background Pavlovian stimuli depends critically on the intrinsic valence of behaviours, and differs between approach and withdrawal.
| The functional architecture of responding involves two fundamental components that are behaviourally [1] and computationally [2] separable: Pavlovian and instrumental. The instrumental component respects the stimulus-dependent contingency between responses and their outcomes (stimulus-response and action-outcome learning) [3]. By contrast, preparatory Pavlovian responses, chiefly involving approach and withdrawal, are elicited by the appetitive or aversive valence associated with predictive stimuli in a manner that is not dependent on the consequences of those responses [3]–[5].
The interactions between the two systems are most evident when automatically-elicited Pavlovian responses interfere with contingent instrumental responding [1], [6]–[9]. For instance, pigeons will strikingly continue to peck at a light predictive of food (a preparatory approach elicited by the appetitive prediction), even if the food is withheld every time they peck the light (the instrumental contingency) [10], [11]. Pavlovian interference likely contributes to many quirks of behaviour such as impulsivity [12], framing and [13], endowment effects [14] and many other “anomalies” [15], including neurological [16]–[19] and psychiatric diseases [20]–[26]. Further, puzzling facets of seemingly purely instrumental behaviour such as the difficulties in learning ‘go’ responses to avoid punishments; or ‘nogo’ to obtain rewards (unpublished data) and even the restrictions in associations evident in ‘evolutionarily preparedness’ [27], [28] might be traced to Pavlovian principles.
However, instrumental and Pavlovian systems share overlapping neural hardware. Their bidirectional interaction is characterised by two key triads: rewards are tied to approach and vigour; and punishments to withdrawal and behavioural inhibition. The neuromodulator dopamine (DA) responds predominantly to rewards [22], [29]–[31], induces behavioural activation and enhances approach [32]–[35]. Each aspect of this triad confounds the role of the phasic DA bursts in the flexible acquisition of instrumental values [36]–[42]. Serotonin appears to lie at the heart of the aversive triad, having been linked to punishments [43]–[45], behavioural inhibition and withdrawal [25], [32], [46]–[52], although dopamine acting via D2 receptors likely also plays a role in linking absence of rewards to nogo [17], [53], [54]. Signatures of both triads are also evident in neural circuits involved in response and choice. In the dorsal striatum, there are interdigitated pathways for ‘go’ and ‘nogo’, with the go pathways again linked positively to rewards via dopamine [16], [18], [55], [56]. The ventral striatum is primarily organized along an appetitive/aversive axis with direct links to approach and withdrawal behaviours [57], [58]. The aversive triad is also tightly linked to the dorsal raphé and the periaquaeductal gray [59], [60].
The main routes to the scientific investigation of these interactions consists of tasks in which Pavlovian stimuli are presented during ongoing instrumental tasks. However, these have as yet not explored the full set of interactions characterising the overlap between the two systems. Two critical confounds remain: The first confound concerns the precise nature of the effect of Pavlovian stimuli on instrumental behaviours. The instrumental behaviours studied have largely been appetitively motivated approach behaviours (in Pavlovian-Instrumental Transfer (PIT) and conditioned suppression tasks, [1], [6]–[8], [61]–[63]), and one instance of aversively motivated withdrawal behaviour [64]. The relative role of the appetitive-aversive motivation axis versus that of the approach-withdrawal axis is unknown. This in turn obscures the nature of the interaction: whether Pavlovian stimuli interact with the value of the instrumental behaviour, or by promoting specific responses [1], or even simply by modulating behavioural activation [5]. Second, the extent to which the separation of reward and punishment processing into opponent motivational structures applies to instrumental as well as Pavlovian learning is incompletely explored [1], [27], [28], [65].
All these issues can simultaneously be addressed in a combined PIT and conditioned suppression task with both approach and withdrawal actions in which the overall motivational component of approach and withdrawal are matched (Figure 1 and Table 1). The task separates the contributions of approach and withdrawal by using two counterbalanced blocks, one involving approach go versus nogo, and the other withdrawal go versus nogo. The comparison between go and nogo controls for effects of behavioural activation or inhibition. In each block, subjects first underwent brief instrumental training (Figure 1A), learning from positive and negative feedback (monetary gains and losses of €0.20) whether to produce a go or a nogo response associated with sorting mushrooms. In the approach block (Figure 1A, top, all 46 subjects), go responses involved moving the cursor onto a mushroom (to collect it), while nogo involved doing nothing, thus not collecting the mushroom. To test for the effect of low-level motor variables, subjects performed one of two types of withdrawal actions. In “throwaway” (24 subjects, Figure 1A, middle), go involved moving the cursor physically away from the mushroom and clicking into an empty blue box; nogo involved doing nothing, and thus keeping the mushroom. Importantly, both approach to and withdrawal from the instrumental stimulus were orthogonal to any approach and withdrawal that might be directed at the Pavlovian background stimulus. In “release” (22 subjects, Figure 1A, bottom), the subjects had to start by pressing the mouse button. Go involved releasing the button to avoid collecting the mushroom; nogo involved continuing to press the button and thereby receiving the mushroom.
In order to orthogonalise the approach-withdrawal and appetitive-aversive axes, the learned instrumental values in approach and withdrawal blocks needed to be matched. To achieve this, both go and nogo responses were, if correct, rewarded. Additionally, to avoid the confound of activation, in each block (i.e. in both approach and withdrawal blocks) the go action was designated as the correct response to half the instrumental stimuli, and the nogo action to the other half (see Table 1). Incorrect responses had opposite outcome contingencies to correct responses, yielding more punishments than rewards. This ensured that go, nogo, approach and withdrawal overall had the same learned association with rewards and punishments. We tested both deterministic and probabilistic outcomes but found no differences.
In the second part of each block, subjects passively viewed unrelated, fractal, stimuli paired with separate auditory tones (Figure 1B). Each compound Pavlovian stimulus was deterministically associated with a monetary gain or loss, i.e. its Pavlovian value was equal to that monetary outcome. Every fifth trial in the Pavlovian block was a query trial (Figure 1C), in which subjects chose the better of two fractal visual stimuli without being informed about the outcome. Finally, in the PIT stage, the instrumental stimuli were presented on a background of fractal Pavlovian stimuli together with the auditory tones, and again without outcome information.
Our task addressed the key confounds described above. With respect to the triads, we found that the Pavlovian influence is action specific: appetitive Pavlovian cues boosted go approach responses and suppressed withdrawal go responses; aversive Pavlovian cues did the opposite. Additionally, subjects were substantially biased against withdrawal, but we found no evidence that the instrumental learning component itself differed between the approach and withdrawal condition.
The key results in this paper concern the interaction of valued Pavlovian stimuli on instrumental choices. We first present a direct analysis of the choice data and reaction times. We then provide a detailed modelling analysis of the data, employing a stringent form of group-level model selection that assesses each model's parsimony by weighing its ability to fit the data against its complexity. The models quantify Pavlovian values , which are the expectations of a gain or loss given Pavlovian stimulus , and instrumental choice values , which are the time-varying expectations of a reward given a response to an instrumental stimulus . The structure of the most parsimonious model implies the influences and interactions that were significant (for instance ruling in a bias against active withdrawal, but ruling out any difference between the instrumental learning rates associated with approach and withdrawal); the values of the parameters in this model indicate the nature of those influences and interactions.
There was no difference between the results for probabilistic and deterministic feedback, and we therefore present the combined data. Analysis of the components of the experiment indicate robust, yet moderate, instrumental conditioning that was stable during the PIT period, combined with highly robust Pavlovian conditioning. Figure 2A shows the instrumental probability of choosing the more rewarded (“correct”) stimulus over time. Subjects rapidly came to prefer the more rewarded action. Preference was weaker for go withdrawal, against which there was a consistent bias. We intended the instrumental preference to be weak to avoid ceiling effects when assessing PIT.
Subjects also exhibited predictable variability on a shorter time-scale: Figure 2B shows the immediate consequences of rewards and punishments on subsequent behaviour. It is notable that punishments did not reduce the repeat probability below chance level (mean is not , one-tailed t-test ). The same was found when analysing go and nogo choices separately: in both cases, was not significantly different from 0.5 (both , two-tailed t-test), and was significantly smaller than (both , paired t-test). Whether this really does represent an insensitivity to punishments depends, however, on the average stay probability, and on how this average stay probability is related to past reinforcements. Subjects were instructed that the outcomes of responses in the PIT block would be counted as in the instrumental block. Figure 2C shows that this led to stable maintenance of the instrumental response tendencies throughout the PIT block. Figure 2D shows that all but one (excluded) subject showed extremely good performance on the Pavlovian query trials interleaved with the Pavlovian training (mean correct ).
Given the success of instrumental and Pavlovian training, we next analysed the raw effect of Pavlovian stimuli on approach and withdrawal choices. Figure 2E shows a highly significant interaction between block and Pavlovian stimulus valence. Relative to neutral stimuli, positive Pavlovian stimuli enhanced approach and inhibited withdrawal go over nogo. Conversely, negative Pavlovian stimuli enhanced withdrawal and inhibited approach go over nogo. A similar analysis looking at the probability of responding incorrectly (outside the blue box) showed no effect of the Pavlovian stimuli in either approach or withdrawal condition and no interaction ( respectively, ANOVA), suggesting that these results were not due to response competition. Note that the withdrawal go probabilities were lower than the approach ones, again reflecting the overall bias against go withdrawal.
Average reaction times for go approach and go withdrawal actions did not differ (, 2-tailed t-test). Against our expectations, Pavlovian stimuli of both positive and negative valence shortened reaction times in a parametric manner relative to neutral Pavlovian stimuli (Figure 2F, p = 0.0310, ANOVA), although this effect was not present in either block separately (p = 0.5502 and p = 0.0781 respectively, ANOVA).
The size of the PIT effect may have been affected by the extent of instrumental learning (and thus the actual learned action values), by response biases, and by generalization from the instrumental to the PIT stage. In addition, there may have been differences in the instrumental learning of approach and withdrawal actions (Figure 2A). We decomposed and analysed all such factors using a detailed reinforcement learning model. This contained explicit parameters capturing all the instrumental and Pavlovian effects in the task, and was fit to the choice data of all subjects. We used group-level Bayesian model comparison [66] to choose amongst a variety of model formulations (reporting scores relative to the final model), and ensured that inference yielded correct parameter estimates when run on surrogate data generated from the assumed underlying decision process.
The final model included parameters associated directly with the instrumental requirements of the task. These comprise one learning rate ; two parameters and representing the bias towards go in the approach and withdrawal blocks; and two separate free parameters and , representing the effective strengths of rewards and punishments.
At a group level, subjects were biased against active withdrawal, but showed no bias for or against approach ( and respectively, two-tailed t-test), the difference being significant (, ANOVA, Figure 4A). Withdrawal biases in the release and throw away experimental subgroups did not differ (, ANOVA), controlling for motor effects. The withdrawal bias accounts for the lower performance on go withdrawal in Figure 2A.
One concern is that differences in the biases might have masked differences in learning (i.e. the reward sensitivities) in the approach and withdrawal conditions. We tested this by allowing for separate reward and punishment sensitivities in the two conditions (Model 6) or separate learning rates (Model 7). The use of these extra parameters was structurally rejected by the model selection process ( respectively for the purely instrumental trials); and the freedom to choose different parameter values in these conditions was duly not used (Figure 5). The absence of any difference in the learning parameters for approach and withdrawal suggests that the instrumental system treated approach and withdrawal entirely equally. We will see below that this was not true for the Pavlovian system.
Although, by design, rewards and punishments were equally informative, subjects chose to rely more on rewards than punishments (Figure 4B). Rewards had a stronger effect than punishments both at a group level and for all individual subjects, the difference being significant (, ANOVA). Indeed, the average punishment sensitivity was not distinguishable from zero (, two-tailed t-test). This remained true when we separately tested subjects who were given deterministic (, two-tailed t-test) and probabilistic (, two-tailed t-test) feedback. Supplementary analyses (Text S1) excluded two further explanations for the punishment insensitivity: first, that it is due to choice perseverance (Figure S1 Text S1); and second that it is due to an emerging maximisation behaviour (Figure S2 in Text S1). Thus, it appears that the pattern seen in Figure 2B is indeed due to a differential sensitivity to rewards and punishments.
We next analysed the generalization of instrumental values from the instrumental to the PIT blocks. Generalization could be imperfect in two ways - the starting values in the PIT block could differ from the ending values in the preceding instrumental block, and the values could then decay over time or trials during the PIT block given the lack of information about the outcomes. We constructed models including such effects, and tested whether their excess complexity was outweighed by their fit to the data.
As expected from the stable raw probabilities of choosing the correct (i.e., more rewarded) option (Figure 2C), a model in which the instrumental values decayed exponentially over time during the PIT block (mimicking extinction) did not provide a good account of the data (Model 9, compared to Model 10 ).
Rather, the final model allowed for the addition of random generalization noise to each . These factors were drawn independently from the same normal distribution for all stimulus-action pairs, and the mean and variance of this distribution were both inferred without constraints (see Methods). Figure 4C visualizes the resulting changes; each dot represents the preference for the go action () for all subjects and all stimuli. The abscissa shows this at the end of the instrumental stage, the ordinate after addition of the noise for the PIT stage. Importantly, there was no systematic difference in mean correct action values either in the instrumental or PIT stage (Figure 4D).
We were mainly interested in the effect of the Pavlovian values on instrumental performance. We therefore fitted unconstrained parameters to separately capture the influence of each of the five Pavlovian stimuli on instrumental go actions in both the approach and withdrawal condition.
All models accounted for performance in the PIT part by adding up instrumental and Pavlovian influences prior to taking a softmax [67], [68]. This amounts to treating instrumental and the Pavlovian controllers as separate experts, each of which ‘voted’ for its preferred action. The model captured in detail, and thereby controlled for, variability in instrumental learning and generalization. The final model predicted the choices of every individual subject better than chance (binomial probability, for every subject, overall predictive probability 0.7544). The maximum a posteriori (MAP) estimates of this model's parameters painted a picture very similar to that seen in the raw data.
Figure 4E shows the parameters of the model related to the influence of each Pavlovian stimulus. The pattern mirrored that seen in the raw data: there are highly significant, and opposite, effects in the approach and withdrawal blocks, with appetitive stimuli (++ and +) promoting approach but inhibiting withdrawal; and aversive stimuli (-- and -) promoting withdrawal but inhibiting approach. At a single subject level, the effect in the approach block was seen in 45/46 subjects (98%), while it was seen in 30 subjects (65%) in the withdrawal block.
Since there was no difference in the learned value of go or nogo actions in either approach or withdrawal blocks, and in either the instrumental learning or the PIT stages (Figure 4D), any PIT effects are unlikely to be due to a preferential association of a Pavlovian stimulus with the learned value of an action. Rather, they reflect the approach or a withdrawal nature of the action.
We included two separate groups of subjects who either performed a throwaway withdrawal action, or a release withdrawal action. This was both to test the contribution of an approach/withdrawal component aimed at the Pavlovian stimuli tiling the background, and in recognition of the sophistication of defensive reactions [27]. Figure 4F shows that Pavlovian stimulus value had a significant, linear effect on both withdrawal action types, and that this overall linear effect did not differ between the two action types. At an individual level, linear correlations were positive for 16 (72%) and 14 (58%) subject in the release and throwaway condition, respectively.
No psychometric measure of anxiety or depression correlated with any of the parameters in the main model.
Our task was designed to look inside the triads of valence, behavioural activation and inhibition, and specific actions associated with Pavlovian influences. This issue has been incompletely explored in the past. Either these triads as a whole have been investigated: aversive actions allowed avoidance of, or escape from, a negative reinforcer; appetitive actions, the acquisition of a reward [6], [8], [64], or, as in negative automaintenance [10], the relevant Pavlovian contingencies have been tightly embedded in the instrumental task. Here, we found that Pavlovian influences distinguished approach from withdrawal when carefully controlling for activation, for appetitive versus aversive instrumental motivation, and for details of the motor execution. Thus, for instance, a Pavlovian stimulus predicting reward had opposite effects on two different instrumental actions (approach and withdrawal) even though both those actions were themselves equally motivated by the acquisition of reward.
Approach and avoidance were defined in two parallel ways: by the cognitive label for the action (‘throw away’, ‘collect’) and by the relation to the stimulus (moving the mouse/finger towards or away from the stimulus). Our task did not set out to distinguish these two contributions (cognitive and motor), and we also did not attempt to quantify subjects' explicit insight into their strategies.
However, both possibilities are important. At a cognitive level, subjects should neglect the Pavlovian stimuli: by design, they are not informative about the instrumental task. Upon entering the PIT stage, subjects were also explicitly instructed to continue doing the instrumental task as before. If despite these facts subjects were cognitively swayed to include the irrelevant backgrounds in their goal-directed decision process, then our finding show that Pavlovian contingencies extend even into cognitive choices. This is of course consonant with a large number of behavioural irregularities in human decision making [12]–[15].
The motor aspects are equally interesting since they suggest a fine level of detail in the architecture of Pavlovian influences. There is quite some evidence for this; for instance, Pavlovian CRs are known to be highly adaptive to the details of the CS (for instance evoking a grooming conditioned response to a rat which functions as a food CS, rather than a gnawing CR [69]) and to the nature of the US [70]. In humans, a plexiglass positioned between subjects and an appetitive US abolishes an increased willingness to pay [71].
The performance on the purely instrumental portion of the task was also revealing. We observed a difference in the instrumental performance of approach and withdrawal action; and this came (unlike in previous tasks) after controlling for the motivational difference between approach and avoidance. Our model-based analysis revealed that the difference was not due to a difference in learning (i.e. a difference in the instrumental parameters relating reinforcements to performance), but due to a static bias against performing a withdrawal go action. Of course, like all other tasks, our instrumental task also had embedded Pavlovian contingencies, and, indeed, a Pavlovian suppression of active withdrawal by the overall appetitive framing of the task (subjects on average chose the correct, rewarded, action more often) could mirror what we saw in the PIT stage of the task. Alternatively, this could be the result of subjects' experiences upon entering an experimental situation in which they are given a computer mouse. We have interpreted such as bias in terms of evolutionary preparedness or programming [2], [9], [24], [50], [72]. That is, the flexibility of the arbitrary outcome-contingent mappings of instrumental control comes at the price of the experience necessary for it to be specified. Pavlovian priors substitute inflexible hard-wired choices that are immediately available for this flexible instrumental adaptativity with its potentially substantial sample complexity (i.e. the potential need for extended experience). Related biases are widely known: dogs will happily learn to run, but not to yawn, for food; teaching a rat to escape is easier than teaching it to avoid the shock [3], [27], [28]; humans perform active go responses slower if instructions are in terms of aversive feedback [51] or if they are followed by aversive information [73]. Finally, in humans, an instructed joystick approach response to a happy face is quicker than a withdrawal response, depending on the cognitive/affective label in a manner similar to our own findings here [74].
Alternative interpretations of the response bias include endowment effects [14], whereby an over-valuation of items notionally in one's possession makes one reluctant to give them up. This is unlikely because such a bias should be present across all instrumental stimuli, i.e. across both stimuli for which a go and a no-go is the more rewarded action (Figure 4). Another possibility is a frame dependence [13]—since we compared go with nogo rather than two alternative go actions against each other. The negative frame associated with sorting to remove bad mushrooms could have inhibited go actions.
One of the central motivations for our investigation was the observation that the neural substrate does not respect the logical independence of reward/punishment and approach/withdrawal. Rather, as we have discussed, these are tied together, via the structure of the striatum and also specific neuromodulators.
While the neural basis for the promotion of approach responses by appetitive stimuli is known to involve both amygdala and striatum [62], [63], [75], the neural bases for the effects of aversive Pavlovian stimuli are less clear. There are no data on withdrawal responses per se, i.e. with positive expectations. Nevertheless, animal models, genetic studies and pharmacological manipulations suggest that serotonin plays a crucial role in the inhibition of active behaviours by aversive expectations [25], [47], [48], [50], [73], [76]–[78]. In humans, there is evidence for the serotonergic mediation of the inhibition of active approach by aversive predictions [51], and of approach responses to stimuli that are predictive of negative reinforcement [73]. It should be noted, though, that, acting via the indirect path and D2 receptors, dopamine itself has also been suggested to be important in mediating ‘nogo’ behaviour due to punishments [18], [53], [79].
Aversive Pavlovian stimuli can also potentiate behaviour [1], [64], [80], [81], with both serotonin and dopamine involved. Dopamine may have a dominant influence in this: it is both known to be released, and influential, in some aversive settings [82]–[85] and has a more evident relationship to vigour [33], [34]. This observation has led to a re-interpretation of previous notions [43] of the opponency between dopamine and serotonin, putting an axis spanning invigoration and inhibition together with spanning reward and punishment [52].
Thus, the literature suggests three predictions for genetic correlates of the Pavlovian influences we observe. When considering these, the caveats concerning the interaction of genetic variation with psychopathology (e.g. anxiety or depression), and with development need to be kept in mind. Nevertheless, the conditioned suppression effect of aversive Pavlovian stimuli on approach should be enhanced by D2 receptors, and hence be positively related to D2 striatal receptor density thought to be modulated by C975T (rs6277; [17]). Second, conditioned suppression should be increased in subjects with higher serotonin levels, i.e. as might be the case with the less efficient (s) allelic variation of the serotonin reuptake transporter (5HTTLPR SLC6A4 [86]). Third, given dopamine's established positive correlation with approach and PIT [87], [88], we expect genetic polymorphisms that boost DA levels, such as the SLC6A3 polymorphism of the dopamine transporter [89], to increase the impact of appetitive Pavlovian stimuli on approach. A similar effect may be expected from DARPP-32, although its closer relationship to synaptic plasticity would also suggest effects on instrumental learning [90]–[92].
Although the learning parameters associated with instrumental approach and withdrawal did not differ, the impact of rewards and punishments on the acquisition of responding was highly asymmetric. In general, subjects neglected punishments, whilst maintaining a fixed sensitivity to reward. This was gratuitous as, in our setting, rewards and punishments were equally informative. It is, however, the case that the optimal strategy can be arrived at by concentrating on either.
Subjects were not globally insensitive to punishments, as their choice behaviour in the Pavlovian learning was highly accurate both for rewards and punishments. Furthermore, it should be emphasized that ascribing punishments a value of zero outcome would still effectively behave as a punishment because a zero outcome is well below the average expectation of correct actions (Figure 4D) and as such would reduce the tendency to emit the action that caused it. The asymmetry has been noted before. Others have fitted models with separate learning rates for rewards and punishments and reported significantly slower learning rates for punishments than rewards [93], [94]. In some restricted regimes, learning rates and inverse temperature parameters can trade off, and we explicitly tested both types of models to address this.
One potential confound is the emergence of determinism. Subject were instructed to perform choices relative to mushrooms. Real world mushrooms are either edible or poisonous, and this dichotomy may have predisposed subjects towards a deterministic, rather than a matching, strategy. (For instance, subjects may have chosen responses based on a classification of the mushrooms into ‘good’ and ‘bad’ ones, rather than on the particular value of a response for a mushroom.) Indeed, in RL settings it is typically optimal to start with a low, exploratory, sensitivity to outcomes, but to increase this over time to encourage exploitation, culminating in a deterministic strategy [2]. However, subjects did not behave deterministically at any point (Figure 2A) and supplementary analyses showed that the time-varying pattern of reinforcement sensitivities this would predict is not observed in the data (Text S1). A further potential confound is the average stay probability. If this were precisely half-way between the stay probabilities after rewards and punishments in Figure 2B, then rewards and punishments would have the same effect relative to the baseline, and hence arguably be equally informative. However, this argument would neglect the fact that the mean stay probability itself must be a function of the reinforcement history; and that this must be included in making inferences about the reinforcement sensitivity.
We have previously made the argument on theoretical grounds that part of the asymmetry observed in appetitive and aversive systems might be due to the inherent difference in how informative rewards and punishments are processed, enshrined again in the architecture of the striatum and neuromodulation [50]. Rewards tell us what to do; punishments tell us what not to do. The former is more informative in naturalistic settings where many options are available but only few are good. The fact that subjects gratuitously rely on rewards rather than on punishments in the present setting may reflect an implicit appreciation of this fact, although our findings are certainly in no way conclusive evidence. Interestingly, it is known that stronger optimality results can be shown for a stochastic learning automata rule called linear reward-inaction, which does not change propensities in the light of punishments but only rewards ([95], [96]; also known as a benevolent automaton [97]), than for a rule that changes propensities for both.
The computational model served several central roles. First, it encapsulated the manifold aspects of behaviour and learning jointly, thereby controlling for them: the bias against withdrawals is not a due to a difference in learning; and variations in learning or generalization do not account for the PIT effects we saw. Secondly, its close fit to the behaviour argues that the PIT effects can be accounted for by a simple superposition of an instrumental and a Pavlovian controller: the action propensities due to both controllers were simply multiplied (as additive factors in an exponential), rather than being allowed to interact in more complex ways.
The simplicity of this interaction eschews questions about peripheral versus central response competition, whether appetitive and aversive systems compete centrally [7], and whether Pavlovian learning is involved in instrumental learning [1]. It takes the view of multiple, separate controllers contributing in parallel [98], and weighting the ultimate choice by the reward expected from that choice. One alternative would be to weigh contributions by different controllers according to their certainty [99], although it is unclear how to compute the Pavlovian controller's certainty.
There are various pressing directions for future studies. First, despite the role the architecture of decision-making has played in the argument, our work does not directly address the neural mechanisms concerned. These could be examined using imaging and pharmacological manipulations.
Second, our task was not designed to distinguish between outcome-specific and general mechanisms [63], [75] as we relied on one, monetary, outcome throughout. Studying different outcomes is important, given evidence for partly parallel pathways through different nuclei of the amygdala and different targets in the nucleus accumbens [100], [101].
Third, we are missing one crucial further orthogonalization to do with the overall framing of the instrumental task. It is important to consider the case in which subjects can at best avoid losing money by doing the correct action [51]. We would expect punishment to maintain its instrumental force in this case; but there could also be a systematic difference in the nature of the Pavlovian influences.
Pavlovian responses are believed to be hard-wired to reflect evolutionarily appropriate attitudes to predictions, being highly adaptive and sensitive to environmental structures [102]. Here, we showed that Pavlovian influences on instrumental behaviour depend on the intrinsic affective label of an action, independent of its learned reward expectation.
It has long been known that prepared or compatible [27], [69] behaviours are easier targets for instrumental conditioning. These intrinsic biases, or priors, may serve a crucial function both by reducing the need for collecting data (i.e. sample complexity) about the effects of actions, and by reducing the need for executing complex processing necessary to work out optimal actions (i.e. computational complexity). Both of these can be expensive or dangerous, particularly in an aversive context. Our findings sharpen the understanding of the relative contribution of Pavlovian and instrumental contingencies in general tasks. We showed clearly that the interaction of Pavlovian and instrumental behaviours is organized along the lines of appetitive and aversive motivational systems, and that a critical contributor to this is the affective nature of actions.
54 healthy subjects of central European origin were recruited from the Berlin area. Subjects were screened for a personal history of neurological, endocrine, cardiac and psychiatric disorders (SCID-I screening questionnaire), and for use of drugs and psychotropic medication in the past 6 months. Subjects received performance-dependent compensation (5–32 Euro) for participation. Three subjects did not meet inclusion criteria and one subject did not complete the task; the data for three further subjects were lost due to a programming error. One further subject was excluded from the analysis because the instrumental task was not satisfactorily performed. The 46 remaining subjects were years old. 59% were female (). The study was approved by the local Ethics Committee and was in accord with the Declaration of Helsinki 2008. Subjects were given detailed information and gave written consent. They were seated comfortably at a table in front of a laptop with headphones and used a mouse with their dominant hand to indicate their choices. The amount earned was indicated by the computer, and the sum paid in cash at the end of the session. The computer task was followed by completion of self-rating scales.
The task was written using Matlab and Psychtoolbox (http://psychtoolbox.org). It consisted of one approach and one withdrawal block separated by a 2 minute break. Each block was in turn divided into a instrumental training, a Pavlovian training and a PIT part. Table 1 illustrates this.
We modified a standard reinforcement learning model to capture the behavioural choices in the experiment. We first describe the main model, and then the alternative control models. Considering first the instrumental part, let be the instrumental stimulus (out of up to 12; i.e. the subscript now designates time rather than identity as in Table 1) presented at trial , and the action (choice) on that trial. An action can be one of four types: go withdrawal and nogo withdrawal in the withdrawal block, and go approach and nogo approach in the approach block. Let also be the reinforcement obtained, either for a punishment, or for a reward. We write the probability of action in the presence of stimulus as a standard probabilistic function of i) the reinforcement expectations associated with that pair on that trial, and ii) a time-invariant, fixed, response bias :(1)(2)where is the instrumental weight of action , and where the variable can take on value for withdrawal go actions, or for the approach go actions. It is always zero for the nogo action. There was no delayed outcome in the instrumental task, and the expectations were thus constructed by a Rescorla-Wagner-like rule with a fixed learning rate . The immediate, intrinsic, value of the reinforcements delivered in the experiment may have different meaning for different subjects. To measure this effect, we added two further parameters: the reward sensitivity and the punishment sensitivity , yielding an update equation for the expectations:This is model 5 in Table 2, which has the lowest score (see below). Alternative models tested on the instrumental data only are as follows: Model 1 assumes that , and that . Model 2 allows only for separate reward and punishment sensitivities and model 4 for separate biases. Model 3 again assumes , and that , but allows for two separate learning rates, i.e. in Equation 3 is replaced by on trials where , and by on trials where . Model 6 and 7 are expansions of the final model, allowing for separate reward and punishment sensitivities (model 6) and for separate learning rates (model 7) in the approach and withdrawal conditions.
Our main measure of interest is the effect of Pavlovian stimuli on the approach and withdrawal actions. Let additionally be the Pavlovian stimulus on trial . We can then write an equation similar to equation 2 for the trials where both instrumental and Pavlovian stimuli were present, but including a term that quantifies the effect of the particular Pavlovian stimulus on the action . This means that the action weights due to the instrumental and Pavlovian controllers are added inside the exponent of equation 2, and that thus the probabilities each controller attaches to a particular action are multiplied and renormalized. The two controllers are therefore treated as two distinct entities, each separately voting for a particular action to be emitted. The influence of each system on action choice is relative to the strength with which the other enhances one particular action. We write the PIT weight of action as:(3)Here we force at all times. The go values can take on 10 separate, inferred, values, meaning that there is one separate parameter for each of the five Pavlovian stimuli in each of the two blocks. Each of these parameters captures how much boosts the go over the nogo action (if ) or the inverse (if ). Note that because these are separately inferred, independent, parameters, this formulation does not impose any assumptions about the effect of the value of the stimulus , or about the relative effect of different stimuli with different values. Hence, this controls for variation in learning during the Pavlovian training block (though the query trials indicate that learning was very robust).
Equation 3 (Model 8 in Table 2) assumes that the stimulus-action values at the end of the instrumental block are perfectly and exactly generalized to the PIT block. We first tested an alternative model (Model 9 in Table 2) that included an exponential extinction factor, letting the values decay on each PIT trial by with . Next, we tested the model described in the main text (Model 10 in Table 2), which allowed for a fixed, Gaussian random offset between the effective values in the instrumental and PIT stages, i.e. we wrote:The noise factor took on value for the nogo action (akin to the bias and variables). It took on a separate value—which was inferred as a separate parameter—for each subject and each stimulus. However, all stimuli shared the same prior distribution for this noise variable. That is, in the E step of our EM procedure, we fitted one Gaussian mean and variance to all the 's that had been inferred for all stimuli for all subjects. In this sense, the generalization factors were drawn from one Gaussian prior whose mean and variance were fitted just like the mean and variance of the other parameters.
For each subject, each model specifies a vector of parameters . Assuming Gaussian prior distributions , we find the maximum a posteriori estimate of the parameters for each subject :where are all actions by the subject. We assume that actions are independent (given the stimuli, which we omit for notational clarity), and thus factorize over trials. The prior distribution on the parameters mainly serves to regularise the inference and prevent parameters that are not well-constrained from taking on extreme values. We set the parameters of the prior distribution to the maximum likelihood given all the data by all the subjects:where . This maximisation is straightforwardly achieved by Expectation-Maximisation [109]. We use a Laplacian approximation for the E-step at the iteration:where denotes a normal distribution over with mean and is the second moment around , which approximates the variance, and thus the inverse of the certainty with which the parameter can be estimated. Finally, the hyperparameters are estimated by setting the mean and the (factorized) variance of the prior distribution to:ansformed before inference to enforce constraints. Unconstrained parameters are inferred in their native space. These model fitting procedures were verified on surrogate data generated from a known decision process.
We fitted a large number of different models to the data, and some of these models differ in their flexibility. For instance, Model 8, which assumes that the instrumental values are generalized exactly to the PIT stage is much less flexible than models 9–10, which allow for an offset. It is important to choose that model which is flexible enough to explain the data, but not so flexible that it would also fit very different data equally well [109].
Ideally, this is achieved by computing the posterior log likelihood of each model given all the data . As we have no prior on the models themselves (testing only models we believe are equally likely a priori), we instead examine the model log likelihood directly. This quantity can be approximated in two steps. First, the integral over [110]:Importantly, however, is not the sum of individual likelihoods, but in turn an integral over the parameters of each individual subject:The second line shows that we approximated the integrals by (importance) sampling times from the empirical prior distribution [109]. These samples were then also used to derive the error bars as the second moments around the maximum:where is a vector of zeros of the same dimension as with only entry set to one. The shifted likelihoods can be easily computed by re-weighting the samples drawn before:Note that while this model comparison procedure does give a good comparative measure of model fit, we still need an absolute measure to ensure that the best model does indeed provide a model fit that is adequate (even the best might be bad). Given each subject's MAP parameter estimate, we compute the total “predictive probability”:(4)where we suppressed the dependence on stimuli on the LHS for clarity. We note that depends on the parameters , which have been fitted to the data. We term it a predictive probability in the sense that it predicts a subject's choice at time given that subject's past behaviour. We emphasize however, that this does depend on the MAP parameters fitted to that subjects' entire choice dataset. Finally, we test whether the expected number of choices predicted correctly exceeds that expected by chance (using a binomial test). The overall predictive probability is given by the geometric mean over all choices and subjects: .
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10.1371/journal.pntd.0001651 | Use of Oxfendazole to Control Porcine Cysticercosis in a High-Endemic Area of Mozambique | A randomized controlled field trial to evaluate the effectiveness of a single oral dose of 30 mg/kg of oxfendazole (OFZ) treatment for control of porcine cysticercosis was conducted in 4 rural villages of Angónia district, north-western Mozambique. Two hundred and sixteen piglets aged 4 months were selected and assigned randomly to OFZ treatment or control groups. Fifty-four piglets were treated at 4 months of age (T1), while another 54 piglets were treated at 9 months of age (T2) and these were matched with 108 control pigs from the same litters and raised under the same conditions. Baseline data were collected on the prevalence of porcine cysticercosis using antigen ELISA (Ag-ELISA), as well as knowledge and practices related to Taenia solium transmission based on questionnaire interviews and observations. All animals were followed and re-tested for porcine cysticercosis by Ag-ELISA at 9 and 12 months of age when the study was terminated. Overall prevalence at baseline was 5.1% with no significant difference between groups. At the end of the study, 66.7% of the controls were found positive, whereas 21.4% of the T1 and 9.1% of the T2 pigs were positive, respectively. Incidence rates of porcine cysticercosis were lower in treated pigs as compared to controls. Necropsy of 30 randomly selected animals revealed that viable cysts were present in none (0/8) of T2 pigs, 12.5% (1/8) of T1 pigs and 42.8% (6/14) of control pigs. There was a significant reduction in the risk of T. solium cysticercosis if pigs were treated with OFZ either at 4 months (OR = 0.14; 95% CI: 0.05–0.36) or at 9 months of age (OR = 0.05; 95% CI: 0.02–0.16). Strategic treatment of pigs in endemic areas should be further explored as a means to control T. solium cysticercosis/taeniosis.
| Porcine cysticercosis is an infection of pigs caused by the larval stage of Taenia solium, a tapeworm that causes taeniosis in humans. The disease is very common in developing countries where it is a serious public health risk and causes significant economic losses in pig production. Many control strategies in developing countries have been of limited impact mainly due to poor socioeconomic and sanitary conditions. An effective treatment of infected pigs using inexpensive drugs may have potential as a long term control tool. We performed a randomized controlled trial to evaluate the effectiveness of oxfendazole treatment for control of porcine cysticercosis. We evaluated the prevalence and incidence of the disease in groups of pigs treated at 4 and 9 months of age and untreated pigs. We found that the prevalence and incidence of the disease in treated pigs was significantly lower than in untreated pigs. We conclude that treatment of pigs with oxfendazole in the last part of the fattening period is cost-effective in controlling porcine cysticercosis in endemic low-income areas but should be integrated with other control measures.
| Taenia solium is the etiologic agent of cysticercosis, an important zoonotic infection involving humans and pigs. The life cycle of this parasite includes pigs as the normal intermediate hosts, harbouring the larval cysts in many parts of the body causing cysticercosis, and humans as definitive hosts, harbouring the adult tapeworm in the intestines causing a condition known as taeniosis. Humans are accidental hosts of cysticerci after ingestion of T. solium eggs from the environment and develop the cysts in their tissues and organs, with the central nervous system (CNS) being a common site of cyst location resulting in neurocysticercosis [1], [2]. Cysticercosis in pigs is endemic in many developing countries of Latin America [3], [4], Africa [5], and Asia [6], where it causes important economic losses resulting from condemnation of infected pork [7], [8]. The disease has been declared preventable and potentially eradicable [9], but in many developing countries it is still a major constraint in pig production mainly due to lack of awareness about its extent, poor socioeconomic conditions and the absence of suitable diagnostic tools and control strategies [10]–[13].
Currently, the diagnosis of porcine cysticercosis in live animals is based on lingual examination that is sensitive only in detecting moderate to heavy infections [14]. Reliable serological tests based on detection of specific antibody and antigen have been developed and proved very useful in confirming diagnosis [15], [16]. Among them the Ag-ELISA has been reported to have high specificity (86.7%) and sensitivity (94.7%), even detecting circulating antigens in pigs harbouring one single T. solium cyst [15], [17] or detecting circulating antigens as early as two to six weeks after infection [17]. However, the detection of circulating antigens technique is unable to distinguish T. solium from T. hydatigena cysticerci, and where the later parasite is highly prevalent the method may be of limited use [18].
Control measures such as improved animal husbandry practices, efficient meat inspection procedures, and health education about hygiene and sanitation have been of limited impact in developing countries where pigs are mainly raised by poor smallholder farmers and marketing of pork is not controlled [19]. However, control of cysticercosis should be possible by eliminating the infection from either pigs or humans, or both for an extended period. Since the pig constitutes a vital link in the transmission cycle of T. solium [20], [21], an effective treatment of infected pigs should interrupt the transmission cycle. Oxfendazole (methyl 5[6]-phenylsulfinyl-2-benzimidazolecarbamate) (OFZ), a benzimidazole anthelmintic commonly used in cattle and small ruminants for treatment and control of gastro-intestinal roundworms, lungworms and certain tapeworms, has been shown to kill all viable T. solium cysticerci in muscles but ineffective against brain cysts in infected pigs [22]–[25]. Pigs treated with OFZ were reported to be refractory to re-infection even in the event of ongoing exposure to T. solium eggs [25]. More importantly, carcasses from treated pigs were reported to have a normal appearance suitable for human consumption after 3–6 months depending on intensity of infection [23], [24]. Surveillance for detection of infected pigs followed by treatment with OFZ could reduce the flow of contaminated pork into the market [22], [23]. Mass porcine chemotherapy with OFZ could, therefore, also be a useful strategy to control T. solium, by providing health as well as economic benefits for rural poor smallholder communities. However, most studies that have addressed the use of OFZ against porcine cysticercosis thus far have mainly focused on efficacy of the drug as opposed to its effectiveness. Therefore, the present study aimed to evaluate the effectiveness of a single oral dose of 30 mg/kg of OFZ against T. solium cysticercosis in pigs reared in smallholder farming systems in a highly endemic area.
The study was conducted in Angónia district located in north-western Mozambique between latitude 14.27°S and 15.28°S, and longitude 33.59°E and 34.38°E. The district is characterized by a humid climate with a rainy season extending from November to mid-March and the dry period from April to October. Four villages were selected randomly from a group of 11 villages within the district with a known high prevalence of porcine cysticercosis [26]. A preliminary visit was made to the selected villages to evaluate villagers' willingness to collaborate in a longitudinal study for control of porcine cysticercosis.
A randomized field study with a control group was conducted, between August 2008 and May 2009, to evaluate the effectiveness of a single oral dose of 30 mg/kg of OFZ treatment against T. solium cysticercosis by comparing the prevalence and incidence of porcine cysticercosis between treatment and control groups. Fifty-four pig litters from same number of households, comprising a total of 216 piglets aged 4 months were selected for the study and followed to the age of 12 months. All piglets were identified and ear-tagged with consecutive numbers and each litter in a household was divided into three groups by randomization. The Group 1 piglets (n = 54) were treated with OFZ at 4 months of age (T1), Group 2 (n = 54) was treated at 9 months of age (T2) while Group 3 (n = 108) served as non-treated controls (C). At the end of the trial, a total of 30 randomly selected pigs (8 from each of the OFZ-treatment groups and 14 from the control group) were purchased from villagers, slaughtered locally and dissected for assessment of T. solium cysticerci.
Blood was collected from all animals in both treatment and control groups in three sampling rounds (4, 9 and 12 months of age), and information regarding sampling date, household, village, sex and age was recorded. Blood samples were obtained from the cranial vena cava into plain vacutainers tubes and allowed to clot at 4°C. Serum was obtained by centrifugation, dispensed into 2 ml aliquots, stored in labelled vials and kept at −20°C until use.
The animals of the two OFZ-treatment groups were first weighed and later given orally a single dose of 30 mg/kg OFZ (Oxfen-C, Lot 800869, Bayer, Isando, South Africa) as a suspension (concentration of 9.06%), while the control group did not receive any treatment. The drug was administered through a dispensing tube attached to a 15 ml drench gun. The pig was firmly restrained and a pig snare was used to stabilize the head. The end of the dispensing tube of the drench gun was passed gently, but firmly, over the back of the tongue to allow the pig to swallow the dispensed suspension and ensure the complete delivery of the drug.
The Ag-ELISA was performed as described by Brandt and others [27] and modified by Dorny and others [15]. Briefly, the serum samples were pre-treated using trichloroacetic acid (TCA) and used in ELISA at a final dilution of 1/4. Two monoclonal antibodies (MoAb) used in a sandwich ELISA were B158C11A10 (Lot K, ITM, Antwerp, Belgium) diluted at 5 µg/ml in carbonate buffer (0.06M/pH 9.6) for coating and a biotinylated MoAb B60H8A4 (Lot 28, ITM, Antwerp, Belgium) diluted at 1.25 µg/ml in phosphate buffered saline-Tween 20 (PBS-T20)+1% new born calf serum (NBCS) as detector antibody. The incubation was carried out at 37°C on a shaker for 30 min for the coating of the first MoAb and for 15 min for all subsequent steps. The substrate solution consisting of ortho phenylenediamine (OPD) and H2O2 was added and incubated without shaking at 30°C for 15 min. To stop the reaction, 50 µl of H2SO4 (4N) was added to each well. The plates were read using an ELISA reader at 492 nm. Sera from two known positive pigs (confirmed at slaughter) were used as positive control. To determine the cut-off, the optical density (OD) of each serum sample was compared with a series of 8 reference negative serum samples at a probability level of 0.1% using a modified Student's t-test [28].
Carcass dissection was performed as described by Phiri et al [14] with slight modifications. Briefly, skeletal muscle groups were excised from the left half carcasses together with the complete heart, tongue, head and neck muscles, psoas muscles, diaphragm, lungs, kidneys, liver and brains. Dissection was done in such a way that all fully developed cysts could be revealed (i.e. each slice was about 0.5 cm thick). Animals were considered infected if viable cysts were found in the carcass.
Data were entered and analysed using STATA version 9.1 (Stata Corporation, College Station, TX, USA). Prevalence estimates were calculated for pigs that were bled in each sampling round. Incidence was estimated as the number of new cases occurred per unit of animal time at risk, during a period between two consecutive sampling rounds. Confidence intervals were calculated for prevalence and incidence of porcine cysticercosis in both treatment and control groups. Chi-square test and statistical comparison of rates were used to compare the prevalence and incidence, respectively. Logistic regression models were fitted to examine the role of factors potentially associated to prevalence of porcine cysticercosis. Examined factors included treatment time, sex of the pig, pig husbandry practices and village. The statistical significance level was set at 5%.
The study was conducted with ethical approval from the scientific board of the Veterinary Faculty, Eduardo Mondlane University. All animals were handled in strict accordance with good animal practice as defined by the OIE's Terrestrial Animal Health Code for the use of animals in research and education. Study permissions were obtained from the Livestock National Directorate, village leaders and pig owners. Due to high level of illiteracy among villagers, an oral consent was obtained from pig farmers in the presence of a witness, who signed on their behalf. At the end of the trial all animals were treated with OFZ, and pig farmers were informed not to slaughter their animals before 4 weeks after the treatment.
All examined pigs (n = 216) were of the local breed (Landim), mostly males (55%) and the majority (93.1%) were deliberately left to roam freely. A total of 570 samples were collected along three sampling rounds from 216 pigs. From these animals, 32 (14.8%) were sampled once, 184 (85.2%) were sampled twice, and 170 (78.7%) were sampled three times. Altogether 46 animals were lost to follow-up, out of which 24 (22.2%) were from the control group, 12 (22.2%) from T1 group and 10 (18.5%) were from T2 group. The main reasons for losses of animals to follow-up were death, sale or refusals to allow sampling due to absence of the head of the household. Overall baseline prevalence at 4 months of age was 5.1% (95% CI = 2.6%–8.9%) and did not differ significantly (p>0.05) among comparison groups.
This study in Angónia district has evaluated the effectiveness of OFZ treatment in pigs, as a strategy to control T. solium cysticercosis in a highly endemic area in which pigs are constantly exposed to the parasite. A substantial benefit of treating pigs with OFZ using the single oral dose of 30 mg/kg body weight was clearly demonstrated, since the prevalence and incidence in groups of treated pigs was significantly lower compared to the group of untreated pigs. All pigs that were infected at the time of treatment with OFZ were found negative in the subsequent sampling round (5 and 3 months later for T1 and T2, respectively). Interestingly, this result was observed in animals raised under constant exposure to T. solium eggs [26] but were in keeping with previous studies conducted under controlled settings reporting a clear effect of OFZ in killing cysts when given as a single dose of 30 mg/kg [22]–[24], [29].
A significant number of negative control pigs (51/79) got infected whereas 1 of the 2 infected pigs treated at 4 months and none of the 18 infected pigs treated at 9 months were found re-infected at the end of the study. The latter result is very convincing of induction of a high level protective immunity whereas the former may raise some speculations. One could argue that the result may be explained by differences in individual immune responsiveness or even exposure, as animals become susceptible to new infections after treatment. Also, it can be speculated that there were false serological results or even cross reactions with T. hydatigena infection, though this is unlikely as none of the 51 pigs found infected in control group had fluctuating positivity and all pigs (7/30) that were found positive at necropsy had no T. hydatigena cysticerci. Nevertheless, in accordance with our findings, naturally infected pigs treated with OFZ under field conditions were not re-infected for at least 3 months. These results support conclusions from previous studies [23], [25] and speculations that treated pigs remain immune to re-infection for at least 12 weeks [24].
However, although the effectiveness of OFZ can be regarded as good, the prevalence and incidence of T. solium cysticercosis in T1 pigs increased during the study period. The observed increase after treatment is most likely related to new infections in animals being fully susceptible at the time of treatment. Indeed, nearly all of the T1 pigs (51/54) were negative by Ag-ELISA at the time of treatment, thus being at risk of infection. On the other hand, the prevalence and incidence of T. solium cysticercosis in T2 pigs reduced significantly from treatment at 9 months to 12 months of age. This strategy, although also involving possible treatment of uninfected pigs, showed better results and the potential to be used as a control strategy for T. solium cysticercosis in our settings considering the lifespan of pigs is usually less than one year. Our findings are in line with an earlier field study that showed a clear effect of mass treatment with OFZ at 30 mg/kg body weight in decreasing the prevalence and incidence of porcine cysticercosis, however that study had utilised an antibody-detection method which measures exposure and not necessarily active infection [30].
The significant decrease in the risk of porcine cysticercosis infection at the age of 12 months observed in this study if pigs were treated with OFZ, either at 4 or 9 months of age, was observed in a context of poor living conditions in the study area, where most pigs were left to roam freely and no other control interventions were implemented. This finding, and the low cost of the drug (approximately 0.018 USD/dose), corroborates previous conclusions considering OFZ as a potential effective control tool for porcine cysticercosis in resource-poor endemic areas [22]–[24], [29]. However, it should be borne in mind that many strategies to control T. solium cysticercosis in developing countries have shown promising results [7], [13], [21], [31], [32] but none has fully succeeded up to date, mainly due to poor socioeconomic and sanitary conditions [7], [13]. Control measures targeting pigs alone would be ineffective as they cannot prevent the spread of cysticercosis in humans and pigs [21], [33], [34], thus there is a need for integration with other T. solium control strategies in the long term [34]–[36]. Moreover, inexpensive and reliable diagnostic tests in live animals are needed for monitoring the effect of interventions in endemic countries. The Ag-ELISA used in this study, though considered very sensitive [37] may have drawbacks in monitoring the effectiveness of treatment in pigs as cysticercal antigen levels take some time to disappear from circulation after treatment depending on the intensity of infection [24]. Furthermore, the technique may not detect brain cysts [24], [25], though in our study brain cysts were only found in some of the control pigs, and currently it does not allow differentiating T. solium from T. hydatigena cysticerci [18].
The results presented in this study showed that OFZ treatment in the last part of the pigs' fattening period is effective to control porcine cysticercosis but is not a stand-alone approach because in high endemic areas a certain number of animals will inevitably get infected after treatment and before slaughter. Although effective, its strategic use as a control tool in T. solium endemic areas should be further explored, particularly with regard to availability, formulation, regimen of administration, safety and marketing of pigs. Despite these concerns and considering that any strategy to control T. solium by targeting pigs has a potential to provide economic incentives to poor smallholder pig farmers, treatment of pigs with OFZ, if integrated with other control measures such as treatment of human tapeworm carriers, ending open human defecation, education, and community-based inspection and sales restrictions, should be considered an important, cost-effective measure to reduce the transmission of T. solium infections in endemic low-income areas.
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10.1371/journal.pgen.1005059 | Genome-Wide Association Studies in Dogs and Humans Identify ADAMTS20 as a Risk Variant for Cleft Lip and Palate | Cleft lip with or without cleft palate (CL/P) is the most commonly occurring craniofacial birth defect. We provide insight into the genetic etiology of this birth defect by performing genome-wide association studies in two species: dogs and humans. In the dog, a genome-wide association study of 7 CL/P cases and 112 controls from the Nova Scotia Duck Tolling Retriever (NSDTR) breed identified a significantly associated region on canine chromosome 27 (unadjusted p=1.1 x 10-13; adjusted p= 2.2 x 10-3). Further analysis in NSDTR families and additional full sibling cases identified a 1.44 Mb homozygous haplotype (chromosome 27: 9.29 – 10.73 Mb) segregating with a more complex phenotype of cleft lip, cleft palate, and syndactyly (CLPS) in 13 cases. Whole-genome sequencing of 3 CLPS cases and 4 controls at 15X coverage led to the discovery of a frameshift mutation within ADAMTS20 (c.1360_1361delAA (p.Lys453Ilefs*3)), which segregated concordant with the phenotype. In a parallel study in humans, a family-based association analysis (DFAM) of 125 CL/P cases, 420 unaffected relatives, and 392 controls from a Guatemalan cohort, identified a suggestive association (rs10785430; p =2.67 x 10-6) with the same gene, ADAMTS20. Sequencing of cases from the Guatemalan cohort was unable to identify a causative mutation within the coding region of ADAMTS20, but four coding variants were found in additional cases of CL/P. In summary, this study provides genetic evidence for a role of ADAMTS20 in CL/P development in dogs and as a candidate gene for CL/P development in humans.
| Cleft lip with or without cleft palate (CL/P) is a commonly occurring birth defect that can lead to a lifetime of complications in affected children. To better understand the genetic cause of these disorders, we investigated CL/P in both dogs and humans. Genome-wide association studies in both species independently identify ADAMTS20 as a candidate gene for CL/P development. In dogs, a deletion within a functional domain of ADAMTS20 is responsible for CL/P in the Nova Scotia Duck Tolling Retriever dog breed. In humans, an associated region containing the same gene, ADAMTS20, was identified in a study population of native Guatemalans. Subsequent sequencing in humans was unable to identify a causative mutation within the coding region of ADAMTS20 in the Guatemalan cohort; however, sequencing of ADAMTS20 in additional cases with CL/P identified four novel coding variants. This work provides genetic evidence for a role for ADAMTS20 in CL/P development in both dogs and humans.
| Nonsyndromic orofacial clefts, notably cleft lip (CL) with or without cleft palate (CL/P) and isolated cleft palate (CP), are the most common craniofacial birth defects in humans and represent a substantial personal and societal burden. Clefts affect approximately 1 in 700 individuals[1], with a lifetime cost of treatment in the U.S.A. estimated at $200,000[2–5]. Rates vary dramatically depending on population, with higher rates of CL/P found in Asians, South Americans, and American Indians compared with Caucasians [6], while populations of African descent are the least often affected[7]. Interestingly, the CL/P birth prevalence differs between genders (males more often affected), but the CP prevalence does not[6]. Although clefts can be surgically repaired, affected individuals often undergo multiple craniofacial and dental surgeries, as well as speech, hearing, and orthodontic therapies. Furthermore, individuals born with an orofacial cleft have increased incidence of mental health problems, higher mortality rates through all stages of life[2,8], and a higher risk of various cancer types (including breast, brain, and colon cancers) that extends to their family members[9–12].
Orofacial clefts are complex birth defects resulting from genetic variations, environmental exposures, and their interactions[3]. Before the advent of genome-wide approaches, evaluation of candidate genes revealed at best modest associations with a number of genes[13,14]; further, despite exhaustive mutation screens, coding mutations were found in less than 10% of study participants, leaving a large portion of the genetic etiology unexplained[15–18]. Genome-wide studies, using both linkage and association methods[18], have identified a number of different genes and genomic regions likely to contribute to the risk of orofacial clefts; both rare and common variants have been implicated in studies of Caucasian and Asian populations[16–18] with distinct SNPs associated in each ethnicity.
In addition to human genetic studies, a variety of model organisms have been utilized to further understand the development of orofacial clefts. A number of mammalian species exhibit orofacial clefting, with mice being the most often studied; however, mouse models often exhibit CP alone and very rarely display CL/P. In contrast, the domestic dog has spontaneous and naturally occurring CL/P with a recessive mode of inheritance documented in several breeds[19–21]. Contributing further to their usefulness as a model organism, common breeding practices have created genetically isolated populations (dog breeds) that result in few haplotypes and extensive linkage disequilibrium[22,23].
Even within closed breeding populations, genetic heterogeneity of orofacial clefts is present within the Nova Scotia Duck Tolling Retriever (NSDTR) dog breed. We previously identified a DLX6 LINE-1 insertion as a cause of cleft palate and mandibular abnormalities in a subset of NSDTRs with orofacial clefts[24]. The DLX6 LINE-1 insertion failed to explain a series of cases with cleft lip, indicating molecular and phenotypic heterogeneity of orofacial clefting within the breed.
Here, we present two parallel genome-wide association studies (GWAS) in the domestic dog and humans. Both studies provide evidence for a role of the same gene, ADAMTS20, in CL/P development. A GWAS investigating the cause of cleft lip within a small cohort of NSDTRs identified an associated interval on canine chromosome 27 that segregates with a more complex phenotype of CL/P and syndactyly. Whole-genome sequencing of these dogs led to the identification of a frameshift mutation within ADAMTS20. A GWAS within a cohort of native Guatemalans with CL/P identified a suggestive association with an interval encompassing ADAMTS20. Sanger sequencing of ADAMTS20 within CL/P cases from the native Guatemalan cohort and additional cases, identified four novel risk variants for CL/P in humans.
To identify loci associated with CL/P in dogs, an allelic genome-wide association was performed using 7 CL/P case dogs and 112 control dogs from within the NSDTR breed. After quality control, association analysis of 110,021 remaining SNPs identified a highly associated region on canine chromosome 27 (unadjusted p-value of 1.1 x 10-13, CFA27: 11419150) (Fig. 1A). A genomic inflation factor (λ) of 1.18 was observed, however, the significance of the positive association and lack of any other associated chromosomal regions led to the investigation of the associated region without further correction for population stratification. After 100,000 permutations to correct for multiple tests, the adjusted empirical p-value was 2.2 x 10-3 with 20 SNPs reaching genome-wide significance (i.e., adjusted p-value ≤ 0.05; Fig. 1B). Underlying this evidence of association was a 2.88 Mb homozygous haplotype spanning from CFA27: 9.29–12.16 Mb in 6 cases used in the GWAS when compared to the controls (Fig. 1C). This homozygous haplotype was not identified in one of the cases with CL/P nor in any of the 112 controls.
Homozygosity mapping of the associated interval was performed in the six full sibling cases not included in the GWAS. These six full sibling cases were excluded from the GWAS analysis in order to reduce possible population stratification. All six cases were found to be homozygous throughout the associated region. Previous studies investigating cleft palate within the NSDTR breed identified two unexplained cases of cleft palate[24]. Homozygosity mapping was also performed in these two unexplained cases and the homozygous haplotype was identified in one of the two CP NSDTRs. Closer inspection of the CP NSDTR with the homozygous haplotype revealed bilateral clefts of the lower alar nasal folds. In summary, a homozygous region concordant with the CFA27-associated interval was identified in 13 of 15 total NSDTRs with orofacial clefts (Table 1). Recombination breakpoints in the 13 dogs reduced the critical interval from 2.88 Mb to 1.44 Mb (CFA27: 9.29–10.73 Mb; Fig. 1D).
Segregation analysis of the associated haplotype was performed in unaffected family members with enough available DNA (parents n = 6; littermates n = 6). None of the 12 unaffected family members were homozygous throughout the associated interval and the parents were all heterozygous, suggesting a recessive mode of inheritance.
The phenotypes observed in the 13 dogs with the homozygous haplotype on CFA27 include a range of clefting phenotypes including bilateral clefts of the lower alar nasal folds (n = 1), bilateral clefts of the lower alar nasal folds and CP (n = 6), a right unilateral complete CL and CP (n = 1), and bilateral complete CL and CP (n = 5) (Fig. 2A-C). To identify additional craniofacial defects, micro computed tomography (microCT) analysis was performed on two severely affected NSDTR cases with bilateral complete CL and CP and three NSDTR controls. MicroCT findings were consistent with the presence of bone and soft tissue clefts of the primary and the secondary palate (Fig. 2D-G). In both affected individuals, bilateral dentoalveolar clefting was evident between the incisors and canine teeth. This clefting was associated with a complete absence of ossification of the dorsal and lateral aspects of the premaxilla, which would normally form a suture with the maxillae and rostral aspect of the nasal bones. The ventromedial component of the premaxilla, from which the incisors arise, appeared largely normal. However, in contrast, to the controls in which the six incisors are evident in the premaxilla, the affected individuals presented with only four incisors in the remnant premaxillary bone. The lateral-most incisors were not integrated in bone and were relatively poorly formed and abnormally oriented in the cleft individuals. Interestingly, the nasal bones were normal in appearance and length. Compared to controls, the rostral portion of each maxillae was hypoplastic (S1 Fig and S2 Fig.). In one of the two individuals, a cleft of the secondary palate extending the full length of maxillary and palatine bones was observed, resulting in free communication between the nasal cavity and oral cavity. In the second individual, one palatal shelf had extended to the midline and had approximated with the anterior nasal septum while the other showed little lateral outgrowth, although the palatal rugae were still evident on both shelves. This asymmetry in shelf growth was mirrored by the asymmetric palatal bone growth. In contrast to the palate, the basisphenoid and basioccipital bones of the cranial base were similar in appearance to that of controls. Of note, however, both affected individuals had smaller (average ~20% smaller relative to skull length) and more laterally positioned tympanic rings compared to the unaffected individuals (Fig. 2FG). In one individual, the tympanic rings were also slightly dimorphic. In one individual there was notable asymmetry in length of one of the component hyoid bones. The mandible, although largely normal, had a slightly more narrow appearance and slightly delayed ossification around the mandibular canines and incisors. In both cleft individuals the forehead was slightly more tapered.
In addition to CL/P, simple complete and/or partial syndactyly of the third and fourth digits was observed in 10 of the 13 dogs (Fig. 2H-J). It is unknown whether or not the 3 remaining dogs had syndactyly. Simple syndactyly demonstrating only soft tissue involvement was observed on radiographs of a paw with complete syndactyly (Fig. 2K). We designate this phenotype cleft lip, palate, and syndactyly (CLPS). The observed phenotypic spectrum of CLPS cases is summarized in Table 2.
To identify variants within the critical interval, whole-genome sequencing was performed on three CLPS NSDTR cases and four control NSDTRs that were homozygous wild type throughout the associated interval and unaffected with CLPS. Within the associated interval (CFA27: 9.29–10.73 Mb) there were 14,167 SNP and in/del variants when compared to the CanFam 3.1 boxer reference genome [22]. Based on the homozygous haplotype observed in the cases, we hypothesized a recessive mode of inheritance and excluded variants that did not segregate with the phenotype. From the set of homozygous variants in the CLPS cases, we excluded variants that were also homozygous in at least one control dog. This reduced the number of variants segregating with the phenotype to 142 (Table 3), when compared to a reference set of 26 control dogs. Breeds of all dogs with whole genome sequence are summarized in S1 Table.
Of these 142 variants, only two were predicted to affect the coding region of genes within the critical interval (S2 Table) [25]. One synonymous coding variant (PUS7L c.278A>G (p. (=))) was identified as homozygous in cases and heterozygous in 10 control dogs (1 NSDTR, 5 Dalmatians, 1 Kelpie, 1 Bearded Collie, 2 Weimaraners). Due to the severe nature of the CLPS phenotype, we hypothesized that the causative allele would occur at a low frequency across all dog breeds. Because approximately one third of the randomly sampled control dogs were heterozygous for PUS7L c.278A>G (p. (=)), we concluded that this variant was unlikely to be causal for the CLPS phenotype. The second variant, ADAMTS20 c.1360_1361delAA (p.Lys453Ilefs *3), was predicted to be a frameshift mutation in the metalloprotease domain resulting in premature truncation of 1461 amino acids from the 1916 amino acid protein (Fig. 3AB) [26]. It was not observed in any of the 30 control dogs.
To confirm the ADAMTS20 c.1360_1361delAA (p.Lys453Ilefs*3) frameshift mutation, Sanger sequencing was performed in cDNA from a CLPS NSDTR case and embryo control (Fig. 3A). In addition to the deletion, seven SNPs were identified in the CLPS NSDTR case and embryo control when compared to CanFam 3.1 Boxer reference sequence (Table 4)[22]. Six of the 7 SNPs did not segregate with the phenotype. ADAMTS20 c.682C >T (p.Val228Leu) segregated within the CLPS NSDTR case, embryo control, and reference sequence, but is a known SNP previously identified in wolves and 15 other dog breeds[27]. Further investigation of this SNP in the 8 NSDTRs with available whole-genome sequence also confirmed that this SNP did not segregate with the phenotype: 1 NSDTR was homozygous for the boxer reference allele, 3 NSDTRs were heterozygous, and 3 NSDTRs were homozygous for the alternate allele (3 cases, 1 control).
We hypothesize that the predicted premature stop codon in ADAMTS20 c.1360_1361delAA (p.Lys453Ilefs*3) results in decreased expression levels when compared to wild type. Quantitative real time PCR analysis was performed on cDNA from heart tissue of CLPS NSDTRs and compared to cDNA from heart tissue of unaffected NSDTRs that were homozygous wild type for the deletion. REST analysis indicated that the ADAMTS20 transcript in CLPS NSDTRs was significantly down regulated by a mean factor of 0.549 (p = 0.005) in CLPS cases when compared to controls[28].
Genotyping was performed in available parents (n = 8), littermates (n = 13), and all NSDTR cases (n = 13 CLPS; 2 unexplained) (Fig. 4). All 13 CLPS cases were homozygous for the deletion. The two unexplained NSDTRs cases without the associated haplotype were homozygous for the wild type allele. We genotyped 97 unrelated control NSDTRs and identified this deletion in the heterozygous form in 3 control dogs. To determine if this allele is found in other breeds, we genotyped 53 dogs with orofacial clefts from 25 breeds and 288 unaffected dogs from more than 70 breeds (S3 Table). All genotyped individuals were homozygous for the wild type allele, suggesting this ADAMTS20 deletion is private to the NSDTR breed.
Our previous work in dogs identified a DLX6 LINE-1 insertion responsible for cleft palate and mandibular abnormalities in a subset of NSDTRs [24]. The ADAMTS20 c.1360_1361delAA (p.Lys453Ilefs*3) deletion was also genotyped in these cases (n = 19). Two of the cases were heterozygous, while the remaining 17 dogs were homozygous wild type for the ADAMTS20 c.1360_1361delAA (p.Lys453Ilefs*3) mutation. Of all control NSDTRs, including relatives, genotyped for both mutations (n = 99), five were heterozygous for both mutations and were phenotypically normal.
To investigate the cause of CL/P in humans, a DFAM allelic association[29] analysis was performed in a Guatemalan study population of 125 nonsyndromic CL/P cases, 420 unaffected relatives (545 total from case families), plus 392 controls with no family history of orofacial clefts (Fig. 5A). No p-value reached a conservative Bonferroni corrected p-value of less than 1.1 x 10-7 (alpha = 0.05), but several SNPs had p-values suggestive of association (p-values ~10-6). Table 5 shows the top 5 CL/P associated SNPs according to the DFAM analysis within the Guatemalan cohort, as well as the corresponding TDT, sib-TDT, and logistic regression p-values. S4 Table lists all SNPs with DFAM p-value less than 0.0001. The QQ plot of the allelic GWAS results shows no evidence of genomic inflation in this family based study (S3A Fig.).
The strongest association observed was for rs10785430 (DFAM p-value = 2.69 x 10-6) on chromosome 12, which mapped to an intron in ADAMTS20. This SNP also showed significant associations (p<0.05) using the TDT (p = 1.8 x 10-4), sib-TDT (p = 3.8 x 10-4), and case-control (p = 0.007) analyses. To further explore this region, we imputed unobserved SNPs on chromosome 12 using the 1000 Genomes reference sample (http://browser.1000genomes.org). Based on SNPs in high LD with rs10785430, association tests revealed a 157kb region of association (from rs11182055 to rs9988939) yielding p-values less than 1x10-4 (Fig. 5B).
We next performed a gene-level analysis with DFAM on 17,578 genes. The QQ plot of gene p-values revealed no significant deviations from expected (S3B Fig.) indicating that the VEGAS method adequately controlled for LD (shown for ADAMTS20 in Fig. 5C). The top 5 associated genes derived from the Guatemalan GWAS using the VEGAS method are summarized in Table 6. ADAMTS20 had the lowest gene-wise p-value (p = 5.3x 10-5), in agreement with the single SNP results.
Previous genome-wide studies were performed in Caucasian and Asian trios[17,18], but did not identify ADAMTS20 as a top hit. However, several SNPs within ADAMTS20 did show nominal significance (p<0.05) in Caucasians[17], so we performed a meta-analysis of the Guatemalan and Caucasian results for the ADAMTS20 SNPs. Overall, smaller p-values were observed for some SNPs after the addition of the Caucasian results, but not for the most significant SNP in the Guatemalans (S4 Fig.). This suggests a distinct genetic etiology for CL/P formation in Guatemalans.
We sequenced all protein coding exons of ADAMTS20 in 20 Guatemalan CL/P cases to determine if a novel, population-specific common variant could explain the association with markers in ADAMTS20; however, no such variants were identified. We also sequenced 19 cases from the Philippines to explore the possibility that rare variants in ADAMTS20 could confer risk of CL/P in other populations. Similarly, we looked for rare coding variants in our Guatemalan cases that could contribute to CL/P risk independent of the association with the common SNP rs10785430. No private variants were found in the Guatemalan cases, but three novel missense variants were found among Filipino cases (S5AB Fig.). All three novel Filipino variants were inherited from unaffected parents. Notably, two of these variants occurred on a common haplotype and were found in both affected children in the family (S5A Fig.). A summary of all of the variants found in the nonsyndromic CL/P cases is found in S5 Table. We also sequenced 44 individuals of diverse ethnicities with CL/P plus syndactyly or limb defects including amniotic bands, polydactyly, and ectrodactyly (which was motivated by the CLPS phenotype observed in dogs which includes syndactyly). From these samples, only one missense variant (chr12: g.43824214C>T (p.A1108T)) was found in an individual with CL/P, facial asymmetry, and a single transverse palmar crease of the left palm. However, this variant did not segregate with clefting in the family (S5C Fig.).
This study presents independent genome-wide association studies that provide evidence of the involvement of ADAMTS20 in the development of orofacial clefts in dogs and humans. The canine study identified a 1.44 Mb region of homozygosity underlying an association on CFA27 where subsequent whole-genome sequencing identified a frameshift mutation in ADAMTS20 that segregated with a complex phenotype of syndromic cleft lip, cleft palate, and syndactyly. The parallel human studies applied combinational-based association statistics to identify suggestive allelic (DFAM) and gene-level (VEGAS) associations with SNPs in ADAMTS20 in a cohort of native Guatemalans with nonsyndromic CL/P. Both studies identify ADAMTS20 as a candidate gene for CL/P development in humans.
This work describes a second causative mutation for an independently segregating locus of orofacial cleft formation within the NSDTR dog breed. CLPS is characterized by a syndromic form of cleft lip, cleft palate, and syndactyly that segregates with a recessive mode of inheritance. This is independent of the previously identified CP1 locus that is characterized by a cleft palate and shortened mandible[24]. A genetic cause has not been identified in two cases of orofacial clefts within NSDTRs, further exemplifying the genetic heterogeneity within the NSDTR that has been previously documented[24]. This heterogeneity mimics what is observed in human cleft cases and is likely indicative of what will be observed in other dog breeds.
ADAMTS20 (a disintegrin-like and metalloprotease with thrombospondin type-1 motifs) is part of a large family of secreted zinc metalloproteases sharing a similar domain organization[26] that are involved in cleaving extracellular matrix (ECM) proteins and processing procollagen[30]. ADAMTS20 cleaves the ECM proteoglycan, versican[26,31], and is involved in a variety of biological processes including promotion of melanoblast survival, palatogenesis, and interdigital web regression[31–33].
Expression studies in mouse embryos identify craniofacial expression of Adamts20 in the first pharyngeal arch, between the medial nasal processes[34], and broad expression in the palatal mesenchyme, where it plays a role in the sculpting and extension of the palate[32]. Adamts20 is also expressed in the developing fore- and hind limbs, the interdigital tissue, and at the medial border of the developing autopod[33,34].
In mice, mutations in Adamts20 are best known to cause a fully penetrant recessive, ventral to dorsal white belted phenotype (bt)[34]. In addition, bt mice have a low penetrance of cleft palate (3%) and soft tissue syndactyly (18%)[32,33]. Full penetrance of cleft palate was observed in bt mice with additional mutations in Adamts9 (Adamts9+/-;bt/bt)[32]. Within the NSDTRs, the CLPS deletion results in 100% penetrance of primary palate clefts. There is variation in the primary palate phenotype that ranges from clefting of the lower alar nasal folds to bilateral cleft lip. Secondary palate clefts are 92% penetrant, but also exhibit some variability in presentation. Syndactyly is likely fully penetrant as it was observed in 10 of the 13 dogs and the status of three remaining full-sibling cases is unknown. NSDTRs have minimal white spotting segregating in the breed, but none of the dogs with the associated haplotype exhibited any obvious midline white markings similar to what is observed in bt mice. This work complements what has been identified within the mouse by providing further evidence for role of ADAMTS20 in cleft palate and syndactyly formation. This highlights that ADAMTS20 should further be investigated for its role in CL/P and craniofacial development.
Previous work describing mutations in ADAMTS20 and other ADAMTS family members may provide insight into the observed phenotype of bt mice and CLPS NSDTRs. ADAMTS proteins share identical N-terminal domains (e.g. metalloprotease), but the type and number of C-terminal ancillary domains vary. These ancillary domains are critical for activity, inhibition, tissue localization, and substrate specificity[35]. Work on ADAMTS13 demonstrated that point mutations do not consistently function as null alleles[36] and deletion of different ancillary domains in ADAMTS5 and ADAMTS13 resulted in mutant constructs that retained partial function depending on the specific domains that were deleted[37–40]. Point mutations have been described in four bt alleles (bt—c.1598C>T p.Pro533Leu; bt9J—c.2451C>T p.Leu761Phe; btBei1—c.2860C>T p.Arg954Ter; bt Mri1—c.4073A>C p.His1357Pro), which are located downstream of the catalytic metalloprotease domain[31,34]. In comparison, the deletion identified in CLPS NSDTRs (c.1360_1361delAA (p.Lys453Ilefs*3)) is closer to the N terminus and within the catalytic metalloprotease domain (Fig. 3B)[31,34]. Furthermore, the allele commonly used to study the bt phenotype (btBei1) is a nonsense mutation that truncates 471 amino acids or 33% of the full-length protein (Fig. 3B)[32,34]. In ADAMTS13, truncation after the spacer domain (a mutation similar to the nonsense btBei1 allele) results in a metalloprotease that is still active[37,38]. The mutation identified in CLPS NSDTRs truncates 1461 amino acids or 76% of full-length protein and may explain the higher penetrance of craniofacial defects and syndactyly observed in CLPS NSDTRs. In summary, this mutation could be the result of a more severe hypomorph than bt, a null, or a species difference. It is also interesting to note that the CLPS NSDTRs do not appear to have the white spotting pattern that is characteristic of the bt mice. Further studies examining the activity and regulation of ADAMTS20 will be necessary to dissect the molecular impact of the CLPS mutation and to determine if it is a null allele or a hypomorph.
ADAMTS20 transcript expression levels are observed at only 55% in CLPS NSDTRs when compared to wild type NSDTRs. Transcripts with premature stop codons often undergo degradation by nonsense mediated mRNA decay to prevent accumulation of truncated proteins[41,42], but often much lower expression levels are observed[43]. Since expression levels were analyzed in neonatal heart tissue, it is possible that a further decrease in expression levels may be observed in the appropriate tissue during the correct developmental time point.
Although no SNP reached genome-wide significance in the human GWAS, the data presents a suggestive association with a SNP within ADAMTS20 (rs10785430). A similar phenotype observed in dogs and mice with mutations in the same gene, combined with the biological relevance of ADAMTS20 to development of the phenotype observed in the Guatemalans, suggests that this gene should be further investigated for CL/P development in humans. These results also indicate that even suggestive loci identified in human GWAS warrant further investigation.
Sequencing of individuals with nonsyndromic CL/P from Guatemala and the Philippines did not identify any coding variants with obvious functional impact. Since the GWAS result suggests the existence of one or more common etiologic variants, it is possible that rare coding variants may yet be found in a subset of individuals with CL/P. We hypothesize that the etiologic variant(s) will be located in regulatory elements of ADAMTS20, perhaps located within introns. We also sequenced ADAMTS20 in an additional cohort of individuals with syndromic CL/P who also had syndactyly or other limb defects. Although we did not find any coding variants in these individuals, ADAMTS20 mutations causing a cleft and syndactyly syndrome may be extremely rare. Variants in and around ADAMTS20 may also act as modifiers of the phenotype in clefting syndromes that including syndactyly as part of the phenotypic spectrum, such as Van der Woude syndrome and the ectodermal dysplasias.
In conclusion, we showed separate association studies in humans and dogs that provide evidence of association between CL/P and variants in ADAMTS20. This complements what is known in the mouse and suggests that ADAMTS20 should be further investigated for its role in CL/P development. Notably, dogs have long been used as models for craniofacial surgical techniques, but this study also demonstrates that they have the potential to be relevant models for the genetics of CL/P. Here we highlighted how dogs are a genetically amenable model organism with naturally occurring cleft lip (the most common orofacial cleft type in humans) and provide a different genetic background to study mutations.
Subjects for this study were recruited from various sites in Guatemala as part of the Pittsburgh Oral-Facial Cleft study, a large research program investigating factors that contribute to the development of CL/P and CP. Recruitment was done in collaboration with the nonprofit organization Children of the Americas (www.childrenoftheamericas.org/).
Individuals and their family members seeking cleft lip and palate repair between 2004 and 2010 at multiple sites in Guatemala (San Juan Sacatepequez, Huehuetenango, Tiquisate, Quiche and Retalhuleu) were invited to participate in our study. This project was approved by both the University of Pittsburgh Institutional Review Board and the Oversight Ethics Committees of each of the participating hospitals; all participants gave informed, written consent in their native languages. Age appropriate assent documents were used for children between 7 and 14 years of age and informed, written consent was obtained from the child, as well as from the parents.
Collection of canine samples in this study was approved by the University of California, Davis Animal Care and Use Committee (protocol #16892).
Buccal swabs, blood, or tissue samples were collected from privately owned NSDTR dogs from the following phenotypic groups: CL/P (n = 13); CP with a previously identified DLX6 LINE-1insertion (n = 19)[24]; CP without a known causative mutation (n = 2; 1 later identified to have CL/P)[24]; healthy littermates of CL/P NSDTRs (n = 13); healthy parents of CL/P NSDTRs (n = 8); control NSDTRs (n = 205). Samples from dogs with orofacial clefts across 25 other breeds (n = 53) were also obtained from privately owned dogs. Blood samples from control dogs (n = 288) across 70 other breeds were collected from the William R. Pritchard Veterinary Medical Teaching Hospital (VMTH). Embryos were collected from pregnant bitches undergoing ovariohysterectomy at the VMTH. Embryos were staged based on measurements of crown-to-rump length and the observation of external features[44].
Gross phenotypic evaluation of the orofacial clefts were performed by a board certified veterinary dentist who is experienced in the evaluation of cleft palate. Further phenotyping was performed by high-resolution microCT imaging of two CLPS NSDTR cases and three NSDTRs controls as previously described[24]. Genomic DNA was extracted from whole blood, tissue samples, or buccal swabs using Qiagen kits (Valencia, CA).
Genome-wide SNP genotyping was performed in 13 cases and 112 controls using the Illumina CanineHD BeadChip with 173,662 markers. All samples had a genotyping call rate of ≥ 90%. 62,546 SNPs were excluded due to a minor allele frequency ≤ 5% and 3,199 SNPs were excluded for a high failure rate (≥ 10%). 110,021 SNPs were used in the final analysis. Chi-square analysis was performed in PLINK[29] on one case from each litter (n = 7) and discordant sibling pairs (n = 4) when available. 100,000 permutations were performed to correct for multiple tests and the genomic inflation factor was calculated in PLINK[29]. Segregation analysis of the associated haplotype was performed in parents (n = 6) and littermates (n = 6) with enough available DNA for genotyping on the Illumina CanineHD BeadChip. Homozygosity throughout the associated interval was analyzed by visual inspection facilitated by color-coding homozygous genotypes in excel. The genotypes are available from the Dryad Digital Repository (doi:10.5061/dryad.j8r8q).
Seven NSDTRs were selected for whole-genome sequencing. Three CLPS NSDTR cases that were representative of the phenotypic spectrum were selected for sequencing: bilateral CL and CP with complete syndactyly of all paws, bilateral cleft of the lower alar nasal folds and CP with complete rear and incomplete front syndactyly, and bilateral cleft of the lower alar nasal folds with CP and incomplete rear syndactyly. Four control NSDTRs were selected for sequencing that were homozygous wild type throughout the associated region identified on CFA27. Two of the four NSDTR controls had normal craniofacial structures. One NSDTR control had a cleft palate and shortened mandible explained by a DLX6 LINE-1 insertion[24] and the remaining NSDTR had a bilateral complete cleft lip with an unexplained genetic cause.
Library preparation and DNA sequencing was carried out by the Ramaciotti Centre at the University of New South Wales, Kensington. Genomic DNA was size selected for 500bp fragments and sequenced on the HiSeq 2000 sequencing platform (Illumina, San Diego, CA) according to vendor’s instructions. Paired-end reads of 101 bp were generated for each sample on a single lane of the sequencer’s flow cell, yielding between 179 and 233 million read pairs per individual. Assuming a genome size of 2.5 Gb, this data reflected raw coverage of 14.4–18.8 fold.
The canine genome sequence (Canfam3.1;[22]) was retrieved from the University of California, Santa Cruz genome browser (UCSC, http://genome.ucsc.edu) and indexed with the Burrows-Wheeler Transform Smith Waterman tool in the Burrows-Wheeler Alignment (BWA) package version 0.6.2[45]. Reads were aligned as pairs to the indexed reference genome using BWA, applying the default parameters for paired-end read alignment using this package. Alignment statistics generated using the idxstats tool within the SAMtools package version 0.1.18[46] indicated average mapped coverage ranging between 14.1 and 16.8 fold per individual. Snpsift was used to sort variants within the 1.44Mb interval by the ‘hom’ ‘any’ case and control filter options[25]. Annotation of remaining variants was performed with SnpEff using the xenoRefGene genes and gene prediction tracks annotation downloaded from the Table Browser window on the UCSC Genome Browser[25]. VCF files of the critical interval are available from the Dryad Digital Repository (doi:10.5061/dryad.j8r8q).
All primers described were designed in Primer 3 (see S6 Table). Expression of ADAMTS20 was evaluated as described previously[24]. Total RNA was isolated from tissue samples using Qiagen QIAamp Blood Mini Kit tissue protocols. RNA was synthesized into cDNA using Invitrogen Superscript III First Strand Synthesis System to RT PCR protocols. ADAMTS20 cDNA was PCR amplified in heart tissue from one NSDTR case and a whole embryo (collected at day 30) control. Areas with high GC content were amplified using Invitrogen AccuPrime GC-Rich DNA Polymerase protocols. PCR products were sequenced on an ABI 3500 Genetic Analyzer and analyzed using Vector NTI (Informax, Frederick, MD, USA). Sequences were aligned to each other and Boxer (Can Fam 3.1) reference sequence to identify any polymorphisms[22].
Primer sequences were generated using Primer3Plus (http://primer3plus.com/) (S6 Table). Semi-quantitative PCR using AmpliTaq Gold DNA Polymerase was performed to test the quality of cDNA and primers, to confirm product size, and to check for the presence of genomic DNA contamination. Real-time PCR was performed using the Rotor-Gene SYBR Green PCR Kit (QIAGEN, Valencia, CA) using a 2-step cycle protocol (35 cycles; Initial denaturation-5 minutes at 95°C; Annealing- 5 seconds at 95°C; Extension- 10 seconds at 60°C; Final Melt curve) on the Rotor Gene Q real-time PCR system. cDNA from heart tissue of 3 neonatal NSDTR controls and 3 neonatal CLPS NSDTR cases were run in triplicates with each replicate containing 4–5 ng template cDNA. All data was normalized to the housekeeping gene, B2M[47]. Amplification and takeoff values were analyzed and graphed by REST2009[28] to determine any significant expression differences in ADAMTS20 transcript levels between case and control cDNA samples.
PCR genotyping was performed according to standard protocols[24] using a shared FAM labeled forward primer (S6 Table). GeneScan 500 ROX size standards were used and the reaction was analyzed on an ABI 3500 Genetic Analyzer. 97 unrelated control NSDTRs, 288 control dogs across 70 breeds, and 53 dogs with orofacial clefts across 25 breeds were genotyped for the deletion. All genotypes were analyzed using ABI GeneMapper software.
From the larger study population, 937 individuals were genotyped (see methods below): 545 from case families (125 affected with nonsyndromic CL/P and 420 unaffected relatives), plus 392 controls with no family history of orofacial clefts. From the genotyping, the population structure of the study subjects was compared to HapMap controls (see S6 Fig.). The results show some European Caucasian admixture based on HapMap controls, plus substantial overlap with the Mexican HapMap Controls. All participants self-identified as Mayan; many spoke Quichean as well as Spanish. Trained health care professionals evaluated cleft phenotypes and ruled out syndromes for each participant. Each participant also provided detailed demographics, medical history, and family history, and female participants provided a detailed pregnancy history. Blood or saliva samples were obtained for DNA extraction using Qiagen kits (Valencia, CA).
Study participants were genotyped for 620,901 markers on the Illumina Human610-Quad (Illumina Inc, San Diego, CA, USA). All individuals had a genotyping rate greater than 90% and therefore were included in the analysis. Deviations from Hardy-Weinberg equilibrium assessed in PLINK[29] found 622 markers with significant deviation from expectation in founder controls (p ≤ 1e-06). An additional 47,687 SNPs were eliminated because of high genotype failure rate (≥10%). An exclusion criterion of a minor allele frequency <5% in founders removed 105,758 SNPs. After removing non-autosomal SNPs a total of 457,969 SNPs remained for the analyses reported here. To further explore a putative association found on chromosome 12 (see results below), we imputed 270,467 SNPs on this chromosome using IMPUTE2 software[48] and the 1000 Genomes Project as the reference sample (http://browser.1000genomes.org). The genotypes and phenotypes for the Guatemalan study population are available in dbGaP (http://www.ncbi.nih.gov/gap), accession number phs000440.v1.p1.
Due to the heterogeneous family structures in our Guatemalan cohort, we performed an association analysis using the DFAM test implemented in PLINK, which integrates a standard TDT, discordant sib-TDT, and Cochran-Mantel-Haenzel clustered-analysis for case-control testing[29]. Independence between each test is established by considering individuals only once in the above statistical tests. For example, participants with relevant standard TDT information were not considered in sib-TDT or Cochran-Mantel-Haenszel clustered analysis. The order of assignment begins with a standard TDT, followed by a sib-TDT, then all remaining unrelated members are considered for the case-control analysis.
To verify the top hits from the DFAM analysis we separately ran a standard TDT (-tdt option in PLINK[29,49], n = 65 trios), a sib-TDT (-dfam command in PLINK in non-founders from nuclear families with multiple siblings and at least one affected family member, n = 49 families), and a case-control test (113 randomly chosen cases and 241 controls). To maximize sample size within these tests, we investigated each using all potential participants with the understanding the derived p-values are not necessarily independent between tests. For case-control analyses, we used logistic regression under the additive genetic model where genotypes were coded by the number of minor alleles (0,1,2) in each case or control.
In addition to the DFAM analysis, we used a multivariate analysis to combine multiple SNPs within a gene into a single statistic. Gene level analysis has the potential benefit of increased power to detect associated regions containing multiple moderate effects[50]. VEGAS is a versatile gene-based test designed to handle any type of data input as long as a p-value can be generated for each individual marker[51]. Within a gene, p-values are converted to an upper tail chi-squared statistic with one degree of freedom and then combined. An empirical null distribution is created from a Monte Carlo simulation on a multivariate normally distributed random vector with a correlation equal to those predicted from a reference population through a Cholesky decomposition matrix. The proportion of simulated test statistics exceeding the observed gene-based test statistic gives the empiric p-value. In this situation, founders from the Guatemala data set were used as a reference population since no publicly available genetic data set sufficiently matches the participants’ genetic background. Gene plots with LD diagrams were generated with Locus Zoom and KGG2.5[52,53].
Primers covering the protein coding exons of ADAMTS20 were designed with Primer3 (http://frodo.wi.mit.edu/primer3/). Primer sequences and annealing temperatures are available in S7 Table. PCR products were sequenced on an ABI 3730XL (Functional Biosciences, Inc., Madison, WI). Chromatograms were then transferred to a Unix workstation, base-called with PHRED (v.0.961028), assembled with PHRAP (v. 0.960731), scanned by POLYPHRED (v. 0.970312), and visualized with the CONSED program (v. 4.0). The functional effects of variants were predicted using the Ensembl database’s Variant Effect Predictor tool[54].
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10.1371/journal.pntd.0007429 | Aedes aegypti microRNA, miR-2944b-5p interacts with 3'UTR of chikungunya virus and cellular target vps-13 to regulate viral replication | RNA interference is among the most important mechanisms that serve to restrict virus replication within mosquitoes, where microRNAs (miRNAs) are important in regulating viral replication and cellular functions. These miRNAs function by binding to complementary sequences mostly in the untranslated regions of the target. Chikungunya virus (CHIKV) genome consists of two open reading frames flanked by 5′ and 3′ untranslated regions on the two sides. A recent study from our laboratory has shown that Aedes miRNAs are regulated during CHIKV infection. The present study was undertaken to further understand the role of these miRNAs in CHIKV replication.
We observe that miR-2944b-5p binds to the 3′ untranslated region of CHIKV and the binding is abated when the binding sites are abolished. Loss-of-function studies of miR-2944b-5p using antagomirs, both in vitro and in vivo, reveal an increase in CHIKV viral replication, thereby directly implying a role of miR-2944b-5p in CHIKV replication. We further showed that the mitochondrial membrane potential of the mosquito cells is maintained by this miRNA during CHIKV replication, and cellular factor vps-13 plays a contributing role.
Our study has opened new avenues to understand vector-virus interactions and provides novel insights into CHIKV replication in Aedes aegypti. Furthermore, our study has shown miR-2944b-5p to be playing role, where one of its target vps-13 also contributes, in maintaining mitochondrial membrane potential in Aedes aegypti.
| Aedes aegypti mosquito transmits pathogenic viruses like chikungunya virus (CHIKV). Inside the vector, the virus replicates in a way so that it is able to survive within the mosquito without causing damage to it. However, once in the mammalian host, it becomes pathogenic and induces death to the infected cells. Amongst several mosquito specific factors that allows or rejects the virus survival, microRNAs play a decisive role. In several studies, miRNAs have shown to be playing role in controlling virus replication either by binding to viral genome or to suppress the expression of any host factor. In the present study, we identified an Aedes miRNA, miR-2944b-5p, which binds to 3'UTR of CHIKV and regulates the replication of the virus in the mosquito. Analysis of the mode of action of this regulation revealed that miR-2944b-5p played a role in maintaining mitochondrial membrane potential during CHIKV replication by targeting cellular factor vps-13.
| Mosquitoes aid the transmission of several pathogenic viruses collectively called arboviruses that mainly belong to the Flaviviridae and Togaviridae families. These are single-stranded RNA viruses that replicate actively within the mosquitoes and reach titers that are then transmitted by the vector to a vertebrate host upon a blood meal. However, mosquitoes employ innate immune responses against arboviruses, thereby restricting their replication in the vector [1].
Among the innate immune responses exhibited by mosquitoes, RNA interference (RNAi) is one of the important mechanisms that play a role in restricting virus replication in mosquitoes [2–6]. This phenomenon functions through different pathways aided by a variety of small-RNA population—small interfering RNAs, virus-derived interfering RNAs, and microRNAs (miRNAs)—that bind to different RNA-binding proteins and are processed in the RNA interference silencing complex (RISC), resulting in the degradation/repression of target molecules [7]. Studies have shown that there is a distinct crosstalk amongst the RNA-silencing pathways aimed to provide an effective RNA-silencing response [8].
miRNAs are short, generally 21–24 bp long single-stranded RNAs, that participate in the degradation of mRNA, thereby inhibiting its translation. The degradation is initiated by the annealing of miRNAs directly to seed sequences in the 3′ untranslated region (UTR) of the mRNA and by the recruitment of specific host proteins [9]. Several studies have reported that cellular miRNAs of the host inhibit the replication of several viruses such as human immunodeficiency virus (HIV), enteroviruses, and influenza virus by binding to coding region of viral genome either directly or indirectly and inhibiting its translation [10–14]. Similarly, several viruses have shown to utilize host miRNAs to their advantage, either to escape host surveillance and maintain viral latency or to promote viral replication [15,16].
Chikungunya virus (CHIKV) is a positive sense single-stranded RNA virus of genus Alphavirus, belonging to family Togaviridae. Transmitted by Aedes aegypti and Aedes albopictus mosquitoes, this virus infects humans, causing acute febrile illness and severe arthralgia. The CHIKV genome contains two open reading frames encoding nonstructural and structural polyproteins and is flanked by a 76 nt long 5′ UTR and a 3′ UTR that ranges between 450–900 nt depending on the lineage, on either side of the open reading frames and an internal subgenomic 5’ UTR, 48 nt long [17]. Recent studies have identified sequence elements involved in viral replication and host interactions, thereby emphasizing the importance of the UTRs of the CHIKV genome in its replication and fitness, both in the vector and in the host [18].
One of the most important interactions between the host/vector and viruses is the interaction of cellular miRNAs with viral UTRs. Among the other interactions that can potentially affect virus replication, are interactions involving the cellular targets of miRNAs that can be beneficially hijacked by the viruses for their own advantages. Whereas there are reports of host miRNAs binding to viral RNAs and restricting viral growth [19], several studies show that host miRNAs may bind to the viral genome to promote viral replication. In one such study HCV miR-122 has been shown to play this role [15,20]. Likewise, many studies have shown that miRNAs targets aid in increase or decrease viral replication thereby posing as host factors [21,22]. Many studies identified a repertoire of mosquito miRNAs that are regulated upon viral infection; amongst which, miR-2944b-5p and miR-2b have shown to have an impact on the pathogenesis of several mosquito-borne pathogens [23,24].
We undertook the present study to further understand the role of these two miRNAs in CHIKV replication in Ae. aegypti and cellular targets affecting CHIKV replication. We performed the luciferase assay and observed that miR-2944b-5p and miR-2b bind to the 3′ UTR of CHIKV and that this binding is abated when the binding sites are abolished. Loss-of-function studies of miR-2944b-5p and miR-2b using antagomirs, both in vitro, reveal an increase in CHIKV replication prominently in the case of miR-2944b-5p, thereby directly implying a role of miR-2944b-5p in CHIKV replication. Analysis of Ae. aegypti cellular targets for miR-2944b-5p reveals that vacuolar protein sorting (vps-13) is a target and plays a role in regulating CHIKV replication in Ae. aegypti cells. Our findings put together provide evidence that CHIKV may be using miR-2944b-5p and its target vps-13 to maintain cellular mitochondrial membrane potential (MMP)/integrity during its replication in mosquito cells. We propose that this could be an approach of the virus to survive in the mosquito cells.
Scientific and ethical approval to carry out this study was obtained from the ICGEB-Institutional Animal Ethics Committee (ICGEB-IAEC). The IAEC approval number is ICGEB/IAEC/280718/VBD-4. The ICGEB-IAEC adheres to national guidelines by Committee for the purpose of control and supervision of experiments on animals (CPCSEA).
The Aag2 cell line (A kind gift from Prof. Alain Kohl, University of Glasgow) was cultured in Schneider Insect Media (HiMedia) at 28˚C. The HEK-293T (American Type Culture Collection-ATCC) cell lines were cultured in DMEM (HiMedia) at 37˚C. All cell lines were supplemented with 10% fetal bovine serum (FBS) (HiMedia) and antibiotics (10,000 U/ml penicillin and 10,000 μg/ml streptomycin; CellClone). CHIKV strain used for infection studies was from a clinical isolate IND-10-DEL1 (ECSA) obtained during a recent outbreak [25].
CHIKV virus was propagated in Vero cells and virus contatining supernatant was collected after 72 hrs and aliquoted to store at -80°C. The infectious virus titer in supernatant was determined by standard plaque assay on Vero cells [26].
For miRNA expression profiling, RNA was isolated using Trizol method. The concentration of RNA was determined using a NanoDrop 2000 spectrophotometer (Thermo Scientific). The purity of RNA was assessed by calculating the ratio absorbance at 260 and 280 nm. All RNA preparations had a ratio of absorbance (260/280 nm) greater than 1.8. cDNA synthesis and PCR assay were performed using a commercial kit (NCode miRNA first-strand cDNA synthesis and qRT-PCR kit; Invitrogen) per the manufacturer’s instructions. The initial miRNA concentration was set at 500 ng. An aliquot (2.5 μl) of the cDNA was used for qRT-PCR using specific forward primers (identical to entire miRNA sequence) for the selected miRNA and the reverse primer was a universal qPCR primer. All the reactions for miRNAs and transcripts analysis were performed in triplicate by qRT-PCR with a PikoReal 96 Real-Time PCR system (Thermo Scientific). RNA for transcript expressions was used from the initial RNA isolated for miRNA using the trizol following manufacturer instructions. Expression levels of the selected miRNAs and transcripts (viral genomic RNA, vps-13 mRNA) were compared with those in controls after normalization against the housekeeping gene (5.8s) (S1 Table) using the cycle threshold (ΔΔCT) method.
All mature miRNAs were mapped against the Ae. aegypti genome using Bowtie, and 250 flanking regions from each side were extracted using in-house Perl scripts. Pre-miRNAs were PCR-amplified from the Ae. aegypti mosquito and cloned in a pmR-mCherry vector (Clontech Lab). The 3′ UTR of CHIKV and putative cellular targets (AAEL011195, AAEL008432, and vps-13) were cloned in a pmir-GLO vector (Promega).
The miR-2944b-5p and miR-2b binding sites at the 3′ UTR of CHIKV were amplified with primers containing desired mutations at seed region for the binding sites of miR-2944b-5p and miR-2b and cloned into the pmirGLO vector for the luciferase assay.
Plasmids expressing UTRs of CHIKV and cellular factors (AAEL011195, AAEL008432, and vps-13) and miRNAs were transfected in HEK-293T cells using JetPRIME Transfection reagent as per the manufacturer’s instructions. Luciferase activities were measured using a luminometer according to the manufacturer’s instructions (Glomax20/20 luminometer, Promega) 24 h post-transfection using the Dual-Glo luciferase reporter assay system (Promega). Renilla luciferase activity was normalized using firefly luciferase activity for each sample. Empty pmirGLO vector along with miR-2944b-5p cloned in pmR-mCherry vector was taken as control for miRNA miR-2944b-5p binding to the UTR's of cellular factors (AAEL011195, AAEL008432, and vps-13). Empty pmR-mCherry vector along with CHIKV 3'UTR pmirGLO vector was used as a control in case of miRNA miR-2944b-5p and miR-2b binding to the CHIKV 3'UTR.
For dsRNA preparation, the vps-13 was cloned in pGEMT-easy vector (Promega, USA) and was in vitro transcribed with T7 and SP6 polymerase using Riboprobe combination kit (Promega, USA). As a mock or control, dsRNA for gfp was used. The dsRNAs were purified using Trizol (Invitrogen, USA) method described earlier and stored till further use.
Antagomirs (miRNA inhibitors) for miRNAs were purchased from Ambion (miRNA Inhibitors are chemically modified, single stranded nucleic acids designed to specifically bind to and inhibit endogenous microRNA molecules). The Aag2 cells were grown in 6-well plates and allowed to reach a confluence of 70%. At this point, the cells were transfected with 100 picomoles of the antigomirs (Antimir-2b, Antimir-2944b-5p, and a scrambled miRNA, that served as negative control) using Cellfectin II 2000 (Invitrogen). In case of the dsRNA, 2μg of RNA was transfected in each well as per manufacturer's protocol. The gfp dsRNA was also transfected that served as the negative control. After 24 hrs, the cells were infected with media containing virus with MOI 1.
UTR sequences of Ae. aegypti genes were downloaded from VectorBase and targets of miR-2944b-5p were predicted using RNAhybrid tool. The targets were filtered on the basis of complementarities of the miRNAs with the targets and energy of the miRNA:target i.e, ≤ -30 Kcal/mol.
Female mosquitoes (4–5 days old) were divided into three batches of 20 mosquitoes each. The first batch was injected with 69 nl of PBS. The second and third batches of mosquitoes were injected with 69 nl of 100 μM negative control RNA (scrambled miRNA) and miR-2944b-5p antagomir respectively. For dsRNA study, 800ng of dsRNA was injected in each mosquito. The mosquitoes were allowed to recover for 24 hrs and were fed on an infected meal. The whole mosquitoes were then stored 24 hrs post-feeding in Trizol at −80°C until RNA extraction. Knockdown of miRNA expression post-injection was checked by miRNA qRT-PCR and for CHIKV genomic RNA, primers for E1 gene were used.
Four-day-old female Ae. aegypti mosquitoes were orally fed with blood containing CHIKV virus using a glass blood feeder. The infectious blood meal contained 50% defibrinated blood (Source: Balb/C Mice), 10% FBS, and 40% DMEM containing freshly propagated purified virus to result in an estimated titer of 106 plaque-forming units per milliliter.
The 20×104 cells were seeded on autoclaved coverslips one per well in a 24-well and were transfected with miR-2944b-5p inhibitor, vps-13 dsRNA, followed by CHIKV infection (MOI 1). CHIKV infection was done 24 hrs post transfection and after 24 hrs post infection cells were subsequently stained with JC-1(Dye to measure mitochondrial potential) (BD MitoScreen JC-1 Kit, Becton Dickinson, USA) for 15 min at 37°C and nuclei were stained with DAPI. The MMP was detected using microscopy experiments. The ratio JC-1 red fluorescence and JC-1 green fluorescence was used to measure the MMP or total polarized mitochondria in cells. NIS-Elements (Nikon) was used for acquiring the images as Z-stacks using a Nikon EclipseTi-E laser scanning confocal microscope equipped with a 60×/1.4 NA Plan Apochromat DIC objective.
Data were shown as standard error of the mean. The experiments were repeated at least thrice and each experiment included at least three treatments. The error bars generated were from experimental replicates. Statistical analysis of experimental data was performed using GraphPad Prism (version5) using Student's t-test when comparing two conditions. Data from different treatments were subjected to an analysis of variance (ANOVA). For multiple comparisons, Tukey’s test or Dunnett’s test was performed. Values of P<0.05 represented with an asterisk were considered significant.
Several studies have established that viruses contain binding sites for host miRNAs in their UTRs [10,27,28]. In order to explore whether Aedes miRNAs do have binding sites in the CHIKV 3′ UTR region, we identified two miRNAs, namely, miR-2944b-5p and miR-2b, that were most significantly regulated upon CHIKV infection in a previous study [23], and set out to perform the Dual-Glo luciferase assay using the pmR-mCherry and pmirGLO system (Fig 1A). To confirm the expression of miRNAs after transfection we performed the qRT-PCR (Fig 1B). The assays revealed that miR-2b and miR-2944b-5p exhibited a significant level of binding to CHIKV 3′ UTR, with 60% and 80% reduction in relative percentage of Luciferase/Renilla luminescence, respectively (Fig 1C). Also, to confirm if binding to CHIKV 3'UTR is specific to miR-2944b-5p we also selected another miRNA, namely, miR-13-3p, having no predicted binding site at CHIKV 3'UTR, as a negative control, to perform luciferase assay, and results showed that mir-2944b-5p is specific in binding to its target region in CHIKV-3'UTR (S1 Fig). The assay further showed that the relative percentage of luciferase/renilla luminescence of miR-2944b-5p was slightly higher than that of miR-2b. Further, to confirm that these are the binding sites of seed regions of miRNAs, we mutated the binding sites of CHIKV 3′ UTR and performed luciferase assays. Reversal of luciferase expression in the mutated clones confirmed the role of these sites in the UTR–miRNA interaction (Fig 1D).
Further to test whether the binding of miRNAs to CHIKV 3′ UTR had any functional relevance to the CHIKV infection, we silenced these miRNAs using antagomir (miRNA inhibitor) in Ae. aegypti cells and infected them with CHIKV to check for any changes in virus replication. Optimum silencing of the miRNAs was achieved at 24 hrs post-transfection (S2 Fig), and when the cells were infected with CHIKV (MOI 1) 24 hrs post-silencing, we observed that at 24 hrs post infection CHIKV replication increased more than two fold, (P<0.05), when miR-2944b-5p was silenced (Fig 2A). These results were promising enough to hypothesize that miR-2944b-5p was probably playing a more direct role in CHIKV replication in Ae. aegypti. After confirming the regulation of CHIKV replication by miR-2944b-5p in Aag2 cells, Ae. aegypti mosquitoes were used for further validation. The Ae. aegypti mosquitoes were injected with Anitimir-2944b-5p to silence miR-2944b-5p, also scrambled miRNA served as a mock. Post miR-2944b-5p silencing in four-day-old mosquitoes, we infected the mosquitoes by feeding them with CHIKV-spiked defibrinated blood. After CHIKV infection, the mosquitoes were collected at different time points, 24hrs, 48hrs, 72hrs for RNA isolation and qRT-PCR analysis. Monitoring CHIKV replication in mosquitoes in which miR-2944b-5p was knocked down revealed, a three fold, increase (P<0.0001) in CHIKV infection than that observed in the cell lines (Fig 2B), thereby confirming that the miRNA was playing an important role in CHIKV replication in Ae. aegypti.
Target prediction of miR-2944b-5p amongst Ae. aegypti transcripts revealed a total of 28 putative targets based on the selection criteria (Fig 3A). Three most significant putative targets were taken for validation based on their statistical significance (p≤0.001) and free energy cutoff -31kcal/mol. We performed luciferase assay to confirm the binding of miR-2944b-5p towards these three annotated targets. The result showed AAEL010484 (vps-13) with more than 75% reduction in the relative percentage of luciferase/renilla luminescence and was taken forward for further validations (Fig 3B and 3C). Furthermore, the expression level of vps-13 was observed to be affected with the miR-2944b-5p inhibition using Antimir-2944b-5p in Aag2 cell line and Ae. aegypti mosquito(Fig 3D and 3E). The expression level of miR-2944b-5p was also analyzed at different time point after the inhibition using Antimir (S3 Fig).
Relative expression of vps-13, at different time points post CHIKV infection, were checked in Aag2 cell line and Ae. aegypti mosquito. The results showed more than two fold increase in the level of vps-13 suggesting the role of vps-13 in CHIKV replication (Fig 4A). Relative expression of vps-13 in Aag2 cell line and Ae. aegypti mosquito at different time points upon vps-13 dsRNA transfection showed the inhibition and fall in the expression level of vps-13 which was used to study the CHIKV infection level in next experiment (Fig 4B and 4C). Relative expression of CHIKV genomic RNA decreased 24hrs post infection when vps-13 is silenced by dsRNA in Aag2 cells and Ae. aegypti mosquito. For negative control control gfp dsRNA was used (Fig 4D and 4E). Collectively all this data showed the regulation of vps-13 on CHIKV replication.
Finally, we wanted to evauate the independent roles of miR-2944b-5p and its target, vps-13 on CHIKV replication in Ae. aegypti cells. For this purpose, we silenced vps-13 and miR-2944b-5p simultaneously and studied the impact of silencing on CHIKV replication in Aag2 cells. As described in the previous sections, 2944b-5p inhibition increased the CHIKV replication significantly. Simultaneous silencing of miR-2944b and vps-13 however revealed that CHIKV replication decreased as compared to when only miR-2944b-5p was silenced, thereby suggesting the vps-13 could be modulating CHIKV replication by interacting with miR-2944b-5p (Fig 5). This modulation of CHIKV replication by vps-13 and miR-2944b-5p interplay possibly helps persistent CHIKV infection in Ae. aegypti and allowing the cells to survive longer.
After confirming the role of miR-2944b-5p and its target vps-13 in controlling CHIKV replication, we were intrigued to understand the underlying mechanism by which this miRNA and its target could regulate CHIKV replication in Ae. aegypti. Analysis of all the cellular targets of the miR-2944b-5p, including vps-13, revealed that most of the targets were prominently involved in regulating mitochondrial functions. It is known that CHIKV induces severe oxidative stress in the host it infects. Among the several organelles involved in regulating the cellular processes during viral replication and in combating oxidative stress, mitochondria are known to play a critical role [29,30]. Considering its importance in CHIKV infection, we decided to evaluate MMP in the Aag2 cells. The MMP was evaluated by the ratio of the mitochondrial dye, JC-1 based on its ability to enter the mitochondria that is depicted as red fluorescence vs its availability in the cytoplasm as seen as green fluorescence. A higher ratio of red JC-1 by green JC-1 depicts an increase in MMP, which in turn reveals better mitochondrial integrity. The confocal microscopy results clearly showed that vps-13 had a significant role in maintenance of MMP in Ae. aegypti cells. Also, CHIKV infection did not hamper the cellular MMP clearly emphasizing that CHIKV is not pathogenic in Ae. aegypti cells. Upon miR-2944b-5p silencing in uninfected cells, we did not observe a significant increase in cellular MMP. However, miR-2944b-5p silencing and subsequent CHIKV infection revealed an increase in the cellular MMP, leading us to believe that the miRNA has a direct role to play during CHIKV infection. Further, our data (Fig 4A) revealed that there was an overexpression of vps-13 upon CHIKV infection. Silencing of vps-13 and subsequent CHIKV infection, revealed an increase in the cellular MMP albeit marginally (Fig 6A and 6B).
The present study provides evidence for the functional relevance of a mosquito miRNA binding to the 3′ UTR of an arbovirus and regulating its replication in the insect cells. It is known that miRNAs are regulatory in their function and participate in several cellular processes such as development, reproduction, and immunity [31–34]. Recent studies have shown that apart from these cellular functions, host miRNAs control translation and replication of viruses through binding to the 5' UTR [15,20,35], 3' UTR as well as coding regions of viral genome [36]. All these studies have reported miRNAs interacting with human pathogenic viruses of both DNA and RNA origin. However, studies on miRNA interactions with arboviruses that utilize different hosts for their propagation and transmission are scant [23,24]. Owing to the distinct defense mechanisms between the mammalian and insect systems, it is of immense interest to understand how arboviruses are able to survive in such diverse environments, which was the premise of our present study.
RNA viruses have a high mutation rate due to the absence of proofreading ability [37]. In spite of this, these viruses do retain miRNA binding sites within their genome under selective pressure, where downregulating its own replication is necessary for persistent infection [38,39]. As seen in the present study, Ae. aegypti miRNAs were found to possess binding sites in the 3′ UTR of CHIKV and examination of the functional relevance of this binding proved that miRNAs interfere with viral replication using completely different mechanisms. A recent study from our laboratory showed that the Ae. aegypti miRNA, miR-2b regulated CHIKV replication by regulating its cellular target, ubiquitin related modifier [40]. The present study additionally revealed that miR-2b bound to CHIKV 3'UTR, albeit not having much biological relevance in directly controlling CHIKV replication. However, the present study has thrown light on another Aedes miRNA, miR-2944b-5p, in controlling CHIKV replication in a more direct manner. Furthermore, this study shows that regulation of viral replication by Aedes miRNA is not a straightforward phenomenon; rather a complex interaction involving the mosquito’s miRNA, its targets and the virus that results in modulating the cell’s function, in this case, favorably for the virus. Our results are in concurrence with similar findings in other studies involving arboviruses, where in a study miR-142-3p restricts the replication of the North American (NA) eastern equine encephalitis virus (EEEV) [35]. These recent in-depth analyses of Aedes miRNAs reveal that there is a strong level of interaction between the mosquito miRNAs and arboviruses either directly, as seen in the present study, or through their targets as recorded elsewhere [41].
In addition to viruses possessing binding sites to vector miRNAs, another important aspect that deserves rationalization is the length and structure of the viral 3′ UTR. Among the genotypes of CHIKV, it is known that the length of the 3′ UTR varies and has a direct implication on CHIKV inter-genotypic virulence in the vector [18]. Analysis of miR-2944b and miR-2b binding with 3′ UTRs of all the CHIKV lineages revealed differences in the binding energies (S4 Fig). In the Asian lineage of CHIKV, duplication has been shown to occur in the 3’UTR [18], and our analysis reveal that miR-2b and miR-2944b-5p possessed two binding sites at the point of duplication thereby reducing the level of confidence of its binding capacity in other lineage of CHIKV. The present study was performed using the Indian Ocean island lineage and still had significant effect of CHIKV replication. These observations prompt us to believe that these virus–vector interactions are specific and pivotal for the extent of the pathogenicity of CHIKV and warrants further in-depth studies utilizing strains from all CHIKV lineages.
Analysis of miR-2944b-5p across the kingdom revealed that this miRNA was restricted to the insects and that it was not present in the mammals. This prompted us to hypothesize that this miRNA could be involved in a vector-specific function with respect to CHIKV replication and might play a role to differentiate some survival feature specifically imparted to mosquito vectors. Analysis of its targets revealed that this miRNA was involved in regulation to mitochondrial functions and membranes. Amongst the significant targets, presence of vps-13 led us to hypothesize the possible involvement of mitochondria during CHIKV infection. In previous studies vps-13 has been shown to have role in maintaining the function of mitochondria and membrane morphogenesis in other systems [42,43]. It has been prominently shown that mitochondria are organelles which play a critical role during virus infection [29,44]. They are vital for energy production and oxidative phosphorylation and are also reported to participate in an array of cellular functions including production of reactive oxygen species [45], apoptosis [46], and calcium homeostasis [47]. Many of the above-mentioned phenomena are affected upon virus infection in a cell. It has been reported that during the early stages of viral replication, some viruses try to avoid apoptosis so as to efficiently replicate [48]. Analysis of MMP during CHIKV replication in vector cells in the present study revealed that the MMP was affected as virus replication progressed. In the case of mosquito cells, mitochondrial integrity is regulated and maintained in the presence of CHIKV and vps-13 plays role in controlling it. These findings suggest that either the mosquito cells have defense mechanisms that regulate viral growth or the virus could be manipulating the mosquito cells to ensure their survival within the cells, in any case vps-13 plays a significant role in manipulating the virus replication.
Several reports have highlighted the role of miRNAs in regulating the function, metabolism, and morphology of mitochondria [49–51]. In the present study, miR-2944b-5p seems to be involved in stabilizing the cells during CHIKV infection in Aag2 cells. As evidenced by the miRNA profiling during CHIKV infection in Ae. aegypti cells [23], miR-2944b-5p gets over-expressed during CHIKV replication (S5 Fig) and this over-expression could be related to its role in stabilizing Ae. aegypti cells during CHIKV infection. Based on our findings, we propose a mechanism explaining the role of miR-2944b-5p in maintaining CHIKV in mosquito cells wherein, upon CHIKV infection, miR-2944b-5p is differentially regulated and helps in maintaining the integrity of mitochondria involving vps-13 to help persistent virus replication (Fig 7).
The present study provides evidence for direct binding of an Aedes miRNA, miR-2944b-5p to the 3′ UTR of CHIKV. The study also stipulates a possible role of miR-2944b-5p and vps-13 in maintaining the MMP in a cell infected with CHIKV. It is quite possible that there may be other cellular factors that may be involved in the execution of this feature; nevertheless, our results provide strong evidence for the role of Aedes specific miRNAs and cellular targets in the phenomenon of CHIKV infection in Aedes cells. One novel strategy for translating these findings to address public health concerns is to take advantage of the fact that this miRNA is insect specific and is not present in the mammalian systems, thereby making it a good candidate for regulating CHIKV infection in the humans; however, further in-depth studies are essential to address this hypothesis.
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10.1371/journal.pntd.0002896 | Community Knowledge and Attitudes and Health Workers' Practices regarding Non-malaria Febrile Illnesses in Eastern Tanzania | Although malaria has been the leading cause of fever for many years, with improved control regimes malaria transmission, morbidity and mortality have decreased. Recent studies have increasingly demonstrated the importance of non-malaria fevers, which have significantly improved our understanding of etiologies of febrile illnesses. A number of non-malaria febrile illnesses including Rift Valley Fever, dengue fever, Chikungunya virus infection, leptospirosis, tick-borne relapsing fever and Q-fever have been reported in Tanzania. This study aimed at assessing the awareness of communities and practices of health workers on non-malaria febrile illnesses.
Twelve focus group discussions with members of communities and 14 in-depth interviews with health workers were conducted in Kilosa district, Tanzania. Transcripts were coded into different groups using MaxQDA software and analyzed through thematic content analysis.
The study revealed that the awareness of the study participants on non-malaria febrile illnesses was low and many community members believed that most instances of fever are due to malaria. In addition, the majority had inappropriate beliefs about the possible causes of fever. In most cases, non-malaria febrile illnesses were considered following a negative Malaria Rapid Diagnostic Test (mRDT) result or persistent fevers after completion of anti-malaria dosage. Therefore, in the absence of mRDTs, there is over diagnosis of malaria and under diagnosis of non-malaria illnesses. Shortages of diagnostic facilities for febrile illnesses including mRDTs were repeatedly reported as a major barrier to proper diagnosis and treatment of febrile patients.
Our results emphasize the need for creating community awareness on other causes of fever apart from malaria. Based on our study, appropriate treatment of febrile patients will require inputs geared towards strengthening of diagnostic facilities, drugs availability and optimal staffing of health facilities.
| Understanding the awareness of the community on non-malaria febrile illnesses is crucial, especially during the recent decline of malaria episodes of malaria. This study conducted focus group discussions with communities to assess their awareness of non-malaria febrile illnesses. In addition, in-depth interviews with health workers were conducted to explore their views and practices related to diagnosis and management of these illnesses. We identified that the awareness of the study participants on non-malaria febrile illnesses was low and the majority believed that most instances of fever are due to malaria. Moreover, the participants could not mention the right causes of fever and many had inappropriate beliefs about possible causes of fever. Health workers from our study looked for non-malaria febrile illnesses when a febrile patient had negative mRDT result or there was persistence of fever following completion of anti-malarial dosage. Shortages of diagnostic facilities were identified as one of the impediments to proper diagnosis of febrile illnesses. These findings indicate the need for creation of public awareness on causes of fever other than malaria. We recommend appropriate measures be taken by the government and other stake holders to improve health care services delivery particularly at primary health care facilities.
| Febrile illnesses due to different etiological agents are the common causes of morbidity and mortality in developing countries [1]. Malaria has been the leading cause of fever in sub- Saharan Africa for many years [2]. For instance, in Tanzania, malaria was contributing to about 42% of hospital diagnoses and 32% of hospital deaths in the last decade [3]. Accordingly, presumptive treatment of all febrile illnesses in children under five years with anti-malarial drugs was adopted as policy in many countries of sub-Saharan Africa [4]. However, in recent years, there has been gain in malaria control strategies which has led to decreased malaria prevalence particularly in endemic countries [5]–[7]. The decrease in malaria burden has also been indicated by the 2012 World Malaria Report where there is a good achievement in worldwide reduction of malaria transmission, morbidity and mortality [8]. The decline in malaria transmission is mainly a result of increased coverage of different malaria control strategies that have been implemented for several years. This includes the use of long lasting insecticide treated bed nets, indoor residual spraying, intermittent presumptive treatment of malaria during pregnancy or intermittent presumptive treatment of malaria to infants and treatment with effective anti-malaria drugs such as Artemisinin-based Combination Therapies [5], [9]. The decrease in malaria transmission led World Health Organization (WHO) to change its policy in 2010 where anti-malarial treatment is initiated after parasitological confirmation [10]. However, the decline in trend of malaria transmission in many malaria-endemic countries corresponds to an increasing proportion of febrile patients who are diagnosed as not having malaria [11], [12]. Recent studies have increasingly demonstrated the importance of non-malaria fevers, which have significantly improved our understanding of etiologies of febrile illnesses [13], [14]. In this regard, a reasonable proportion of febrile illnesses are now ascribed to be non-malaria febrile illnesses [13] and episodes of such diseases are reported to increase [12]. In Tanzania, diseases such as respiratory tract infections, urinary tract infections, typhoid fever and rotavirus infection are among non-malaria febrile illnesses that have been commonly affecting people particularly children [15]–[18]. A study conducted in Dar es Salaam and Ifakara had shown that among 1005 children, 498 (50%) had acute respiratory infection, while 54 (5.4%) had urinary tract infections and 33 (3.3) had typhoid fever [18]. Diseases such as Rift Valley Fever (RVF), dengue fever, Chikungunya virus infection, leptospirosis, tick-borne relapsing fever, Q-fever, rotavirus infection and brucellosis have also been reported in Tanzania [13], [14], [19]–[24]. A recent study conducted in northern Tanzania has reported the occurrence of 55 (7.9%) cases of Chikungunya virus infection, 40 (33.9%) cases of leptospirosis, 24 (20.3%) cases of Q-fever and 16 (13.6%) cases of brucellosis among 870 admitted febrile patients [13]. Some non-malaria febrile illnesses may contribute to high morbidity and mortality in humans. For instance, rotavirus takes the lives of more than 8,100 Tanzanian children under five each year [25]. Furthermore, the most recent outbreak of RVF in 2006/2007 which occurred in 10 regions of Tanzania mainland [26] and in other countries such as Kenya and Somalia were associated with widespread morbidity and mortality in humans [27].
The diagnosis of non-malaria febrile illnesses poses a challenge since many of these illnesses may have similar symptoms with malaria and thus making their clinical diagnosis difficult [28]. Also, non-malaria febrile illnesses could have common overlapping manifestations and therefore, this absence of specific symptoms make it difficult to distinguish several non-malaria febrile conditions that often occur in the same area [29]. Clinical overlap between diseases may result in inappropriate antimicrobial therapy and therefore, laboratory tests for differential diagnosis of causative agent are essential.
Following a long tradition of regarding malaria as the leading cause of fever, it is important for the community to understand the other causes of fever apart from malaria particularly during this period when the episodes of malaria related fevers are reported to decrease [8], [11], [30]. Understanding the awareness of the community on non-malaria febrile illnesses is critical and relevant particularly in management and control of such illnesses. Despite their importance, only few studies aiming at assessing the awareness of the communities regarding non-malaria febrile illnesses have been conducted in Tanzania [31], [32]. Therefore, this study intended to contribute in filling the information gap by assessing the knowledge and attitude of the communities regarding non-malaria febrile illnesses. In addition, the study explored treatment seeking behaviors for febrile illnesses among community members. Following the longstanding practice of treating most fevers as malaria, health workers may still treat febrile patients with anti-malarial drugs even if the patients had a negative test results. Studies from Tanzania and other countries like Zambia, Uganda and Burkina Faso have indicated that febrile patients were prescribed anti-malarial drugs following negative mRDT/microscopy result [33]–[36]. Therefore, there is a need to know the management of febrile patients following the decline in the incidence of malaria. The current study also assessed health workers' practices related to diagnosis and treatment of febrile patients.
The study was conducted in Kilosa district which is one of the six districts in Morogoro region, located in eastern Tanzania. The district borders with Tanga and Manyara regions to the north and Mvomero district and Mikumi National Park to the east. On the western border are Dodoma and Iringa regions whereas to the south it borders with Kilombero district. The district lies between latitudes 6° south and 8° south and longitudes 36°30′ east and 38° west. The area has semi humid climate with an average rainfall of 800 mm annually. The early rains start in November and end in January followed by heavy rainfall between March and May. The district experiences a long dry season from June to October and the average annual temperature is 24.6°C. The district has an area of 14,245 square kilometers and has a population of 438,175 people [37]. It consists of a mixture of different ethnic groups predominantly Kaguru, Sagara and Vidunda. The main economic activities are crop production and livestock keeping. More than 77% of people are subsistence farmers and major crops cultivated include maize, cassava, rice, paddy and sorghum whereas the major cash crops are sisal, sugarcane, cotton and oilseeds.
Kilosa was selected due to its possession of intensive human activities with livestock as well as its proximity to wildlife from the Mikumi National Park (figure 1), what was expected to be a good interface for zoonotic diseases such as RVF and Brucellosis [38]. Administratively, Kilosa district is divided into 9 divisions, 37 wards and 164 villages [39]. In terms of health care services, Kilosa district has 71 health facilities and among these, there are 3 hospitals, 7 health centers and 61 dispensaries [40]. However, the number of villages exceeds the number of health facilities and hence most health facilities serve more than one village. Kilosa district is an area with holoendemic malaria transmission with seasonal peaks following the long and short rainy seasons [41]. According to Tanzania HIV and Malaria Indicator Survey, in 2007–2008 malaria prevalence was estimated to be 15.7% in Morogoro region [42] and decreased to 13% in the year 2011–2012 [43]. The common non-malaria febrile illnesses that have been reported in Kilosa district include acute respiratory diseases, UTIs and typhoid fever [24]. Data from a platform for health monitoring and evaluation in Tanzania (Sentinel Panel of Districts) have shown that in the year 2011, acute respiratory diseases and UTIs comprised of 20% and 2.5% respectively of total recorded illnesses (77,862) in outpatient department in children aged less than 5 years [44].
This study was specifically conducted in 6 divisions, namely Kimamba, Kilosa town, Magole, Masanze, Rudewa and Ulaya. Within these divisions, 12 wards namely Dumila, Chanzuru, Magomeni, Kilosa, Msowero, Zombo, Ulaya, Kimamba, Mkwatani, Kasiki, Masanze and Rudewa were purposively selected based on (i) geographical representation within the district e.g. Zombo is in the south western part of the district, whereas Dumila ward is in north-eastern part (figure 1), (ii) the presence of government health facilities (iii) connectivity of the wards and ease of accessibility by road.
We conducted a cross-sectional study in which qualitative data collection methods were used. Focus group discussions (FGDs) with members of the communities were conducted to assess their knowledge, attitude and perception of community members on non-malarial febrile illnesses. In-depth interviews (IDIs) with health workers were conducted in order to obtain their views about practices related to diagnosis and management of non-malarial febrile illnesses.
Parents, guardians or caregivers aged between 18–59 years for children of less than 10 years were eligible to participate in FGDs. This group of participants was targeted because febrile illnesses have been shown to be common in children and contribute to high proportion of hospital admissions globally, with significant morbidity and mortality [13], [18], [45]. The participants were recruited from different hamlets within the study wards with the assistance of local government and villages leaders. In total 12 FGDs were conducted in urban, peri-urban and rural areas in the selected wards of which 5 FGDs were with men and 7 involved women. Each FGD comprised 6–8 people, but women and men were separately interviewed to give the participants freedom to talk during the discussions. Two FGDs were conducted per day and each FGD took about 60 to 90 minutes. In each of Dumila, Rudewa, Chanzuru and Ulaya wards 2 FGDs were separately conducted for men and women. In Masanze and Zombo wards 2 FGDs were conducted with women and the remaining 2 FGDs (1with men and 1with women) involved participants selected from Kilosa, Kasiki and Magomeni wards.
For IDIs, only health workers who were on duty and attended patients (prescribers) in the health facilities during the study period were eligible to participate into the study. Health workers from 12 health facilities located in Dumila, Chanzuru, Ulaya, Zombo, Kilosa, Magomeni, Msowero, Kimamba, and Mkwatani wards were interviewed. Two health workers were interviewed from each health facility and only one health worker was interviewed at a time. In-depth interviews with health workers on average lasted for one hour and 2 IDIs were conducted per day.
Focus group discussions and IDIs were conducted by a skilled and experienced social scientist who was assisted by an observer and note taker. All discussions and interviews were conducted based on a prepared semi-structured interview guide that consisted of questions corresponding to the research topic. To ensure accuracy of the information, the data collection tool was translated from English to Kiswahili and then back-translated. The interview guide for FGDs consisted of questions about their knowledge on non-malaria febrile illnesses, health care seeking behaviors and their recommendations on non-malaria febrile illnesses (supporting information S1). Health workers were asked questions on the awareness of the communities on non-malaria febrile illnesses, communities' health seeking behaviors, how they perform diagnosis and treatment of febrile patients and their recommendations on proper management of non-malaria febrile illnesses (supporting information S1). During the interviews and discussions, notes were taken and conversations were digitally recorded. Field notes were expanded on the same day of the interview/discussion. All FGDs and IDIs were held in Swahili language which is the most widely spoken language by the community (national language).
FGDs and interviews were transcribed verbatim and translated from Swahili to English. Thereafter, the transcripts were converted into rich text format and imported into MaxQDA, a software for qualitative data analysis [46]. Text files were independently reviewed by the two researchers (IM and CM) before agreeing on the different themes and categories. In case of differing interpretations, the discussion between the researchers took place until the final agreement was reached. The findings were also validated by the interviewing researcher (BC). The agreed themes and categories were then coded. The retrieved segments were analyzed using thematic content analysis and their respective codes were exported to Excel for quantitative analysis.
Ethical approval was obtained from Institutional Review Board of Ifakara Health Institute (IHI/IRB/No: 01-2013) and Medical Research Coordinating Committee of Tanzania's National Institute for Medical Research (NIMR/HQ/R.8a/Vol.1X/1472). A written informed consent was obtained from each respondent and participant prior to IDIs or FGDs. To protect identification of the respondents and FGD participants, all personal information that could identify the study participants were only used during the analysis and omitted from the final reports. The participants were assured of anonymity in the presentation and publishing of the data.
In total, 93 participants were interviewed during FGDs, of which 56 were women and 37 were men. Most of the study participants 87 (93.5%) were subsistence farmers and the majority 73(78.5%) had primary level of education. The demographic characteristics of the study population are shown in Table 1. A total of 12 health facilities were visited, among which were 1 hospital, 2 health centers and 9 dispensaries. Fourteen health workers were interviewed, including 8 clinical officers, 1 assistant clinical officer, 3 nurses and 2 medical attendants. Among 14 health workers, 6 were males and 8 were females. Eleven health workers (11/14) had work experience ranging from 1–20 years whereas 3 health workers (3/14) had work experience of 21–40 years. The study identified five major themes as follows:
The study participants were asked to explain what they know about the term “fever” (“homa” in Swahili language). There were different levels of understanding among the participants. The responses provided by majority of the participants did not associate fever with high body temperature. They described fever as an illness condition such as malaria, colic, rheumatism and sleeping sickness or associated it with symptoms such as headache, coughing, rashes and body pain. Others reported that they did not know the exact meaning of the term fever. There were few participants who described fever as a raise of body temperature (hot body).
When the participants were asked to mention the causes of fever in children, things such as change of weather (cold, high temperature) and sunlight were listed by many participants. The participants believed that exposure to a cold environment or prolonged stay under the sun by itself can lead to fever. Only a few participants mentioned the right cause of fever which included illnesses like measles, tuberculosis (TB), typhoid fever and UTIs and other participants associated fever with symptoms/clinical signs such as flu, coughing and diarrhea.
Moreover, inappropriate beliefs were perceived as causes of fever by the participants. For example, the presence of false teeth (meno ya plastiki in Swahili language), breastfeeding after long-term sunlight exposure of the mother as well as cessation of pulsation in the fontanel were mentioned by some participants.
When the FGD participants were asked to mention the causes of fever other than malaria, several of them listed diseases or symptoms which were neither associated with fever nor non-malaria febrile illnesses. The commonly reported diseases/symptoms were headache, colic, hernia and abdominal pain. However, some participants admitted to be unaware of such illnesses. Only a small number of the participants mentioned the correct non-malaria febrile illnesses such as typhoid fever, UTIs (dirty urine), pneumonia, measles and tuberculosis (TB) and sleeping sickness. Both men and women participants had similar level of knowledge, but participants older than 30 years were more knowledgeable than those who were younger. Moreover, the participants from FGDs conducted in rural areas had limited knowledge in comparison with participants from urban and semi-urban areas.
It was also revealed that despite the decrease of malaria, the participants believed that most instances of fever were due to malaria. This was noted when several participants mentioned only malaria as the cause of fever. The participants explained that when their children get fever, they mostly associated it with malaria. This was also acknowledged when the participants were asked to describe the meaning of the term fever (theme 1). A considerable number of the participants explained fever is malaria:
During the interviews, health workers were asked to give their views about the knowledge of the community members on non-malaria febrile illnesses. All health workers reported that majority of community members were not aware or had little knowledge on these illnesses. Health workers explained that fevers were perceived to be caused by malaria by several members of communities. This situation was reported to be a challenge to health workers particularly when they want to obtain a comprehensive history of illness from patients since patients explain to health workers that they are suffering from malaria. Health workers identified this wrong perception as an impediment to proper management of patients and emphasized the need for change of attitude.
In our study the most common reason for unawareness of the community on non-malaria febrile illnesses reported by the majority of health workers was lack of health education. Health workers pointed out that health education is offered at health facilities but it has not reached a wider community. Several community members live in remote villages and they rarely visit health centres for health care. Health workers emphasized that health education on diseases associated with non malaria fevers will help to create awareness to members of the community.
Findings from the FGDs revealed that community members from the study population sought treatment from both the health facilities and traditional healers. When queried on their health seeking behavior, the majority of the study participants reported sending their febrile children to the nearby health facility. However, when asked for any alternative treatment, almost half of the participants reported to seek treatment from the traditional healers. Therefore, this indicates that health facilities and traditional healers were both utilized by the participants. Even though several participants reported seeking treatment from health facilities, but interviewed health workers explained that community members rarely visit health facilities. They said the majority of community members live in remote areas and thus long distances pose as an impediment for visiting health facilities. In addition, health workers stated that the habit of seeking treatment from traditional healers was practiced by some members of the community. They further pointed out that sometimes children were sent to health facilities when they were terminally ill. Likewise, some parents/guardians admitted to health workers that delays to send their children to health facilities was due to prior consultations made from traditional healers.
Our findings have also shown that seeking treatment from the traditional healers was sought when there was a persistent fever following treatment from health facilities. If children still had fever after completion of anti-malarial dosage, majority of the participants opted to consult traditional healers for further treatment. Only a few mentioned taking their children back to the health facilities.
This study found that self-medication was commonly practiced by several participants. Many participants reported purchasing drugs from pharmacy/drug shops without prior medical prescription. They explained that they prefer using anti-malarial drugs for the treatment of fever in children. The practice of self-medication was reported by FGD participants from wards located near the health facilities as well as wards which were far off from health facilities. During the interviews with health workers, all of them acknowledged that self-medication was commonly practiced by members of the communities. Heath workers further pointed out that some members of the communities would still visit health facilities if they had found no improvement following self-medication. Health workers explained that the habit of self-medication delays provision of prompt and proper treatment and in most cases results into death. The common reported reasons which influenced many members of the community to opt for self-medication were poor health services from health facilities, shortages of drugs, lack of diagnostic facilities, long distance to the nearby health facility and inability to afford health care charges.
In our study, we found that the diagnosis of febrile patients was mostly done by mRDTs or clinical symptoms/signs as presented by patients with the assistance of Integrated Management of Childhood Illness guidelines. Interviewed health workers explained that mRDTs were used to distinguish malaria from non-malaria fevers and the majority (10/14) prescribed anti-malarial drugs only to patients with mRDT positive result. When patients had negative mRDT result, health workers reported looking for other causes of fever based on clinical signs and history of the fever. Only a few health workers (4/14) stated initiating anti-malarial drugs even if patients had negative mRDT, as one health worker said:
Moreover, when mRDTs were not available, majority of health workers relied only on clinical manifestations of the patient. When asked to describe symptoms or signs which guide them to make a conclusive diagnosis of febrile illnesses such as malaria, typhoid fever or urinary tract infections, most health workers (12/14) mentioned presence of fever, vomiting, headache, loss of appetite and diarrhea as typical symptoms of malaria. With regards to UTIs, pain during urination was mentioned by majority of health workers as a definitive symptom whereas fewer health workers (2/14) considered urine coloration (from yellowish to milky colour) and small urine volume as symptoms of UTIs. For typhoid fever, symptoms such as abdominal pain and diarrhoea were commonly listed by the majority of health workers.
It was also revealed that when mRDTs were available many febrile patients tested negative and hence other causes of fever were very likely to be considered. However, in the absence of mRDTs, health workers said several febrile patients were suspected to have malaria and were treated with anti-malarial drugs. They considered that reliance on clinical signs and symptoms only, is prone to lead to misdiagnosis and over-prescription of anti-malarial drugs.
Opinions of health workers towards management of persistent fevers following completion of anti-malarial dosage were quite divergent. While majority of health workers (10/14) reported opting for symptomatic diagnosis of non-malaria febrile illnesses and appropriate prescription of antibiotics following failed malaria treatment, however fewer health workers (4/14) reported to switch from first line anti-malarials (artemisinin-based combination therapy) to second line anti-malarial treatment (quinine).
Proper management of non-malaria febrile illnesses is largely dependent on the capacity of the health facility to perform accurate diagnosis and treatment of these illnesses. Health workers in this study repeatedly reported lack of diagnostic facilities, shortage of trained health workers, and stock-out of medications as major barriers to proper management of non-malaria febrile illnesses. Our study revealed that from the 12 health facilities, only 2 had diagnostic facilities for a few febrile illnesses; the dispensary had Widal test for detection of typhoid and a microscope used in diagnosis of UTIs and mRDTs, while the health center only had a microscope. The remaining health facilities (10/12) had no diagnostic tests except mRDTs in few health facilities. Health workers explained that they experienced challenges in managing febrile patients without tools for laboratory investigation. Even though mRDT was named as the only diagnostic test which was used to rule out malaria from febrile patients, stock-out of mRDTs was mentioned as an ongoing problem. Health workers said that the supply of mRDTs from the government to the health facilities was normally done on a quarterly basis. However, all health workers repeatedly reported receiving inadequate mRDTs and there were frequent delays in supply of mRDTs. It was clearly stated by health workers that in most cases half way through a quarter, they experienced lack of mRDTs. Among 12 health facilities, only 4 had mRDTs available at the time of our visit. Health workers insisted need for diagnostic tools for malaria and non-malaria febrile illnesses.
During the interview with health workers, stock-out of medication was mentioned as a common problem. Drug shortages were reported in most (9/12) health facilities although a few (3/12) had a few boxes of basic drugs such as ALU, antibiotics (amoxicillin, septrin and metronidazole) and pain killer (paracetamol).
With regards to staffing, our study revealed significant shortage of health workers particularly in health facilities located in rural areas. Among visited health facilities, four (a hospital, two health centers and one dispensary) had more than two health workers at the level of clinical officers. The remaining eight health facilities each had only one clinical officer assisted by a nurse/midwife or medical attendant. Some of the interviewed health workers (medical attendants/nurses) explained that although they prescribe drugs to patients, they had inadequate skills to manage febrile patients. Moreover, health workers reported to work beyond normal working hours and thus attend more patients beyond the standard average number of patients per physician.
In this study, we report that the awareness of the community on non-malaria febrile illnesses is low. Most of the study participants had little knowledge on non-malaria febrile illnesses and they considered most instances of fever to be synonymous with malaria. Some FGD participants had inappropriate conceptions about the causes of fever. Although the participants reported seeking treatment from health facilities, visiting traditional healers and self-medication were also practiced. Interviewed health workers reported using mRDTs to distinguish non-malaria causes of fever from malaria. However, mRDT negative patients were diagnosed based on symptoms and clinical signs of the patient. In the absence of mRDTs health workers reported giving anti-malarial drugs to many febrile patients as compared to when mRDTs were available. The lack of diagnostic facilities for non-malarial febrile illnesses, stock-out of mRDTs and shortages of drugs were repeatedly reported by all health workers. Therefore, this study revealed inadequate capacity of health facilities to manage febrile illnesses particularly at primary health facilities (dispensaries and health centers).
Fever is a common medical problem in children which may prompt parents to seek medical care. Our results have shown that some participants could not explain the meaning of the term fever and the majority described fever as an illness condition or associated it with symptoms/signs of the disease. In addition, other participants mentioned change of weather and exposure to sunlight as causes of fever in children. The present findings are consistent with the study from Uganda where the study participants used the term fever to describe an illness condition [47] and in Saudi Arabia where more than 70% of parents had a poor understanding of the definition of fever [48]. Knowledge and perception of the community about meaning of fever and its possible causes can influence the decision on seeking treatment from health facility [47]. Additionally, our findings found that the study participants had misconceptions about the causes of fever. Previous studies have also indicated wrong perceptions such as exposure to cold, teething, exposure to sunlight and a warm drink were perceived as causes of fever [49]. Our results indicate the need for correction of such misconceptions within communities as these beliefs would often distract or delay them from seeking treatment from health facilities.
We have indicated minimal awareness of community members on non-malaria febrile illnesses. However, a few participants particularly parents aged more than 30 years were more knowledgeable about non-malaria febrile illnesses as compared to younger parents. Likewise, all interviewed health workers admitted that the communities lack knowledge or had little knowledge on these illnesses. This finding is consistent with results from other studies in Northwestern Ethiopia and Zimbabwe which reported very low awareness of the community regarding non-malaria febrile illnesses [50]–[52]. However, contrary to our findings, previous studies conducted in India, Malaysia and Sri Lanka revealed high level of community awareness on diseases like Chikungunya virus, dengue fever and leptospirosis [53]–[55]. The lack of health education was mentioned by interviewed health workers as a major reason for minimal awareness on non-malaria febrile illnesses. In Tanzania, health education is mainly offered at health facilities [56] however, most people who live in rural areas have limited access to health care facilities. Despite the decrease in malaria incidences, the concept among communities that most fevers are due to malaria was evident. This concept was vividly shown by our results whereas majority of the participants believed that fever is caused by malaria. In addition, interviewed health workers complained that many febrile patients attended health facilities with the assumption that they were suffering from malaria. Our findings correlate with results from other studies in Tanzania, where the term malaria was used to describe fever [57] and any kind of fever was believed to be malarious [58]. This concept is outdated and an obstacle to proper management of patients thus there is a need for creating community awareness on causes of fever other than malaria.
With regards to treatment seeking for febrile illnesses, although some FGD participants reported seeking treatment primarily from health facilities, however, other participants sought treatment from traditional healers. These findings however agree with previous studies in Tanzania where a large percentage of members of the community sought treatment from health care facilities and only a few opted for traditional health care [22], [59]. However, further analysis of health seeking preferences has revealed that those who preferred to seek health care from health facilities also consulted traditional healers since almost half of our study participants reported visiting traditional healers. This observation is in agreement with the report in 2007 from the ministry of Health and Social Welfare (Tanzania), where it was estimated that 60% of those seeking health care services from facilities also depended on traditional healers [60]. Furthermore, although the participants sought health care from health facilities, when symptoms persist they would switch to traditional healers. This finding was shown by the participants who acknowledged going to traditional healers if they were not cured from health facilities. This is also in agreement with a study conducted in Congo, where different treatment options (traditional and health care facilities) were utilized whereby patients initially visited health facilities but when the drugs did not work, they changed to herbs [47]. These findings therefore indicate the need for improving the diagnosis and management of febrile patients at health care facilities so that patients get cured after receiving proper and prompt treatment from health facilities. Alternatively, misdiagnosis and mistreatment of febrile patients at health facilities could continue influencing communities to seek for alternative treatment as revealed by our findings.
Self-medication without medical prescription was found to be a common practice among many FGD participants. Self-medication with anti-malarial drugs has widely been practiced in several countries including Tanzania [22], [61]–[63]. Our findings have also shown this trend, where anti-malarial drugs were self-prescribed by majority of the participants. This behaviour contributes to delay in seeking medical care from health facilities. Also, irrational use of anti-malarial drugs could lead to drug wastage and increased risk of developing parasite resistance [64]. The most commonly reported reasons for self-medication practices were poor health services, shortage of drugs, long distance to the nearby health facility and inability to afford health care charges. In 1994, it was estimated that 72% of Tanzania's population lived within 5 km and 93% within 10 km of health care facilities [65]. However, due to rapid growing population especially in rural areas, the established primary health care system cannot suffice the community demand. At present, the number of villages exceeds the number of health facilities and hence most health facilities serve more than one village [66]. Considering the global decline in malaria prevalence and the reduction in the proportion of fevers due to malaria [11] there is an urgent need to prevent self-medication behavior. Improvement of health service delivery particularly at primary health care facilities will help to reverse this behavior.
The current study has shown that health workers used mRDTs to distinguish non-malaria causes of fever from malaria. However, for the diagnosis of mRDT negative children, health workers reported to rely on symptoms and clinical signs to distinguish non-malaria febrile illnesses. In Tanzania, there has been a roll out of mRDT that was introduced at all levels of health care for parasitological confirmation of malaria prior to treatment [67] as per WHO recommendations [10]. The majority of the interviewed health workers reported giving anti-malarial drugs to only patients with positive mRDT result and looking for other causes of fever when patients had negative mRDT. However, few health workers reported initiating anti-malarial therapy even if the mRDT result was negative. Our findings are supported with results obtained from Kenya whereby 9% of 1,540 patients with negative mRDT were treated with anti-malarials [68]. Several other studies in Tanzania and Zambia have also reported the use of anti-malarials to patients with negative mRDT results [33], [35]. Being dependent on mRDT for the diagnosis of febrile patients, shortage of mRDTs in most health facilities as previously reported from the other study [69] compels health workers to rely on clinical diagnosis and symptoms such as fever, vomiting and loss of appetite to prescribe anti-malarials. However, non-malaria febrile illnesses such as typhoid fever and dengue fever may have similar symptoms with malaria hence are clinically indistinguishable [29], [70]. In addition, health workers reported that when mRDTs were not used many febrile patients were suspected having malaria clinically, however, when mRDTs were applied very few febrile patients were found positive. This indicates that in most cases, health workers put more emphasis on non-malaria illnesses when patients showed negative mRDT or incase there was persistent fever after completion of anti-malaria dosage. Therefore, in the absence of mRDT there is over diagnosis of malaria and under diagnosis of non-malaria febrile illnesses. Clinical diagnosis for febrile illnesses lacks specificity and has been reported to contribute to misdiagnosis and mistreatment of febrile patients [71]. The use of clinical diagnosis which is a normal practice in most health care facilities in the country [72] stands as a major contributing reason for inability to estimate the true prevalence of febrile illnesses in Tanzania [73],[74].
The proper management of non-malarial febrile illnesses depends highly on the availability of diagnostic facilities, professional health workers, medications, transport and communication infrastructures. However, complaints of lack of diagnostic facilities, shortage of health workers, and stock-out of medications were raised by majority of health workers. We found that most health facilities had no diagnostic tests for febrile illnesses. Some non-malaria febrile illnesses are potentially life-threatening and hence failure to diagnose and treat them can result into prolonged and worsening illness with repeated visits to health facilities [75]. Moreover, inappropriate treatment of febrile patients may contribute to a vicious cycle of increasing ill-health and deepening poverty. Therefore, development of rapid diagnostic tests for non-malaria illnesses is of paramount importance [76], [77]. Such tests will be of great importance at primary level of health care where laboratory facilities are scarce. Frequent shortages of drugs were reported to be commonly encountered at health facilities by the majority of interviewed health workers. Likewise, the ongoing problem with drug shortages in Tanzania, was also reported in previous studies [78], [79]. Our findings also have revealed staff shortages in most of the health facilities. Staffing levels and numbers documented in this study are contrary to recommendations by the ministry of health, where a dispensary norm is to have 2 clinicians (clinical officers or assistant medical doctors) and 2 nurses; whereas a health center is intended to be staffed with 4 clinicians and 9 nurses [80]. The challenge of health workforce crisis in Tanzania has been previously reported in other reports as well as by the Tanzanian ministry of health and social welfare [81], [82]. Health workers from our study reported working beyond normal working hours which is also reflected when one considers the ratio of doctor to patients in Tanzania, which stands at 1∶30,000 being far below the standard set by WHO [83]. The issue of staff shortages has led to some health facilities to be operated by unprofessional health workers like medical attendants or nurse/midwife who are not entitled to prescribe drugs other than emergency medication [84]. Similar findings have been found from studies in Tanzania and Uganda where unqualified health workers contributed to inefficiencies in the health service [85] and bypassing of primary health services in rural areas [86].
FGD participants were selected from different divisions, wards and hamlets, hence they were from diverse demographic backgrounds and their views were a representation of the general population in the district. Interviewed health workers were selected from different health care levels, dispensary to hospital levels. This was purposively done to grasp a wide scope of attitudes and practices by health care staff from the few health facilities which were visited. During focused group discussion with communities more discussions were done with women as compared to men, but this was purposively done because women are responsible for child care in the family hence their views were expected to give a balanced representation of the household health. However, the results of this study showed that both men and women had similar level of knowledge when it comes to perceptions of non-malaria febrile illnesses. It is also possible that some information was lost during the translation of the transcripts before analysis.
This study has demonstrated that the awareness and level of knowledge of communities on non-malaria febrile illnesses was low. Knowledge from this and other similar studies will provide insights into better and practicable methods for improving the management of febrile patients. The wrong perception among communities, whereas fever is understood as being synonymous with malaria, as encountered in this study pose a challenge to the health sector and thus we emphasizes the need of creating public awareness regarding causes of fever other than malaria. Community misconceptions on fever and its causes must be addressed since such beliefs often distract or delay treatment seeking from health care facilities. It is also crucial that relevant authorities intervene against existing habits of self-medication and seeking treatment from traditional healers. Appropriate treatment of febrile patients will require inputs geared towards strengthening of diagnostic facilities, drugs availability and optimal staffing of health facilities. Therefore, it is advisable that the government and other stakeholders should take appropriate measures to improve health care services delivery.
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10.1371/journal.pgen.1003574 | DNA Methylation Restricts Lineage-specific Functions of Transcription Factor Gata4 during Embryonic Stem Cell Differentiation | DNA methylation changes dynamically during development and is essential for embryogenesis in mammals. However, how DNA methylation affects developmental gene expression and cell differentiation remains elusive. During embryogenesis, many key transcription factors are used repeatedly, triggering different outcomes depending on the cell type and developmental stage. Here, we report that DNA methylation modulates transcription-factor output in the context of cell differentiation. Using a drug-inducible Gata4 system and a mouse embryonic stem (ES) cell model of mesoderm differentiation, we examined the cellular response to Gata4 in ES and mesoderm cells. The activation of Gata4 in ES cells is known to drive their differentiation to endoderm. We show that the differentiation of wild-type ES cells into mesoderm blocks their Gata4-induced endoderm differentiation, while mesoderm cells derived from ES cells that are deficient in the DNA methyltransferases Dnmt3a and Dnmt3b can retain their response to Gata4, allowing lineage conversion from mesoderm cells to endoderm. Transcriptome analysis of the cells' response to Gata4 over time revealed groups of endoderm and mesoderm developmental genes whose expression was induced by Gata4 only when DNA methylation was lost, suggesting that DNA methylation restricts the ability of these genes to respond to Gata4, rather than controlling their transcription per se. Gata4-binding-site profiles and DNA methylation analyses suggested that DNA methylation modulates the Gata4 response through diverse mechanisms. Our data indicate that epigenetic regulation by DNA methylation functions as a heritable safeguard to prevent transcription factors from activating inappropriate downstream genes, thereby contributing to the restriction of the differentiation potential of somatic cells.
| Animal bodies are constructed from many different specialized cell types that are generated during embryogenesis from a single fertilized egg, and acquire their specific characteristics through a series of differentiation steps. After being committed to a specific cell type, it is generally difficult for differentiated cells to convert to other cell types, at least partly because the cells maintain some memory or mark of their developmental history. Such cellular memory is mediated by “epigenetic” mechanisms, which function to stabilize the cell state. DNA methylation, a chemical modification of genomic cytosine residues, is one such mechanism. Genomic DNA methylation patterns in early embryonic cells are established in a cell-type-dependent manner, and these specific patterns are propagated through cell divisions in a clonal manner. However, our understanding of how DNA methylation controls cell differentiation and developmental gene regulation is limited. In this study, using an in vitro model of differentiation, we obtained evidence that DNA methylation modulates the cell's response to DNA-binding transcription factors in a cell-type-dependent manner. These findings extend our understanding of how cellular traits are stabilized within specific lineages during development, and may contribute to advances in cellular engineering.
| Development is based on a series of cell-fate decisions and commitments. Transcription factors and epigenetic mechanisms coordinately regulate these processes [1], [2]. Transcription factors play dominant roles in instructing lineage determination and cell reprogramming [3], [4]. Transcription factor and co-factor networks regulate cell-specific gene programs, allowing a given transcription factor to be used repeatedly in different cellular and developmental contexts [5]. In addition, epigenetic mechanisms, which establish and maintain cell-specific chromatin states (or epigenomes) during differentiation and development [6], modulate the functions of transcription factors in cell-type-dependent manners [7], [8]. Alterations of chromatin states can increase the efficiency of transcription factor-induced cell reprogramming [9], [10] and lineage conversion in vivo [11], [12]. However, how epigenetic mechanisms and transcription factor networks coordinately regulate cell differentiation remains elusive.
DNA methylation at cytosine-guanine (CpG) sites is a heritable genome-marking mechanism for epigenetic regulation, modulating gene expression through chromatin regulation [13]. Genome-wide DNA methylation profiles have revealed that the methylated CpG in the mammalian genome is specifically distributed in a cell-type-dependent manner [14]–[16], and the methylated CpG sites are dynamically reprogrammed during embryogenesis and gametogenesis [17]–[19]. The DNA methylation profile is established and maintained by three DNA methyltransferases (DNMTs), Dnmt1, Dnmt3a, and Dnmt3b [20], together with DNA demethylation mechanisms [21]. Dnmt1 is required for the maintenance of DNA methylation profiles, whereas Dnmt3a and Dnmt3b are required to establish them. The inactivation of Dnmt1 or both Dnmt3a and Dnmt3b in mice leads to early embryonic lethality, showing that DNA methylation has essential roles in mammalian embryogenesis [22]–[24]. DNA methylation is involved in various cell-differentiation processes, and several studies have identified the underlying mechanisms for specific cases [25]–[29]. However, the roles of DNA methylation in differentiation and development remain largely unexplored.
The evolutionarily conserved zinc-finger transcription factor GATA family controls tissue-specific gene expression and cell-fate determination in many cell types [30]. Gata4 is broadly expressed in endoderm- and mesoderm-derived tissues as well as in pre-implantation embryos. Gata4 functions in endoderm formation, cardiac morphogenesis, the establishment of regional identities in the small intestine, and tissue-specific gene expression in the liver and osteoblasts [31]–[36]. Even though Gata4 has a broad expression profile, it still has cell-specific functions, which are determined largely by transcription factor and co-factor networks. Unique interactions with cardiogenic transcription factors and co-factors allow Gata4 to regulate cardiac gene expression specifically in cardiac progenitor cells and their derivatives [37]. In contrast, the overexpression of Gata4 alone causes mouse ES cells to differentiate into extra-embryonic primitive endoderm cells [38], indicating that Gata4 functions as a master regulatory transcription factor for endoderm specification in ES cells. It is likely that Gata4 is unable to activate a cardiac gene program in ES cells, because of the lack of cardiac transcription factors and co-factors. However, it remains unclear how the endoderm-instructive function of Gata4 is suppressed in non-endoderm tissues, such as mesoderm. Epigenetic mechanisms such as DNA methylation may modulate the cell-specific functions of Gata4.
Here, we have established an in vitro experimental system to test the downstream output of Gata4 in two defined cell types, ES and mesoderm progenitor cells, using a drug-inducible Gata4 and an ES-cell differentiation protocol. Using this experimental system, we examined the effect of DNA methylation on Gata4-induced endoderm differentiation and developmental gene regulation during mesoderm-lineage commitment. Our findings suggest that DNA methylation restricts the endoderm-differentiation potential in mesoderm cells and controls the responsiveness of developmental genes to Gata4.
To explore the role of DNA methylation in the context-dependent function of transcription factors, we focused on Gata4 as a model. Gata4 instructs the primitive endoderm fate in ES cells [38], while it regulates various endoderm and mesoderm tissue-specific genes in somatic cells [30]. In this study, we took advantage of a drug-inducible Gata4 construct where the Gata4 coding region is fused with the ligand-binding domain of the human glucocorticoid receptor (Gata4GR) [39]. The activation of Gata4GR by adding dexamethasone (Dex), a glucocorticoid receptor ligand, drove the differentiation of wild-type (WT) ES cells into the primitive endoderm lineage, in which all the cells were positive for the primitive endoderm marker Dab2 (Figure S1A–S1D, LIF(+) condition). However, when the ES cells were first differentiated for 3 days by withdrawing leukemia inhibitory factor (LIF) from the ES maintenance medium, the cells became resistant to the Gata4-induced endoderm differentiation (Figure S1A–S1D, LIF(−) condition), showing that the endoderm-instructive function of Gata4 is suppressed after somatic cell differentiation.
To investigate the Gata4 response in a defined somatic cell population, we employed a mesoderm differentiation protocol, in which ES cells were co-cultured with OP9 stroma cells [40] without LIF for 4 days and then sorted to isolate the Flk1 (also known as VEGFR2 or KDR)-positive (+) population [41] (Figure 1A). Flk1(+) cells derived from ES cells are considered to be equivalent to a mixture of primitive and lateral mesoderm [41], and these cells can differentiate into several mesoderm derived lineages. To eliminate less differentiated cells (including mesendoderm) and ensure their mesoderm commitment, we isolated the Flk1(+)/E-cadherin(−) population by flow cytometry [42] (Figure 1B).
Flk1(+) mesoderm cells derived from WT ES cells can efficiently differentiate into vascular mural cells, which express alpha-smooth muscle actin (SMA), when cultured on type IV collagen [43] (Figure 1A, 1C, 1D, WT Dex−). When we activated Gata4GR by adding Dex, the WT Flk1(+) mesoderm cells also differentiated into mural cells, and the entire cell population was positive for SMA staining at an intensity similar to that of cells without Gata4 induction, although some of the Gata4-induced cells were noticeably smaller in size (Figure 1C, 1D, WT Dex+). We found no round, Dab2-positive cells among the WT Flk1(+) cells that differentiated with Gata4 induction (data not shown). These results indicated that the endoderm-instructive function of Gata4 was suppressed after the differentiation of ES cells into Flk1(+) mesoderm.
We next examined whether DNA methylation was involved in the suppression of the endoderm-instructive function of Gata4 in Flk1(+) mesoderm cells. For this, we used Dnmt3a−/−Dnmt3b−/− double-knockout (DKO) mouse ES cells, which have no de novo methylation activity and low DNA methylation levels at many loci [24], [44]. DKO ES cells expressing Gata4GR differentiated efficiently from ES cells into primitive endoderm in the presence of Dex, similar to WT ES cells (Figure S2). We obtained the DKO Flk1(+) mesoderm population at the same high efficiency as the WT Flk1(+) cells (Figure 1B), and the DKO Flk1(+) cells differentiated into SMA(+) mural cells with a similar efficiency to WT Flk1(+) cells (Figure 1C, 1D, DKO Dex−), indicating that DNA hypomethylation does not by itself inhibit ES-cell differentiation into Flk1(+) mesoderm and SMA (+) mural cells. We then tested the response of the DKO Flk1(+) mesodermal cells to Gata4 activation (Figure 1A). After 4 days of induction with Gata4, most of the differentiated DKO Flk1(+) cells stained weakly for SMA, although the intensity was somewhat variable (Figure 1D, DKO Dex+). Strikingly, a small number of cells in the population (about 1%) had a round, endoderm-like morphology and were positive for the primitive endoderm marker Dab2 (Figure 1C, 1E, DKO Dex+). Morphologies of the SMA-positive cells (flat and large cytoplasm) and the Dab2-positive cells (round and small cytoplasm) were distinct (Figure 1C–1E). These results indicated that some DKO Flk1(+) mesoderm cells were converted to endodermal identity in response to Gata4.
Dnmt3a and Dnmt3b have transcriptional repression activities that are independent of their enzymatic activities [45], [46]. To examine whether this lineage conversion was DNA methylation-dependent, we prepared Flk1(+) mesoderm cells from Dnmt1−/− (KO) ES cells [23] expressing Gata4GR (Figure S3A), in which DNA methylation in the genome is extensively decreased due to the loss of maintenance methylation activity. Although overall tendencies for low growth and survival were observed, the Dnmt1−/− Flk1(+) cells efficiently differentiated into SMA(+) cells without Gata4 induction, whereas Dab2(+) primitive endoderm-like cells emerged when Gata4 was induced (Figure S3B, S3C). These results indicated that it was the loss of DNA methylation that promoted the Flk1(+) mesoderm cells to convert their lineage to endoderm in response to Gata4. These results also exclude the contribution of clonal effects caused by genetic or epigenetic changes associated with individual cell lines unrelated to DNMT functions.
We observed similar results using DKO Flk1(+) mesoderm cells obtained using a different mesoderm differentiation condition, in which ES cells were cultured on type IV collagen-coated dishes [41] (Figure S4A). Although the recovery of the Flk1(+) population from DKO ES cells was low (3%) under this condition (Figure S4B), the DKO Flk1(+) cells differentiated into SMA-positive mural cells with an efficiency similar to that of the WT Flk1(+) cells (Figure S4C,D, Dex−), confirming the commitment of the DKO Flk1(+) cells to the mesoderm fate. Using this condition, we observed that Gata4 reproducibly induced highly efficient differentiation of DKO Flk1(+) mesoderm cells into the endoderm, in which most cells were Dab2-positive with an endoderm-like, round cell morphology, while no such cells were observed in the WT Flk1(+) cell cultures (Figure S4C, S4D, WT DKO Dex+). We used RT-PCR and microarray analysis to obtain gene expression profiles of these cell populations, and found that when DKO Flk1(+) mesoderm cells were cultured for 4 days with Gata4 activation (DKO Flk1+Dex+), their gene expression profile was similar to that of primitive endoderm cells that were derived directly from ES cells (Figure S4E–S4G).
However, we found that this culture condition was not stable; although it initially gave more efficient Gata4-induced differentiation of DKO Flk1(+) mesoderm cells into the endoderm (Figure S4C, S4D, DKO Dex+) compared to the OP9 co-culture condition (Figure 1C–1E, DKO Dex+), later, the efficiency of the both conditions became similar. We speculated that stroma-cell-free culture systems, such as the type IV collagen condition, may be more sensitive to factors such as the serum lot used in the culture medium. Because of the consistent differentiation properties and the high recoveries of Flk1(+) mesoderm cells, we used the OP9 co-culture condition for mesoderm differentiation in the remaining analyses.
The above results indicated that DNA methylation is involved in the suppression of the endoderm-instructive function of Gata4 in mesoderm cells. Thus, we wondered how the DNA-hypomethylated mesoderm cells reprogrammed their transcription profiles from mesoderm to endoderm in response to Gata4. Previous studies in other cell-differentiation models suggested two possibilities: (1) the loss of DNA methylation together with Gata4 activity de-represses a few gatekeeper genes for endoderm differentiation, and these gatekeepers then activate the endoderm transcriptional program [27], [29]; (2) alternatively, the loss of DNA methylation allows Gata4 to directly activate its endoderm downstream genes [26]. To address these possibilities, we dissected the temporal changes of the transcriptome in response to Gata4 in Flk1(+) mesoderm cells (Figure 2A). RNA was isolated at several time points (up to 72 hr) from WT or DKO Flk1(+) cells cultured with or without Gata4 activation, and their genome-wide transcriptional profiles were analyzed using microarrays (Figure 2A, Flk1+ mesoderm). For comparison, we also obtained the temporal transcriptome of ES cells in response to Gata4 at similar time points (Figure 2A, ES).
We first examined how many genes were differentially expressed as a result of the loss of Dnmt3a/Dnmt3b or Gata4 activation at 72 hr (Figure 2B). In total, 941 genes were expressed at a more than fourfold higher level in DKO Flk1(+) mesoderm cells cultured without Gata4 activation, compared to WT cells (Figure 2B, WT Dex− vs. DKO Dex−, up), which may represent genes directly repressed by DNA methylation. The gene ontology (GO) terms related to immune response, meiosis, and gametogenesis were significantly enriched in this gene set (Table S1), which is consistent with a previous report showing that promoters of germline- or inflammation-associated genes are methylated de novo during mouse embryogenesis in a Dnmt3-dependent manner [47]. Because DKO Flk1(+) cells without Gata4 activation properly differentiated into mural cells with a cell morphology indistinguishable from that of WT Flk1(+) cells (Figure 1C, 1D), the upregulation of these 941 genes seemed to have little impact on the cellular phenotype of mesodermal differentiation. In contrast, Gata4 activation in both the WT and DKO Flk1(+) cells resulted in differential expression of hundreds of genes, for which the GO terms related to various developmental processes were enriched (Figure 2B, Table S1).
We then extracted the genes that responded to Gata4 preferentially in DKO cells with hypomethylated DNA (Figure 2C–2E, Figure S5). The overlap between (i) the genes expressed more highly in DKO cells than in WT cells with Gata4 induction (WT Dex+<DKO Dex+, >2-fold change) and (ii) the genes expressed more highly in DKO cells with Gata4 induction than in the same cells without Gata4 induction (DKO Dex−<DKO Dex+, >2-fold change) separated the Gata4-responsive genes (710 genes) from the DNA methylation-sensitive genes (974 genes) (Figure 2C, Table S2). Based on the further overlap with (iii) the genes expressed at higher levels in DKO cells after 72 h of mesodermal differentiation with Gata4 induction than in the cells immediately after mesodermal differentiation (DKO 0 hr<DKO Dex+, >2-fold change), we identified 320 genes that responded to Gata4 at the 72 hr time point preferentially on the DKO background in Flk1(+) mesoderm cells (Figure 2D). To extract early response genes to Gata4, we identified the same overlaps in gene sets differentially expressed at 24 hr (146 genes, Figure S5). Based on the overlap of these 146 genes with the Gata4 responsive genes at 72 hr, we identified 94 genes that responded to Gata4 within 24 hr and lasted for at least 72 hr, preferentially on the DKO background, in Flk1(+) mesoderm cells (Figure 2E, Table S3). These results indicated that a significant number of genes became hyper-responsive to Gata4 in DNA-hypomethylated DKO mesoderm cells.
We then examined the time course of the expression profiles of these Gata4-hyper-responsive genes in Flk1(+) mesoderm and ES cells with or without Gata4 induction (Figure 3A, Figure S6). These Gata4-hyper-responsive genes were divided into two groups by hierarchical clustering (Table S3): group 1 genes responded to Gata4 in ES cells (upper part, Figure 3A, Figure S6), whereas group 2 genes did not (lower part, Figure 3A, Figure S6).
Consistent with the endoderm-instructive function of Gata4 in ES cells [38], group 1 contained many genes expressed in endoderm-derived tissues, such as the liver, intestine, and stomach (BioGPS, http://biogps.org/) [48]. Endoderm transcription factor genes (Sox17, Foxa2, Elf3, Foxq1) as well as endoderm lineage-specific genes (Aqp8, Sord, Akr1b8, Pga5) were upregulated in response to Gata4 in the DKO Flk1(+) mesoderm cells to the same extent as in ES cells (Figure 3B, Figure S7A). In addition, several genes whose expression was not restricted to endoderm-derived tissues (as listed in BioGPS) showed a similar expression profile (Figure S7B). It should be noted that the group 1 genes showed almost no response to Gata4 in WT Flk1(+) mesoderm cells (Flk1+ WT Dex−, Figure 3A, Figure S6). These results suggest that the upregulation of group 1 genes represents the ectopic activation of the endoderm genetic program in the DNA-hypomethylated DKO mesoderm in response to Gata4.
Endoderm transcription factor genes Sox7 and endogenous Gata4, which also have mesoderm functions, were expressed in the Flk1(+) mesoderm cells before Gata4 activation, but their expression remained only in the DKO Flk1(+) mesoderm cells in response to Gata4 (Figure S7D). Interestingly, several primitive endoderm genes (Hnf4a, Fgfr4, Amn, S100g) responded to Gata4 specifically in the DKO mesoderm, but the extent of their response was modest compared to that in the ES cells (Figure S7C). No detectable response of other primitive endoderm genes, such as Gata6 and Snai1, to Gata4 was observed in the DKO mesoderm (Figure S7E).
Group 2 contained many genes involved in heart development and function. Cardiac transcription factor (Nkx2-5, Myocd, Tbx18, Lbh, Tbx5) and heart-specific genes (Ednra, Tnnt2, Fhod3, Acaa2, Ryr2, Kcnj5) were upregulated in response to Gata4 preferentially in the DKO Flk1(+) mesoderm within 24 hr (Figure 3C, Figure S7F). In addition, several genes that are highly expressed in skeletal muscles or osteoblasts (Fbxo32, Thbs4, Gyg, Pdlim3, Leprel4) showed a similar response in the DKO Flk1(+) mesoderm cells (Figure S7G). These results are consistent with the cardiac and other mesodermal functions of Gata4 [36], [37]. Because Gata4 regulates cardiac genes through its cooperation with other cardiac transcription factors and co-factors [37], it is likely that the cardiac genes did not respond to Gata4 in ES cells because of the lack of such co-factors.
In contrast, since Flk1(+) mesoderm includes cardiac progenitors [49], Flk1(+) cells may be competent to activate the expression of cardiac genes. Consistent with this idea, unlike the group 1 genes, the group 2 genes, including the cardiac genes, responded weakly to Gata4 in WT Flk1(+) mesoderm cells (Flk1+, WT Dex+, Figure 3A, Figure S6). These results suggested that the response of the cardiac genes in group 2 represents the precocious expression of the cardiac gene program in DNA-hypomethylated DKO Flk1(+) mesoderm cells. In addition, group 2 contained genes highly expressed in endoderm-derived tissues (Tspan8, Aldh1a1, Psen2; Figure S7H), which may represent the ectopic activation of the definitive endoderm program.
To determine whether the loss of Dnmt3a/Dnmt3b permits Gata4 to directly activate downstream target genes, we examined the immediate response to Gata4 activation by analyzing transcriptome changes occurring within 3 hr. Within the entire transcriptome, the expression of 64 genes were significantly increased, by more than 2-fold, 3 hr after Gata4 activation in DKO Flk1(+) mesoderm cells. Fifteen of these genes overlapped with the Gata4-hyper-responsive genes identified in Figures 2D and S5 (data not shown). Among them, both group 1 genes (Aqp8, Akr1b8, Elovl7, Pga5, Figure 4A) and group 2 genes (Nkx2-5, Myocd, Ednra, Lbh, Thbs4, Mrap, Figure 4B) immediately responded to Gata4 in the DNA-hypomethylated DKO mesoderm cells, but not in the WT cells. We also confirmed these results by RT-qPCR (Figure S8). These results suggested that DNA methylation contributes to suppress Gata4 from directly activating these genes.
To gain insight into how DNA methylation modulates Gata4 activation, we examined the Gata4-binding sites in WT and DKO Flk1+ mesoderm cells by ChIP and high-throughput sequencing (ChIP-seq), using anti-Gata4 antibodies. To obtain a large number of cells for the ChIP-seq experiment, WT or DKO ES cells expressing Gata4GR were differentiated in a large-scale culture on OP9 stroma cells, and the Flk1(+) cells were purified by magnetic-activated cell sorting. Gata4 was activated for 3 hr before the cell purification by adding Dex. As controls, WT and DKO ES cells expressing Gata4GR but without Dex treatment were subjected to the analysis. We identified 20,410 peaks for WT Gata4-activatd Flk1(+) cells and 22,733 peaks for DKO Gata4-activated Flk1(+) cells using the DNAnexus software tools.
To validate the Gata4-ChIP-seq peaks, we performed two independent motif analyses in the MEME Suite software package (http://meme.nbcr.net/) [50]. Using the JASPAR CORE vertebrate motifs (http://jaspar.genereg.net/) [51] and UniPROBE mouse transcription factor motifs (http://thebrain.bwh.harvard.edu/uniprobe/) [52], ab initio motif discovery analysis by DREME [53] identified the most highly enriched motifs in the Gata4-ChIP-seq peak regions from both WT and DKO Flk1(+) cells with Gata4 activation as Gata factor-binding motifs (Figure 5A). Similarly, central motif enrichment analysis by CentriMo [54], which assumes that the direct DNA-binding sites tend toward the center of the ChIP-seq peak region, identified the three most highly ‘centrally enriched’ motifs as Gata-factor-binding motifs, using the same motif databases (Figure S9). These results indicated that Gata4-binding sites were highly enriched in the Gata4-ChIP-seq peaks.
We next examined the Gata4 peaks and DNA methylation states of individual Gata4-response genes. Among 146 genes that transcriptionally responded to Gata4 within 24 hours specifically in DKO Flk1(+) mesoderm cells (Figure S5), 70 were associated with the Gata4 peaks in DKO Flk1(+) mesoderm cells, and 52 were associated with DKO-specific Gata4 peaks within a 5-kb distance (data not shown). We then searched for the genes in which either promoter regions [55] or Gata4 peak regions were differentially methylated between WT and DKO mesoderm cells and/or between ES and mesoderm cells, by bisulfite sequencing analysis.
Aqp8 has a low-CpG promoter, and a DKO-specific Gata4 peak was observed in its intronic region in DKO-mesoderm cells (Figure 5B). Gata4 also bound to the same intronic region in WT ES cells in response to Gata4 activation as revealed by ChIP-qPCR (Figure S10). Both regions were highly methylated in WT but not in DKO mesoderm cells. Thus, the DNA methylation of these regions might affect their Gata4-binding ability or the downstream response of Gata4. However, these regions were moderately or highly methylated in WT ES cells, in which Aqp8 responded to Gata4 (Figure S7). Thus, the DNA methylation in these regions may have different functions between ES and differentiated somatic cells, as observed in the retrotransposon IAP [56]. Sox7 has a high-CpG promoter, and DKO-specific Gata4 peaks were observed at this promoter region (Figure 5C). While the 5′-upstream and promoter region of Sox7 was unmethylated at the undifferentiated ES cell stage, this region was de novo methylated, highly at the distal part, during mesoderm differentiation. Similar de novo methylation during mesoderm commitment and inverse correlation with Gata4 peaks were observed at the Cldn7 locus (data not shown). The Gata4-responsive endoderm gene Lgmn was associated with two DKO-specific Gata4 peak regions that were highly methylated in Flk1(+) mesoderm cells (Figure S11A). One of the Gata4 peaks, located in the 3′ region of the neighboring gene Rin3, was methylated de novo during mesoderm differentiation. Since Rin3 itself did not respond transcriptionally to Gata4, the Gata4 peak located at Rin3 may contribute to Lgmn's transcription. Mrap and Thbs4, which have high-CpG promoters, were associated with Gata4 peaks within the gene or the neighboring gene in both WT and DKO mesoderm cells (Figure S11B, S11C). The promoter of Mrap was heavily methylated in a Dnmt3-dependent manner, consistent with a previous study [16], while the promoter of Thbs4 was de novo methylated during mesoderm differentiation. These promoter methylations may modulate downstream response of these genes in response to Gata4 binding. We also confirmed by luciferase reporter assay that at least some short fragments associated with Gata4 peaks had enhancer activities in response to Gata4 activation (Figure S12). Note that we also observed DKO-specific Gata4 peak regions that remained locally unmethylated in WT mesoderm cells (data not shown). These Gata4 peaks may represent cooperative binding with other Gata4 molecules or co-factors, or higher-order chromatin state changes induced by a decrease in DNA methylation. Taken together, the Gata4-peak and DNA-methylation profiles suggested that DNA methylation modulates cellular responses to Gata4 through diverse mechanisms.
Collectively, our results show that a significant number of developmental genes, including transcription factors and terminal differentiation genes, were promptly and simultaneously activated in DKO mesoderm cells by Gata4. This finding supports the model in which DNA methylation globally restricts the responsiveness of downstream genes to Gata4, rather than controlling a few gatekeeper genes.
In this study, we characterized the role of DNA methylation in the output of the single transcription factor Gata4 in defined cell types using an ES-cell differentiation method. Mesoderm cells derived from Dnmt3a/Dnmt3b-deficient ES cells were hyper-responsive to Gata4 and activated inappropriate developmental programs. Gata4 induced ectopic expression of endoderm downstream genes and precocious activation of cardiac and other downstream genes in DNA-hypomethylated mesoderm cells; these genes do not respond or respond only weakly to Gata4 in WT cells, suggesting that inappropriate Gata4 target genes such as endoderm genes are repressed in a DNA methylation-dependent manner. Our results indicate that epigenetic regulation by DNA methylation ensures the proper spatial and temporal developmental gene regulation by Gata4 and stabilizes differentiated cellular traits against the possible influences of natural fluctuation or environmental perturbations.
We showed that a fraction of Dnmt3a/Dnmt3b-deficient mesoderm cells, but not WT cells, can convert to endoderm cells in response to Gata4. This effect could be attributable to de-differentiation or the response of a small population of immature cells. However, our data suggest that these possibilities are unlikely. First, we purified the Flk1(+)/E-cadherin(−) cells by flow cytometry, which removes the immature mesendoderm population [42]. Second, Gata4 globally induced endoderm genes in Dnmt3a/Dnmt3b-deficient mesoderm cells, and some genes were expressed at the same level as in Gata4-activated ES cells, the whole population of which differentiates to endoderm. Third, many endoderm genes responded to Gata4 within 12 hr in Dnmt3a/Dnmt3b-deficient mesoderm, and some even responded within 3 hr. These results suggested that Gata4 globally and promptly activates an endoderm gene program in a large population of Flk1(+) mesoderm cells on the Dnmt3a/Dnmt3b-deficient background, but not in WT cells. Thus, it is unlikely that relatively slow processes such as de-differentiation or reversion to pluripotent states [57] are involved in this endoderm differentiation.
Using the mesoderm cells differentiated with OP9 stroma cell co-culture, we found that only a small fraction of the cultured mesoderm cells was differentiated into endoderm based on Dab2 staining (∼1%), even though the expression of endodermal genes were significantly increased (Figure 3, Figure S6, Figure S7). This implies that some cells retaining the mesodermal phenotype express both endoderm and mesoderm genes. During transcription factor induced-somatic cell reprogramming to pluripotent cells, partially reprogrammed cell clones express both stem cell-related genes and lineage-specific genes together [10]. We suggest a model in which an endoderm gene program is activated in a large mesoderm population, priming it for endoderm differentiation, but only a small subset of this population accomplishes the primitive endoderm differentiation.
We showed that the loss of DNA methylation allowed mesoderm cells being converted to endoderm cells in response to Gata4 using both Dnmt1-deficient and Dnmt3a/Dnmt3b-deficient mesoderm cells. However, the low efficiency and incomplete reprogramming of the conversion suggest that additional mechanisms such as cell-specific trans-factors or other epigenetic signatures [58], [59] may also restrict the Gata4-induced endoderm differentiation in Flk1(+) mesoderm cells. These other mechanisms may be coordinately regulated or maintained by DNA methylation. We obtained variable ratios of endoderm differentiation from Gata4-activated Dnmt3a/Dnmt3b-deficient Flk1(+) mesoderm cells when we used the stroma cell-free condition with type IV collagen-coated dishes for mesoderm cell formation (Figure S4 and data not shown). This variation in differentiation efficiency may be due to effects of such restriction mechanisms other than DNA methylation. It is possible that differentiation conditions without stroma cells are more sensitive to various factors in cell culture, which may affect gene expression or epigenetic signatures of the Flk1(+) mesoderm cells differentiated with this condition.
DNA methylation restricts cell differentiation potential during development. Trophectoderm differentiation is restricted in mouse ES cells, and DNA methylation is involved in this process through the DNA methylation-dependent silencing of trophectoderm transcription factor Elf5 [27]. Pancreatic β cell identity is maintained by DNA methylation-dependent silencing of the lineage-determining transcription factor Arx [29]. In these cases, DNA methylation suppresses a limited number of gatekeeper transcription factors. The loss of DNA methylation de-represses these transcription factors, which subsequently activate their downstream transcriptional programs. In contrast, we showed that, in our cellular models, DNA methylation stabilizes mesoderm identity during cell differentiation by restricting the responsiveness of downstream genes to the transcription factor Gata4.
We found that the induction of Gata4 together with the loss of DNMTs, but not Gata4 alone, activates the endoderm gene program in mesoderm cells and promotes endoderm differentiation. During this process, Gata4 promptly activates many endoderm genes, including both transcription factors and terminal differentiation genes with a similar time frame, suggesting that DNA hypomethylation allows Gata4 to activate endodermal target genes directly in mesoderm cells. However, we cannot exclude the possibility that endoderm transcription factors such as Sox17 and Foxa2, which respond to Gata4 early, may contribute more than other proteins to the endoderm differentiation phenotype.
Our results also showed that the loss of DNA methylation alone does not induce endoderm differentiation, showing that the role of DNA methylation is permissive, not instructive, in this differentiation. This is consistent with a previous study of astrocyte differentiation showing that DNA methylation suppresses the responsiveness of embryonic neuroepithelial cells to the gliogenic LIF signal during mouse embryogenesis [26]. The binding of the transcription factor STAT3 to the promoter region of the astrocyte gene Gfap is suppressed in a DNA methylation-dependent manner. Similarly, DNA methylation restricts the responsiveness of neuroepithelial cells to Notch signaling by suppressing the binding of the transcription factor RBP-J to the Hes5 gene promoter [60]. These reports suggest that regulation of the responsiveness of downstream genes to transcription factors is likely to be a broadly used mechanism of DNA methylation-dependent gene regulation. In hematopoietic stem cells (HSCs), a deficiency of Dnmt1 results in impaired self-renewal and skewed myeloid/lymphoid differentiation [61], [62], whereas the inactivation of Dnmt3a leads to an increase in self-renewal associated with the incomplete repression of HSC genes such as Runx1 [63]. Thus, it is likely that DNA methylation modulates cellular differentiation by multiple pathways and mechanisms.
Several transdifferentiation studies have shown that one or a few transcription factors are sufficient to convert somatic cell fate within several days [3], [64]–[67]. It is likely that there are many genes downstream of transcription factors initiating trans-differentiation, but this aspect has yet to be analyzed in depth. Our study focused on the initial processes during cell fate conversion, and uncovered the contribution of DNA methylation in restricting the global response to the single transcription factor Gata4. Several studies have also suggested a link between DNA methylation and transcription factor-induced cell reprogramming to pluripotency (i.e. iPS cells) [4]. DNA methylation and de-methylation are closely correlated with the epigenetic memory of the original donor cells, and this may contribute to the variable differentiation propensity of iPS cells [68]–[71]. In addition, overall efficiency of the reprogramming process can be improved when somatic cells are treated with DNMT inhibitors [10]. Although, the reprogramming to pluripotency is different from Gata4-induced transdifferentiation in that it requires a much longer period of time, DNA methylation-dependent mechanisms similar to those described here may be involved in the reprogramming process.
DNA methylation regulates gene expression by various mechanisms [72]. The promoter regions of germ-cell-specific genes, inflammation-response genes, and some tissue-specific genes are methylated de novo by Dnmt3a/Dnmt3b around the implantation stage of mouse embryogenesis. The expression of these genes is increased by the loss of DNA methylation, indicating that DNA methylation directly represses their transcription [47]. In contrast, in neuronal progenitor cells, the gene body region of neural genes is methylated in a Dnmt3-dependent manner, and the expression of these genes is decreased by the loss of DNA methylation, suggesting that DNA methylation is required to maintain the expression of these genes [73]. Whole-genome DNA methylation analysis showed that the DNA methylation state of distal regulatory enhancers changes dynamically and is linked to changes in the expression of adjacent genes [16], and that the DNA methylation changes of enhancers are driven by transcription-factor binding [16], [74].
In this study, we showed that groups of developmental genes downstream of Gata4 become hyper-responsive to this transcription factor on a Dnmt3a/Dnmt3b-deficient background. The loss of DNA methylation together with Gata4 activation induces the expression of these genes, but the loss of DNA methylation alone does not alter their expression, indicating that DNA methylation does not directly regulate the transcription of these genes. This finding implies that the transcriptome for a given methylome depends on the composition of the transcriptional regulators in a cell. This notion is consistent with previous reports that genome-wide DNA methylation profiles are not well correlated with gene expression [15], [63]. Further mechanistic studies will be necessary to connect the DNA methylome to cellular phenotypes.
In conclusion, our results extend our understanding of the role of DNA methylation in cell differentiation and the stabilization of cellular traits. Together with its feature of clonal inheritance [75], DNA methylation is likely to function as a memory of a cell's developmental history. Elucidation of the mechanisms of DNA methylation targeting and its interaction with chromatin may provide insight into the role of epigenetic regulation in development and cellular reprogramming.
Dnmt3a−/−Dnmt3b−/− DKO ES cells (clone 16aabb), Dnmt1−/− ES cells (clone 36), and WT J1 ES cells were described previously [23], [24]. WT, DKO, and Dnmt1−/− ES cell clones stably expressing dexamethasone (Dex)-inducible Gata4 were generated by introducing by electroporation an expression plasmid for Gata4 fused with the ligand-binding domain of the human glucocorticoid receptor (Gata4GR) driven by the CAG promoter [39], followed by selection with L-histidinol dihydrochloride (HisD) (clones J1G4.211, 16G4.3, and 36G4.3, respectively). ES cells were maintained on gelatinized culture dishes in either ES medium, consisting of Glasgow Minimum Essential Medium (GMEM, Sigma) supplemented with 10% fetal calf serum (FCS), 0.1 mM nonessential amino acids (Invitrogen), 1 mM sodium pyruvate, 0.1 mM 2-mercaptoethanol, and 2000 U/ml LIF, or the same medium except for the replacement of 10% FCS with 10% Knockout Serum Replacement (KSR, Invitrogen) and 0.5% FCS. OP9 stromal cells, kindly provided by Dr. Shin-ichi Nishikawa, were maintained in α-Minimum Essential Medium (α-MEM, Invitrogen) supplemented with 20% FCS.
For in vitro differentiation by LIF withdrawal, ES cells were cultured overnight in ES medium containing 10% FCS, then differentiation was induced by replacing the medium with medium lacking LIF, after a wash with phosphate-buffered saline (PBS). For primitive endoderm (PE) differentiation, 100 nM Dex was added to ES cells stably expressing Gata4GR, in ES medium containing 10% FCS for 4 days [39]. For the time-course analysis, the cells were recovered by trypsinization at the indicated times after the addition of Dex, and RNA was isolated. For Flk1(+) mesoderm differentiation, ES cells were cultured either on type IV collagen-coated dishes or on OP9 stromal cells [40], [41].
For the type IV collagen-coated dish method, 1×105 WT ES cells or 5×105 DKO ES cells were plated on a type IV collagen-coated 10-cm dish (BioCoat, BD Biosciences) in differentiation medium (α-MEM supplemented with 10% FCS and 50 µM 2-mercaptoethanol) and cultured for 4 days. The cultured cells were then collected using 0.25% trypsin-EDTA, and single-cell suspensions were stained using an allophycocyanin (APC)-conjugated anti-Flk1 antibody (AVAS12, eBioscience), a biotinylated anti-PDGFRα antibody (APA5, eBioscience), and phycoerythrin-conjugated streptavidin (eBioscience). For the OP9 stroma co-culture method, 2×105 WT ES cells or 2.4×105 DKO or Dnmt1−/− ES cells were plated on a 10-cm dish with confluent OP9 stromal cells in the differentiation medium for 4 days.
The cultured cells were collected using 0.25% trypsin-EDTA, and single-cell suspensions were stained using an APC-conjugated anti-Flk1 antibody, a biotinylated anti-E-cadherin antibody (Eccd2, TaKaRa Bio or DECMA-1, eBioscience), and phycoerythrin-conjugated streptavidin. Flk1(+), Flk1(+)/PDGFRα(+), and Flk1(+)/E-cadherin(−) cells were sorted by a FACSAria (BD Biosciences), and the flow cytometry profiles were visualized with FlowJo software (Tree Star). The sorted cells were further cultured on type IV collagen-coated dishes in differentiation medium in the absence or presence of 100 nM Dex for 4 days or the indicated times. For the short-term Gata4-response experiment, ES cells were plated on a 10-cm dish with confluent OP9 stroma cells and cultured for 4 days as described above. One, two, or three hours before cell collection, 100 nM Dex was added to the cell culture. The cells were collected by trypsinization, and the Flk1(+)/E-cadherin(−) cells were sorted as described above. The sorted cells were directly used for RNA isolation.
Cells grown on gelatin- or type IV collagen-coated dishes were washed in PBS, fixed with 4% paraformaldehyde for 10 min at room temperature, and permeabilized with 0.5% Triton X-100 for 10 min. After being blocked in 4× saline-sodium citrate (SSC) containing 3% BSA and 0.2% Tween 20 for 30 min at 37°C, the cells were incubated with primary antibodies in detection buffer (4× SSC containing 1% BSA and 0.2% Tween 20) for 1 hr at 37°C, washed twice with 4× SSC, and incubated for 1 hr at 37°C with secondary antibodies conjugated with Alexa Fluor 488 or Alexa Fluor 555. For DNA staining, fixed cells were incubated with 0.2 µg/mL 4′,6-diamidino-2-phenylindole dihydrochloride (DAPI) or 1 µg/mL Hoechst 33342 and then washed in 4× SSC.
The following antibodies were used: anti-Disabled-2/p96 (Dab2) mouse monoclonal antibody (clone 52, BD Biosciences, 610464), anti-Gata4 rabbit polyclonal antibody (Santa Cruz Biotechnology, sc-9053), anti-alpha-SMA mouse monoclonal antibody (clone 1A4, Sigma, A5228), anti-Sox17 polyclonal goat antibody (R&D Systems, AF1924), goat anti-mouse IgG conjugated with Alexa Fluor 488 (Invitrogen, A11017), goat anti-rabbit IgG conjugated with Alexa Fluor 488 or Alexa Fluor 555 (Invitrogen, A11070, A21430), and rabbit anti-goat IgG conjugated with Alexa Fluor 488 (Invitrogen, A21222).
For RT-PCR, total RNA was isolated with TRIzol Reagent (Invitrogen), and the first-strand cDNA was synthesized from 1–5 µg of total RNA with random hexamer primers and SuperScript II reverse transcriptase (Invitrogen), according to the manufacturer's protocol. Primer sequences and PCR conditions are listed in Table S4.
For RT-qPCR, cytoplasmic RNA was isolated with the RNeasy Mini Kit (Qiagen) according to the manufacturer's cytoplasmic RNA protocol with the DNase digestion option. The first-strand cDNA was synthesized from 400 ng of total RNA with SuperScript VILO cDNA synthesis kit (Invitrogen), according to the manufacturer's protocol. Expression levels of genes of interest in cDNA samples were quantitated by real-time PCR using FastStart SYBR Green Master (Roche Applied Science), based on a standard curve using genomic DNA. The RT-qPCR data were normalized by values of three housekeeping genes, Gapdh, Rps21 and Rps27a as internal control genes. Primer sequences and PCR conditions are listed in Table S4.
For the Affymetrix microarray analysis, total RNA was isolated with TRIzol reagent and purified using an RNeasy Mini Column (Qiagen). The cRNA probe was prepared using a two-cycle target-labeling assay and hybridized to Affymetrix MOE430v2 oligonucleotide arrays as recommended by the manufacturer (Affymetrix). RNA from two independent experiments were used as duplicates for each experimental condition. The microarray data were analyzed using the Affy package [76] of the Bioconductor suite of programs [77] in combination with the eXintegrator system [78] (http://www.cdb.riken.jp/scb/documentation/). Data from the raw .CEL files were used either to calculate expression values using RMA expression values or as input to the eXintegrator system. A Principal Component Analysis (PCA) was carried out using the R “prcomp” function [79], and differentially expressed genes were identified using the SAM algorithm [80].
For Agilent microarray analysis, total RNA was isolated with an RNeasy Plus Micro Kit with a gDNA Eliminator column (Qiagen) in most cases. For the short-term Gata4-response experiment (0–3 hr), cytoplasmic RNA was isolated with the RNeasy Mini Kit (Qiagen) according to the manufacturer's cytoplasmic RNA protocol with the DNase digestion option. RNA quality was checked by electrophoresis on an Agilent 2100 Bioanalyzer (Agilent Technologies). Fifty nanograms of total RNA was labeled by a Low Input Quick Amp Labeling Kit (Agilent Technologies) and hybridized to a Mouse Gene Expression 8x60k Microarray (Agilent Technologies) according to the manufacturer's instructions. For 72 hr-time-course analysis, each RNA was labeled in triplicate (Flk1(+) cells at 0 hr and at 12, 24, 36, 48 and 72 hr in the presence of Dex) or in duplicate (others). For 3 hr-time-course analysis, RNA from two independent experiments for WT or DKO cells expressing Gata4GR transgene (WT+Gata4GR and DKO+Gata4GR) were labeled and used as duplicate for each time point, while each RNA from one experiment was labeled in duplicate for DKO cells without the Gata4GR transgene (DKO). The microarray data were quantile-normalized and analyzed using custom R scripts with the Limma package [81], the Bioconductor package suite [77], and custom Perl scripts. Probe values from the same gene were merged into the mean values to calculate the gene expression values, and the analyses described below were performed only for genes that had a GeneSymbol. To identify differentially expressed genes, we used empirical Bayes methods [82]. Genes that had a p value <0.01 and fold change >2 or 4 were selected. Unsupervised hierarchical clustering was performed in the clustering module for Perl [83] with 1 - (Pearson correlation coefficient) as a distance and average linkage. The clusters were visualized by Java Treeview (http://jtreeview.sourceforge.net/) [84] and custom Perl scripts. Venn diagrams were generated using the BioVenn web application (http://www.cmbi.ru.nl/cdd/biovenn/) [85]. Gene ontology analysis at Biological Process level 4 (BP4) was performed using DAVID (http://david.abcc.ncifcrf.gov/) [86] version 6.7 with default parameters.
The microarray data have been deposited into the Gene Expression Omnibus (GEO) database (accession number GSE36814 for the experiment using the type IV collagen-coating condition by Affymetrix microarray analysis and GSE36313 for the experiment using the OP9 co-culture condition by Agilent microarray analysis).
ChIP was performed using the ChIP-IT Express chromatin immunoprecipitation kit (Active Motif, Rixensart, Belgium) according to the manufacturer's instructions. Briefly, WT or DKO ES cells differentiated on 15-cm dishes for 4.5 days using OP9-co-culture were treated with Dex for 3 hours and then collected by trypsinization. The differentiated cells were stained with a biotin-conjugated anti-Flk1 antibody (AVAS12, eBioscience) followed by streptavidin microbeads (Miltenyi Biotec), and then sorted with a magnetic cell separation system (MACS, Miltenyi Biotec). The isolated mesoderm cells were crosslinked with 1% formaldehyde for 10 min at room temperature, then the formaldehyde was quenched by adding glycine to a final concentration of 0.125 M. Chromatin was sonicated to an average size of 0.3–0.5 kb using Covaris shearing technology (Covaris, Massachusetts, USA). A mixture of equal amounts of three anti-Gata4 antibodies (sc-1237 and sc-25310, Santa Cruz Biotechnology and L97-56, BD Biosciences), bound to magnetic beads (Active Motif), was added to the sonicated chromatin, and the mixture was incubated for 4 hours at room temperature. After the beads were washed, the chromatin was eluted, and the crosslinking was then reversed. The DNA was purified with a MinElute DNA purification kit (Qiagen). The resultant ChIP DNA was quantified using a Bioanalyzer (Agilent Technologies) and a Quant-it dsDNA assay kit (Invitrogen). Undifferentiated WT or DKO ES cells without Dex treatment were used as controls for ChIP.
Libraries for high-throughput sequencing were prepared with the Illumina ChIP-seq DNA Sample Prep Kit according to the manufacturer's instructions. High-throughput sequencing using an Illumina Hiseq and mapping of the resulting reads were performed by Hokkaido System Science Co., Ltd. Japan. Data analysis and visualization of the sequence reads were performed with DNAnexus software tools (https://dnanexus.com/). The ChIP-seq raw data have been deposited into the GEO database (accession code GSE41361). Of the initial 250,590,286 reads for WT Flk1+ cells and 392,760,766 reads for DKO Flk1+ cells obtained in the Gata4-ChIP-seq experiment, 209,514,577 (83.61%) and 368,177,514 (93.74%) were mapped to the mouse reference genome (NCBI v37, mm9), respectively.
The ChIP-seq peaks for Gata4 were determined with DNAnexus software tools using the following settings: KDE (kernel density estimation) bandwidth = 30, ChIP candidate threshold = 5.0, Experiment to background enrichment = 3.0, Minimum ratio of confident to repetitive mapping in region = 3.0. Peak calling with DNAnexus software tools identified 20,410 peaks for WT Flk1+ cells and 22,733 peaks for DKO Flk1+ cells using WT or DKO ES ChIP-seq reads, respectively, as background controls. For DKO-specific Gata4 peak calling, 10,636-enriched peaks were identified from the comparison between WT and DKO Flk1+ cells. The nearest Refseq genes (within 5 kb) were identified from the Gata4 peaks. Transcription-factor binding-site motif analysis for the Gata4 peak sequences was performed using the MEME-ChIP suit (http://meme.nbcr.net/) [50].
For ChIP-qPCR, 100 nM Dex was added to WT ES cells stably expressing Gata4GR cultured on 15-cm dishes in ES medium containing 10% FCS. At 3 hours after the addition of Dex, the cells were crosslinked with 1% formaldehyde for 10 min at room temperature on the dishes; then the formaldehyde was quenched by adding glycine to a final concentration of 0.125 M. The fixed cells were recovered by scraping with a rubber policeman in ice-cold PBS and collected by centrifugation (Dex+). WT ES cells without Dex treatment were used as controls (Dex−). Chromatin sonication, ChIP with the mixture of three anti-Gata4 antibodies, and purification of ChIP DNA were performed as described for ChIP-seq, except for using Protein G FG beads (Tamagawa seiki) instead of magnetic beads in the kit. Relative abundance of regions of interest in precipitated DNA to input DNA was quantitaed by real-time PCR using Thunderbird SYBR qPCR Mix (TOYOBO) with the comparative CT method. Gata4 enrichment was calculated as fold change of relative abundance for Dex+ to that for Dex−. Primer sequences and PCR conditions are listed in Table S4.
Genomic DNA was isolated from ES cells or Flk1(+) mesoderm cells with a QIAamp DNA micro kit (Qiagen) and subjected to bisulfite conversion with an EpiTect Bisulfite kit (Qiagen), according to the manufacturer's instructions with a slight modification [87]. Target sequences of the bisulfite-converted DNA were amplified by PCR, and 24 clones for each sample were sequenced. The primers for bisulfite sequencing were designed using MethPrimer (http://www.urogene.org/methprimer/) [88]. The bisulfite sequencing data were analyzed using QUMA (http://quma.cdb.riken.jp/) [89]. Primer sequences and PCR conditions are listed in Table S4.
Constructs used for the luciferase reporter assays of Gata4-ChIP target sequences were based on the pFgf3_1.7k-luc vector [39], in which a fragment of DNA encompassing 1.7 kb of sequence immediately 5′ of the Fgf3 coding region containing GATA binding sites [90] was inserted into pGL4.10 (Promega). We generated the pFGF3_0.8k-luc vector by removing the 5′-half (0.9 kb) of FGF3 1.7 kb fragment from the pFgf3_1.7k-luc vector with digestion of 5′-end XhoI site on GL4.10 vector and internal AflII site. Luciferase reporter vectors containing Gata4-ChIP target sequences were constructed by inserting a 0.2–0.3 kb fragments centered around Gata4-ChIP-seq peaks amplified with HotStarTaq DNA polymerase (Qiagen) using primers containing XhoI site (forward) and AflII site (reverse) sites, between the XhoI and AflII sites of the pFgf3_0.8k vector (See also Figure S12A). The following Gata4-ChIP peak regions were used for the construction and luciferase reporter assay; Aqp8 (intron), Grk5 (promoter), Sord (promoter), Sox7 (promoter), Lgmn (intron), Myocd (intron), and Spon1 (3′UTR). Primer sequences are listed in Table S4.
For transfection of reporter plasmids, 1×104 cells were seeded in each well of a 96-well plate in ES medium containing 10% FCS, and incubated with 330 ng reporter plasmid and 8 ng of the internal control plasmid pRL-CMV (Promega), together with Lipofectamine 2000 (Invitrogen), following the manufacturer's protocol. At 3 hr after the transfection, 100 nM Dex was added. Luciferase assays were performed at 30 hr after the addition of Dex using a Dual-luciferase assay kit (Promega).
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10.1371/journal.pgen.1007916 | An African-specific haplotype in MRGPRX4 is associated with menthol cigarette smoking | In the U.S., more than 80% of African-American smokers use mentholated cigarettes, compared to less than 30% of Caucasian smokers. The reasons for these differences are not well understood. To determine if genetic variation contributes to mentholated cigarette smoking, we performed an exome-wide association analysis in a multiethnic population-based sample from Dallas, TX (N = 561). Findings were replicated in an independent cohort of African Americans from Washington, DC (N = 741). We identified a haplotype of MRGPRX4 (composed of rs7102322[G], encoding N245S, and rs61733596[G], T43T), that was associated with a 5-to-8 fold increase in the odds of menthol cigarette smoking. The variants are present solely in persons of African ancestry. Functional studies indicated that the variant G protein-coupled receptor encoded by MRGPRX4 displays reduced agonism in both arrestin-based and G protein-based assays, and alteration of agonism by menthol. These data indicate that genetic variation in MRGPRX4 contributes to inter-individual and inter-ethnic differences in the preference for mentholated cigarettes, and that the existence of genetic factors predisposing vulnerable populations to mentholated cigarette smoking can inform tobacco control and public health policies.
| An exome-wide association study revealed a significant association between menthol cigarette use and coding variants in MRGPRX4, which encodes a G-protein coupled receptor expressed in sensory neurons. The variant haplotype is found only in populations of African ancestry, and encodes a receptor that displays reduced agonism by Nateglinide. Our findings indicate genetic variation contributes to the high rate of menthol cigarette use in African Americans.
| Cigarette smoking remains a leading cause of preventable disease and mortality in the United States, contributing to >480,000 deaths annually [1]. Although the overall rates of smoking have declined dramatically over the last 50 years [1], the use of mentholated cigarettes has not, and has actually increased in some groups [2, 3]. Menthol is a flavoring additive commonly used in cigarettes and tobacco products. It is thought to reduce the harshness of cigarette smoke due to its cooling and anesthetic properties [4–6]. Menthol cigarettes currently account for about 30% of the cigarette market in the U.S. [7]. Scientific evidence suggests that the use of mentholated cigarettes leads to increased smoking initiation among youth and reduced rates of cessation [8, 9]. This has led the FDA to conclude that menthol cigarettes likely pose a public health risk above that of nonmenthol cigarettes [10, 11].
The prevalence of menthol cigarette smoking varies markedly between demographic groups, and is especially high among young adults and in African Americans [3, 9, 12]. In the U.S., nearly 83% of African-American smokers use menthol cigarettes, compared to 24% of white and 32% of Hispanic smokers. Whether this disparity has a genetic basis, or is attributable solely to social or cultural factors, is not known.
Menthol is known to interact with transient receptor potential (TRP) channels, including TRPM8 [13] and TRPA1 [14]. Although one study found that common variants in TRPA1 were associated with menthol tobacco use among European-American smokers [15], this finding awaits replication. Variations in the TAS2R38 bitter taste receptor gene appear to have a modest effect on smoking and on menthol cigarette use [16–20], but no comprehensive analysis of the role of variation in these and other genes in menthol cigarette smoking has been carried out to date.
To determine whether inherited variations in the protein-coding regions of the genome contribute to menthol cigarette smoking, we performed an exome-wide association study using a population-based cohort of African Americans (AA) and European Americans (EA) from Dallas, Texas. The findings were replicated in a cohort of African-American smokers from Washington, DC.
The discovery cohort included 561 participants (394 AA and 167 EA) from the Dallas Heart Study (DHS) and the Dallas Biobank (Table 1). The average age of participants was 55±11.0 (SD) years, and 60% were women. Nearly 78% of DHS AA and 86% of Biobank AA subjects reported smoking mentholated cigarettes, compared to 33% of European Americans (P<0.001), consistent with national trends [3]. Menthol smokers were younger than non-menthol smokers among African Americans (P<0.05), but there was no difference in age among European-American smokers. The prevalence of menthol smoking was not significantly different between DHS and Biobank AA after adjusting for age (P = 0.59). In the replication cohort (Schroeder), most of the participants (N = 424, 57.2%) were menthol smokers (Table 1). A higher percentage of menthol smokers than non-menthol smokers were female (39.6% versus 24.3%, P<0.001) consistent with previous literature [12]. No differences were found in the mean age of menthol smokers and non-menthol smokers (P = 0.41).
A total of 52,298 variants were tested for association with menthol cigarette smoking in the Dallas cohort. Genomic control [21] value was acceptable (λgc = 1.05) and QQ-plot of P-values showed no systematic inflation of association results (S1 Fig). No variant met our exome-wide significance threshold (9.6x10-7). We therefore decided to investigate the top variants with a suggestive level of significance (P<1x10-4) in greater detail. A total of three variants reached this level of significance in our exome-wide screen (Table 2), and these were genotyped in an additional cohort of 741 AA smokers from Washington DC (Schroeder cohort). While no association was found with two of the three variants (Table 2), the third variant, rs7102322 in the gene MRGPRX4, was strongly associated with menthol smoking in the replication cohort (P = 2.1x10-6). Meta-analysis of the two samples together revealed an even lower P-value that exceeded criteria for genome-wide significance (P = 1.6x10-8, Table 2).
The rs7102322 variant was seen exclusively in African-American participants (minor allele frequency [MAF] = 8% in the Dallas cohorts, 5% in Schroeder) and was not observed in European Americans (0% in DHS EA). Among the AA participants in the Dallas cohorts, the allele frequency of the variant was five-to-eight fold higher in menthol smokers compared to non-menthol smokers (10.4% vs 1.3%, odds ratio (OR) = 8.5, P = 5.6x10-5 (Table 3). A similar magnitude of difference was seen in the Schroeder cohort (7.0% vs 1.3%, OR = 6.3, P = 2.1x10-6, Table 3). Although limited by low power, our analyses found highly similar differences in the MRGPRX4 allele frequencies between menthol and non-menthol smokers in males and females (S1 Table). To determine whether the lower frequency of the rs7102322 variant in the Schroeder cohort (5%) was influenced by admixture, we estimated the percentage of African and European ancestry in a subset of this cohort. This indicated that Schroeder cohort participants indeed have a higher degree of European admixture compared to West Africans and African Americans from other regions of the U.S. (S2 Fig). Further analyses that included ancestry informative markers and an inferred proportion of African ancestry at this locus maintained strong support for association with menthol smoking (P<1e-5), indicating that the observed association is unlikely to be due to differential admixture (S4 and S5 Tables).
The MRGPRX4 gene encodes a Mas-related G-protein coupled receptor member X4, which is expressed in nociceptive neurons of the dorsal root ganglia and trigeminal neurons, and may regulate pain and somatosensation [22–24]. The MRGPRX4 rs7102322 variant encodes an asparagine-to-serine substitution at codon 245 (N245S). The residue is conserved in chimpanzees, and resides immediately 5’ to a region highly conserved across primates (Fig 1).
To evaluate whether rs7102322 SNP was in linkage disequilibrium (LD) with another functional variant in the MRGPRX4 locus, we examined data from the 1000 Genomes Project [25]. Consistent with our observations, the rs7102322 variant was observed solely in African-ancestry populations (MAF = 11.5% in Africans and 8% in African Americans in Southwest U.S.). The rs7102322 variant was in LD with the SNP rs61733596[A/G], which encodes a synonymous substitution at codon 43 (T43T) in MRGPRX4. Genotyping the rs61733596 variant in the Schroeder cohort confirmed that this variant is in complete linkage disequilibrium (R2 = 1) with rs7102322.
To identify whether additional coding variants in MRPGRX4 were associated with menthol cigarette smoking, we sequenced all exons of MRPGRX4 in a subset of Dallas cohort participants (N = 389, Table 4). This analysis confirmed that the rs7102322 (N245S) variant was in complete LD with rs61733596 (T43T), which showed an equivalent association with menthol smoking (OR = 3.3, P = 0.007). No other coding variant in MRGPRX4 was in linkage disequilibrium with N245S or associated with menthol cigarette smoking in this group.
MRGPRX4 is an orphan G protein-coupled receptor (GPCR) expressed in mammalian sensory neurons [22, 23]. Although the endogenous ligand(s) for this receptor are not known, the potassium channel modulator Nateglinide has been identified to be a highly efficacious agonist and was used to demonstrate that this receptor couples predominantly to Gαq [26]. We used a cell-based approach to determine if the N245S variant affected the responsiveness of either MRGPRX4 β-arrestin or Gαq downstream signaling in response to Nateglinide. We first generated FLAG-tagged, codon-optimized wild-type (WT) and N245S variant constructs for the PRESTO-Tango β-arrestin recruitment assay and then generated stable, tetracycline-inducible cell lines for the FLAG-tagged WT MRGPRX4 and the N245S variant. Notably, all N245S variant constructs in this study also included the synonymous variant T43T. To measure membrane expression levels of WT MRGPRX4 and N245S+T43T, we used an established whole-cell ELISA assay [27] in HTLA cells. Using a 1-way ANOVA, we determined that N245S+T43T was expressed slightly but significantly more than the WT receptor in HTLA cells and in the tetracycline-inducible stable cells (Fig 2A and 2B).
We then examined the effect of the agonist Nateglinide on the recruitment of β-arrestin in the PRESTO-Tango recruitment assay, which provides a quantitative measure of receptor activation and downstream signaling [28, 29]. We found that Nateglinide had equal potency at both N245S+T43T and WT receptors, but the N245S+T43T variant displayed a dramatic reduction of fold activation (42 fold) in β-arrestin recruitment, significantly less than the WT receptor (123.9 fold) (P<0.001, Fig 2C, S6 Table). In an independent quantitative assay, we also examined the effect of the variant on G protein signaling using the G protein-dependent phosphatidylinositol (PI) hydrolysis assay. We observed that the maximal (Emax) PI hydrolysis values following Nateglinide addition were significantly reduced in cells expressing the N245S+T43T variant compared with those expressing the WT receptor (PI Hydrolysis P<0.001, Fig 2D, S6 Table). Together, these data demonstrate that despite an apparent increase in N245S+T43T expression, the variant has significantly reduced arrestin and G protein signaling in comparison to WT.
To determine whether (-)-menthol, the additive present in menthol cigarettes, alters the activity of MRGPRX4 WT or the N245S+T43T variant, we added the compound and repeated the functional assays. In the Tango assay, (-)-menthol alone showed no agonist activity at MRGPRX4 (up to 1 mM) (S3 Fig). We then tested whether (-)-menthol altered agonist-induced activity of the WT and N245S+T43T receptors. Increasing concentrations of (-)-menthol were added to each assay together with Nateglinide (Fig 3). We observed that 100 μM and 300 μM (-)-menthol significantly reduced the Emax of the agonist Nateglinide on the WT (P<0.001) and N245S+T43T (P<0.001) in the arrestin pathway (Fig 3A and 3B) but not in the G protein pathway as measured using the PI hydrolysis assay (Fig 3C and 3D). To determine whether (-)-menthol’s modulatory effect differed significantly between WT and N245S+T43T variant, we calculated ΔΔlog(Emax/EC50) [30] for our reference agonist Nateglinide in the presence or absence of 100 and 300 μM (-)-menthol and found that (-)-menthol modulated WT and the variant equivalently (Fig 3E). A comparison of the fold change activation of β-arrestin recruitment revealed that 300 μM (-)-menthol significantly reduces Nateglinide-induced activation of the N245S variant when compared to WT (P<0.001, Fig 3F), similar to the differences in fold change for non-menthol conditions (Fig 2C). To test for non-specific effects of menthol on cells or cell membranes, we tested the effect of menthol on the unrelated D2 dopamine receptor in the PRESTO-Tango assay. This control showed that (-)-menthol had no modulatory effect on this receptor in this assay (S3 Fig, panel C). Similarly, (-)-menthol and Nateglinide had no effect on PI hydrolysis in cells where tetracycline was not added (i.e., with no MRGPRX4 receptor expression) (S3 Fig, panel D).
We also performed further studies using bioluminescence resonance energy transfer (BRET) as another measure of MRGPRX4 activity. Basal BRET in the Gq/βγ dissociation assay (an index of constitutive receptor activity) was reduced at MRGPRX4 N245S relative to WT (Fig 4A, 0.24 ± 0.025 vs 0.3 ± 0.015, t(3) = 4.986; p = 0.0155). Conversely, while basal activity of both WT and MRGPRX4 N245S was found to be increased by menthol (F(3,18) = 32.89, p < 0.0001), there was no effect of or interaction with genotype. Increasing amounts of menthol resulted in significantly elevated activity (Fig 4B, Linear effect, F(1,28) = 42.83; p < 0.0001) at all increments except between 50 and 100 μM (Fig 4B). Under agonist stimulation conditions, the probe Nateglinide showed an approximate 2-fold greater potency at MRGPRX4 N245S compared to WT (Table 5, EC50 values of 10.49 ± 0.61 μM vs 5.25 ± 0.25 μM). This effect was significant (F(1,18) = 7.44, p = 0.0343) accounting for 43.21% of the variance between the two populations. No effect of menthol on potency nor interaction with genotype were indicated. Similarly, efficacy of the probe Nateglinide (Emax) was not affected by menthol at any concentration (Fig 4C), though Emax as calculated by net BRET was reduced at MRGPRX4 N245S vs WT (-0.067 ± 0.001 vs -0.113 ± 0.006, F(1,6) = 15.51, p = 0.0076).
To provide further evidence for a role of MRGPRX4 in somatosensation, we used RT-PCR with RNA obtained from human thoracic dorsal root ganglia (DRG). The DRG serves to relay sensory information to the central nervous system, with the thoracic DRGs receiving sensory input from the lungs and airway. Using RT-PCR primers covering the length of MRGPRX4, following by sequencing of the RT-PCR products to ensure they originated from MRGPRX4 rather than any of the other closely related MRGPRX genes, we found clear expression in this tissue (S4 Fig), consistent with a role for MRGPRX4 in somatosensation in tissues exposed to cigarette smoke.
We have identified a variant haplotype of MRGPRX4 that is associated with increased prevalence of menthol cigarette smoking. This variant is found solely in individuals of African ancestry, and increases the odds of menthol use 5-to-8 fold among cigarette smokers. Cell-based assays of MRGPRX4 receptor function identified menthol as a novel negative modulator for this receptor, acting to reduce the responsiveness of this G protein-coupled receptor to its only known agonist at the WT and African-specific coding variant further.
While our understanding of MRGPRX4 gene function is limited, the members of this gene family are expressed in primary sensory neurons and are believed to be involved in somatosensation and nociception, including pruritus [22–24]. Although the natural ligand(s) for MRGPRX4 have not yet been identified, it is of interest that the MRGPRX4 agonist Nateglinide, a drug used to treat Type 2 diabetes, has been reported to have pruritus as a side effect [31]. Together, this suggests that menthol may act outside of the taste sensory system and may exert an anesthetic effect, which is further enhanced by the African-specific form of this receptor, which has dampened signaling capacity.
The MRGPRX4 variant associated with menthol cigarette smoking is relatively uncommon, with a MAF 8% in African Americans. Therefore, this variant alone cannot account for all of the difference in menthol cigarette smoking prevalence between African Americans and other ethnic groups. Thus, it is likely that other factors contribute to these differences. Surprisingly, we did not observe any consistent association at loci previously reported to be associated with menthol cigarette smoking (such as TRPA1 and TAS2R38), or the gene encoding the TRPM8 channel, which has been shown to be the target for menthol action in the somatosensory system. This may be due to the small size of our discovery cohort, which was powered to discover only large effect sizes (OR >2–3). The TRPA1 variants previously linked to menthol smoking had more modest effect sizes (odds ratios 1.3–1.4); thus, our study may have had insufficient power to detect their effects. We also genotyped the TRPA1 SNPs in our Schroeder cohort (N = 741) and could not replicate these associations, suggesting that factors other than power may be responsible for the difference in the results. The previously described association of TRPA1 variants with menthol smoking was restricted to heavy smokers, and was not observed in lighter smokers. Although our discovery cohort likely included a substantial proportion of light smokers, our replication cohort included mostly heavy smokers, which suggests that the lack of association in TRPA1 is unlikely to be explained entirely by the difference in phenotype.
Another possibility is that menthol smoking preferences are regulated by TRPA1 or TRPM8 non-coding variants that were not captured by the Exome chip or whole-exome sequencing. However, we have previously sequenced the exons and adjacent intronic regions of TRPM8 and TRPA1 in the Schroeder and Dallas cohorts and could not find variants with a consistent association with menthol smoking. Nevertheless, these genes remain plausible candidates and further studies, including larger samples of precisely phenotyped individuals, are warranted.
There are currently no crystal structures of the MRGPRX4 receptor available to conclusively determine the location and role of the N245S variant uncovered in this study. Based on a sequence alignment of MRGPRX4 with a published computational model of the related receptor MRGPRX2 [32], N245 (D in MRGPRX2) appears to be located in the third extracellular loop (EL3) of the receptor. Here, N245S reduces Nateglinide-induced agonism in both arrestin and G protein signaling pathways despite an apparent increase in membrane expression. EL3 has been demonstrated in the serotonin receptor 5HT2B to be involved in sterics of ligand binding and the kinetics of ligand and receptor interactions [28]. Thus, it is possible that the N245S variant changes the steric or kinetic properties of Nateglinide binding that influence arrestin and G protein signaling, though further studies will be needed to dissect the mechanism of this effect.
The strengths of our study are the use of a population-based sample including both African-American and European-American smokers, and a replication in a large independent cohort of African Americans. Unlike previous studies that looked at candidate polymorphisms, we performed a hypothesis-free exome-wide screen that provided broad and dense coverage of variation in the coding regions of the genome.
One limitation of our study, as mentioned above, is the relatively small size of our discovery cohort, which provided adequate power to discover only variants with large effect sizes (OR>2–3), and may have missed other genetic variants with lower allele frequencies or smaller effect sizes. Nevertheless, our approach represents an unbiased investigation into the genetic determinants of menthol cigarette smoking in a multiethnic cohort. Another potential limitation is that our data on menthol cigarette use was based on self-report, thus some individuals may have been misclassified with regard to their phenotype. However, our estimates of prevalence of menthol cigarette smoking among ethnic groups were consistent with national estimates, suggesting that misclassification error, if present, is likely small. Likewise, if such misclassification in the Schroeder population led to an overestimate of the association between MRGPRX4 and menthol smoking due to hidden population substructure, this is likely to be small because we found minimal evidence for heterogeneity within this group by large-scale SNP genotyping. Finally, not all participants responded to our questionnaire, thus results may not generalize to other populations. However, responders were similar to non-responders in terms of age and ethnicity (see Materials and Methods).
Menthol is known to exert its effects through transient receptor potential (TRP) channels TRPM8 [33, 34], and to a lesser extent TRPA1 [14]. TRPM8 is also known to mediate menthol-induced analgesia [35–37], and studies have shown that even low levels of menthol in tobacco, below those required to produce mint-like taste or aroma in tobacco, can activate TRPM8 [38]. Although our study was underpowered to detect variants with small effects, we found no evidence of association between variants at the TRPM8 and TRPA1 loci and menthol use, suggesting that variation in these menthol receptors is not a major contributor to the differential use of menthol cigarettes among African Americans.
Menthol cigarettes have been identified as a major threat to public health that have a disproportionate effect on ethnic minorities [39]. Our data suggest that ancestry-specific variants in genes involved in nociception contribute to both inter-individual and inter-ethnic differences in menthol cigarette smoking. The existence of population-specific genetic variants presents a new risk factor for menthol cigarette use, and suggests that the existence of this risk factor can inform health policies and tobacco regulatory actions designed to reduce health disparities in the United States.
Participants gave written informed consent under protocols approved by the IRB of the University of Texas Southwestern Medical Center (protocol #STU 112013–048), and the Western IRB (protocol #20131296). The overall study was carried out under protocol #01-DC-0230 and was approved by National Institutes of Health Combined Neurosciences IRB.
In the Dallas cohorts, genomic DNA was extracted from circulating leukocytes. A total of 4,591 DHS participants and 4,975 African-American participants from the Biobank were previously genotyped using Illumina Infinium HumanExome BeadChip v12.1, which captured >200K markers, including protein-altering variants (>90%), disease-associated variants from previously published genome-wide association studies, ancestry-informative markers, and other variants. Genotypes were called using Illumina GenomeStudio software. Samples were excluded if they met the following criteria: genotype call rate <99%, duplicate sample, discordant duplicate pair, or genotyped gender did match the stated gender. Variants were excluded based on a call rate of <99% or a deviation from Hardy-Weinberg equilibrium in African Americans with P <0.0001. Of the individuals who were successfully genotyped, 561 had data on menthol cigarette smoking and were included in the present study. After quality filtering, 116,212 autosomal variants were polymorphic in our study sample. Due to the relatively small sample size, we removed variants with MAF<1% (<10 carriers). After exclusions, 52,298 autosomal variants were available for analysis.
In the Schroeder population, DNA was collected using Oragene saliva collection kits and extracted according to the manufacturer’s protocol (Genotek Inc., Kanata, Ontario, Canada). Variants identified in the Dallas cohort were assayed by Sanger sequencing, using a dedicated set of primers (S2 Table). DNA chromatograms were analyzed and checked individually in order to evaluate the presence of calling errors with the Lasergene suite (DNASTAR, Madison, Wisconsin). In addition, 24 menthol smokers and 24 non-menthol smokers were randomly chosen and genotyped using the Illumina HumanOmni1 Chip that assayed 1,140,419 SNPs genome-wide to estimate ancestry levels. Variants were excluded based on a call rate of <99% or a deviation from Hardy-Weinberg equilibrium with P <0.0001. High-quality variants were further pruned for linkage disequilibrium (r2<0.1). Ancestry was inferred using ADMIXTURE software v.1.3.0 [43].
Exons of MRGPRX4 were sequenced in a subset of Dallas participants (N = 389) using whole-exome sequencing, as part of a related investigation into the role of genetic variation in smoking behaviors. Sample preparation and whole-exome sequencing were performed at the McDermott Center Next-Generation Sequencing core. Three micrograms of genomic DNA was sonicated using a Covaris S2 ultrasonicator (Covaris, Woburn, MA), purified, and assessed using an Agilent 2100 Bioanalyzer (Agilent Technologies, Santa Clara, CA). DNA was end-repaired, and 3′ ends were adenylated and barcoded with truncated adapters. PCR amplified libraries were purified with AmpureXP beads (New England Biolabs, Ipswich, MA) and assayed using an Agilent 2100 Bioanalyzer. A 750 ng aliquot of the fragment library was concentrated by vacuum to 3.5 μL and hybridized and captured with a SureSelect Human All Exon V4 kit (Agilent Technologies, Santa Clara, CA). Following hybridization, the captured library was amplified and index tags were added to the adapters. DNA was again purified with AmpureXP beads, and fragment sizes were assayed using the Agilent 2100 Bioanalyzer. Paired-end sequencing (150 basepairs) was performed using an Illumina Hiseq 2500. We achieved sufficient coverage depth to provide mean coverage of >115x for targeted bases, with over 94% of target bases covered at least 20x in 95% of the samples (>91% of target bases covered ≥20x in >99% of the samples).
Reads were aligned to the human reference genome build GRCh37 using the Burrows-Wheeler Alignment Tool [44], and variants were called using the Genome Analysis Toolkit (GATK) HaplotypeCaller [45]. Only high-quality variants (GQ >80% and allele depth >20x) were retained for analysis. Variants were annotated using SnpEff [46].
In the Dallas cohorts, ancestry was inferred using principal component analysis implemented in EIGENSTRAT [47]. Exome-wide association analysis was performed using PLINK v1.90p [48]. Significance was determined based on a likelihood-ratio test [49], using an R [50] plug-in function for logistic regression. An additive genetic model was assumed, and the analysis was adjusted for age, gender, and 6 leading principal components of ancestry. All variants reaching a significance threshold P<1x10-4 in the discovery cohort were tested for replication in the validation cohort. The analysis was performed in PLINK, with adjustment for age and gender. The association results were combined using random-effects inverse-variance weighted meta-analysis.
Fresh frozen human dorsal root ganglia was obtained from the National Disease Research Interchange (https://ndriresource.org) under protocol DDRD6 001 001. Following tissue preparation on a Covaris CPO2/S2, RNA was purified using RNeasy Quick start (Qiagen) according to the manufacturer’s instructions and including a DNase digestion. The resulting total RNA was used as template for cDNA synthesis using Superscript IV First-Strand Synthesis System (Invitrogen) with the Oligo d(T)20 primers. Subsequent PCR primers (S8 Table) were used to generate PCR products that were analyzed on a 1% agarose gel (S4 Fig), followed by dideoxy- Sanger sequencing to confirm the RT-PCR products originated from MRGPRX4, rather than the other, closely related MRGPRX gene family members.
Baseline characteristics of participants were compared using t-tests or analysis of variance for continuous variables and chi-square tests for categorical variables. Analyses were performed using R v3.2.1 statistical analysis software [50]. Presto-tango assay data were analyzed in GraphPad Prism V6.07 using an F-test for each EC50 and Emax parameters. ΔΔlog(Emax/EC50) plots were calculated as described in Kenakin et al [30]. BRET assay data were analyzed in Graphpad Prism 7.0 using non-linear regression models. Net BRET was calculated by subtracting values from the no-drug condition. Data comparing effects of menthol were analyzed as two-way repeated measures ANOVA (n = 4 biological replicates, 2 technical replicates for each condition per plate). Post-hoc analyses were performed using a Tukey correction for multiple comparisons.
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10.1371/journal.pcbi.1003661 | On the Origins of Suboptimality in Human Probabilistic Inference | Humans have been shown to combine noisy sensory information with previous experience (priors), in qualitative and sometimes quantitative agreement with the statistically-optimal predictions of Bayesian integration. However, when the prior distribution becomes more complex than a simple Gaussian, such as skewed or bimodal, training takes much longer and performance appears suboptimal. It is unclear whether such suboptimality arises from an imprecise internal representation of the complex prior, or from additional constraints in performing probabilistic computations on complex distributions, even when accurately represented. Here we probe the sources of suboptimality in probabilistic inference using a novel estimation task in which subjects are exposed to an explicitly provided distribution, thereby removing the need to remember the prior. Subjects had to estimate the location of a target given a noisy cue and a visual representation of the prior probability density over locations, which changed on each trial. Different classes of priors were examined (Gaussian, unimodal, bimodal). Subjects' performance was in qualitative agreement with the predictions of Bayesian Decision Theory although generally suboptimal. The degree of suboptimality was modulated by statistical features of the priors but was largely independent of the class of the prior and level of noise in the cue, suggesting that suboptimality in dealing with complex statistical features, such as bimodality, may be due to a problem of acquiring the priors rather than computing with them. We performed a factorial model comparison across a large set of Bayesian observer models to identify additional sources of noise and suboptimality. Our analysis rejects several models of stochastic behavior, including probability matching and sample-averaging strategies. Instead we show that subjects' response variability was mainly driven by a combination of a noisy estimation of the parameters of the priors, and by variability in the decision process, which we represent as a noisy or stochastic posterior.
| The process of decision making involves combining sensory information with statistics collected from prior experience. This combination is more likely to yield ‘statistically optimal’ behavior when our prior experiences conform to a simple and regular pattern. In contrast, if prior experience has complex patterns, we might require more trial-and-error before finding the optimal solution. This partly explains why, for example, a person deciding the appropriate clothes to wear for the weather on a June day in Italy has a higher chance of success than her counterpart in Scotland. Our study uses a novel experimental setup that examines the role of complexity of prior experience on suboptimal decision making. Participants are asked to find a specific target from an array of potential targets given a cue about its location. Importantly, the ‘prior’ information is presented explicitly so that subjects do not need to recall prior events. Participants' performance, albeit suboptimal, was mostly unaffected by the complexity of the prior distributions, suggesting that remembering the patterns of past events constitutes more of a challenge to decision making than manipulating the complex probabilistic information. We introduce a mathematical description that captures the pattern of human responses in our task better than previous accounts.
| Humans have been shown to integrate prior knowledge and sensory information in a probabilistic manner to obtain optimal (or nearly so) estimates of behaviorally relevant stimulus quantities, such as speed [1], [2], orientation [3], direction of motion [4], interval duration [5]–[8] and position [9]–[11]. Prior expectations about the values taken by the task-relevant variable are usually assumed to be learned either from statistics of the natural environment [1]–[3] or during the course of the experiment [4]–[6], [8]–[11]; the latter include studies in which a pre-existing prior is modified in the experimental context [12], [13]. Behavior in these perceptual and sensorimotor tasks is qualitatively and often quantitatively well described by Bayesian Decision Theory (BDT) [14], [15].
The extent to which we are capable of performing probabilistic inference on complex distributions that go beyond simple Gaussians, and the algorithms and approximations that we might use, is still unclear [14]. For example, it has been suggested that humans might approximate Bayesian computations by drawing random samples from the posterior distribution [16]–[19]. A major problem in testing hypotheses about human probabilistic inference is the difficulty in identifying the source of suboptimality, that is, separating any constraints and idiosyncrasies in performing Bayesian computations per se from any deficiencies in learning and recalling the correct prior. For example, previous work has examined Bayesian integration in the presence of experimentally-imposed bimodal priors [4], [8], [9], [20]. Here the normative prescription of BDT under a wide variety of assumptions would be that responses should be biased towards one peak of the distribution or the other, depending on the current sensory information. However, for such bimodal priors, the emergence of Bayesian biases can require thousands of trials [9] or be apparent only on pooled data [4], and often data show at best a complex pattern of biases which is only in partial agreement with the underlying distribution [8], [20]. It is unknown whether this mismatch is due to the difficulty of learning statistical features of the bimodal distribution or if the bimodal prior is actually fully learned but our ability to perform Bayesian computation with it is limited. In the current study we look systematically at how people integrate uncertain cues with trial-dependent ‘prior’ distributions that are explicitly made available to the subjects. The priors were displayed as an array of potential targets distributed according to various density classes – Gaussian, unimodal or bimodal. Our paradigm allows full control over the generative model of the task and separates the aspect of computing with a probability distribution from the problem of learning and recalling a prior. We examine subjects' performance in manipulating probabilistic information as a function of the shape of the prior. Participants' behavior in the task is in qualitative agreement with Bayesian integration, although quite variable and generally suboptimal, but the degree of suboptimality does not differ significantly across different classes of distributions or levels of reliability of the cue. In particular, performance was not greatly affected by complexity of the distribution per se – for instance, people's performance with bimodal priors is analogous to that with Gaussian priors, in contrast to previous learning experiments [8], [9]. This finding suggests that major deviations encountered in previous studies are likely to be primarily caused by the difficulty in learning complex statistical features rather than computing with them.
We systematically explore the sources of suboptimality and variability in subjects' responses by employing a methodology that has been recently called factorial model comparison [21]. Using this approach we generate a set of models by combining different sources of suboptimality, such as different approximations in decision making with different forms of sensory noise, in a factorial manner. Our model comparison is able to reject some common models of variability in decision making, such as probability matching with the posterior distribution (posterior-matching) or a sampling-average strategy consisting of averaging a number of samples from the posterior distribution. The observer model that best describes the data is a Bayesian observer with a slightly mismatched representation of the likelihoods, with sensory noise in the estimation of the parameters of the prior, that occasionally lapses, and most importantly has a stochastic representation of the posterior that may represent additional variability in the inference process or in action selection.
Subjects were required to locate an unknown target given probabilistic information about its position along a target line (Figure 1a–b). Information consisted of a visual representation of the a priori probability distribution of targets for that trial and a noisy cue about the actual target position (Figure 1b). On each trial a hundred potential targets (dots) were displayed on a horizontal line according to a discrete representation of a trial-dependent ‘prior’ distribution . The true target, unknown to the subject, was chosen at random from the potential targets with uniform probability. A noisy cue with horizontal position , drawn from a normal distribution centered on the true target, provided partial information about target location. The cue had distance from the target line, which could be either a short distance, corresponding to added noise with low-variance, or a long distance, with high-variance noise. Both prior distribution and cue remained on screen for the duration of the trial. (See Figure 1c–d for the generative model of the task.) The task for the subjects involved moving a circular cursor controlled by a manipulandum towards the target line, ending the movement at their best estimate for the position of the real target. A ‘success’ ensued if the true target was within the cursor radius.
To explain the task, subjects were told that the each dot represented a child standing in a line in a courtyard, seen from a bird's eye view. On each trial a random child was chosen and, while the subject was ‘not looking’, the child threw a yellow ball (the cue) directly ahead of them towards the opposite wall. Due to their poor throwing skills, the farther they threw the ball the more imprecise they were in terms of landing the ball straight in front of them. The subject's task was to identify the child who threw the ball, after seeing the landing point of the ball, by encircling him or her with the cursor. Subjects were told that the child throwing the ball could be any of the children, chosen randomly each trial with equal probability.
Twenty-four subjects performed a training session in which the ‘prior’ distributions of targets shown on the screen (the set of children) corresponded to Gaussian distributions with a standard deviation (SD) that varied between trials ( from 0.04 to 0.18 standardized screen units; Figure 2a). On each trial the location (mean) of the prior was chosen randomly from a uniform distribution. Half of the trials provided the subjects with a ‘short-distance’ cue about the position of the target (low noise: screen units; a short throw of the ball); the other half had a ‘long-distance’ cue (high noise: screen units; a long throw). The actual position of the target (the ‘child’ who threw the ball) was revealed at the end of each trial and a displayed score kept track of the number of ‘successes’ in the session (full performance feedback). The training session allowed subjects to learn the structure of the task in a setting in which humans are known to perform in qualitative and often quantitative agreement with Bayesian Decision Theory, i.e. under Gaussian priors [5], [9]–[11]. Note however that, in contrast with the previous studies, our subjects were required to compute each trial with a different Gaussian distribution (Figure 2a). The use of Gaussian priors in the training session allowed us to assess whether our subjects could use explicit priors in our novel experimental setup in the same way in which they have been shown to learn Gaussian priors through extended implicit practice.
After the training session, subjects were randomly divided in three groups ( each) to perform a test session. Test sessions differed with respect to the class of prior distributions displayed during the session. For the ‘Gaussian test’ group, the distributions were the same eight Gaussian distributions of varying SD used during training (Figure 2a). For the ‘unimodal test’ group, on each trial the prior was randomly chosen from eight unimodal distributions with fixed SD ( screen units) but with varying skewness and kurtosis (see Methods and Figure 2b). For the ‘bimodal test’ group, priors were chosen from eight (mostly) bimodal distributions with fixed SD (again, screen units) but variable separation and weighting between peaks (see Methods and Figure 2c). As in the training session, on each trial the mean of the prior was drawn randomly from a uniform distribution. To preserve global symmetry during the session, asymmetric priors were ‘flipped’ along their center of mass with a probability of . During the test session, at the end of each trial subjects were informed whether they ‘succeeded’ or ‘missed’ the target but the target's actual location was not displayed (partial feedback). The ‘Gaussian test’ group allowed us to verify that subjects’ behavior would not change after removal of full performance feedback. The ‘unimodal test’ and ‘bimodal test’ groups provided us with novel information on how subjects perform probabilistic inference with complex distributions. Moreover, non-Gaussian priors allowed us to evaluate several hypotheses about subjects’ behavior that are not testable with Gaussian distributions alone [22].
We first performed a model-free analysis of subjects' performance. Figure 3 shows three representative prior distributions and the pooled subjects' responses as a function of the cue position for low (red) and high (blue) noise cues. Note that pooled data are used here only for display and all subjects' datasets were analyzed individually. The cue positions and responses in Figure 3 are reported in a coordinate system relative to the mean of the prior (set as ). For all analyses we consider relative coordinates without loss of generality, having verified the assumption of translational invariance of our task (see Section 1 in Text S1).
Figure 3 shows that subjects' performance was affected by both details of the prior distribution and the cue. Also, subjects' mean performance (continuous lines in Figure 3) show deviations from the prediction of an optimal Bayesian observer (dashed lines), suggesting that subjects behavior may have been suboptimal.
Our model-free analysis showed that subjects' performance in the task was suboptimal. Here we examine the source of this apparent suboptimality. Subjects' performance is modelled with a family of Bayesian ideal observers which incorporate various hypotheses about the decision-making process and internal representation of the task, with the aim of teasing out the major sources of subjects' suboptimality; see Figure 1e for a depiction of the elements of decision making in a trial. All these observers are ‘Bayesian’ because they build a posterior distribution through Bayes' rule, but the operations they perform with the posterior can differ from the normative prescriptions of Bayesian Decision Theory (BDT).
We construct a large model set with a factorial approach that consists in combining different independent model ‘factors’ that can take different ‘levels’ [8], [21]. The basic factors we consider are:
Observer models are identified by a model string, for example ‘BDT-P-L’ indicates an observer model that follows BDT with a noisy estimate of the prior and suffers from occasional lapses. Our basic model set comprises 24 observer models; we also considered several variants of these models that are described in the text. All main factors are explained in the following sections and summarized in Table 2. The term ‘model component' is used through the text to indicate both factors and levels.
For each observer model and each subject’s dataset we evaluated the posterior distribution of parameters , where is in general a vector of model-dependent parameters (see Table 2). Each subject's dataset comprised of two sessions (training and test), for a total of about 1200 trials divided in 32 distinct conditions (8 priors 2 noise levels 2 sessions). In general, we assumed subjects shared the motor parameter across sessions. We also assumed that from training to test sessions people would use the same high-noise to low-noise ratio between cue variability (); so only one cue-noise parameter () needed to be specified for the test session. Conversely, we assumed that the other noise-related parameters, if present (, , , ), could change freely between sessions, reasoning that additional response variability can be affected by the presence or absence of feedback, or as a result of the difference between training and test distributions. These assumptions were validated via a preliminary model comparison (see Section 5 in Text S1). Table 2 lists a summary of observer models and their free parameters.
The posterior distributions of the parameters were obtained through a slice sampling Monte Carlo method [29]. In general, we assumed noninformative priors over the parameters except for motor noise parameter and cue-estimation sensory noise parameter (when present), for which we determined a reasonable range of values through an independent experiment (see Methods and Text S3). Via sampling we also computed for each dataset a measure of complexity and goodness of fit of each observer model, the Deviance Information Criterion (DIC) [30], which we used as an approximation of the marginal likelihood to perform model comparison (see Methods).
We compared observer models according to a hierarchical Bayesian model selection (BMS) method that treats subjects and models as random effects [31]. That is, we assumed that multiple observer models could be present in the population, and we computed how likely it is that a specific model (or model level within a factor) generated the data of a randomly chosen subject, given the model evidence represented by the subjects' DIC scores (see Methods for details). As a Bayesian metric of significance we used the exceedance probability of one model (or model level) being more likely than any other model (or model levels within a factor). In Text S1 we report instead a classical (frequentist) analysis of the group difference in DIC between models (GDIC), which assumes that all datasets have been generated by the same unknown observer model. In spite of different assumptions, BMS and GDIC agree on the most likely observer model, validating the robustness of our main findings. The two approaches exhibit differences with respect to model ranking, due to the fact that, as a ‘fixed effect’ method, GDIC does not account for group heterogeneity and outliers [31] (see Section 4 in Text S1 for details). Finally, we assessed the impact of each factor on model performance by computing the average change in DIC associated with a given component.
After establishing model SPK-P-L as the ‘best’ description of the data among the considered observer models, we examined its properties. First of all, we inspected the posterior distribution of the model parameters given the data for each subject. In almost all cases the marginalized posterior distributions were unimodal with a well-defined peak. We therefore summarized each posterior distribution with a point estimate (a robust mean) with minor loss of generality; group averages are listed in Table 3. For the analyses in this section we ignored outlier parameter values that fell more than 3 SDs away from the group mean (this rule excluded at most one value per parameter). In general, we found a reasonable statistical agreement between parameters of different sessions, with some discrepancies in the unimodal test session only. In this section, inferred values are reported as mean SD across subjects.
The motor noise parameter took typical values of screen units ( mm), somewhat larger on average than the values found in the sensorimotor estimation experiment, although still in a reasonable range (see Text S3). The inferred amount of motor noise is lower than estimates from previous studies in reaching and pointing (e.g. [10]), but in our task subjects had the possibility to adjust their end-point position.
The internal estimates of cue variability for low-noise and high-noise cues ( and ) were broadly scattered around the true values ( and screen units). In general, individual values were in qualitative agreement with the true parameters but showed quantitative discrepancies. Differences were manifest also at the group level, as we found statistically significant disagreement for both low and high-noise cues in the unimodal test session (-test, ) and high-noise cues in the bimodal test session (). The ratio between the two likelihood parameters, , differed significantly from the true ratio, ().
A few subjects () were very precise in their decision-making process, with a power function exponent . For the majority of subjects, however, took values between and (median ), corresponding approximately to an amount of decision noise of of the variance of the posterior distribution (median ). The range of exponents is compatible with values of ( number of samples) previously reported in other experiments, such as a distance-estimation task [33] or ‘intuitive physics’ judgments [35]. In agreement with the results of our previous model comparison, the inferred exponents suggest that subjects' stochastic decision making followed the shape of a considerably narrower version of the posterior distribution () which is not simply a form of posterior-matching ().
The Weber's fraction of estimation of the parameters of the priors' density took typical values of , with similar means across conditions. These values denote quite a large amount of noise in estimating (or manipulating) properties of the priors. Nonetheless, such values are in qualitative agreeement with a density/numerosity estimation experiment in which a change of in density or numerosity of a field of random dots was necessary for subjects to note a difference in either property [36]. Although the two tasks are too different to allow a direct quantitative comparison, the thresholds measured in [36] suggest that density/numerosity estimation can indeed be as noisy as we found.
Finally, even though we did not set an informative prior over the parameter, the lapse rate took reasonably low values as expected from a probability of occasional mistakes [28], [37]. We found , and the inferred lapse rate averaged over training and test session was less than for all but one subject.
We examined the best observer model's capability to reproduce our subjects' performance. For each subject and group, we generated datasets simulating the responses of the SPK-P-L observer model to the experimental trials experienced by the subject. For each simulated dataset, model parameters were sampled from the posterior distribution of the parameters given the data. For each condition (shape of prior and cue type) we then computed the optimality index and averaged it across simulated datasets. The model's ‘postdictions’ are plotted in Figure 10 as continuous lines (SE are omitted for clarity) and appear to be in good agreement with the data. Note that the postdiction is not exactly a fit since (a) the parameters are not optimized specifically to minimize performance error, and (b) the whole posterior distribution of the parameters is used and not just a ‘best’ point estimate. As a comparison, we also plotted in Figure 10 the postdiction for the best BDT observer model, BDT-P-L (dashed line). As the model comparison suggested, standard Bayesian Decision Theory fails to capture subjects' performance.
For each subject and group (training and test) we also plot the mean optimality index of the simulated sessions against the optimality index computed from the data, finding a good correlation (; see Figure 11).
Lastly, to gain an insight on subjects' systematic response biases, we used our framework in order to nonparametrically reconstruct what the subjects' priors in the various conditions would look like [2], [3], [8], [9] (see Methods). Due to limited data per condition and computational constraints, we recovered the subjects' priors at the group level and for model SPK-L, without additional noise on the priors (P). The reconstructed average priors for distinct test sessions are shown in Figure 12. Reconstructed priors display a very good match with the true priors for the Gaussian session and show minor deviations in the other sessions. The ability of the model to reconstruct the priors – modulo residual idiosyncrasies – is indicative of the goodness of the observer model in capturing subjects' sources of suboptimality.
We have explored human performance in probabilistic inference (a target estimation task) for different classes of prior distributions and different levels of reliability of the cues. Crucially, in our setup subjects were required to perform Bayesian computations with explicitly provided probabilistic information, thereby removing the need either for statistical learning or for memory and recall of a prior distribution. We found that subjects performed suboptimally in our paradigm but that their relative degree of suboptimality was similar across different priors and different cue noise. Based on a generative model of the task we built a set of suboptimal Bayesian observer models. Different methods of model comparison among this large class of models converged in identifying a most likely observer model that deviates from the optimal Bayesian observer in the following points: (a) a mismatching representation of the likelihood parameters, (b) a noisy estimation of the parameters of the prior, (c) a few occasional lapses, and (d) a stochastic representation of the posterior (such that the target choice distribution is approximated by a power function of the posterior).
Subjects integrated probabilistic information from both prior and cue in our task, but rarely exhibited the signature of full ‘synergistic integration’, i.e. a performance above that which could be obtained by using either the prior or the cue alone (see Figure 5). However, unlike most studies of Bayesian learning, on each trial in our study subjects were presented with a new prior. A previous study on movement planning with probabilistic information (and fewer conditions) similarly found that subjects violated conditions of optimality [23].
More interestingly, in our data the relative degree of suboptimality did not show substantial differences across distinct classes of priors and noise levels of the cue (low-noise and high-noise). This finding suggests that human efficacy at probabilistic inference is only mildly affected by complexity of the prior per se, at least for the distributions we have used. Conversely, the process of learning priors is considerably affected by the class of the distribution: for instance, learning a bimodal prior (when it is learnt at all) can require thousands of trials [9], whereas mean and variance of a single Gaussian can be acquired reliably within a few hundred trials [11].
Within the same session, subjects' relative performance was influenced by the specific shape of the prior. In particular, for Gaussian priors we found a systematic effect of the variance – subjects performed worse with wider priors, more than what would be expected by taking into account the objective decrease in available information. Interestingly, neither noise in estimation of the prior width (factor P) nor occasional lapses that follow the shape of the prior itself (factor L) are sufficient to explain this effect. Model postdictions of model BDT-P-L show large systematic deviations from subjects' performance in the Gaussian sessions, whereas the best model with decision noise, SPK-P-L, is able to capture subjects' behavior; see top left and top right panels in Figure 10. Moreover, the Gaussian priors recovered under model SPK-L match extremely well the true priors, furthering the role of the stochastic posterior in fully explaining subjects' performance with Gaussians. The crucial aspect of model SPK may be that decision noise is proportional to the width of the posterior, and not merely of the prior.
In the unimodal test session, subjects' performance was positively correlated with the width of the main peak of the distribution. That is, non-Gaussian, narrow-peaked priors (such as priors 1 and 6 in Figure 12b) induced worse performance than broad and smooth distributions (e.g. priors 4 and 8). Subjects tended to ‘mistrust’ the prior, especially in the high-noise condition, giving excess weight to the cue ( is significantly lower than it should be; see Table 3), which can be also interpreted as an overestimation of the width of the prior. In agreement with this description, the reconstructed priors in Figure 12b show a general tendency to overestimate the width of the narrower peaks, as we found in a previous study of interval timing [8]. This behavior is compatible with a well-known human tendency of underestimating (or, alternatively, underweighting) the probability of occurrence of highly probable results and overestimating (overweighting) the frequency of rare events (see [27], [38], [39]). Similar biases in estimating and manipulating prior distributions may be explained with an hyperprior that favors more entropic and, therefore, smoother priors in order to avoid ‘overfitting’ to the environment [40].
In building our observer models we made several assumptions. For all models we assumed that the prior adopted by observers in Eq. 2 corresponded to a continuous approximation of the probability density function displayed on screen, or a noisy estimate thereof. We verified that using the original discrete representation does not improve model performance. Clearly, subjects may have been affected by the discretization of the prior in other ways, but we assumed that such errors could be absorbed by other model components. We also assumed subjects quickly acquired a correct internal model of the probabilistic structure of the task, through practice and feedback, although quantitative details (i.e. model parameters) could be mismatched with respect to the true parameters. Formally, our observer models were not ‘actor’ models in the sense that they did not take into account the motor error in the computation of the expected loss. However, this was with negligible loss of generality since the motor term has no influence on the inference of the optimal target for single Gaussians priors, and yields empirically negligible impact for other priors for small values of the motor error (as those measured in our task; see Text S3).
Suboptimality was introduced into our observer models in three main ways: (a) miscalibration of the parameters of the likelihood; (b) models of approximate inference; and (c) additional stochasticity, either on the sensory inputs or in the decision-making process itself. Motor noise was another source of suboptimality, but its contribution was comparably low.
Miscalibration of the parameters of the likelihood means that the subjective estimates of the reliability of the cues ( and ) could differ from the true values ( and ). In fact, we found slight to moderate discrepancies, which became substantial in some conditions. Previous studies have investigated whether subjects have (or develop) a correct internal estimate of relevant noise parameters (i.e. the likelihood) which may correspond to their own sensory or motor variability plus some externally injected noise. In several cases subjects were found to have a miscalibrated model of their own variability which led to suboptimal behavior [33], [41]–[43], although there are cases in which subjects were able to develop correct estimates of such parameters [10], [44], [45].
More generally, it could be that subjects were not only using incorrect parameters for the task, but built a wrong internal model or were employing approximations in the inference process. For our task, which has a relatively simple one-dimensional structure, we did not find evidence that subjects were using low-order approximations of the posterior distribution. Also, the capability of our models to recover the subjects' priors in good agreement with the true priors suggest that subjects' internal model of the task was not too discrepant from the true one.
Crucial element in all our models was the inclusion of extra sources of variability, in particular in decision making. Whereas most forms of added noise have a clear interpretation, such as sensory noise in the estimation of the cue location, or in estimating the parameters of the prior, the so-called ‘stochastic posterior’ deserves an extended explanation.
We introduced the stochastic posterior model of decision making, SPK, with two intuitive interpretations, that is a noisy posterior or a sample-based approximation (see Figure 7 and Text S2), but clearly any process that produces a variability in the target choice distribution that approximates a power function of the posterior is a candidate explanation. The stochastic posterior captures the main trait of decision noise, that is a variability that depends on the shape of the posterior [33], as opposed to other forms of noise that do not depend on the decision process. Outstanding open questions are therefore which kind of process could be behind the observed noise in decision making, and during which stage it arises, e.g. whether it is due to inference or to action selection [46].
A seemingly promising candidate for the source of noise in the inference is neuronal variability in the nervous system [47]. Although the noisy representation of the posterior distribution in Figure 7b through a population of units may be a simplistic cartoon, the posterior could be encoded in subtler ways (see for instance [48]). However, neuronal noise itself may not be enough to explain the amount of observed variability (see Text S2). An extension of this hypothesis is that the noise may emerge since suboptimal computations magnify the underlying variability [49].
Conversely, another scenario is represented by the sampling hypothesis, an approximate algorithm for probabilistic inference which could be implemented at the neural level [19]. Our analysis ruled out an observer whose decision-making process consists in taking the average of samples from the posterior – operation that implicitly assumes a quadratic loss function – showing that averaging samples from the posterior is not a generally valid approach, although differences can be small for unimodal distributions. More generally, the sampling method should always take into account the loss function of the task, which in our case is closer to a delta function (a MAP solution) rather than to a quadratic loss. Our results are compatible with a proper sampling approach, in which an empirical distribution is built out of a small number of samples from the posterior, and then the expected loss is computed from the sampled distribution [19].
As a more cognitive explanation, decision variability may have arisen because subjects adopted a probabilistic instead of deterministic strategy in action selection as a form of exploratory behavior. In reinforcement learning this is analogous to the implementation of a probabilistic policy as opposed to a deterministic policy, with a ‘temperature’ parameter that governs the amount of variability [50]. Search strategies have been hypothesized to lie behind suboptimal behaviors that appear random, such as probability matching [51]. While generic exploratory behavior is compatible with our findings, our analysis rejected a simple posterior-matching strategy [25], [26].
All of these interpretations assume that there is some noise in the decision process itself. However, the noise could emerge from other sources, without the necessity of introducing deviations from standard BDT. For instance, variability in the experiment could arise from lack of stationarity: dependencies between trials, fluctuations of subjects' parameters or time-varying strategies would appear as additional noise in a stationary model [52]. We explored the possibility of nonstationary behavior without finding evidence for strong effects of nonstationarity (see Section 6 in Text S1). In particular, an iterative (trial-dependent) non-Bayesian model failed to model the data in the training dataset better than the stochastic posterior model. Clearly, this does not exclude that different, possibly Bayesian, iterative models could explain the data better, but our task design with multiple alternating conditions and partial feedback should mitigate the effect of dependencies between trials, since each trial typically displays a different condition from the immediately preceding ones.
In summary, we show that a decision strategy that implements a ‘stochastic posterior’ that introduces variability in the computation of the expected loss has several theoretical and empirical advantages when modelling subjects' performance, demonstrating improvement over previous models that implemented variability only through a ‘posterior-matching’ approach or that implicitly assume a quadratic loss function (sampling-average methods).
The Cambridge Psychology Research Ethics Committee approved the experimental procedures and all subjects gave informed consent.
Twenty-four subjects (10 male and 14 female; age range 18–33 years) participated in the study. All participants were naïve to the purpose of the study. All participants were right-handed according to the Edinburgh handedness inventory [53], with normal or corrected-to-normal vision and reported no neurological disorder. Participants were compensated for their time.
Subjects were required to reach to an unknown target given probabilistic information about its position. Information consisted of a visual representation of the a priori probability distribution of targets for that trial and a noisy cue about the actual target position.
Subjects held the handle of a robotic manipulandum (vBOT, [54]). The visual scene from a CRT monitor (Dell UltraScan P1110, 21-inch, 100 Hz refresh rate) was projected into the plane of the hand via a mirror (Figure 1a) that prevented the subjects from seeing their hand. The workspace origin, coordinates , was cm from the torso of the subjects, with positive axes towards the right ( axis) and away from the subject ( axis). The workspace showed a home position (1.5 cm radius circle) at cm and a cursor (1.25 cm radius circle) that tracked the hand position.
On each trial 100 potential targets (0.1 cm radius dots) were shown around the target line at positions , for , where the formed a fixed discrete representation of the trial-dependent ‘prior’ distribution , obtained through a regular sample of the cdf (see Figure 1d), and the were small random offsets used to facilitate visualization ( Uniform(−0.3, 0.3) cm). The true target was chosen by picking one of the potential targets at random with uniform probability. A cue (0.25 cm radius circle) was shown at position . The horizontal position provided a noisy estimate of the target position, , with the true (horizontal) position of the target, the cue variability and a normal random variable with zero mean and unit variance. The distance of the cue from the target line, , was linearly related to the cue variability: cues distant from the target line were noisier than cues close to it. In our setup, the noise level could only either be low for ‘short-distance’ cues, cm ( cm), or high for ‘long-distance’ cues, cm ( cm). Both the prior distribution and cue remained on the screen for the duration of a trial.
After a ‘go’ beep, subjects were required to move the handle towards the target line, choosing an endpoint position such that the true target would be within the cursor radius. The manipulandum generated a spring force along the depth axis ( N/cm) for cursor positions past the target line, preventing subjects from overshooting. The horizontal endpoint position of the movement (velocity of the cursor less than 0.5 cm/s), after contact with the target line, was recorded as the subject’s response for that trial.
At the end of each trial, subjects received visual feedback on whether their cursor encircled (a ‘success’) or missed the true target (partial feedback). On full feedback trials, the position of the true target was also shown (0.25 cm radius yellow circle). Feedback remained on screen for 1 s. Potential targets, cues and feedback then disappeared. A new trial started 500 ms after the subject had returned to the home position.
For simplicity, all distances in the experiment are reported in terms of standardized screen units (window width of 1.0), with and 0.01 screen units corresponding to 3 mm. In screen units, the cursor radius is and the SD of noise for short and long distance cues is respectively and .
Subjects performed one practice block in which they were familiarized with the task (64 trials). The main experiment consisted of a training session with Gaussian priors (576 trials) followed by a test session with group-dependent priors (576–640 trials). Sessions were divided in four runs. Subjects could take short breaks between runs and there was a mandatory 15 minutes break between the training and test sessions.
Each session presented eight different types of priors and two cue noise levels (corresponding to either ‘short’ or ‘long’ cues), for a total of 16 different conditions (36–40 trials per condition). Trials from different conditions were presented in random order. Depending on the session and group, priors belonged to one of the following classes (see Figure 2):
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10.1371/journal.pgen.1008338 | Reduction of mRNA export unmasks different tissue sensitivities to low mRNA levels during Caenorhabditis elegans development | Animal development requires the execution of specific transcriptional programs in different sets of cells to build tissues and functional organs. Transcripts are exported from the nucleus to the cytoplasm where they are translated into proteins that, ultimately, carry out the cellular functions. Here we show that in Caenorhabditis elegans, reduction of mRNA export strongly affects epithelial morphogenesis and germline proliferation while other tissues remain relatively unaffected. Epithelialization and gamete formation demand a large number of transcripts in the cytoplasm for the duration of these processes. In addition, our findings highlight the existence of a regulatory feedback mechanism that activates gene expression in response to low levels of cytoplasmic mRNA. We expand the genetic characterization of nuclear export factor NXF-1 to other members of the mRNA export pathway to model mRNA export and recycling of NXF-1 back to the nucleus. Our model explains how mutations in genes involved in general processes, such as mRNA export, may result in tissue-specific developmental phenotypes.
| The Central Dogma of Biology schematically highlights the transmission of genetic information stored in DNA, through RNA, to the formation of proteins. This general flow implicates RNA export from the nucleus to the cytoplasm and proper protein localization within the eukaryotic cell. Ultimately, proteins are the cell’s structural and catalytic functional units. As a result, cells differentiate into one cell type or another (such as epithelial, muscle, neuron…) and exhibit specific shape and functionality. Here we describe, in a C. elegans model, how mutations in genes involved in a general and ubiquitous mechanism, such as mRNA export, may result in tissue-specific developmental phenotypes that show up in processes that are highly demanding of cytoplasmic transcripts like epithelialization and gamete formation. A deep understanding of the mechanisms underlying the "connectors" shown in the Central Dogma of Biology is key both to unravel the general genetic control of an organism’s development and, at the same time, contribute to a better understanding of tissue-specific diseases.
| Cell differentiation and morphogenesis rely on the expression of specific genes that are translated into proteins in specific sets of cells to ensure the correct formation of the organs and body plan. The physical separation between genomic DNA and the cytoplasm in eukaryotic cells makes it necessary to export RNA through the nuclear envelope (NE) [1,2,3,4,5]. This nucleo-cytoplasmic transport is highly conserved [6] and our understanding of its mechanism comes from a variety of model organisms including yeast, nematodes, fruit flies and vertebrates [4,5,7,8].
mRNA biogenesis and export are tightly coordinated by sequential assembly of appropriate ribonucleoprotein complexes named the THO complex (named after the yeast tho2 subunit was identified as a suppressor of the Transcriptional defect of Hpr1 by Overexpression), the TREX (TRanscription EXport) complex and the THSC/TREX-2 (Transport/export complex 2) complex [4,5,9,10,11]. Briefly, during transcription, a group of proteins called the THO complex is recruited to chromatin. This complex is needed for transcription elongation, mRNA export and genome integrity [12,13]. The metazoan THO complex contains THOC1/2/3/5/6/7 (THO complex in yeast: Hpr1, Tho2, Mtf1 and Thp2) [14]. Next, additional proteins UAP65, Aly/REF and CIP29 (Sub2p, Yra1p and Nab2 in yeast) bind the THO subunits to build the transcription-export complex (TREX complex) which couples transcription with mRNA export [15].
After the messenger ribonucleoprotein (mRNP) has been generated, the conserved nuclear RNA export factor 1 (NXF1/TAP) is recruited through direct interaction with several TREX components [2,16]. NXF1 family export factors are composed of multiple domains. At the N terminus is the RNA recognition motif (RRM) [17]. Next, a leucine-rich repeat domain (LRR) is required for NXF1-mediated export [18]. This domain is followed by a nuclear transport factor 2, NTF2-like domain, that heterodimerizes with a protein known as p15 or NXT [19,20,21,22]. Efficient mRNA export from the nucleus to the cytoplasm requires the formation of this complex. The remaining C-terminal domain, TAP, also known as the NXF1 ubiquitin-associated domain (UBA), permits translocation through the central channel of the nuclear pore complex (NPC) by interacting with FG-Nups (phenylalanine-glycine (FG) reach nucleoporins) [6,20,21,23,24]. Finally, the THSC/TREX-2 (transport/export complex 2), binds the mRNP to the nucleoplasmic side of the NPC. The transit of mRNP through the nuclear pore is mediated by direct interaction of NXF1-p15 with the nucleoporins that line the pore [25]. Once in the cytoplasm, mRNA can be stored in large ribonucleotide protein particles (RNP), as happens in the so-called germ granules (known as P granules in C. elegans) or it can be directly translated into proteins [26,27,28] (Fig 1).
Over the last decades, C. elegans has emerged as a powerful model for studying cell differentiation and morphogenesis. C. elegans has a simple body plan. Schematically, it can be divided into two cylindrical layers of tissues and organs separated by a fluid-filled space (pseudocoelom). From outside to inside, the outer layer constitutes the body wall, which consists of the cuticle and an epithelium called the epidermis (also known as the hypodermis) [29], the excretory system, neurons and muscles. The inner system is comprised of the gonad and another epithelial tube composed of the pharynx and intestine [30]. The anterior portion of the pharynx and the external epidermis remain linked by nine cells called the arcade cells. Absence of this arcade cell epithelium leads to a Pun (pharynx unattached) phenotype where the pharynx detaches from the mouth during development and forms a confined cell cluster in the interior of the animal [30,31,32,33].
Several pathways contribute to cell fate specification and epithelialization of arcade cells. It occurs after the epidermis and pharynx have epithelialized. The process is very fast (less than 10 minutes), during mid-embryogenesis after the embryonic cell divisions are complete [33]. Recent studies show that PAR-6/PARD6A is required for polarizing the arcade cells to define typical apical and basolateral domains [34,35,36]. In C. elegans epithelial cells, both domains are separated by the adherens junctions (CeAJ). Thus, the CeAJ contains proteins that mediate adhesion such as HMR-1/cadherin, HMP-1/-catenin, HMP-2/-catenin, and VAB-9/claudin [37,38]. In addition, DLG-1/Discs large and the coiled-coil protein AJM-1, are also part of the CeAJ although they are located slightly more basally [39,40].
In this study we show that reducing mRNA export strongly affects epithelial formation and germline proliferation in C. elegans. Previous studies revealed that both processes require specific gene expression programs. Our findings indicate the existence of feedback mechanisms that activate expression of specific genes to compensate for the lack of mRNAs in the cytoplasm such as those involved in mRNA export or cytoskeletal rearrangements. Finally, we suggest a model to explain the mRNA export pathway and recycling of the export factor NXF-1 back to the nucleus in C. elegans to close the export cycle.
To discover genes involved in embryonic morphogenesis, we performed a genetic screen for embryonic lethal worms with the pharynxes unattached to the mouth (Pun phenotype). Thus, we identified a thermo-sensitive (ts) mutant allele, t2160, whose embryos arrested at late stages with a highly penetrant Pun phenotype (87.5% (n = 176)) and body elongation defects (81% (n = 60)) (Fig 2A and 2B, Table 1). Three experimental lines demonstrated that t2160ts is an allele of the C. elegans nuclear export factor 1 (nxf-1) [41]. First, using the whole genome sequencing approach (WGS) and CloudMap/Hawaiian Variant Mapping (http://usegalaxy.org/cloudmap)[42], we identified a homozygous A-to-G transition at the 14414501 position of chromosome V, in the C15H11.3/nxf-1 gene that caused a VAL to ALA substitution. The t2160ts mutation was located in the RRM (RNA recognition motif) domain of NXF-1 (Fig 2C). Second, t2160ts failed to complement a knockout deletion of the nxf-1(ok1281) gene. Third, the t2160ts embryonic lethality was successfully rescued with a plasmid containing 1096 bp of the gene promoter, the nxf-1 genomic region and 1313 bp of the 3' UTR (S1 Fig). To determine whether the ts effect of the VAL to ALA substitution was likely due to synthesis or to folding [43] we performed a ts curve assay during embryo development (S2 Fig). In an up-shift curve, worms grown to adulthood at 15°C (permissive temperature) were allowed to lay eggs which were sequentially shifted to 25°C (restrictive temperature). In those embryos, the maternal product was therefore synthesized at the permissive temperature. However, 100% of those embryos died when shifted to 25°C at the two-cell stage, and the lethality did not fall under 50% until mid-embryogenesis when most cell divisions and epithelialization are completed. The maternal product is enough to complete the development of a maternally rescued homozygous embryo at 25°C from a heterozygous nxf-1(t2160ts)(+/-) hermaphrodite mother. Together with the down-shift curve, these results show the NXF-1 requirement during embryonic cell proliferation, differentiation and morphogenesis and strongly indicate that t2160ts mutation alters the protein conformation when exposed to 25°C.
Since the t2160ts mutation affects the nxf-1 gene, we decided to check intracellular mRNA distribution. To examine the polyadenylated RNA localization, we performed FISH (fluorescent in situ hybridization) analysis. Hybridization with an oligo-dT probe against poly(A) showed mRNA preferentially accumulated in the nucleus rather than the cytoplasm of nxf-1(t2160ts) mutants. In contrast, the signal was mostly dispersed in the cytoplasm of wild-type (WT) embryo cells (Fig 2D). This indicates that t2160ts is a loss-of-function mutation in the nxf-1 gene that impairs mRNA export. A phenotypic analysis of the available alleles and nxf-1 RNAi reveals that the t2160ts mutation leads to reduced activity but is not a null allele of nxf-1. nxf-1 (ok1281) knockout maternally rescued or nxf-1 RNAi-fed L1 larvae led to larval arrest, whereas nxf-1 (t2160ts) or RNAi performed under mild conditions (RNAi diluted with L4440 bacterial RNAi empty vector at a 1:1 ratio) led both to the same embryonic Pun phenotype and body elongation defects in the F1 embryos. Heterozygous worms ok1281/ t2160ts exhibited an intermediate phenotype: they reached adulthood but were sterile (Table 1).
To check NXF-1 localization in vivo and throughout development, we created transgenic lines expressing NXF-1 fused to 3xFLAG and eGFP (enhanced green fluorescent protein) (S1B Fig). The transgene successfully rescued the nxf-1(t2160ts) mutation indicating that the construct was functional and did not change the function of NXF-1. eGFP expression was detected in all stages of the C. elegans life cycle. As expected for a nuclear export factor, NXF-1::3xFLAG::eGFP showed nuclear localization during embryogenesis, larval and adult stages. NXF-1 showed dynamic localization during cell division: it was detected as nuclear but diffused to the cytoplasm during mitosis (S1 Movie). In addition, NXF-1::3xFLAG::eGFP was detected in granules in the oocyte cytoplasm (Fig 3).
To assess the developmental defects of nxf-1(t2160ts) embryos, we performed 4D microscopy and compared it to that of WT N2 embryos (S2 Movie). Since the nxf-1(t2160ts) mutant is temperature sensitive, worms (WT and mutant) were grown at 15°C. Then, the worms where transferred to 25°C degrees and allowed to grow overnight (O/N) prior to selecting the embryos. The Pun pharynxes of the nxf-1(t2160ts) mutant embryos displayed several characteristics that are consistent with normal tissue differentiation, such as the presence of a distinct pharyngeal lumen and sustained rhythmic pumping. In further support of these results, we observed strong expression of several GFP markers indicating the presence of differentiated muscle (Pmyo-2::GFP) [44], and neurons (Pric-19::GFP) [45] in the Pun pharynxes (Fig 4A and 4B).
In addition to these transgenes, we tested the expression of the pha-4 gene. The pha-4 transcription factor is the central selector regulator gene for the C. elegans pharynx and its activity is essential for all pharyngeal development [46]. PHA-4 determines the identity and morphogenetic program of all the pharyngeal precursors by directly regulating many genes expressed in the pharynx and arcade cells at different time intervals [36,47,48,49,50,51,52,53,54]. pha-4 expression was detected in the nuclei of the intestine, pharynx and arcade cells, both in the nxf-1(t2160ts) mutant and WT. All nine arcade nuclei could be identified and were located approximately between the pharyngeal and epidermal cells (Fig 4C), indicating that developmental programs properly differentiate pharyngeal cells in the nxf-1(t2160ts) mutant. Furthermore, normally expressed CDH-3::GFP [55] in the developing arcade cells, lateral epidermal cells and seam cells suggested proper differentiation of epithelial cells (Fig 4D).
In contrast, expression of the pxIs10 [Ppha-4::GFP::CAAX + (pRF4) rol-6(su1006)] transgene that generates a GFP fused to the isoprenylation sequence (CAAX) of mig-2 [56], under control of the pha-4 promoter to drive GFP to the plasma membrane of the same cells [30,53], revealed that although the intestinal and pharyngeal cell membranes glowed in both WT and mutant embryos, fluorescence was not detected in the arcade cell membrane of the mutant (Fig 4E). This indicated the existence of membrane or cortex defects specifically in the arcade cells of the nxf-1 (t2160ts) mutant.
To further assess whether epithelialization defects in arcade cells caused the Pun phenotype in nxf-1(t2160ts) mutants, we monitored the localization of the fluorescent reporter for the C. elegans apical surface polarization protein PAR-6/Par-6 and the expression and localization of C. elegans apical junction (CeAJ) components (Fig 4F, S3A Fig): the classical cadherin-catenin complex (HMR-1/E-cadherin, JAC-1/p120-catenin, HMP-1/alpha-catenin) and the more basal AJM-1, DLG-1/disk large complex and SAX-7/L1CAM that has been proposed to function as a transmembrane component of this complex (S3 and S4 Figs) [57]. Partial loss of NXF-1 activity affected normal expression of CeAJ proteins. We detected a strong increase in DLG-1::dsRed expression and a moderate decrease of AJM-1::GFP, SAX-7::GFP and HMR-1::GFP expression in both the epidermis and the gut, that may be a direct consequence of the less efficient export of transgenic mRNA in the nxf-1 (t2160ts) mutant. However, the more dramatic change in nxf-1(t2160ts) mutants was the absence of apical junctions in the arcade cells and the mislocalization of the PAR-6 polarity protein that shows an ectopic and non-polarized localization in arcade cells and also, to a lesser degree, in pharyngeal and intestinal cells as occurs in WT (Fig 4F, S5 Fig), indicating that NXF-1 is essential for arcade cells to form a polarized epithelium. In addition, proper epidermal morphogenesis was disrupted, and some cells failed to adopt their normal elongated form (Fig 4, S3 Fig, S4 Fig).
Finally, to determine whether other epithelia, such as intestine, could also be affected to a lesser extent, we analyzed WT and nxf-1 (t2160ts) L1 larvae intestinal morphology. Although the mutants showed a fully developed intestine, their morphology was not completely normal and showed a wider lumen than in the WT, all along the gut duct. This defect was already detectable during embryogenesis (S6 Fig), indicating that although less sensitive than arcade and hypodermal cells, intestinal epithelia is also affected by limited mRNA export.
In summary, a partial loss of activity in the nxf-1(t2160ts) mutant predominantly affects epithelial tissues (mainly arcade cells) causing pharynx attachment defects and body elongation arrest by affecting cell-cell membrane contacts but not cell differentiation.
To discern whether the unattached pharynx and body elongation defects observed in nxf-1(t2160ts) mutants were due to a specific function in morphogenesis or to impaired mRNA export in tissues with a high demand of mRNA, we knocked down other mRNA export machinery components and evaluated both phenotypes. Thus, we depleted NXT-1/p15 and the DEAD-box helicase, HEL-1/UAP56 by RNAi. NXT-1/p15 is an ortholog of the Ran-GDP-binding nuclear transport factors NXT1 and NXT2, that heterodimerize with NXF-1 to bind to nucleoporins and facilitate export of poly(A) RNA [2,58,59]. HEL-1/UAP56 is a DEAD-box helicase, essential for mRNA export in C. elegans [59,60] (Fig 5).
L1 larvae of the strain ST65 (ncIs13[ajm-1::GFP]) [61], expressing AJM-1::GFP, fed with RNAi clones of the nxf-1 and hel-1 genes, arrested at the L2 stage. L1 animals depleted of NXT-1 reached adulthood but were sterile and 50% of them showed protruding vulva (S7 Fig). When the RNAi experiment started at the L4 stage, all the worms progressed to adulthood and laid eggs that arrested at early embryonic stages [41,59]. To obtain a partial reduction of the mRNA export activity, we performed RNAi experiments by feeding L4 stage worms with bacterial nxf-1 and nxt-1 RNAi diluted with L4440 (bacterial RNAi empty vector) at a 1:1 ratio for a “milder” RNAi effect. F1 embryos developed further and died at later stages. 71% (n = 28) of the nxf-1 RNAi embryos, 62% (n = 29) of the nxt-1 RNAi embryos and 27% of the hel-1 RNAi (L4 normal conditions) embryos showed unattached pharynxes with missing expression of AJM-1::GFP in arcade cells and body elongation defects (Fig 5, Table 1).
Thus, we concluded that inhibition of the mRNA export machinery by RNAi depletion of its individual components leads to a catastrophic arrest during development. In contrast, a partial reduction in mRNA export predominantly affects epithelial tissues, mainly arcade cells, causing pharynx attachment defects and body elongation arrest.
To assess whether other proteins involved in mRNA biogenesis and processing also affect embryonic morphogenesis in C. elegans, we knocked down components of the exon junction complex (EJC) and scored the unattached pharynx and body elongation defects. The EJC complex remains stably bound within mRNPs and serves as a binding platform for factors involved in mRNA packaging, export, translation and nonsense-mediated decay (NMD). Depletion of C. elegans EJC has a partial effect on mRNA splicing fidelity [62]. This complex provides a link between several steps of the mRNA life cycle [reviewed in: 63,64,65].
L1 larvae of the transgenic strain ST65 (ncIs13[ajm-1::GFP]) [61], expressing AJM-1::GFP, were fed with RNAi clones of the genes mag-1/Mago-nashi and its binding partner rnp-4/Y14 (components of the C. elegans EJC) [66]. Depletion of these two genes caused lethality of the F1 embryos which arrested with elongation defects [this study,59,66]. Depletion of RNP-4/Y14 did not cause nuclear accumulation of poly(A) RNAs, suggesting that C. elegans Y14 orthologue plays an essential role in C. elegans development, but is not directly associated with mRNA export [59]. The AJM::GFP reporter showed that the hypodermis was disorganized in these embryos. In contrast, pharyngeal and intestinal tissues were evident in those arrested embryos. The CeAJ in the foregut was properly formed and the pharynx was completely elongated (Fig 5).
Our results indicate that whereas the epidermis is highly sensitive to different processes affecting mRNA metabolism, such as biogenesis, processing or export, arcade cells are specifically more sensitive to mRNA export defects. This suggests the existence of different mechanisms for epithelialization or different levels of mRNA requirements for the different types of epithelia during morphogenesis.
To further explore the consequences of reducing the activity of nxf-1 in other tissues, we expanded the analysis to the C. elegans germline. Oocyte production requires high levels of transcription and translation to accumulate enough maternal product for embryonic development [67,68]. DAPI staining of gonads at the one-day adult stage shows that the number of mitotic germ cells was strongly reduced in the nxf-1(t2160ts) mutant (Fig 6). N2 and nxf-1(t2160ts) worms were grown at 15°C until the L4 larval stage and then moved overnight to 25°C before scoring the gonad nuclei. The mitotic region of nxf-1(t2160ts) gonads had 105.9±3.4 nuclei (mean±standard error/SE) (n = 20), which is half the number of nuclei in the mitotic region of WT worms 205.36±2.9 (mean±SE) (n = 11) grown under the same conditions. In C. elegans, germline proliferation is governed by GLP-1/Notch-receptor and other effectors that mediate the transition from mitosis to meiosis [69,70]. Although, we did not find significant differences between gene expression of those factors in nxf-1(t2160ts) vs WT nematodes (S8 Fig), the inefficient transport of their mRNAs to the cytoplasm could affect the extension of the mitotic region. The number of nuclei in mitosis was determined by counting phosphorylated histone H3 (pH3)-positive nuclei in dissected gonads. Immunostaining with an anti-pH3 antibody marks cells in the late M phase [71]. This reduction in phosphorylated histone H3 is not caused by a lower level of histone expression (S9 Fig) but likely reflects the less proliferative state of the nxf-1 (t2160ts) mutant gonad. The reduction of the average number of mitotic cells observed in nxf-1(t2160ts) (3.58±0.36 (mean±SE) (n = 29)) versus the WT (8.88±0.69 (mean±SE) (n = 18)) further confirmed the diminished germline proliferation in the nxf-1(t2160ts) mutant (Fig 6).
As a canonical cell cycle progression mechanism, CDC25 dephosphorylates CDK1 to allow entry into mitosis. In C. elegans, CDK-1 is phosphorylated at the Tyr15 inhibitory residue upon DNA damage [72,73]. Phosphorylation of tyrosine (Tyr15) and threonine (Thr14) in the ATP-binding loop of CDK-1 prevents activation of the CDK/cyclin complex hindering entry into mitosis. To understand how loss of function of nxf-1 disrupts the mitotic cell cycle, we performed immunostaining of adult gonads with antibodies against phosphorylated Tyr15 CDK-1. Our results showed an increase in Tyr15 phosphorylation of CDK-1 in the nuclei of the gonadal proliferative region of nxf-1(t2160ts) mutant animals (S10 Fig). This increase was higher than that caused by irradiation with ionizing radiation (IR) in WT animals. The absence of significant changes in the expression of cdc-25 or cdk-1 (S9 Fig) in nxf-1 (t2160ts) vs WT and immunostaining with antibodies specific to phosphorylated proteins suggests that the reduction in germline proliferation is achieved by control of the cell cycle machinery by phosphorylation. To further check whether cell cycle impairment was an nxf-1(t2160ts)-specific phenotype or a consequence of reduced RNA export, we assayed other genes involved in RNA export. RNAi depletion of the DEAD-box helicase HEL-1/UAP56 also increased Tyr15 phosphorylation of CDK-1 in the gonadal proliferative region (S10 Fig). We next extended the analysis of cell proliferation to the cell cycle progression in the developing embryo. Consistent with the results observed in the gonad, 4D microscopic analysis shows that embryonic cell division is significantly slower in nxf-1 (t2160ts) mutants compared to WT embryos under the same conditions (Fig 7). Taken together, these results suggest that RNA export reduction impairs mitotic cell cycle progression in C. elegans.
Since the germline mitotic rate was reduced upon RNA export impairment, we further investigated its role in maintenance of meiosis. The RAD-51 protein is involved in DNA repair by homologous recombination and it is a marker of double-strand breaks (DSBs) undergoing processing [74,75]. Although the expression of rad-51 was not affected by nxf-1(t2160ts) mutation (S9 Fig), we observed a massive accumulation of RAD-51 in the pachytene/diplotene region of nxf-1(t2160ts) mutant gonads (S11 Fig). However, depletion of hel-1 by RNAi did not produce similar RAD-51 foci (S11 Fig). This result suggests that this phenotype is not directly caused by the reduction of RNA export, but instead may reveal an additional function of nxf-1 in genome stability. Once nuclei enter the meiotic pathway and complete the premeiotic S-phase, physiological double-strand breaks (DSBs) are generated through the action of a specialized topoisomerase enzyme SPO-11 [76]. Chromosomes align and synapse, and recombination is largely completed by late pachytene. This mechanism for initiation of meiotic recombination is conserved throughout eukaryotes. As a consequence, RAD-51 foci fail to form in spo-11 mutants, indicative of an absence of DSBs [76]. To assay whether the increased levels of RAD-51 in nxf-1(t2160ts) mutants were due to a deregulation of SPO-11 activity, we knocked down spo-11 by RNAi in an nxf-1(t2160ts) mutant background. Depletion of spo-11 did not suppress the formation of RAD-51 foci in the nxf-1(t2160ts) mutant, indicating that they are independent of SPO- 11 activity (S11 Fig).
To gain insight into the transcriptional consequences of reducing mRNA export, we performed RNA-seq analysis of nxf-1(t2160ts) mutant worms and compared the gene expression profile to that of N2 WT worms. Since nxf-1(t2160ts) is a temperature sensitive mutant, synchronized one-day old adult-stage WT and nxf-1(t2160ts) worms grown at 15°C were shifted to 25°C for 12–16 hours before RNA extraction. Three biological replicas of each analysis were performed. RNA extraction, deep sequencing and quantitative differential expression analysis were performed as described in Material and Methods. Raw sequence data generated in this study are available at the Gene Expression Omnibus (GEO) data repository (Accession number GSE116737). Statistical analysis with the DeSeq and Edger bioinformatics algorithms showed 1117 statistically significant downregulated genes and 834 statistically significant upregulated genes in nxf-1(t2160ts) mutants vs WT (S12 Fig).
Our KEGG pathway analysis [77] of these sets of genes revealed that mRNA export reduction in the nxf-1(t2160ts) mutant led to activation of RNA transport and mRNA surveillance pathways. nxf-1 expression itself, its binding partner nxt-1/p15 and other genes involved in RNA transport are significantly upregulated when nxf-1(t2160ts) is mutated. This result likely suggests the existence of a transcriptional feedback mechanism that activates mRNA export in response to low levels of cytoplasmic RNA. Similar transcription-translation feedback loops (TTFL) in which genes are transcribed until their protein products accumulate and are transported into the nucleus, thus inhibiting positive elements from the promoter region of the gene so that transcription is halted, have been described from yeast to mammals [78, 79, 80, 81, 82]. Consistently, genes involved in other aspects of the RNA life cycle such as ribosome biogenesis pathways appear as significantly downregulated, which again suggests a regulatory transcriptional response to adapt the number of ribosomes to the few transcripts available in the cytoplasm (Table 2, S1 Table, S2 Table, S12 Fig).
In addition, a Gene Ontology analysis [77] of the same sets of differentially expressed genes revealed that they do not randomly fall within different molecular function categories. Instead, the significantly upregulated set in the nxf-1 mutant is highly enriched for GTPase binding, RasGTPase binding, small GTPase binding, actin binding and cytoskeleton protein binding genes. On the other hand, the set of significantly downregulated genes is enriched in genes involved in oxidative phosphorylation and mitochondrial ATP synthesis (Table 2, S12 Fig). These results suggest that reduction of mRNA export has a deep impact on cytoskeletal dynamics that could underlie the nxf-1(t2160ts) epidermal and mitochondrial defects. These results prompted us to specifically study the cytoskeleton and mitochondrial network in the nxf-1(t2160ts) mutant.
Cytoskeletal growth and rearrangement require the translation of specific mRNAs that code for structural components and regulatory proteins connected to the cytoskeleton [83]. To examine the actin filament network in WT and nxf-1(t2160ts) embryos, we used phalloidin staining. Whereas WT embryos accumulated actin at the nascent apical surface at the onset of epithelialization, we observed a decrease in filamentous actin (F-actin) staining in the mutant. In addition, actin remained dispersed in the arcade cells of nxf-1(t2160ts) embryos compared to WT (Fig 8). Next, we evaluated the mitochondrial network morphology by discriminating between four types of mitochondrial shapes: connected, intermediate, fragmented and very fragmented [84]. C. elegans WT embryonic cells show a connected mitochondrial network in their cytoplasm (S13 Fig). In contrast, nxf-1 (t2160ts) embryos grown at 25°C showed a general dotted pattern of Mitotracker staining in their cytoplasm, indicating the additional presence of fragmented-type mitochondria (S13 Fig). To further validate this observation, we analyzed mitochondrial morphology in adult muscle cells, a tissue where mitochondria are highly abundant and evident. To do so, animals were grown for 8 days at 25°C and scored at day 1 post L4, day 4 and day 8. 64% (n = 47) of the nxf-1(t2160ts) body wall muscle cells already showed a fragmented pattern of mitochondrial network at day 1 (S14 Fig). A higher percentage (76%) (n = 46) of nxf-1(t2160ts) muscle cells still had the fragmented phenotype at day 8, whereas in WT worms, only 14% (n = 21) of muscle cells showed this mitochondrial morphology (S14 Fig). This fragmented mitochondrial network observed in nxf-1(t2160ts) is detectable in different types of embryonic cells and not restricted to epithelia. Therefore, it does not seem to be the cause of morphogenetic defects but rather a result of cytoskeletal defects [85].
These data, as a whole, point to a model in which the decrease in cytoplasmic mRNA available for actin rearrangement could explain the reduction and disorganization of the actin cytoskeletal network in nxf-1(t2160ts) mutant embryos, leading to cell attachment and elongation defects [86,87]. The transcriptional activation of genes coding for small GTPase, actin and cytoskeleton binding proteins further supports the existence of a transcriptional feedback mechanism that activates the expression of those genes in response to the cellular requirements of cytoskeletal rearrangements.
To get deeper insight into the molecular mechanism by which NXF-1 acts in the cell, we identified C. elegans NXF-1 co-immunoprecipitated protein partners using LC-MS/MS (liquid chromatography-mass spectrometry/mass spectrometry). We expressed NXF-1::3xFLAG::eGFP to immunoprecipitate NXF-1 along with its protein partners and used the N2 WT strain as the negative control. Immunoprecipitations (IPs) from three replicate JCP519 and N2 worm extracts were eluted from the beads by competitive elution with the 3xFLAG peptide. Next, immunoprecipitates were resolved by SDS-PAGE, and stained with Coomassie Blue. Proteins were identified by LC-MS/MS (Table 3). Co-immunoprecipitated proteins fall into the following two main categories:
The formation and maintenance of specialized organs depend on developmental signaling pathways that regulate cell proliferation and differentiation, as well as establishment of the correct architecture by regulating cell-cell adhesion, cytoskeletal organization and apical-basal polarity within the constituent cells. For this to happen, gene expression has to be tightly regulated in all the steps; from transcription to mRNA export and translation [1,3]. Three levels of regulation control formation of the arcade cell epithelium: first, the transcriptional level; second, the level of protein expression; and third, the protein localization to nascent adherens junctions [36].
Nuclear export factor 1 (NXF-1), but not its ortholog, NXF-2, has been shown to play an essential role in mRNA export in C. elegans [41,59,60]. However, the consequence of NXF-1 partial loss-of-function was not examined previously. Isolation of the nxf-1(t2160ts) thermo-sensitive mutant provides an invaluable tool for analyzing the spatial and temporal in vivo role of mRNA transport during development. nxf-1(t2160ts) mutation results in mRNA accumulation in all cell nuclei. This inability to export mRNA primarily disrupts epidermal and pharyngeal morphogenesis during embryonic development. Mutant embryos do not elongate properly and show problems with epidermal cell organization. In addition, although the pharynx was evident and the pharyngeal lumen was visible, in most of the cases (87.5%, n = 176) it was unattached to the mouth.
Our genetic analysis revealed that the observed phenotypes were not the result of cell fate mis-specification, but rather cell morphogenetic defects. Expression of pha-4, a transcription factor that regulates pharyngeal development [46,100]; myo-2/Myosin-3, pharyngeal muscle myosin [44] and ric-19/ICA1, which is expressed in nervous system [45], revealed that the fate of major pharyngeal components was properly specified. In contrast, expression of membrane-tagged GFP [32,56] and the apical junction markers: PAR-6/PARD6A; DLG-1/Discs large [101,102]; AJM-1 [62] and HMR-1/E-cadherin [103] indicated that Pun pharynxes of nxf-1(t2160ts) animals are possibly a result of lost cell polarity and failed epithelialization of arcade cells. Similar expression patterns of apical markers have been observed in pha-1 mutants [104].
These developmental defects do not reflect a specific function of NXF-1, but rather the consequence of the reduction of mRNA export. Disruption of other nuclear export factors such as NXT-1/p15 and HEL-1/UAP56 also led to similar embryonic lethality, epidermal defects and the Pun phenotype. Thus, although it affects all cells, hypodermal and especially pharyngeal development seem to be particularly sensitive to a reduction in the efficiency of mRNA export. The arcade cell epithelium forms extremely rapidly, in less than 10 min, while epidermal epithelialization takes over 30 min [32,33]. Therefore, pharyngeal morphogenesis probably requires extremely tight temporal control over the differentiation process.
As a consequence of the low amount of cytoplasmic mRNA, nxf-1 mutation causes upregulation of genes involved in mRNA export and downregulation of ribosomal RNAs. aly-1/ALYREF, eef-1A.2/EEF1A1, npp-14/NUP214, npp-21/TPR, nxt-1/p15 and nxf-1 itself, among others, appear significantly upregulated in nxf-1(t2160ts) worms compared to WT N2 animals (S1 Table, S2 Table). Such a feedback mechanism has also been described in Drosophila Schneider cells (S2 cells) in which blocking the NXF1-mediated mRNA export pathway results in upregulation of export factors [105].
A similar feedback regulatory mechanism also seems to operate for genes involved in cytoskeletal rearrangement. nxf-1 loss of function causes the lack of an apical junction in arcade cells (Fig 5, S3 Fig, S4 Fig) and a dramatic reduction in filamentous actin in nxf-1 mutant embryos (Fig 8). Our transcriptomic analysis shows a significant upregulation of genes involved in cytoskeletal maintenance: GTPase binding, Ras GTPase binding, small GTPase binding, Rho GTPase binding, actin binding, and cytoskeletal binding proteins. This overexpression likely occurs as a feedback mechanism due to an insufficiency of cytoplasmic mRNAs necessary for cytoskeletal maintenance and rearrangement. Transcriptional activation of these genes is indeed a critical step during epithelial polarization and cytoskeletal reorganization [87].
Studies in Drosophila suggest a functional connection between SBR/NXF1 and the cytoskeleton [106]. In early D. melanogaster embryos, SBR/NXF1 marks the spindles of dividing nuclei [107]. We found the HCP-1/CAGE1 protein among the NXF-1 interactors in the immuno-precipitation experiments. HCP-1 is a centromere-associated protein involved in the fidelity of chromosome segregation [108]. The key role of HCP-1 is to target CLS-2/CLASP to kinetochores which promote the polymerization of kinetochore-bound microtubules [109]. Detection of HCP-1 suggests that NXF-1 may play a role in mitotic spindle assembly independently of mRNA transport. This functional connection between NXF1 and the embryonic mitotic spindle may underlie the slow cell division rate and the DNA breaks observed in C. elegans nxf-1 (t2160ts) mutants. D. melanogaster sbr10 and sbr5 mutants have morphological spindle defects in their first meiotic division [110]. Moreover, the sterile males of sbr12 mutant flies display immobile spermatozoa which exhibit disturbances in mitochondrial morphology and cytokinesis similar to those described here [106,107].
In addition to the defects in epidermal and pharyngeal morphogenesis, nxf-1 loss of function reduces the gonadal mitotic regions in C. elegans. The reduced number of mitotic germ cells in nxf-1(t2160ts) animals and the small number of cells in M-phase could be explained by mitotic delay of cells entering into the M-phase, which leads to mitotic defects and increased CDK-1 phosphorylation levels (Fig 6, S10 Fig). C. elegans germline proliferation is governed by GLP-1/Notch-receptor and other regulators [69, 70]. Our results suggest that efficient mRNA export of those and/or other factors is key to proper mitotic progression in the C. elegans gonad. Thus, knockdown of HEL-1/UAP56 also leads to increased CDK-1 phosphorylation levels (S10 Fig). Interestingly, UAP56/HEL-1 associates with the mitotic apparatus in HeLa cells. When UAP56/HEL-1 was knocked down, chromosome misalignment and mitotic delay at prometaphase were frequently observed in mitotic cells. Chromosome misalignment causes activation of the spindle assembly checkpoint (SAC) which arrests mitotic progression at prometaphase [111].
Interestingly, not only mitosis but also meiosis is affected in nxf-1(t2160ts) animals. The massive accumulation of RAD-51 foci in the meiotic region suggests the existence of multiple DNA breaks. Importantly, knockdown of other mRNA export factors such as HEL-1/UAP56 does not lead to the same accumulation of RAD-51 foci, suggesting that they are not caused by the lack of mRNA export (S11 Fig). These breaks could form as a consequence of the impaired cytoskeleton dynamics during chromosome pairing or could reflect the existence of torsional stress at the DNA fiber level upon NXF-1 downregulation. This mechanical stress activates ATR which seems to modulate nuclear envelope plasticity and to promote chromatin detachment from the nuclear envelope [112,113]. Unexpectedly, high levels of mRNA from a transgene containing the hsp-16.2 promoter, GFP, and the unc-54 3’UTR (hsp-16.2::gfp::unc-54 (3’UTR)), has been detected in the so-called “expression zone” [26] that overlaps with the region where we see the meiotic RAD-51 accumulation in the nxf-1(t2160ts) mutant. Additional studies in C. elegans show that the heat shock hsp-16.2 gene promoter relocates to the nuclear periphery after heat shock [114]. These findings suggest the existence of a yet unknown stress response mechanism in the late pachytene/diplotene germ cells.
In summary, mRNA export is required in all tissues and organs. However epithelial cells that undergo a rapid morphogenetic transformation during development (such as arcade cells and epidermis) and the germline (the only proliferative tissue in adult nematodes) appear to be highly sensitive to reductions in the mRNA export rate in C. elegans. Many proteins involved in mRNA export have been implicated in cancer, developmental and neural diseases [1,115,116,117,118,119]. It has been shown that NPC can be reprogrammed as part of the oncogenic transformation process, the result of a viral infection or during oxidative and metabolic stress [1,8]. Interestingly, bioinformatic research predicts NXF1 to be a probable tumor suppressor gene (TSG) [120]. A deeper understanding of the processes involved in mRNA export from nucleus to cytoplasm is required. Basic aspects of their relationship to stress and DNA damage response remain an open question. This knowledge will shed light on many aspects of biology ranging from cell differentiation to morphogenesis and disease.
Standard methods were used to culture and manipulate C. elegans strains [121]. Worms were grown on NGM (nematode growth media) agar plates. Plates were previously seeded with an LB (Luria-Bertani) liquid culture of the Escherichia coli strain OP50 (Uracil auxotroph, E. coli B., ampicillin resistant from CGC) overnight at 37°C (ampicillin (100 mg/ml) and nystatin (0.004%)), and air-dried.
When larger amounts of worms were needed (for IP experiments), egg-seeded plates were used. Egg plates were prepared as described [122]. Normal NGM plates were seeded with 5ml egg mix and air-dried.
In this study, worms were grown at 15°C and 25°C. The nxf-1(t2160ts) mutant is temperature sensitive so it was maintained at 15°C. Before all experiments, worms were shifted to the non-permissive temperature of 25°C. The C. elegans strains used in this study are listed in S3 Table. Their genotypes, characteristics and sources are shown.
3D FISH protocol [123] was followed. Embryos were fixed on slides using the freeze-crack procedure. For hybridization, the probe against the poly-A sequence of mRNA (40T) labeled with Cy3 fluorochrome (Sigma) was added to the hybridization buffer and slides were incubated for 2–3 days at 37°C.
Phalloidin staining [124] was performed. Embryos were fixed on slides using the freeze-crack procedure. After cracking, eggs were fixed for 20 minutes in fix/permeabilization solution (4% PFA; 0.2% Triton X-100; 50mM PIPES pH 6.8; 25mM HEPES pH 6.8; 10.2mM EGTA; 2mM MgCl2), then slides were rehydrated/permeabilized by three 5-minute washes in 1X PBS in a Coplin jar, followed by 90 minutes of incubation with CytoPainter Phallooidin-iFluor 488 solution (Abcam). Slides were washed 2 times in 1X PBS and mounted by adding a drop of ProLongTM Diamond Antifade Mountant with DAPI (Invitrogen).
Young adult worms were dissected in dissection buffer (1X egg buffer, 0.02% Tween-20, 0.2mM Levamisole and Milli-Q H2O). Dissected gonads on slides were fixed in fixation buffer (1X egg buffer, 0.02% Tween-20, 4% formaldehyde and Milli-Q H2O) covered with a coverslip (24x24 mm), incubated for 5 minutes and dipped in liquid nitrogen. Coverslips were flipped away and slides were incubated in Coplin jars in precooled (-20°C) 1:1 acetone: methanol solution for 10 minutes. Next, slides were washed three times (10 minutes each) in 1% Triton PBS buffer followed by another 5-minute wash with 0.1% Tween-20 PBS. Samples were blocked for 20–30 minutes in a Coplin jar with 10% fetal bovine serum diluted in 0.1% Tween PBS. Slides were pre-blocked for 20–30 minutes using Image- iT FX Signal Enhancer (Invitrogen). Slides were incubated with the desired first antibody, washed three times (10 minutes each) in 1% Triton PBS buffer, stained with the appropriate secondary antibody, and mounted by adding a drop of ProLongTM Diamond Antifade Mountant with DAPI (Invitrogen). The following antibodies were used: anti-RAD-51 (1:10000, SDIX 2948.00.02); anti-pH3 (detects pSer 10 H3, 1:400, Santa Cruz Biotechnology sc-8656R); anti-pTyr15 CDK-1 (1:10000, CALBIOCHEM 213940); goat anti-rabbit IgG (H+L), Alexa Fluor 555 (1:1000, Thermo Fisher Scientific); goat anti-rabbit IgG (H+L), Alexa Fluor 488 (1:1000, Thermo Fisher Scientific).
In order to immunoprecipitate NXF-1 and Co-IP their interactors, protein extracts from JCP519 (nxf-1(t2160ts) V; jcpEx6[pAZ09(Pnxf-1::nxf-1::3xFLAG::eGFP::nxf-1UTR)]), were used. Extracts from WT worms were used as the negative control. A large amount of protein extract was needed, and thus 8 to 10 NGM egg plates were used. Protein extracts were measured using the BCA Protein Assay Kit (Fisher Scientific) according to the manufacturer’s instructions. IP/Co-IPs were performed with Anti-FLAG M2 Magnetic Beads (Sigma) composed of the murine derived ANTI-FLAG M2 monoclonal antibodies attached to superparamagnetic iron impregnated 4% agarose beads. The eluted IPs were run on Mini-PROTEAN TGX Precast Gels by sodium dodecyl sulfate-polyacrylamide gel electrophoresis (SDS-PAGE) and proteins were separated according to their molecular weights [125]. Next, the gels were stained with Coomassie Blue and bands were excised. Proteomic analysis was performed at the CIC Biogune proteomics platform (https://www.cicbiogune.es/org/plataformas/Proteomics).
C. elegans biolistic bombardment was performed as described [121], with few modifications. We used the Biolistic PDS-1000/He Particle Delivery System (Bio-Rad). This system uses high-pressure helium, released by a rupture disk and a partial vacuum, to propel a macrocarrier sheet with millions of microscopic DNA-coated gold particles toward target worms at a high velocity. In this work, JCP495 (nxf-1(t2160ts) V) was successfully rescued by bombardment with plasmids pAZ07 (Pnxf-1::nxf-1::nxf-1UTR) and pAZ09 (Pnxf-1::nxf-1::3xFLAG::eGFP::nxf-1UTR) (S1 Fig). The JCP519 (nxf-1(t2160ts) V; jcpEx6[pAZ09(Pnxf-1::nxf-1::3xFLAG::eGFP::nxf-1UTR)]) strain expressing NXF-1::3XFLAG::eGFP was generated by gene bombardment using the plasmid pAZ09.
In this study, RNAi was achieved by feeding worms with the bacteria that produced the desired dsRNA. RNAi clones of nxf-1, hel-1 and rnp-4 (our lab), as well as nxt-1 and mag-1 [126] were used in this study. The empty L4440 vector in HT115 cells was used as a control. For a “mild” RNAi effect, bacterial RNAi clones of nxf-1 and nxt-1 were diluted with L4440 at a 1:1 concentration.
For microscope preparations, worms were monitored on NGM plates under a Leica Stereo microscope (MZ16FA). DIC was performed on a fluorescent Leica microscope (DM600B) equipped with a Hamamatsu Orca-ER C10600 camera fitted with DIC optics. C. elegans embryos, larvae and adults were mounted on 4.5% agar pads and observed under DIC optics [127]. Images were captured with Micro-manager software (https://micro-manager.org/) and processed with XnView software and ImajeJ or Fiji software.
Confocal microscopy imaging was performed with a Zeiss 780 confocal microscope (immunofluorescence and phalloidin staining experiments). Images were acquired and processed using ZEN lite open software from Zeiss and ImageJ/Fiji.
Relative fluorescence image data obtained from ImageJ/Fiji was statistically analyzed with IBM SPSS Statistic 21, and the representative graphs were created with GraphPad Prism 6 software.
In this study, the nxf-1(t2160ts) strain was backcrossed with the Hawaiian (CB4856) strain. Around 3000 F2 recombinants (t2160ts)/(Hawaiian-CB4856) were singled out. 560 thermo-sensitive F2 t2160ts/Hawaiian recombinants were obtained. Total DNA extraction of 560 C. elegans worms (560 recombinants (t2160ts)/(Hawaiian-CB4856)) was performed using the Plant/Fungi DNA Isolation Kit (Norgen Biotek Corp.) following the manufacturer’s instructions. This kit enabled us to isolate total DNA from a small number of worms.
Using the Hawaiian single-nucleotide polymorphism (SNP) mapping method, we backcrossed the nxf-1(t2160ts) mutant with the polymorphic Hawaiian strain [128]. Next, we isolated the newly generated F2 recombinants homozygous for the nxf-1(t2160ts) mutation, (Hawaiian-CB4856)/nxf-1(t2160ts). Using 205 ng genomic DNA obtained as described, sequencing libraries were constructed using the NEXTflex Rapid DNA-Seq Kit according to the manufacturer’s instructions (Bioo Scientific). DNA quality and integrity were evaluated by Experion Automated Electrophoresis System (Bio-Rad) and the concentration was calculated using qPCR. Libraries were prepared at the genomic platform of the CIBIR (http://cibir.es/es/plataformas-tecnologicas-y-servicios/genomica-y-bioinformatica) and sequenced on an Illumina HiSeq 15000. The quality of DNAseq results was assessed using FastQC(http://www.bioinformatics.babraham.ac.uk/projects/fastqc/). Paired-end 100-bp sequencing yielded a theoretical mean coverage of 245X of the C. elegans genome. The FastQ files were analyzed using a Cloud-Based Pipeline for Analysis of Mutant Genome Sequences (Cloudmap tool, https://usegalaxy.org/u/gm2123/p/cloudmap) with standard parameters following Cloudmap workflow [42].
Total RNA extraction from C. elegans worms was performed using the RNeasy Mini Kit (Qiagen) following the manufacturer’s instructions. Four 99x16.2 mm worm plates of nxf-1(t2160ts) and WT worms were used.
RNA deep sequencing was performed at the genomic platform of the CIBIR (http://cibir.es/es/plataformas-tecnologicas-y-servicios/genomica-y-bioinformatica). Expression analysis was performed by DESeq2 [129] and edgeR [130] as described [131, 132].
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10.1371/journal.pcbi.1004381 | Multiscale Estimation of Binding Kinetics Using Brownian Dynamics, Molecular Dynamics and Milestoning | The kinetic rate constants of binding were estimated for four biochemically relevant molecular systems by a method that uses milestoning theory to combine Brownian dynamics simulations with more detailed molecular dynamics simulations. The rate constants found using this method agreed well with experimentally and theoretically obtained values. We predicted the association rate of a small charged molecule toward both a charged and an uncharged spherical receptor and verified the estimated value with Smoluchowski theory. We also calculated the kon rate constant for superoxide dismutase with its natural substrate, O2−, in a validation of a previous experiment using similar methods but with a number of important improvements. We also calculated the kon for a new system: the N-terminal domain of Troponin C with its natural substrate Ca2+. The kon calculated for the latter two systems closely resemble experimentally obtained values. This novel multiscale approach is computationally cheaper and more parallelizable when compared to other methods of similar accuracy. We anticipate that this methodology will be useful for predicting kinetic rate constants and for understanding the process of binding between a small molecule and a protein receptor.
| We estimated the kon rate constant of four biochemically relevant ligand-receptor systems using milestoning theory. All results closely resemble experimentally and theoretically determined results, indicating that this technique may be applied toward accurate estimation of binding rate constants for additional ligand-receptor systems of biomedical interest.
| Estimating kinetics is an important and challenging task in computational biophysics. The kinetic rate constants of ligand-receptor interactions, in particular the kon and koff values, play an important role in enzymology[1] and drug discovery[2]. Kinetic rate constants of ligand-receptor association and dissociation are important determinants of drug efficacy[2], and the optimization of these quantities is an important problem in medicinal chemistry. Although these values may often be measured experimentally, an accurate computational estimate would be attractive in cases where experimental measurement is expensive or difficult. In addition, advances in computational power, particularly in parallel computing, offer great potential for methods that take advantage of the vast and increasing power of computation.
As indicated in Eq 1, ligands typically bind to receptors according to a second order reaction process with a rate constant of kon. Unless a nonreversible reaction occurs, ligands typically unbind from their receptors according to a first order process with a rate constant of koff.
A number of computational techniques exist to predict rate constants. The timescale of kinetic events vary wildly in biomolecular systems, and can extend between 108 events per second to less than 1 event per hour[1] for a single reaction event at physiological concentrations of reactants. For computational methods that estimate kinetic quantities, there is typically a high correlation between accuracy and computational cost. Explicit all-atom molecular dynamics (MD) is one approach to estimate the kon between a protein and a small molecule[3–6]. Though it offers a relatively high degree of accuracy, this technique involves extensive cyberinfrastructure overhead or access to specialized hardware such as the Anton machine[7]. To our knowledge, the longest MD simulations to date are limited to the low millisecond range[8].
Various theories and algorithms offer cheaper alternatives to making kinetic approximations using brute-force, all-atom explicit MD simulations. Examples include two closely related techniques: Markov state models (MSM)[9–17] and milestoning[18–23] among many others.
Brownian dynamics (BD) is a simulation method used to model macromolecular diffusion in an aqueous solvent[24]. Compared to MD simulations of intermolecular encounters, BD simulations typically require far less computation to simulate an association event. Due to various approximations, including rigid body dynamics, reduced point-charge interactions, implicit solvent, and a relatively large timestep, millions of protein/small molecule binding or association events can be simulated in 24 hours using modest parallelization. However, the approximations and assumptions made when using BD to simulate molecular binding can also introduce inaccuracies. BD can be used alone to model ligand association[25]. However, an accurate recovery of experimentally determined observables related to a binding process frequently requires additional models to approximate physical effects due to solvation shells and polarization, solvent entropic effects, and solute internal degrees of freedom. Some schemes to include these factors in BD simulations have been implemented[26–29].
Methods for combining the speed of rigid body BD simulations with the precision of all-atom MD simulations to predict kinetics have been used in the past. In a technique invented by Luty, El Amrani, & McCammon, the kon of superoxide dismutase (SOD) with its natural substrate O2− was estimated by partitioning space into a region close to the binding site for simulation with MD, and a region far from the binding site where simulation with BD was more appropriate[30,31]. The statistics of each were combined into a kon estimate using a MSM.
Although Luty et. al.’s original method dramatically decreased the cost to estimate binding kinetics compared to brute-force MD, a number of optimizations can be made to the procedure. Though proportionally smaller, the MD regime was disproportionally more expensive than the BD in Luty et. al.’s initial implementation. In this work, we used milestoning theory instead of a MSM to utilize the transition probabilities and incubation times between states. We modified Luty et. al.’s method by further partitioning the MD component with additional milestones. We also used a first hitting point distribution (FHPD) as the starting phase space points for the milestoning trajectories rather than an equilibrium distribution[20,22], a required procedure in milestoning theory. It is interesting to note that Luty et. al.’s method was remarkably similar to milestoning. Their use of surface states in phase space and a transition matrix to represent traversal between the states was somewhat prescient. However, Luty et. al. did not go so far as to integrate time information into the method to estimate mean first passage times (MFPT), nor did they did use FHPDs. Milestoning proper came later[18] and the formalism has since been extensively developed by others[18–22]. A milestoning model is very similar to a MSM; so much so that milestoning techniques have been used to perform MSM calculations[32], and a number of papers provide extensive comparisons of the two approaches[19,33,34].
In addition to repeating the analysis of SOD made by Luty et. al. with our new method, we also estimated kon values for three additional systems. We calculated kons for two simple, theoretically verifiable “spherical receptor” systems: the rate that a Na+ particle crosses an uncharged sphere of radius 6.0Å, and the rate the same particle crosses a charged sphere of radius 6.0Å (Fig 1). We also estimated the kon of binding between the N-domain of Troponin C (TnC) and its natural substrate Ca2+. Since experimentally measured kons existed for each of the two protein systems mentioned above, we attempted to closely recreate the experimental conditions within our simulations and subsequently recapture the correct kons to validate our methods. Armed with this technique, one can make new attempts to estimate kinetic values for biologically or pathogenically interesting systems.
Molecular dynamics is a simulation technique that uses Newton’s or Langevin’s equations of motion in combination with a specified molecular bond structure, parametrized force fields, and a starting conformation of atomic positions and velocities in order to propagate the dynamics of atoms within a molecular system. Ensembles of conformations or trajectories can be sampled to estimate thermodynamic or kinetic quantities[20,35,36].
All MD simulations were carried out using NAMD 2.9[51]. The MD FHPDs were made with the help of MDAnalysis[52]. All calculations were performed on the Gordon supercomputer at the San Diego Supercomputer Center, the Stampede supercomputer at the Texas Advanced Computing Center, and on local machines.
MD simulations of the charged and uncharged spherical receptor simulations were prepared using a simple 40 Å x 40 Å x 40 Å TIP3P[53] water box, we placed a Cl- in the center of the box for the charged spherical receptor. Both systems contain approximately 7600 atoms. Na+ and Cl- parameters were obtained from the ions94 library of the AMBER ff03 forcefield[54]. The spherical receptor systems were minimized for 10000 steps to allow the water molecules to relax in relation to each other and to the Cl-. Both systems were then equilibrated for 20 ns at a constant temperature of 300K using the Langevin thermostat and constant pressure using the Langevin piston at 1 atm with a damping coefficient of 5 ps−1. The Cl- was constrained to a stationary position in the center of the charged spherical receptor system.
Following this equilibration, four copies were made of the systems, and a Na+ was placed at the milestones located at 7 Å, 8 Å, 9 Å and 10 Å from the center of the water box in the uncharged system (Fig 2), and from the Cl- in the charged system. Two additional milestones were also placed at 6Å and 11 Å. Waters clashing with the Na+ were removed. The system was once again allowed to minimize for another 5000 steps to relax the waters around the ions. Then the system was heated in 10 K increments up to 350 K and then reduced back to 300 K at 2 ps intervals each at constant volume. Then, in order to obtain an ensemble distribution, the systems were simulated at constant temperature at 300 K at constant volume for 20 ns. To this point, all ions have been constrained. In order to obtain a FHPD, 900 position/velocity configurations were uniformly chosen between the 2 ns and 20 ns marks in the ensemble simulations. Velocities were reversed, and the trajectories were allowed to propagate backwards. If the trajectory struck another milestone before re-crossing the one it came from, that trajectory was considered part of the FHPD. All members of the FHPD were then allowed to proceed with their velocities in the forward direction. Each transition event was monitored for future milestoning analysis. Reverse simulations were carried out using a special plugin for NAMD 2.9[55], which allows velocities to be reversed at arbitrary timesteps.
For comparison with the milestoning results, brute-force MD simulations were run and Smoluchowski theory was used to estimate a β, kon, and MFPT for the spherical receptor systems. All brute-force MD simulations were set up with the same parameters as for milestoning above, except that the system was equilibrated for 40 ns and 10000 frames were sampled between the 20 and 40 ns time. Each of the 10000 simulations were started with the Na+ placed on the 10Å milestone and monitored for a crossing event at either the 6Å or the 11Å milestone. The value β was simply the number that crossed the 6Å milestone out of the total number of simulations. The MFPT was the average amount of time that all the simulations lasted before a crossing event.
MD force field (FF) parameters for SOD were obtained as a generous gift from Branco et. al.[56] The system was surrounded by a TIP3P[53] water box with 150 mM NaCl solution. The simulation contained approx. 44,000 atoms. The SOD system was then equilibrated for 80 ns at a constant temperature of 300 K using the Langevin thermostat and constant pressure using the Langevin piston at 1 atm using a damping coefficient of 5 ps−1.
Following equilibration, ten copies were made of the apo system, and O2− was inserted at eight different milestones (located at 4Å-11Å in 1Å increments) from each of the two copper ions in SOD’s two active sites, yielding a total of sixteen different milestones simulated (Fig 3). Waters clashing with O2− were removed. The solvent molecules in the system were minimized for another 5000 steps to relax around the newly placed ions. Then the system was heated in 10 K increments up to 350 K and then reduced back to 295 K at 2 ps intervals each at constant volume. The protein and O2− atom positions were constrained during the minimizations and heating/cooling. In order to obtain an ensemble distribution, the systems were simulated at a constant temperature of 300 K and constant volume for 200 ns each with an imposed harmonic “spring” force of 300 kcal mol−1 Å−2 that constrained O2− close to a spherical milestone at each system’s proper distance from the SOD active site catalytic copper. In order to obtain a FHPD, 700 position/velocity configurations were uniformly chosen between the 60 ns and 200 ns marks in the ensemble simulations. Velocities were reversed, and the trajectories were allowed to propagate backwards in time. If the trajectory struck another milestone before recrossing the one it came from, that trajectory was considered part of the FHPD. The autoimage function in CPPTraj[57] was used to center the ligand in the waterbox before the reversal stage. All members of the FHPD were then allowed to proceed in the forward direction. Each crossing event was monitored for future analysis. The reversal phases were simulated using a custom plugin for NAMD 2.9[55].
FF parameters for TnC were prepared according to the protocol followed by Lindert et. al.[58] The system was surrounded by a TIP3P[53] waterbox with 100 mM KCl solution. The simulation contained approximately 27,000 atoms. The TnC system was then equilibrated for 100 ns at a constant temperature of 288 K using the Langevin thermostat and pressure using the Langevin piston at 1 atm using a damping coefficient of 5 ps−1.
Following this equilibration, twelve copies were made of the systems, and the Ca2+ was inserted on the binding side of the TnC site II loop at 1 Å increments from 2 Å to 9 Å from the center of mass of the alpha carbons of residues ASP 65, ASP 67, SER 69, THR 71, and GLU 76 (Fig 4). Waters clashing with Ca2+ were removed. The solvent molecules in the system were minimized for another 5000 steps to relax around the newly placed ions. Then the system was heated in 10 K increments up to 350 K and then reduced back to 295 K at 2 ps intervals each at constant volume. The protein and Ca2+ atoms were constrained during the minimizations and heating/cooling cycles. In order to obtain an ensemble distribution, the systems were simulated at a constant temperature of 300 K and constant volume for 100 ns each with an imposed harmonic force of 300 kcal mol−1 Å−2 that constrained Ca2+ close to the spherical surface at each system’s proper distance from the active site center of mass. In order to obtain a FHPD, 700 position/velocity configurations were uniformly chosen between the 30 ns and 100 ns marks in the ensemble simulations. The reversal phase of the TnC system was performed in an identical procedure as the SOD system.
All Brownian dynamics simulations were performed using BrownDye[27] with desolvation forces and hydrodynamic interactions activated. All electrostatics calculations were performed using the Poisson-Boltzmann Equation solver APBS[59]. The solvent dielectric was left at the default of 78, and the permittivity of a vacuum was left at the default of 8.854×10−12 C2N−1m−2. All macromolecular dielectrics were set to 2, while the dielectrics of Ca2+ and O2− were set to 1. A 6–12 hard sphere Lennard-Jones interaction was used. Simulations were distributed across 10 to 20 threads on a local computing node. The BrownDye program bd_top was used to prepare all systems for simulation. A phantom atom of zero charge and zero radius was placed at the center of the active sites in order to detect crossings of spherical milestones. The phantom atom has no effect on the dynamics, but is merely a convenient way to detect surface-crossing events. The BrownDye program nam_simulation was used for simulation, and the program compute_rate_constant was used to aid in the calculation of the association rate constants. Trajectories were processed using the BrownDye programs process_trajectories and xyz_trajectory in combination with in-house Python scripts.
A PQR file for SOD was prepared from the crystal structure PDB ID: 1CBJ[60] using LEaP[61] and DelEE[62] with the AMBER forcefield[63,64] and PROPKA,47 assigned protonation states at a pH of 7.0. A PQR file for O2− was made by hand, with each oxygen given a partial charge of -0.5 and a radius of 1.5 Å. APBS[59] was then used to calculate the electrostatic field at 295 K and a NaCl concentration of 150 mM to approximate conditions used during the experimental measurement of kon for SOD[65]. BrownDye was used to prepare and run 1×106 BD simulations at 295 K with the ligand starting from a b-surface at ~61 Å from the SOD center of mass. Based on experimentally determined diffusion coefficient[66] of 1.5×10−5 cm2s−1, a hydrodynamic radius of 1.45 Å was used for O2− in the simulations (See S1 Text). We used the Browndye default water viscosity of 1.00×10−3 kg m−1s−1 for all BD simulations of SOD. Reactions with both active sites, and also escape events were counted. 1000 configurations of ligand encounters with both active sites (12 Å from catalytic copper) were extracted to make two additional FHPD distributions. 1000 simulations were started from each configuration (2×106 total). These were allowed to react with a surface further down the site (11 Å from the catalytic copper) react with the surface around the other site (12 Å from the other catalytic copper) or escape to infinity. All reaction and escape events were counted to construct the statistics of the transition kernel K and incubation time vector 〈t〉.
A PQR file for TnC was prepared from the NMR structure 1SPY[67]. Partial charges were assigned according the AMBER forcefield[61] using LEaP [63]and DelEE[62] and PROPKA[64] assigned protonation states at a pH of 7.0. A PQR file for Ca2+ was made by hand, given a charge of 2.0 e and an atomic radius of 1.14 Å. APBS[59] was then used to calculate the electrostatic field at 288 K and a KCl concentration of 100 mM to approximate conditions used during the experimental measurement of kon and koff for TnC[68]. A hydrodynamic radius of 3.0 Å was assigned based on an experimentally determined diffusion coefficient[69] of 6.73×10−6 cm2s−1 at 291 K (See SI S1 Text). BD simulations of TnC used an experimentally determined water viscosity of 1.138×10−3 kg m−1s−1 at 288 K[70]. BrownDye was used to prepare and run 1×106 BD simulations at 288K with the ligand starting from a b-surface at ~57 Å from the TnC center of mass. Diffusion to the active site surface, and escapes were counted. 1000 configurations of ligand encounters with the active site (10 Å from binding site center of mass of residues ASP 65, ASP 67, SER 69, THR 71, and GLU 76) were extracted to make a FHPD distribution. 1000 simulations were started from each configuration (1×106 total). These were allowed to react with a surface further down the site (7 Å from binding site center) or escape to an infinite distance. All reaction and escape events were counted to construct the milestoning model.
For our spherical receptor calculations, we used a dielectric of 92 to mimic the dielectric of TIP3P water[71], a permittivity of 8.854×1012 C2N−1m−2, and a diffusion coefficient[69] of 1.33×10−5 cm2s−1 for Na+. Although the dielectric of 92 for water is obtained from MD and was not experimentally measured, the spherical receptors were intended more for demonstration purposes rather than physical realism, and a dielectric of 92 was chosen in an attempt to allow the values obtained using Smoluchowski theory to match what we observe in the brute-force and milestoning MD simulations.
The rate constants k(a), k(b), and k(q) were calculated using Eq 9 for the uncharged spherical receptor and Eq 10 for the charged spherical receptor for the reaction surface, b-surface, and q-surface, respectively. The rate constant k(a) is the theoretical model of the spherical receptor association. For comparison, we deduced k(a) using only k(b), and k(q) by using a transition matrix K obtained from monitoring transitions of the spherical receptor systems in a series of MD simulations. A binding probability β was calculated using Eq 8. The kon for each spherical receptor system was calculated using Eq 12.
The MFPT represents the mean time taken by a particle started on the b-surface and allowed to diffuse before touching either the reaction surface or the q-surface. The MFPT was calculated using Eq 12. The values k(b) and k(q) are obtained using Eq 9 or Eq 10, depending respectively on the absence of presence of a receptor charge.
For each system, the milestoning calculations were performed using custom scripts that used Numpy 1.7, Scipy 0.9.0 and the GNU Parallel tool[72].
Using Smoluchowski theory, milestoning, and brute force MD simulations, the probability β of each system starting on the b-surface and continuing on to touch the reaction surface is listed in Table 1 along with the resulting kon. The MFPT is also listed for the spherical receptor systems. It is important to note that Smoluchowski theory, as we implemented it, makes use of an idealized model of the system where waters are not modeled explicitly, and therefore the MD and milestoning implementations have different diffusion properties from the theoretical model.
Using the stationary probabilities obtained with milestoning of SOD, Eq 5, Eq 6 and Eq 13 below, we constructed a free energy profile for the approach of O2− to the SOD binding site (Fig 5) setting the 10Å milestone to zero energy as a reference.
ΔGi=−kbTln(pi,statpref, stat)
(13)
Where ΔGi is the estimated free energy of milestone i, kB is Boltzmann’s constant, T is temperature, and pi,stat and pref,stat are the stationary probabilities of milestone i and the reference milestone at 10Å, respectively, obtained using Eq 6.
Luty et. al. assumed that the bound state was a spherical surface of radius 6Å centered on the catalytic copper. This location does appear to have a shallow local minimum at 6Å in the free energy as depicted in Fig 5. Because Luty et. al. assumed that the 6Å sphere was the bound state, and because it is the location of a shallow local minimum in the free energy profile in Fig 5, we assume that the catalytic copper and O2− are in a close enough proximity to one another at 6Å that the rapid and essentially irreversible dismutation reaction occurs. Table 2 lists the estimated kon rate constants obtained in this study for the SOD system.
As with the SOD system, we used the stationary probabilities obtained with milestoning of TnC, Eq 5, Eq 6 and Eq 12 to construct a free energy profile for Ca2+ in its approach to the TnC binding site (Fig 6) with the 10Å milestone free energy as the reference. According to this profile, the lowest energy state is located at 3Å from the binding site center. We assume that when the Ca2+ has reached this distance, it is in the bound state. We use a 3Å binding surface for all subsequence milestoning calculations on TnC. The estimated kon rate constants for the TnC system are listed in Table 3.
In addition to the calculation of kon rate constants, the milestoning models and distributions across the states can be used to visualize the path of the ligand in its approach to association within the binding site. The FHPD for SOD at 12 Å is displayed in Fig 7 and the FHPD for TnC at 10 Å is displayed in Fig 8.
The total computational cost of all systems simulated in this study for both MD and BD was approximately 65,000 CPU hours. Computational costs of each simulated system and simulation regime are listed in Table 4. The cost of performing all non-simulation calculations was negligible. Table 4 includes all computer time spent on the supercomputer as well as on local machines. The β, kons and error estimates for all systems were well converged and are reported in the SI (S2–S9 Figs).
The kon calculated using milestoning for the uncharged spherical receptor system matches within 3% to the theoretically determined value and 0.3% to the brute-force MD value. These estimates are well within the bounds of uncertainty introduced by the milestoning model. As a system that can diffuse freely without forces or solvation shells, it is expected that Smoluchowski theory would yield such a close result to simulation. This similarity to a value obtained using well-established theory is a good validation of our basic methodology. The large difference between the MFPT predicted by theory and the MFPTs predicted by milestoning and brute force MD could be due to a difference between the experimentally measured diffusion coefficient of Na+, and the diffusion coefficient that is observed in an MD simulation using the AMBER forcefield.
The kon calculated using milestoning for the charged spherical receptor system differs by 13% from the kon predicted by Smoluchowski theory and by only 6% from the kon obtained by brute force MD simulation. This difference between the simulation-obtained values and the value obtained by theory is likely due to effects caused by the explicit solvent in our simulations, for which this simple implementation of Smoluchowski theory does not account. Very likely, solvation shells have formed around the Cl- placed in the center of the system, as well as the diffusing Na+. Solvation shells create unevenness in the potential of mean force and the position-dependent diffusion coefficient of Eq 4. As such, using Coulomb’s law for the electrostatic potential and a constant diffusion coefficient may not be sufficiently valid assumptions for ions in solution at such close proximity. Previous studies on close NaCl ion pair interactions in dilute solvent show oscillations in the mean force potential of the interionic distance that extend several molecular layers into the solvent[78–80]. Accounting for these factors and using an alternative solution to Eq 4 would likely result in a calculated value much closer to what we obtained using milestoning and the brute force MD. The fact that the milestoning results and the brute-force MD results are so similar supports the validity of the milestoning methodology. Similarly, with the charged receptor, the large difference in the MFPT predicted by theory and the MFPTs predicted by milestoning and brute force MD could be due to a difference between the experimentally measured diffusion coefficient of Na+, and the diffusion coefficient that would be observed in an MD simulation using the AMBER forcefield. It could also be due to the same effects observed on β caused by the aforementioned solvation shells.
SOD is an enzyme found in a wide variety of organisms[73]. It is a homodimer that makes use of a catalytic copper bound in its active site to catalyze the dismutation of the superoxide ion O2− into O2 and H2O2 [65,73]. SOD was the subject of many early enzymology experiments[81] and ligand-receptor binding simulations[38,82].
The SOD kon estimated using milestoning is within a factor of ~1.5 of the experimentally measured kon that this study attempted to emulate. Although this value falls outside the uncertainty bracket calculated for the milestoning model, it is still within the range of kons measured in other studies[73]. The kon we calculated is also close to the value obtained by Luty, et. al. in their seminal study of SOD kinetics[30]. It is well understood that a higher salt concentration slows the rate of O2− binding to SOD[73]. Therefore, the kon measured in this study is likely smaller than the value measured by Luty, et. al. because they simulated MD and BD with a solvent salt concentration of zero. The discrepancy could also be due to differences used by Luty et. al. in their implementations of atomic constraints on the protein, different boundary conditions in the MD phase, and the lack of desolvation forces in the BD phase.
While it is not clear how much error is introduced by using an equilibrium distribution across the milestones, our use of a FHPD should, theoretically, provide a more accurate treatment due to its consistency with formal milestoning theory[18,19]. The insertion of additional states in the MD region also allowed us to obtain much better sampling of transition events than would be available for a comparable computation time if the MD region had been composed of only a single milestone. The FHPD of SOD at 12Å (Fig 7) indicates that O2− approaches directly from the solvent and does not seem to sample much of the protein surface before entering the active site. Although a kon has already been obtained for this system by Luty et. al. using similar methods, our approach offers a number of key improvements and more closely resembles the experimentally obtained rate constant; both insofar as the conditions that the system was exposed to, as well as the final result.
In order to try this milestoning method on a new system, we also calculated the kon of TnC. The troponin complex is a set of proteins that regulates muscle contraction in skeletal and cardiac muscles[67,68,75]. One of the subunits, TnC is attached to the thin filaments of a muscle fiber, and regulates the binding of Ca2+ to the N-terminal domain of TnC[83]. Ca2+ binding triggers changes within the complex, allowing myosin to latch onto the thin filaments and induce muscle contraction. TnC has been extensively studied due to its critical involvement with heart function and failure, and has been marked as a therapeutic target in heart disease and other disorders[68].
Our method is able to determine the kon to a value that is within a factor of ~5 of the experimentally measured kon that our study attempted to emulate. Although this discrepancy falls outside of both the experimental uncertainty as well as the uncertainty of the milestoning calculation, the value is not unreasonable when compared to kon values measured in other studies[74,75]. The FHPD of TnC at 10 Å (Fig 8) indicates that Ca2+ approaches directly from the solvent, probably due to the high desolvation penalty incurred when the highly charged Ca2+ is removed from its aqueous environment. The surface map seems to indicate two close but distinct minima on the FHPD, suggesting that Ca2+ may have two possible routes to binding (Fig 8).
In total, the entire project, including all simulations of all systems analyzed in this study, cost approximately 65,000 hours of CPU usage. The vast majority of this computation was spread across hundreds or thousands of cores at any one time due to the highly parallel nature of milestoning. The total length of MD simulation for our systems required anywhere between 100 and 1600 ns of total MD time each with relatively low uncertainty due to the high rate of sampling along the milestones leading to binding. The cost is significantly less per target than brute force MD simulations run in past studies to observe kinetic events while yielding similar or superior resemblance to experiment[4,5], which were indicated to require between 600 and 15000 ns of MD simulation to achieve even just a single binding event, with some simulations never even yielding a binding event.
Our multiscale MD-BD-milestoning method offers many advantages; yielding predictive kon estimates for biologically relevant molecular systems within a range of experimental measurements at a cost much less than brute-force MD alone and at accuracy much greater than could be obtained using BD alone. This method also benefits from high parallelism due to the spread of MD computation across multiple states. Given a large number of cores and sufficient CPU hours, the MD portion of the calculation can be completed rapidly. Another advantage of this method is its flexibility, giving the user the ability to adjust the cost-to-accuracy balance by performing additional simulation and adding trajectory samples to increase result convergence. Theoretically, this milestoning framework could be used to investigate any biomolecular association where MD and BD simulation methods can adequately model the process. Estimating the binding kinetics between proteins, DNA, small molecules, or any combination thereof ought to be possible, assuming that sufficient sampling effectively constructs the proper FHPD.
The main disadvantage of this method lies in its complexity of concept and implementation (Fig 9), particularly in the maintenance of large numbers of simultaneous simulations. However, with sufficiently robust software-based automation, the burden of maintaining many parallel instances of simulation, as well as simulation preparation and analysis, can be greatly reduced. Another disadvantage of the milestoning framework is that the simulations are still relatively expensive at this time; requiring a supercomputer or cluster to obtain sufficient sampling within a reasonable time frame, although GPU-based MD could potentially alleviate this burden.
We present a new method to estimate kinetic rates. This method uses milestoning to leverage the strengths and minimize the weaknesses of MD and BD, thereby offering an efficient, high-accuracy estimation of kon rate constants. This multiscale method has been successfully used to estimate the kon rate constant for both idealized and realistically sized, biologically relevant systems. Our work demonstrates that milestoning can be used to obtain kinetic quantities of interest with a high resemblance to experiment. We anticipate that this multiscale approach can be used to determine rate constants of interest as well as system-specific binding details that are applicable to drug discovery, biomolecular modeling, and protein-ligand interactions.
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10.1371/journal.pcbi.1004031 | The Formation of Multi-synaptic Connections by the Interaction of Synaptic and Structural Plasticity and Their Functional Consequences | Cortical connectivity emerges from the permanent interaction between neuronal activity and synaptic as well as structural plasticity. An important experimentally observed feature of this connectivity is the distribution of the number of synapses from one neuron to another, which has been measured in several cortical layers. All of these distributions are bimodal with one peak at zero and a second one at a small number (3–8) of synapses.
In this study, using a probabilistic model of structural plasticity, which depends on the synaptic weights, we explore how these distributions can emerge and which functional consequences they have.
We find that bimodal distributions arise generically from the interaction of structural plasticity with synaptic plasticity rules that fulfill the following biological realistic constraints: First, the synaptic weights have to grow with the postsynaptic activity. Second, this growth curve and/or the input-output relation of the postsynaptic neuron have to change sub-linearly (negative curvature). As most neurons show such input-output-relations, these constraints can be fulfilled by many biological reasonable systems.
Given such a system, we show that the different activities, which can explain the layer-specific distributions, correspond to experimentally observed activities.
Considering these activities as working point of the system and varying the pre- or postsynaptic stimulation reveals a hysteresis in the number of synapses. As a consequence of this, the connectivity between two neurons can be controlled by activity but is also safeguarded against overly fast changes.
These results indicate that the complex dynamics between activity and plasticity will, already between a pair of neurons, induce a variety of possible stable synaptic distributions, which could support memory mechanisms.
| The connectivity between neurons is modified by different mechanisms. On a time scale of minutes to hours one finds synaptic plasticity, whereas mechanisms for structural changes at axons or dendrites may take days. One main factor determining structural changes is the weight of a connection, which, in turn, is adapted by synaptic plasticity. Both mechanisms, synaptic and structural plasticity, are influenced and determined by the activity pattern in the network. Hence, it is important to understand how activity and the different plasticity mechanisms influence each other. Especially how activity influences rewiring in adult networks is still an open question.
We present a model, which captures these complex interactions by abstracting structural plasticity with weight-dependent probabilities. This allows for calculating the distribution of the number of synapses between two neurons analytically. We report that biologically realistic connection patterns for different cortical layers generically arise with synaptic plasticity rules in which the synaptic weights grow with postsynaptic activity. The connectivity patterns also lead to different activity levels resembling those found in the different cortical layers. Interestingly such a system exhibits a hysteresis by which connections remain stable longer than expected, which may add to the stability of information storage in the network.
| The connectivity between neurons - i.e., the number of synapses and their transmission efficiencies (weights) - determines information processing and storage in neural networks and, thus, also the cortex. Thus, in order to understand the functionality of cortical neural networks, we have to understand how they generate their connectivity.
Generally, there are two major processes capable of connectivity changes: the first process, so-called structural or architectural plasticity, builds and removes synapses between neurons [1–4]. The transmission efficiency of these synapses is, in turn, modified by the second process named synaptic plasticity [5–7].
Synaptic plasticity was first postulated by Donald O. Hebb [5]. Later on, experiments showed persistent strengthening and weakening of the synaptic transmission efficacy due to neuronal activity [6,8,9]. Besides firing frequencies, different timings of pre- and postsynaptic action potentials play an important role [7,10]. On a longer time scale, it has also been shown, that synaptic weights can be homoeostatically regulated to reach a certain firing frequency in the network [11].
Structural plasticity, on the one hand, refers to the outgrowth and retraction of axons and dendrites, which is primarily taking place during developmental phases or after major injuries of the network structure [1]. On the other hand, it refers to the process of creating and removing synapses, which is the predominant process in adult networks [12]. As the majority of cortical synapses resides on so-called dendritic spines, we can investigate their dynamics to get an intuition about the dynamics of synapses. Spines are highly motile structures which can appear and disappear on a time scale of hours to days. Their lifetime has been shown to depend on their head-volume [3, 12, 13]. Furthermore, this head-volume correlates with the strength of the excitatory postsynaptic potentials (EPSPs) from the corresponding synapse [14] - the electro-physiological equivalent of the synaptic weight. It has also been shown that stimulation protocols which potentiate or depress the synaptic weight enlarge [15] or shrink [16] the spine head respectively. Thus, synaptic plasticity influences the spine head volume, which determines the probability of structural changes. This indicates a strong interaction of synaptic and structural plasticity.
A recent long-term in vitro study revealed that the dynamics of the spine volume, and, therefore, the removal of synapses, can be treated as a random process on longer time scales [13]. Furthermore, this study confirms the influence of synaptic plasticity mechanisms on this random process.
Therefore, the emergence of neuronal connectivity should be understandable from the interaction between synaptic and structural plasticity. To explore this interaction experimentally, the changes of the weights of synapses would have to be monitored on the time scale of structural changes [17]. Unfortunately experiments have not been extended to this time scale yet.
Thus, as the two processes cannot be measured simultaneously, their interaction currently can only be investigated by theoretical models. For both processes there are already plenty of commonly used mathematical formulations: Synaptic plasticity has been modelled, e.g., by the Hebbian rule [18], the Oja rule [19], the Bienenstock-Cooper-Munro rule [18, 20], or even by arbitrary polynomials in pre- and postsynaptic activity and the synaptic weight [21] for rate-based systems. Further on, there is also a variety of spike-timing-dependent plasticity rules (see, e.g., [22–24]). Mechanisms, which prevent divergence of weights, have been proposed for both classes [18, 25]. On the other hand, there are also several structural plasticity models: Some of them describe the activity-dependent restructuring of networks during development or after major injuries [26–28], whereas others tried to describe connection changes from an information theoretic point of view [29, 30].
The interaction of synaptic and structural plasticity has mostly been addressed by simulation studies [31–33]. These studies already showed the importance of structural plasticity to generate certain statistical network features (e.g., network motifs [34, 35]), but provide little analytical understanding how these features are generated.
In order to obtain a better analytical understanding, a two neuron system and the probability distribution of the number of synapses from one neuron to the other is used as a benchmark, because it has been measured experimentally. In those experiments, the number of synapses between two neurons is estimated by quantal analysis of the excitatory postsynaptic potentials from patch clamp experiments [10, 36–39]. The distributions of the number of synapses between two neurons obtained by this method typically peak between three and eight synapses and shows very low probabilities for one or two synapses (Fig. 1A). The fraction of unconnected neuron pairs in those experiments reaches from 75%−99%, such that the distribution has a second very large peak at zero synapses. Due to this large number of unconnected pairs, the connected part of the experimental distributions is based on very few (10–50) data points resulting to large statistical errors (see Text S1). Thus, these experimental data should be interpreted qualitatively rather than quantitatively.
In a first model, which was proposed to understand how these distributions emerge [40], the synapses of a connection are generated from a pool of potential contacts with a certain probability. Thereby, the probability for a certain number of potential synapses was estimated from morphological reconstructed neurons. Nevertheless, morphology on its own was not sufficient to explain the distribution of the actual number of synapses. It turned out that the probability of small numbers of synapses is strongly suppressed, which means that synapses are not created independently but rather in a collective manner. However, newly established connections typically have less synapses than old ones [3]. Thus, there must be an underlying dynamic process, which influences the stability of synapses, such that their creation appears collective on a longer time scale. The stabilisation of spines in vivo depends on experience-dependent (synaptic) plasticity [41, 42]. As the collective-formation model does not include activities or activity-dependent plasticity, it describes synapse formation only in a coarser way. A model which includes the interaction of synaptic and structural plasticity should provide more insight into the dynamics that lead to the shape of the experimental distribution (Fig. 1A).
In fact, another theoretical study already demonstrated [43] that these experimental distributions can be reproduced with a structural plasticity rule, which is modulated by a synaptic-plasticity-related quantity. Particularly, the authors of this study assume stochastic changes between three possible synaptic states (absent, silent or active) with probabilities modulated by a quantity which resembles an abstract version of spike-timing-dependent plasticity. Hereby, the influence of an active synapse on the postsynaptic neuron is assumed to be constant and not coupled to this plasticity-related quantity. Although this study already shows that this specific interaction of structural and synaptic plasticity can in fact yield biologically reasonable connectivity, important questions are still open: First, under the broad variety of existing synaptic plasticity rules [5, 17, 20, 21, 44], which class of learning rules can exhibit the experimentally observed behaviour in combination with stochastic structural plasticity? Second, the above discussed study [43] assumes fixed firing frequencies, although the firing frequencies between the different experimental locations differ [45–47] and, thus, may influence the structural changes. Moreover, activity levels possibly act as control signal, which drives connectivity to change in a specific way, such that it exhibits certain non-random features (see [34, 35, 40]). Therefore, it is important to evaluate, if and how changes in activity influence the connectivity.
To tackle these questions, in this study, we propose a stochastic model for structural plasticity based on weight-dependent probabilities. Using this model, we calculate the stationary probability distribution for the number of synapses between two neurons. By means of a simple approximation, we derive model-constraints under which this probability distribution corresponds to the biological measured distributions. Interestingly, one of these constraints requires the synaptic weight to grow with the postsynaptic activity, which corresponds to the Hebbian idea of synaptic plasticity. Then, we show that one example system, which matches these constraints, can explain the experimentally observed distributions from different cortical layers by different neuronal activities, which relates well to experimental observations. Considering these suitable activities as a working point of the system, we explore its activity-dependent dynamics around this working point. We find, that increasing and decreasing activity does in fact influence the connectivity strongly. More precisely, the number of synapses undergoes a hysteresis loop when changing either pre- or postsynaptic activity, which suggests potential for memory storage.
In the following, we present a biologically plausible mathematical model with which the (long-term) interaction between synaptic and structural plasticity is investigated. In this model, the process with the slowest time scale is structural plasticity, which operates from days to weeks (see, e.g., [48–50]). We assume that on this time scale the spiking-dynamics of the neurons, which take place in several milliseconds, need not to be modelled explicitly. Therefore, we use rate-based neuron models. The firing frequency vi of such a neuron i is determined by the input-output-relation F, which is an increasing function of the inflowing current:
v
i
=
F
[
∑
j
∑
k
=
1
S
i
j
w
i
j
,
k
v
j
+
I
i
]
.
The first component of this current are signals, which are transmitted from other neurons via synapses and calculated as the product of the presynaptic neurons’ firing frequencies vj and the synaptic weights wij,k of the kth synapse from j to i: wij,k vj. Hereby, Sij denotes the number of synapses from neuron j to neuron i. The second component Ii represents other neuron-specific influences such as leakage currents, inhibitory inputs or inputs from other cortical layers or brain areas (e.g., thalamus). Note, for better readability in this paper we do not indicate the time-dependence of activities, weights, number of synapses or stimulations. To eliminate covariant model parameters, we normalize the activities vi to the interval [0, 1]. Due to the diversity of experimentally measured input-output-relations we use the logistic function F[x] = 1/ (1 + exp(−x)), if not stated differently. This function is both analytically tractable and it includes the transition from convex to concave input-output relation. This transition is a common feature of most biological neurons, such that we can make qualitative predictions.
As already mentioned above, to model structural plasticity, the morphology of the two neurons and their dendritic and axonal trees is abstracted to a number of potential synapses Pij [51], where each potential synapse represents a location, where a dendritic spine can bridge the gap between axon and dendrite. If multiple spines can do this close to each other (clustered spines [52]), each of them is counted as a potential synapse. Thus, in the following, a connection between two neurons is described by the number of potential synapses Pij, the number of realised synapses Sij and their weights wij,k (Fig. 1B).
At each of the Pij − Sij locations, where no synapse exists, a synapse can be formed with a constant probability pbuild (formation rate). If a synapse already exists, its weight is adapted by a synaptic plasticity rule. The mathematical formulation of this rule should only depend on local quantities of the synapse: pre- and postsynaptic activity and the synaptic weight itself [21]. Furthermore, it is required that the rule leads to stable (bounded) weights w i j * [ v i,v j ] (also called fixed weights) for given pre- and postsynaptic activities.
The removal of an existing synapse can also happen with a certain probability pdel. Inspired by biological weight-volume correlation [14] and the volume-lifetime-dependency [3, 12, 13], we model this probability as a decreasing function of the synaptic weight resulting from the plasticity rule (Fig. 1B, inset):
p
d
e
l
[
w
i
j
]
=
p
b
u
i
l
d
ρ
exp
(
-
a
2
w
i
j
4
/
3
)
(1)
where ρ scales the maximum deletion probability relative to the building probability and, thereby, determines whether deletion happens faster or slower than build-up, and a determines the influence of the weight. The exponent 4/3 has been inspired from spine-volume-dynamics from [13]. However, the results presented below, are insensitive to the exact mathematical form of this function (see Text S5).
We now investigate which connectivity emerges from the model above. Therefore, we calculate the probability p[S] that one single plastic connection from neuron j to neuron i has S synapses in the long-term equilibrium (rest of the network fixed; Fig. 1B). The resulting distribution is especially interesting, because it can be compared to experimental results ([10, 36–39], Fig. 1A). Furthermore, it can be calculated analytically, which we show in the following.
To describe one single plastic connection from neuron j to neuron i, the currents from all neurons other than i and j can be included into Ii. The only current which influences the connection results from the presynaptic activity vj. Note, this effective Ii is now specific for the single connection from j to i. Multiple connections on the same postsynaptic neuron would have different values for Ii.
In the following, some indices will be omitted for better readability: P := Pij, S := Sij and I := Ii. The influence Ij will not be used as it is completely determined by vj = F(Ij).
For given postsynaptic influence I and presynaptic activity vj, we can now calculate the equilibrium probability distribution of the number of synapses p[S] on this connection in the following way: We assume that after each structural change all weights wij,k converge to their fixed point before the next structural change takes place. Thus, we separate the time scales of both plasticity mechanisms as the time scale of structural changes is much longer than the one for synaptic changes [48] which has been similarly applied in [43]. Thus, for a fixed S, we can calculate the fixed weights w i j* [ v i * [ S ],v j ] and activities v i * [ S ]. Knowing the fixed weight for S, we can calculate the deletion probability for S synapses. Thereby, deletion probability p d e l ( w i j* [ v i * [ S ],v j ] ) and building probability pbuild only depend on the current number of synapses but not on past values. The system can thus be treated as Markov-process with the number of synapses as states. For each of those states, we can calculate the probability to jump to any other state from the deletion and building probability and, thereby, create a transition matrix. The long-term equilibrium distribution of the number of synapses on the plastic connection (stationary distribution of the Markov-process) can now be calculated from the (dominant) eigenvectors of the transition matrix.
However, this requires solving a system of equations and is not expressible by a simple formula, which would allow us to investigate the influence of the different components of our model. Thus, to approach the shape of this distribution in a more analytical way, the so-called first-step-approximation can be used [43]. In this approximation the system is only allowed to create or remove one synapse at one time step. Then, the probability flow between two neighbouring states S − 1 and S is in equilibrium given by:
p
[
(
S
−
1
)
→
S
]
=
p
[
S
→
(
S
−
1
)
]
(
detailed balance
)
withp[(S−1)→S]=
p[S−1]
︸
state probability
⋅
(P−S+1)⋅
p
build
︸
transition probability
p[S→(S−1)]=
p[S]
︸
state probability
⋅
S⋅
p
del
[
w
ij
∗
[
v
i
[S],
v
j
] ]
︸
transition probability
.
From this we deduce the ratio of the probabilities between two neighbouring states in equilibrium
Δ
p
[S]:=
p[S]
p[S−1]
=
(P−S+1)
S
p
build
p
del
[
w
ij
∗
[
v
i
[S],
v
j
] ]
.
(2)
By using these ratios, the whole distribution can be recursively calculated as multiples of p[S = 0], which can then be derived from the requirement that the probability distribution sums up to one (see Eq. 7). This provides us with a formula to calculate p[S] (Eq. 6), if we know all activities v i * [ S ] or weights w i j * [ S ] for S ∈ [0,P]. For the model parameters we use, the equilibrium probability distributions obtained by this approximation closely resemble those from a full simulation of the dynamics (see Text S4).
With Equation 2 we have a tool to calculate an approximation of the equilibrium distribution. With this equation, we now want to explore which qualitatively different shapes this distribution can take and how the interaction between neuron model, synaptic and structural plasticity influence it. For this, two distributions are considered to be qualitatively different, if they differ in number and arrangement of their local extrema (peaks and valleys).
As we will show, these extrema are already fully determined by Equation 2, which can, furthermore, be transformed such that the influences of the neuron model and structural plasticity can be mathematically treated independently.
To distinguish qualitatively different probability distributions p[S], we first identify the number and arrangement of their local extrema. For this, we treat p[S] as a function of S. Along this line, the logarithm of the ratios given in Equation 2
Δ
lnp
[S]:=ln(
p[S]
p[S−1]
)=
ln
p
build
+ln(
P−S+1
S
)
︸
:=−
p
cf
−ln
p
del
w
ij
∗
[
v
i
[S],
v
j
]
︸
:=
p
d
(3)
behaves like a discrete version of the derivative of this function. If Δlnp[S] is positive, the function p[S] grows with S. If Δlnp[S] is negative, p[S] will have smaller values for larger S. Therefore, as a necessary condition for extrema, hence peaks and valleys, in p[S], we simply have to determine the zero-crossings Δlnp[S] = 0. Moreover, the sufficient condition for a peak would be a zero crossing from positive to negative values, and for valleys it is the other way around.
To see the influence of the plasticity models more clearly, we rewrite Equation 3 depending on postsynaptic activities v i * in the following way: One can assume that the postsynaptic activity v i * [ S ] grows (strictly monotonically) with the number of synapses. In this case we can invert this relationship to S [ v i * ]. Then, we can rewrite Δln p as a function of the postsynaptic activity v i *. The zero-crossings of Δ ln p [ v i * ] are then given by the solutions of the equation
p
c
f
[
v
i
*
]
=
p
d
[
v
i
*
]
(4)
with
p
cf
[
v
i
∗
] := −ln
p
build
−ln(
P−S[
v
i
∗
]+1
S[
v
i
∗
]
)
and
p
d
[
v
i
∗
] := −ln
p
del
[
w
ij
∗
[
v
i
∗
,
v
j
] ].
Number and types of extrema of the equilibrium distribution of synapses in a connection is determined solely by the intersection topology (crossing points) of p c f [ v i * ] with p d [ v i * ], where fine details of these functions will not matter. This is an important notion as it allows us to analyse types of neural activation function interacting with types of plasticity rules asking whether or not their interaction will reproduce the biologically observed synaptic distributions. Hence, we can restrict the analysis to the different, possibly existing, generic cases how such crossing points could arise from different shapes of p c f [ v i * ] and p d [ v i * ].
Note, in Equation 4 the influence of the deletion probability and synaptic plasticity is represented by pd. The term pcf contains an offset term pbuild and combinatorial factors, which are shaped by properties of the neuron model via S [ v i * ]: we find that that p c f [ v i * ] takes a characteristic S-shape (see Fig. 2A, middle), where curvature may vary following the input-output (neural activation) function (Fig. 2A, left and right). The function pd changes slowly across variable input frequencies but could in principle take any shape. Still, under these constraints, there are only a few possible intersection topologies between p c f [ v i * ] with pd existing, depicted in Fig. 2B. As long as we have slowly changing curvatures of the neural activation functions (Fig. 2A), which represents the biologically relevant situation, we will observe maximally two extrema of the synaptic distributions. We have sorted and numbered these six cases by the number of intersections or extrema in the probability distribution. The resulting qualitatively different probability distributions are sketched in Fig. 2C. It can be seen that the biologically observed case (Fig. 1A) is represented by case 6, but we will show in the following (see section “Connection between two neurons can show hysteresis”) that also some of the other cases play an important role for the dynamics of the system.
Although case 4 also shows two maxima at zero and at higher values, the second peak for number of synapses would only be at the maximum number of synapses P. Thus, the observed falling flank of the upper peak would have to stem from the distribution of potential synapses. As investigations of these distributions show a much shallower tail [40], case 4 will not be considered in the following.
As already mentioned, the experimental distributions of the number of synapses between two neurons [10, 36–39] are based on too small datasets to interpret them as a significant quantitative measurement. However, the shape of the probability distribution is quite similar for all datasets (Fig. 1A), such that we consider this qualitative shape to be a general property of biological neural networks (see Text S1). This common distribution shape exhibits a strong peak at S = 0 followed by a probability minimum for one or two synapses and a second peak between three to eight synapses. As this distribution shape corresponds to case six in Fig. 2C, we can identify the class of biological realistic plasticity rules and neuron models, which yield case six dynamics. Following the results from the previous section, this case necessarily needs two intersections between pd and pcf (i.e. necessary condition: two sign-changes of Δln p) at which the sign of Δln p has to change from negative to positive and from positive to negative (sufficient condition). In the following, we will translate these conditions to properties of the neuron model and the plasticity rule. We will show that, as a necessary condition, the fixed synaptic weight must grow with the postsynaptic activity. Note, this describes a rather generic condition, which, however, is very often not fulfilled by standard learning rules. Nonetheless, we will show that a large class of realistic rules obeys this and especially if the system is embedded in a recurrent network.
Necessary condition In the first step, we determine under which conditions we can obtain two intersections between pd and pcf: If we look at pcf, we find that it is always increasing with S and, thus, also with v i *. Therefore, any monotonously decreasing or constant p d [ v i * ] can intersect it maximally once. This would lead to maximally one sign-change of Δlnp and to the cases 1–4 (Fig. 2). Thus, to exhibit case six, p d [ v i * ] necessarily needs to have a positive slope. Following Equation 1, we obtain that p d ∝ w i j 4 / 3 which is a monotonously increasing function of wij. Thus, instead of investigating the slope of pd, we can also evaluate whether the derivative d w i j [ v i * , v j ] / d v i * is positive, i.e. whether the synaptic weight grows with the postsynaptic activity.
As the plastic connection can be part of a recurrent network, the postsynaptic activity can also influence the fixed weight by feeding back to the presynaptic activity. This dependency can be approximated by a Taylor series v j ≈ r 0 + r 1 v i + O [ v i 2 ]. Note, here we take the unusual perspective of presynaptic activity as a function of the postsynaptic one. We restrict our following analysis to two representative systems: a feed-forward system (r0 = const,r1 = 0) and a linear-feedback system (r0 = 0, r1 = 1).
In these two systems we now evaluate, if the condition of weights growing with postsynaptic activity is met for the following commonly used rate-based learning rules: the Hebb rule and the fixed-threshold Bienenstock-Cooper-Munro (BCM) rule with hard boundaries on the weights, the Oja rule, the BCM rule with sliding threshold [18], and the Hebbian and the fixed-threshold BCM rule with weight-dependent synaptic scaling [25]. Although the time scales of synaptic scaling (minutes to days) and structural plasticity (days to months) slightly overlap, we still apply the Markov-system-approximation for the rules including synaptic scaling. This approximation is supported by the similarity of the probability distributions resulting from the (approximated) analysis and from the simulation of the full system dynamics (see Text S4).
Surprisingly, we find that among the investigated rules a positive slope of the v i * − w i j *-relation is only found by the Hebb rule with synaptic scaling in the linear feedback system or by the BCM rule with synaptic scaling in both systems, hence with and without feedback. Thus, our model predicts that, at least for Hebb-like synaptic plasticity, feedback plays a crucial role in generating the biological distribution of the number of synapses. This relates well to the finding that recurrent microcircuits are overproportionally abundant in cortical networks [34, 35]. When evaluating the v i * − w i j *-relation for a recently published calcium-based plasticity rule [24], we find that also this biologically more detailed rule shows a growing v i * − w i j *-relation. Although we only show a limited set of rules here, many other rules, as, e.g., Hebbian learning with soft bounds and weight decay [21], fulfil this necessary constraint (see Supporting Table S1 for more rules). In general, the analysis we present here can be used as a tool to judge whether a learning rule of interest has the potential to generate a certain connectivity.
Sufficient condition So far, we only set up a necessary condition for two extrema. In our second step, we ensure the right order of the extrema (minimum - maximum). For this, the average curvature of Δlnp must be negative (at least between the two zero crossings), i.e., the difference between the curvatures of pd and pcf must be negative. This could be achieved, either by a strong(er) negative curvature in pd (compare case 61 in Fig. 2B) or a strong(er) positive curvature in pcf (compare case 62 in Fig. 2B). As the common rate-based learning rules mostly do not lead to a strong negative curvature (compare Fig. 3), a positive curvature of pcf seems to be more plausible. As shown above, this can be achieved by a neuron model which has an input-output-curve with negative curvature (concave) in the relevant interval of v i * (resulting in case 62). On the other hand, when assuming very low frequencies, where biological neurons typically have convex input-output-relations, it is more plausible that the relation between fixed weight and postsynaptic activity is sublinearly growing (i.e. negatively curved), which would result in case 61. Although, the commonly used rate-based learning rules typically do not show this behaviour, there are synaptic plasticity rules which fulfil this constraint. For example the v i * − w i j *-relation of a calcium-based plasticity rule [24] fulfils the required behaviour (Fig. 3 rightmost panel). Thus, in either case, also the sufficient condition translates to a biologically reasonable constraint.
In summary, this analysis predicts that biological connectivity can be generated by a weight-modulated structural plasticity rule under biological reasonable constraints. We therefore conclude, that structural rewiring in cortex could be regulated by the synaptic weight or its morphological correlates.
We now want to verify that an example combination of neuron model and synaptic plasticity rule, which fulfils the above conditions, can account for experimental data - here the distribution of connections between cortical layer IV cells [36].
As input-output-curve for this example, we use the logistic function (1 + exp(−x))−1, which has the required negative curvature for positive inputs. Thus, one would expect that the experimental data can be explained for postsynaptic activities above 0.5. For the synaptic plasticity rule, we use a fixed-threshold BCM rule with weight-dependent synaptic scaling [25] in the feed-forward system (r0 = const, r1 = 0):
d
w
i
j
d
t
=
μ
(
v
j
v
i
(
v
i
−
θ
)
−
κ
−
1
(
v
i
−
v
t
s
s
)
w
i
j
2
)
where θ is the LTP / LTD threshold of the BCM rule, κ a parameter which determines the influence of a single presynaptic neuron on the input, and vtss the characteristic activity of synaptic scaling [25]. The learning rate μ sets the time scale of the convergence of synaptic plasticity, but does not influence the equilibrium distribution as long as it is faster than the structural plasticity time scale.
For this model, we calculate the long-term equilibrium distributions p[S] for a broad range of presynaptic activities vj and postsynaptic influences I (on a 446 x 357 grid). In Fig. 4A, one example equilibrium distribution for vj = 0.656 and vi[S = 0] = 0.2975 is compared with the experimental distribution.
As the experimental distribution have so little statistics, standard statistical test fail to compare model and experiment. Therefore, we evaluated if the experimental distributions would be a probable outcome, when sampling from the model distribution randomly, by using a Monte-Carlo test (Fig. 4C, see Methods for details). Like in [40] we define 95% confidence regions as the activity regions, where the model distribution is statistical not significantly different from the experimental one (p-value > 5%), i.e. where the model can account for the experimental distribution (Fig. 4B). For a better comparability, the postsynaptic influence I is transformed to the resulting postsynaptic firing frequency v i * [ S = 0 ] for zero synapses (without presynaptic influence). The resulting confidence region spans a broad range of presynaptic activities and postsynaptic stimulations. However, choosing one of the parameters restricts the other one quite strongly, which indicates, that data could stem from different activity levels, but depends strongly on the right combination between pre- and postsynaptic influences.
We now want to see if the set of plasticity parameters, which was used to account for the layer IV connections, would also be able to explain the distributions from other cortical layers and areas. Although the properties of neurons and synapses could be very different in different layers and areas, we analyse whether the activities can cause the differences in the experimental distributions. Therefore, we also calculate the confidence regions for connections between visual cortex layer V cells [10] and barrel cortex layer II/III cells [38, 39] as well as connections from barrel cortex layer IV to layer II [37] (Fig. 5A). We find that all distributions can be explained by the same model and parameters, but the activities where the model can account for the data differ for each dataset. However, these layer-specific activities are consistent with each other and relate well to biologically observed activities: When comparing the datasets which are taken from rats barrel cortex, we find that the connections from layer IV are on average explained by higher presynaptic activities than connections from layer II. Also, the postsynaptic activity seems to be higher for connections to layer IV. This corresponds to experimental observation of the spontaneous activities in the rat barrel cortex [45, 46] as well as model predictions of the responsiveness of different cortical layers [53]. In both cases, cortical layer IV exhibits higher activities than layer II. Furthermore, the two confidence regions for layer II/III experiments are perfectly overlapping, while - as expected - the dataset with more statistics leads to a smaller confidence region. This supports the hypothesis that activity can be the parameter which causes the differences of the experimental distributions. On the other hand, the confidence regions are also partly overlapping. This indicates, that different activities are not even necessary to explain the experiments. Either way, one parameter-set for the synapses can be used for all layers.
In the following, we obtain a quantitative estimate for the activities in different layers from a model which is more closely matched to biology. For this, we repeated the calculations of the activity confidence regions for layer II and layer IV intra-layer connections from somatosensory cortex with an adaptive exponential integrate-and-fire neuron [54] and the calcium-based spiking plasticity rule [24] described above. We first obtain the input-output-curve of the neuron when stimulated with a constant current. Then, we determine the fixed weight of the plasticity rule when stimulated with pre- and postsynaptic Poisson-spike trains as a function of their frequencies. Interpolation between the simulated values provides us with continuous functions for which we can apply the analysis described above.
As above, we find also for this biologically more reasonable system that the ordering of the activity confidence regions corresponds to the experimental measurements. Furthermore, we assumed that the pre- and postsynaptic activity must be equal for intra-layer connection. Along this line, we estimate the activities in different layers as the intersection between the vi = vj-cline (blue line in Fig. 6) and the activity confidence region of that layer. For the vi = vj-cline, we used both the baseline postsynaptic activity vi(S = 0) (Fig. 6A) and the expected postsynaptic activity calculated for the stationary distribution (Fig. 6B). Although the resulting frequencies do not match the experimental values exactly, our simple model, which uses the same input-output-relation for all neurons and neglects influences of recurrences or inhibition, predicts frequencies on the right order of magnitude [45] of about one Hertz.
In this section, we demonstrate that the structural plasticity model does not require fine tuning to yield bimodal distributions and, thus, that they are a general feature of the proposed model. Along this line, we evaluate the influence of altered structural plasticity parameters on the size and position of the confidence region in activity space for the system used in Fig. 5A.
First, we investigate the influence of an altered number of potential synapses. For this, we use the estimated probability distributions of the number of potential synapses p[P] from [40] instead of the fixed number P = 12. Along this line, we calculate the equilibrium distributions for each P ∈ [1, 20] separately. Then, we sum those distributions weighted with the probabilities p[P]. Again, we test, if this summed distribution is statistically different from the experimental distribution and obtain 95% confidence regions (Fig. 5B). We find that the shape of the confidence regions resembles the one for P = 12 potential synapses, but is overall larger (see Fig. 5B), and that the confidence regions for the P-distributions are slightly shifted to higher presynaptic activities. This is not surprising, because the mean probability mass of the P-distribution lies below P = 12 and smaller P increase pcf by decreasing the combinatorial factors. To maintain the same intersection structure for smaller P, larger weights and larger activities are required in pd.
Second, we vary the parameters a and ρ which determine height and width of the deletion probability pdel. For each combination (a,ρ) we determined the area in the activity space as well as the averaged presynaptic activity and postsynaptic stimulation of the 95% confidence region for layer IV data [36] on a grid of 90x90 values (Fig. 5C-F).
It turns out that an increase in a a or ρ shifts the confidence regions to lower presynaptic activities and higher postsynaptic stimulations (Fig. 5E and F; schematically summarised in Fig. 5C ). Also, there is a corridor in the a−ρ space where the confidence regions reach a maximal area. The relation of a and ρ along that corridor follows a negative linear function (Fig. 5D). Only parameter sets far away from this maximal corridor eventually lead to a disappearance of the confidence region, and, in general, the system still shows the desired behaviour when the optimal parameters are varied by 10 – 20%.
For the neuron model and synaptic plasticity rule we use here, the largest area of the confidence regions is obtained when the presynaptic activity is larger than the postsynaptic baseline activity v i * [ S = 0 ]. Choosing approximately equal pre- and postsynaptic activities leads to smaller confidence regions in this system, but the model can still account for the data. A very large confidence region only means that the system behaviour is very similar over a large range of activities, which indicates that this behaviour is robust to changes in activity. On the contrary, a more confined confidence region means that a significant change of the distribution can be achieved by a smaller change in activity, which indicates that the connectivity can be controlled by changing the activities and stimulations more easily.
The size of the confidence region, however, can only predict the sensitivity of the equilibrium distribution to activity changes. In the following, we answer the question how an altered activity changes the shape of the distribution. For this, we evaluate activity-dependent changes for the above example model which was already shown to exhibit biological realistic behaviour for a certain combination of pre- and postsynaptic influences. We now use this combination as a putative working point of the biological system, keep one of the influences at the value of this working point and calculate the equilibrium distributions when varying the other.
As predicted by the confidence regions, the shape of the distributions are altered strongly by these changes: If pre- or postsynaptic influences are weak, there is only one probability maximum at S = 0 (Fig. 7A for pre- and Fig. 7B for postsynaptic influence). For higher influences, the second peak emerges and for even stronger influences the probability mass shifts to the upper peak until the peak at S = 0 eventually vanishes.
In our example system, changing postsynaptic influence only appears to alter the height of the probability peaks, which are present at the working point, leaving their shape unchanged, whereas changing presynaptic activities also shift the position of the upper peak until it reaches the maximum number of synapses.
In order to understand this behaviour, we treat S as a continuous quantity and calculate the values of S where building and deleting a synapse are equally probable (Fig. 7A-B black lines). These points separate the states S (intervals for continuous S) where the system is expected to increase or decrease its number of synapses. If we treat the stochastic process like a dynamical system which would be following the net probability flow, these points correspond to attractive (solid line) and repulsive (dashed line) fixed points. If the net probability flow points towards one of the boundaries S = 0 or S = P, we also add this boundary as an attractive state.
We see that these fixed points correspond to the shape of the distributions: for both low pre- or postsynaptic stimulation there is only the attractive state at S = 0. For stronger stimulations a pair of repulsive and attractive states is generated (comparable to a saddle node bifurcation in dynamical systems). For high postsynaptic stimulation, a second bifurcation leads to the disappearance of the repulsive fixed-point and the attractive state at S = 0, leaving only the upper attractive state. For presynaptic activity the second bifurcation does not occur if S is a continuous variable. However, it effectively does take place when the two lower fixed points lie closer together than the discrete states of S can resolve.
As we showed above, a necessary condition for the existence of two fixed points is that the fixed-weights resulting from synaptic plasticity grow with increasing postsynaptic activities. Given this, synaptic plasticity maps an increase in the number of synapses onto larger synaptic weights, which finally results in a decrease of the deletion probability. At some point, synapse deletion becomes as probable as synapse creation. This point can be viewed as a threshold: All numbers of synapses below this threshold (below dashed line in Fig. 7A and B) yield systems which remove synapses more likely than they create them and converge to S = 0. For all higher numbers of synapses the system converges into the upper attractor. This bistability emerges from structural plasticity only due to the growing v i * − w i j *-relation. A bistability of the weights themselves, as observed, e.g., for the BCM rule, is not necessary and also not used here (e.g., our vi(S = 0) = 0.2975 is larger than the LTP/LTD-threshold θ = 0.08 of the BCM rule). However, a potential bistability of the weights could even strengthen the bimodality of the synapse-distributions and enhance the effects observed in this study.
Beside the bistability, we also observe regimes with one fixed point. In these regimes, synaptic plasticity does not only map changes in the number of synapses, but also the external pre- and postsynaptic stimulations onto the weights. For example, at very small stimulations, the weights are small and the deletion probability is much larger than the build-up probability. Therefore, the changes in the weights due to the number of synapses may not be sufficient to create a regime where synapse creation dominates such that there is no upper attractor.
These examples show that the activity-dependent change of the equilibrium distribution can only be understood from the interaction of synaptic and structural plasticity. Along that line, Equation 4 provides insight into the fixed points and bifurcations which govern the system dynamics.
In dynamical systems theory, the bifurcation structure described above is associated with hysteresis [55]. A hysteresis, in turn, would mean, that temporary changes in activity leads to persistent changes in the system’s dynamic. This would be a very desirable feature of the system, as it indicates the possibility to store information in the connectivity. However, for a stochastic system a hysteresis does not necessarily emerge, because in contrast to a dynamical system which can remain in a certain state for infinite times without losing information, the stochastic dynamics in the long-term equilibrium yields a stationary distribution which is independent of the system’s history. But, if activities change faster than this equilibrium can be reached, the connection between two neurons could in principle exhibit a hysteresis. To test this, we simulate its behaviour while we repeatedly increase the presynaptic activity or postsynaptic influence stepwise until they are close saturation (v = 0.99) and then decrease them again to v = vtss. Thus, the system is on average exposed to an intermediate stimulation, where both attractors exist, but experiences both very high and very low stimulations during one stimulation cycle. As an indicator, whether the system behaves differently after low as compared to after high stimulation, we use the average number of synapses. We calculate this number for each step of such a stimulation cycle in the following way: the number of synapses is averaged over the time-interval with constant influences (separately for increasing and decreasing). Afterwards, the time-averages for each step are also averaged over all cycles.
Indeed, the average number of synapses shows a hysteresis loop when changing either influence (Fig. 7C and D). This means that the number of synapses can in fact depend on the system’s history. As already mentioned, this is only possible if the system does not reach its stationary probability distribution.
In the activity regime with two attractors, the probability that the Markov-Chain describing our structural plasticity process jumps from one basin of attraction to the other becomes very small. Thus, if the system is allowed to stay in the activity regime with two attractors for sufficiently small times in our simulation, we expect no transitions between the basins of attraction. Thereby, the two parts of the Markov-Chain become effectively unconnected (i.e. ergodicity breaking) and the system can be expected to stay in one basin of attraction. By de- or increasing the stimulation, one of the attractors vanishes and the whole Markov-Chain becomes effectively connected again with higher probabilities. Thus, the probability mass quickly shifts towards the remaining attractor. When the system is then brought back into the two-attractor regime, the Markov-chain separates again into two effectively unconnected parts and the system is very likely to stay in the attractor, it has been brought to.
However, the disconnection of the two parts is not due to a changing probability to jump to another state, as at least pbuild is constant. Instead, the disconnection is a result of the low probability to be at the boundary of the basin of attraction, where creating (or deleting) one synapse already leads to a transition to the other basin of attraction. Thus, the probabilities p[S] of the number of synapses between the two peaks must be very low. As this is a feature of the experimental probability distributions, we expect all systems, which exhibit biological connectivity and a structural plasticity, which can be described similarly, to undergo this effective disconnection of the parts of the Markov-Chain and, thus, also to show a hysteresis. Furthermore, these low probabilities, to be at the boundary of the basin of attraction, can only emerge due to intermediate states between the two attractive fixed points. These states only exist for the multisynaptic system.
We presented a biological plausible, simple model for stochastic structural plasticity in adult networks and analysed its interaction with different synaptic plasticity rules and neuron models. Remarkably, we find that biological connectivity, i.e. the probability distribution of the number of synapses from one neuron to another, can only be explained when this structural plasticity interacts with synaptic plasticity rules leading to increasing synaptic weights along with stronger postsynaptic activities, which corresponds to the basic idea of Hebbian plasticity [5]. Additionally, the firing frequency of the neuron likely grows sublinearly with the input current. We further show that a model, which fulfils these constraints, can account for experimental datasets from different cortical layers at different activities, which are ordered in the same way as experimentally observed activities in those layers. Furthermore, although this is not explicitly implemented in our model, the connectivity can be controlled via pre- and postsynaptic stimulation. Along this line, we demonstrate that the number of synapses may exhibit a hysteresis when altering the pre- or postsynaptic stimulation. The hysteresis emerges from a very small probability that the system is close to the boundary of its actual basin of attraction. This can be assumed for all systems which exhibit the experimentally observed connectivity in long-term equilibrium.
In this work, we use rate-based neurons and rate-based synaptic plasticity rules. However, there exist synaptic plasticity mechanisms on faster time scales, as, for instance, spike-timing-dependent plasticity (STDP, [56]). On the time scale of our model, the effects of such plasticity mechanisms would appear as fluctuations of the weight or fluctuation of the volume of the corresponding dendritic spine respectively.
Accordingly, it has also been shown that the volume fluctuations partly depend on NMDA-receptor activation [13], which is associated with STDP. However, blocking of the NMDA-receptors shows that there are also other fluctuation sources.
As a consequence of such fluctuation, the weights or volumes are rather broadly distributed. Still, for each weight or volume in the distribution there is a certain probability for deletion in a certain time interval, as the fluctuations could have driven it beyond some deletion threshold. As both, the individual deletion probability and the distribution are unknown, we assume that one can directly calculate the expected deletion probability for the whole weight (volume) distribution from the characteristic weight wij emerging from synaptic plasticity via equation 1. Thus, the fluctuations can be viewed as the underlying reason for the stochastic deletion in this model and faster plasticity mechanisms should be already implicitly included. Along this line, the parameter a would correlate with the width of the resulting weight distribution and, so, with the strength of the fluctuations. As already described, volume or weight fluctuations are the basis of the synapse deletion probability in our model, such that we hypothesise that it already covers the influence of faster plasticity mechanisms.
The above argumentation relies on the assumption that spike-timing-dependent plasticity on average leads to undirected changes of the weight, which can be modelled as fluctuations. However, persistent strong temporal correlation between neural activities could alter the resulting synaptic weights strongly. In the case when the weights only depend on correlation of the firing between pre- and postsynaptic neuron, we could use this correlation instead of the postsynaptic firing in our analysis. Then, to fulfil the necessary condition for case six, the weight would have to grow with this correlation. Moreover, it is also reasonable that the correlation saturates at some point and, thus, also the sufficient condition can be fulfilled. Therefore, even when synaptic weights depend on correlations between the neuronal firing, the essential statements of our analysis remain true.
Yet, firing correlations and rates can also jointly determine the synaptic weight, together with many other factors like conduction delays, sub-threshold potentials [57] or molecular concentrations [24]. In that case, the interaction between the plasticity mechanisms would yield even more complex dynamics and our analysis has to be extended by these mechanisms.
In the presented model, we assume that the number of potential synapses between two neurons is constant. For adult networks, this is realistic, because the number of potential synapses is derived from the morphology of axons and dendrites, which has been observed to be quite stable in adult networks [12, 58]. However, there is evidence for large-scale morphological changes of axons and dendrites during development or after major injuries [1, 59]. Moreover, changes of the morphology depend on calcium concentration [60–62], which can be seen as a low pass filtered version of the neural activity. Thus, in order to account for early development or recovery from major injuries, our model would have to be extended by a dynamic number of potential synapses, which depends on the neuronal activity (see, e.g., [28]). However, for healthy adult networks this seems not to be necessary.
Although experiments show an experience-dependent rate of spine formation [42, 49, 52, 63] or removal, the presented model assumes activity-independent synapse formation or removal probabilities. We do so because these effects might as well be a result of experience-dependent synaptic plasticity of the weights which leads to stabilisation or destabilisation of the corresponding dendritic spines [64, 65] without any direct activity dependence in the building or removal probability. However, we want to shortly discuss if and how an explicit activity-dependence of the probabilities would qualitatively change the presented results. For this, one has to consider that the equilibrium distributions of the number of synapses between two neurons are dominantly determined by the ratio between building and removal probability. Thus, if both formation and removal probability change similarly, their ratio, and, thus, the equilibrium probability distributions of the number of synapses, will not change and our results remain valid. Only the dynamics of the system would become faster or slower.
Otherwise, when the activity-dependent changes can be written as factors in pdel and pbuild, only the ratio of those changes will influence the equilibrium probability distribution, while the absolute values will only influence the speed of convergence. Thus, when only considering the equilibrium probabilities, the whole activity-dependence can be modelled by a activity dependence of one of the probabilities: e.g., a building probability which increases with activity (compare [66]) corresponds to a deletion probability which decreases with activity. Both cases can be modelled by a (postsynaptic) activity-dependent term with a negative slope in pd. As a consequence of that, the v i * − w i j *-relation would need an even stronger positive slope to fulfil the necessary condition.
Only when the building probability decreases or the removal probability increases with activity (homoeostasis) the dynamic of the system would change qualitatively and would not yield the same constraints as we find in our analysis.
The topology of neuronal networks has always been proposed to provide information storage capacity. Accordingly, the emergence of a hysteresis implies that the dynamic of a single connection is influenced by its past and, thus, also stores information about it. Systems with similar attractor structures (hysteresis) have been shown to store information, e.g., in computer-hard-drives but also in biological [67] and neuronal systems [68, 69].
It has already been shown that an autonomous network with homoeostatic structural plasticity follows a hysteresis during connectivity build-up in a self-organized way [26]. In contrast, the hysteresis loop in the current study is controlled by external stimulations.
In previous studies, the storable information of a single synapse is deduced from the number of possible topologies of a network, given by the number of possibilities to select a certain number of synapses from the pool of potential synapses [70, 71]. This relies on the assumption, that two different selections of the same number of synapses can be distinguished, which is only possible when considering the morphology of the neural system. However, the behaviourally relevant output of neural networks is rather determined by neuronal and synaptic dynamics than by morphology itself. Thus, we suggest that two topologies must be distinguishable from the network dynamics in order to represent two different states. In our model, by construction, all possible choices of the same number of synapses from one presynaptic neuron yield the same postsynaptic dynamic. Thus, they cannot be distinguished and code the same state. This leads to a decrease in the information capacity per synapse, which could be prevented by extending our model by morphology or other ways to distinguish multiple synapses (delays, shapes of postsynaptic potentials, etc.).
A further decrease in information capacity per synapse can be expected from fluctuations of the number of synapses. For example, on the time scale of the shown hysteresis, these fluctuations effectively allow us to determine only the basin of attraction the system is in. Thus, the sum of the information capacity of all synapses on one connection is maximally one bit.
The advantage of a multiple-synapse connection for information storage as compared to single-synapse systems reveals itself in the duration of learning and forgetting: The system is fluctuating around the attractive states and only transits to the other basin of attraction with a very low probability. This implies that the transition- or forgetting-time of the system exceeds the time, which is needed to build or remove a single synapse.
It was shown that in a structurally similar class of models - the so-called cascade models [72, 73] - the lifetime of memories is enhanced. In these models, connections can be in a weak or a strong state (comparable to the two basins of attraction in our model) consisting of several sub-states (number of synapses). Two plasticity mechanisms, synaptic plasticity and metaplasticity, are modelled by stochastic transitions between states and sub-states respectively. In our model, both transitions would be changes in the number of synapses, which either end up in the same or in the other attractor. Although, in our model, the sub-states are not exactly arranged as a cascade, the dynamics of the single connection is determined by the interaction of multiple exponential processes with widely ranging time scales (see Text S6). Due to the structural similarity, we would expect in our system similar power-law forgetting of stored memories as in the cascade model, which relates well to forgetting measured in humans [74–77].
Besides the functional form of the forgetting curve, another important problem has to be solved to model learning by a neuronal system: the system must always be plastic enough to form new memories, but also keep traces of old memories during acquiring new ones. This problem has been termed the plasticity-stability dilemma [78]. In the context of synaptic plasticity, neuronal systems with bistable synapses, which only switch their state due to extraordinary (high or low) activities [79], have been proposed to solve this problem [80]. In the context of structural changes, the results from above as well as previous work [40, 43] suggest that bistable dynamics also govern the number of synapses between two neurons. Our results now demonstrate that collective dynamics of synapses can store information about the system’s history and that, comparable to synaptic plasticity case, the number of synapses can be shifted to either basin of attraction due to high or low activities. Thus, we expect that the here proposed interaction of synaptic plasticity and structural plasticity can be used similarly to tackle the plasticity-stability dilemma for memories stored in the network structure.
Each connection from neuron j to i in our model has a certain number of potential synapses Pij. At each time step of the simulation and each of those locations, the probability to create a functional synapse is pbuild = const. The number of realised synapses is denoted by Sij and each of these synapses has a weight wij,k with k ∈ {1, …, Sij}. The time development of those weights is described by a synaptic plasticity rule, a differential equation for the weight wij,k, which only depends on local quantities accessible by the synapse (pre- and postsynaptic activities (vj,vi) and the weight itself) and is required to have a stable fixed point w i j * [ v i,v j ]. Every realised synapse can be deleted with a weight-dependent probability
p
d
e
l
[
w
i
j
,
k
]
=
p
b
u
i
l
d
ρ
exp
(
−
a
2
w
i
j
,
k
4
/
3
)
.
Neuronal activities are determined by a nonlinear function F (the input-output curve) of the inflowing currents:
v
i
=
F
[
∑
j
≠
i
∑
k
=
1
S
i
j
w
i
j
,
k
v
j
+
I
i
]
where Ii denotes neuron specific influences from outside the modelled network, e.g., inhibitory input or thalamic afferents. For the function F we use the sigmoidal function F[x] = (1 + exp(−x))−1 if not stated differently.
Synaptic plasticity is fast compared to structural changes [48]. Thus, we assume that the weight has converged to its fixed point w i j * before a structural change takes place. As the fixed weight and the corresponding fixed postsynaptic activity v i * can be determined only from the actual number of synapses Sij, the deletion probability also only depends on Sij. Thus, if we interpret the number of synapses as states of the system, the transition probability to any other state only depends on the actual state. Therefore, similar as in [43], the system can be treated as a Markov process. The transition probabilities from l to k synapses are given by
M
kl
={
∑
x=0
min{l,P−k}
(
l
x
)
p
del,l
x
⋅
(1−
p
del,l
)
l−x
︸
remove x synapses
(
P−l
k+x−l
)
(1−
p
build
)
P−k−x
⋅
p
build
k+x−l
︸
form k − l + x synapses
if k>l
∑
x=0
min{k,P−l}
(
l
l−k+x
)
p
del,l
l−k+x
⋅
(1−
p
del,l
)
k−x
︸
remove l − k + x synapses
(
P−l
x
)
(1−
p
build
)
P−l−x
⋅
p
build
x
︸
form x synapses
if k≤l.
(5)
where p d e ll = p d e l ( w i j * [ v i [ S = l ],v j ] ). As this is a strictly positive stochastic matrix, the Frobenius-Perron-theorem guarantees the existence of a stationary distribution, which can be calculated as the eigenvector for eigenvalue 1. This calculation may be numerically difficult as the entries of the matrix are distributed over many orders of magnitude (e.g., p b u i l d 0…p b u i l d P i j).
However, to estimate the stationary distribution analytically one can use the first step approximation [43]. In this approximation, we allow the system only to increase or decrease its number of synapses by one during one time step. Thus, the states of the Markov-process are connected as a sequence. Once the system has reached its stationary state, the system is in detailed balance, i.e. the probability flow between two neighbouring states S − 1 and S cancel out, such that the probability of either state remains constant:
p[(S−1)→S]=p[S→(S−1)] (
detailed balance
)
with
p
[
(
S
−
1
)
→
S
]
=
p
[
S
−
1
]
︸
state probability
⋅
(
P
−
S
+
1
)
⋅
p
b
u
i
l
d
︸
transition probability
p
[
S
→
(
S
−
1
)
]
=
p
[
S
]
︸
state probability
⋅
S
⋅
p
d
e
l
[
w
i
j
∗
[
v
i
[
S
]
,
v
j
]
]
︸
transition probability
.
From this we calculate the ratio between the probabilities of two neighbouring states in the stationary probability distribution
p
[
S
]
p
[
S
−
1
]
=
(
P
−
S
+
1
)
S
p
b
u
i
l
d
p
d
e
l
[
w
i
j
∗
[
v
i
[
S
]
,
v
j
]
]
only by knowing the fixed weights w i j * [ v i [ S ] , v j ] for each number of synapses. The state probabilities can now be recursively calculated from p[0]
p[S]=p[0]⋅(
P
S
)
p
build
S
∏
S
^
=1
S
p
del,
S
^
−1
with
p
del,S
:=
p
del
[
w
ij
∗
[
v
i
[S],
v
j
] ].
(6)
Finally, p[0] can be obtained from normalization
p[0]=
(
∑
S=0
P
(
P
S
)
p
build
S
∏
S
^
=0
S
p
del,
S
^
−1
)
−1
.
(7)
The investigated synaptic plasticity rules [18–20, 25] are summarised in Table 2.
As an example of a realistic spiking plasticity rule, we simulated the calcium-based plasticity rule proposed in [24]:
τ
w
d
w
(
t
)
d
t
=
γ
p
(
1
−
w
(
t
)
)
Θ
[
c
(
t
)
−
θ
p
]
−
γ
d
w
(
t
)
Θ
[
c
(
t
)
−
θ
d
]
d
c
(
t
)
d
t
=
−
c
(
t
)
/
τ
c
+
∑
k
C
pre
δ
(
t
−
t
p
r
e
,
k
)
+
∑
l
C
post
δ
(
t
−
t
p
o
s
t
,
l
)
where Θ denotes the Heavyside-stepfunction and tpre,k and tpost,l are the times of the kth presynaptic spike and the lth postsynaptic spike. We neglected the bistable potential term, as such a variation can be assumed to have no effect on the ability of this rule to account for various plasticity experiments [24]. However, this allows us to integrate the differential equations analytically between two occurring spikes (see Text S7). At low rates, this integration method allows simulation until weights have converged.
To obtain the fixed weight for a certain combination of pre- and postsynaptic rates, 500 connections were simulated simultaneously with stimulation from independent Poisson-inputs with these rates. After fixed time intervals, which covered at least 200 spikes from each site, the mean and standard deviations of the weights in the ensemble were evaluated. The simulation was stopped when the mean fluctuated around a stationary value and these fluctuations were smaller than the target accuracy of 0.005. For both, Fig. 3 and 6, the parameters for cortical slices were used [24]: τw = 346.361 s, θp = 1.3, γp = 725.085, θd = 1.0, γd = 331.909 τc = 22.7 ms, Cpre = 0.5617, Cpost = 1.2396. For the feedforward system in Fig. 3, we used a presynaptic activity of vj = 8 Hz. The error bars represent one standard deviation.
As the experimental datasets [10, 36–39] only contain little statistics and several numbers of synapses which were not observed, standard statistical tests are not suitable for our study. Instead, we use the following method to generate p-values to test if the experimental data can result from a model distribution p[S]: we sample from this distribution Nexp times, where Nexp represents the number of neuron pairs investigated in the experiments (this number sometimes had to be estimated by dividing the number of connected pairs by the connection probability in that experiment). The sampled data is sorted into a relative frequency histogram psample and compared to the model distribution p[S] by determining the squared error
S
E
:
=
∑
S
=
0
P
(
p
[
S
]
−
p
s
a
m
p
l
e
[
S
]
)
2
.
This process is repeated for NMC times (typically NMC = 1000) to obtain an estimate for the probability distribution of the error SE which results from randomly sampling from p[S]. Finally, we evaluate the squared error SE for the experimental distribution pexp[S] and determine the p-value as the probability to obtain a larger squared error from random sampling.
We simulated the adaptive integrate-and-fire neuron given in [54] stimulated by noisy current I (σnoise = 0.05⋅I) with a forward Euler algorithm. The average spiking rate as a function of the input current was determined over intervals of 2500 s with currents increasing by steps of 0.5 pA in an interval from 600 pA to 1450 pA. The weights resulting from the calcium-based plasticity rule were simulated as described above and scaled with w0 = 0,035 nA Hz−1. Both simulated curves were then transformed to continuous functions by interpolating between the simulated values. For those functions, the fixed postsynaptic activities and weights were solved and used in the further analysis as described for Figs. 4 and 5.
Simulations were carried out in C. For each time step of the simulation, the following calculations were performed for all neurons: (1) calculate new firing rates from the standard sigmoidal input-output curve; (2) calculate weights from the learning rule by using a classical Runge-Kutta algorithm (4th order) for integration; (3) calculate deletion probabilities and delete synapses; (4) create synapses at vacant potential synapses. New weights were initialized with
w
0
=0.05
κ
(1−
v
tss
)
−1/2
.
For the fixed-threshold BCM rule with weight-dependent scaling
d
w
i
j
d
t
=
μ
(
v
j
v
i
(
v
i
−
θ
)
−
κ
−
1
(
v
i
−
v
t
s
s
)
w
i
j
2
)
the following parameters were used in all simulations: μ = 0.2, κ = 9, θ = 0.08, and vtss = 0.1.
The synapse creation probability was set to pbuild = exp(−16.0) and the removal probability was determined using ρ = 0.125 and a = 2.0.
To obtain the hysteresis curves, the stimulations I{i,j} were repeatedly altered in a way that either the presynaptic activity or the postsynaptic activity at S = 0 synapses increased in steps of 0.01 until they reached 1.0 and then decreased again until they reached 0.05 (= 1 stimulation cycle). Each of those stimulations was applied for an interval of 6 ⋅ 105 time steps for the postsynaptic and 6 ⋅ 106 steps for presynaptic hysteresis curve. For each of those stimulation intervals, the average number of synapses of the stimulation-interval was saved. After simulation, these time averages were averaged over all stimulation cycles (2612 for postsynaptic and 1141 for presynaptic hysteresis).
|
10.1371/journal.pbio.1001540 | When the Most Potent Combination of Antibiotics Selects for the Greatest Bacterial Load: The Smile-Frown Transition | Conventional wisdom holds that the best way to treat infection with antibiotics is to ‘hit early and hit hard’. A favoured strategy is to deploy two antibiotics that produce a stronger effect in combination than if either drug were used alone. But are such synergistic combinations necessarily optimal? We combine mathematical modelling, evolution experiments, whole genome sequencing and genetic manipulation of a resistance mechanism to demonstrate that deploying synergistic antibiotics can, in practice, be the worst strategy if bacterial clearance is not achieved after the first treatment phase. As treatment proceeds, it is only to be expected that the strength of antibiotic synergy will diminish as the frequency of drug-resistant bacteria increases. Indeed, antibiotic efficacy decays exponentially in our five-day evolution experiments. However, as the theory of competitive release predicts, drug-resistant bacteria replicate fastest when their drug-susceptible competitors are eliminated by overly-aggressive treatment. Here, synergy exerts such strong selection for resistance that an antagonism consistently emerges by day 1 and the initially most aggressive treatment produces the greatest bacterial load, a fortiori greater than if just one drug were given. Whole genome sequencing reveals that such rapid evolution is the result of the amplification of a genomic region containing four drug-resistance mechanisms, including the acrAB efflux operon. When this operon is deleted in genetically manipulated mutants and the evolution experiment repeated, antagonism fails to emerge in five days and antibiotic synergy is maintained for longer. We therefore conclude that unless super-inhibitory doses are achieved and maintained until the pathogen is successfully cleared, synergistic antibiotics can have the opposite effect to that intended by helping to increase pathogen load where, and when, the drugs are found at sub-inhibitory concentrations.
| We take an evolutionary approach to a problem from the medical sciences in seeking to understand how our knowledge of rapid bacterial evolution should shape the way we treat pathogens with antibiotic drugs. We pay particular attention to combinations of different drugs that are purposefully used to produce potent therapies. Textbook orthodoxy in medicine and pharmacology states one should hit the pathogen hard with the drug and then prolong the treatment to be certain of clearing it from the host; how effective this approach is remains the subject of discussion. If the textbooks are correct, a combination of two antibiotics that prevents bacterial growth more than if just one drug were used should provide a better treatment strategy. Testing alternatives like these, however, is difficult to do in vivo or in the clinic, so we examined these ideas in laboratory conditions where treatments can be carefully controlled and the optimal combination therapy easily determined by measuring bacterial densities at every moment for each treatment trialled. Studying drug concentrations where antibiotic synergy can be guaranteed, we found that treatment duration was crucial. The most potent combination therapy on day 1 turned out to be the worst of all the therapies we tested by the middle of day 2, and by day 5 it barely inhibited bacterial growth; by contrast, the drugs did continue to impair growth if administered individually.
| Our arsenal of antimicrobials boasts a wide diversity of drugs and we continue to invest in the search for new ones [1]. Yet bacteria adapt so readily to their ambient environment that all antibiotics in clinical use have bacteria that resist them [2],[3]. A Staphylococcus aureus infection traced in vivo yielded over thirty de novo mutations from a 12-week therapy, each mutation conferring an increase in drug resistance [4]. With such a rapidly evolving foe and antibiotic discovery programmes waning substantially [3], determining optimisation principles that maintain the efficacy of the antibiotic repertoire already in our possession represents one of the keenest challenges confronting the scientific community.
And yet drug-resistance evolution has been called ‘conceptually uninteresting’ [5]. This view is the result of assuming a fixed timeline: a pathogen is treated with antibiotics, resistance traits emerge, sweep through the population and fix. The more efficient the drug, the greater selection for resistance and the sooner resistance fixes. The only mitigating action we can take is hit early, hit hard and kill drug-susceptible cells before they accumulate, so the old argument goes [6].
Bacteria are hardest hit by multi-drug combinations. Developed for over 70 years [1],[7],[8], combinations are key in our fight against microbes [9], viruses [10] and cancers [11]. Combinations said to be synergistic, where two drugs hit the pathogen much harder than each drug alone, are highly prized [1],[12],[13]. Indeed, the rapid deployment of synergistic antibiotics should, according to the same logic, be the fastest way of clearing a bacterium.
To make our discussion more precise we say that a pair of bacteriostatic antibiotics of equal efficacy is synergistic if a 50-50 weighted combination of both drugs inhibits growth more than the two single-drug treatments when measured over one day of bacterial growth [8],[14]–[16]. (Strictly speaking, we ask this for all (θ,(1−θ))-combinations where θ is any value between 0 and 100%, not just 50-50, as shown in Figure 1.) With this definition we can formulate a null hypothesis, H0: a synergistic drug combination also inhibits growth synergistically if the treatment lasts longer than a day. Put differently, if the 50-50 combination treatment is more efficient than both single-drug monotherapies on the first day of treatment, it should also be more efficient on subsequent days to be deemed synergistic.
Any in vitro test of H0 necessitates the use of antibiotic concentrations that support measurable population densities, the treatments we can use to test it are, as a result, necessarily constrained to a sub-inhibitory dosing regime. We must therefore question how relevant this study can be to antibiotic use in vivo, we argue that it is relevant for the following reasons. Drug interactions are often determined by one-day checkerboards and isoboles [17], like those illustrated in Figure 1, but by their very nature checkerboards only provide insight into the interaction inside the sub-inhibitory regime as isoboles can only be calculated if cells grow. Moreover, drug concentrations can sweep downwards from their highest values to sub-inhibitory concentrations during treatment ([18], Figure 1), repeatedly so for intermittent dosing regimens [19],[20]. The different diffusivities small antibiotic molecules exhibit in different tissue can create substantial inhomogeneities in concentration [21] resulting in a potential spatiotemporal mosaic of selection for resistance [18],[22] whereby treatment can reduce pathogen load in some, but not all, organs [23]. Indeed, spatial diffusion itself creates concentration gradients with rapid, super-exponential decay away from a point source. It is therefore essential to understand how antibiotic combinations mediate resistance at all dosages within this mosaic, including sub-inhibitory, particularly as resistance is known to be selected for at very low concentrations, well below the minimal inhibitory concentration [24].
Now, we argue that treatments with the greatest short-term efficacy do not necessarily lead to the lowest bacterial densities later. A simple construction accounting for both density-dependent and frequency-dependent selection on drug resistance suffices to explain why. Consider three scenarios with two drugs, ‘A’ and ‘B’. A bacterium is either unchallenged by antibiotics, challenged with drug A only (or drug B only) or else treated with the optimally synergistic combination of both, as in Figure 2(a). The no-drug treatment sees the cells grow, to carrying capacity say, without selecting for drug-resistant phenotypes. The synergistic combination inhibits drug-susceptible cells optimally, better than the two monotherapies, and so, by the end of day 1, the lowest bacterial load of all is observed in this treatment. However, suppose some cells exhibit genetic or epigenetic adaptation conferring resistance; such cells may even have been present in low frequencies at the start of treatment. It is now in the synergistic line that drug-resistant phenotypes fare best as they have fewer competitors for the extracellular metabolites needed for growth.
To clarify how this might arise, imagine a population of bacteria with two subpopulations of drug-susceptible and resistant cells and suppose extracellular metabolites are shared equally among all the growing cells. As the growth of susceptibles is suppressed more at greater synergies, more metabolites become available for resistant cells in those treatments. However, resistant cells necessarily grow faster than susceptible cells do when the drugs are present, with a greater fitness difference at greater synergies. Thus the total population density can be increased by the synergy even when the number of drug-susceptible cells present is reduced. Now, if resistant cells are absent or at low frequencies at the beginning of treatment, the exposure to antibiotics must be long enough to allow the resistants to achieve densities comparable to the susceptibles and so the treatment duration then needs to be long enough for the claim in the previous sentence to be true. This process is illustrated in Figure 2.
This idea, known as ‘competitive release’ [25] has been tested in treatments of malaria in vivo using mice [5] where higher drug concentrations have been shown to select for higher parasite load but competitive release makes new predictions for antibiotic therapy, for combinations in particular. First, the optimal combination is not robust: the best way of deploying a drug pair depends on how long the treatment lasts. Second, and as a result, the favoured property of antibiotic synergy is not necessarily robust to adaptations that confer drug resistance. Not only will synergy decay with time, it can be lost completely and replaced with an antagonism because more potent combinations have paradoxically selected for larger bacterial load. Thus the theory of competitive release is not consistent with our null hypothesis and provides an evolutionary rationale for rejecting it.
A toy mathematical model captures the verbal argument completely and shows that synergy loss can be viewed as a form of tipping point. Imagine a bacterial population consisting of cells susceptible to both antibiotics at density S(t), where t is time. Suppose there is a completely resistant phenotype, R(t), and μ is the mean rate in a random Poisson process by which susceptible cells gain resistance. The dimensionless variable θ between zero and one controls the drug combination and k(θ) = 1+θ(1−θ) measures the efficiency of each combination at drug concentrations (A,B) = (A0θ, B0(1−θ)). Here A0 and B0 are normalising concentrations, chosen so that each drug achieves equal inhibitory effect at a defined time. Note that k(θ)is maximised when θ = 1/2. This value represents a 50-50 combination therapy whereby (A,B) = (A0/2, B0/2).
The toy model is the following logistic growth equation modified to include antibiotics:(1a)(1b)where and R(0) = 0. We therefore begin with susceptible cells but no resistant ones. Figure 2(b) shows the population densities that result from this model, Δt(θ) = S(θ,t)+R(θ,t), plotted as a function of θ for increasing values of time t.
For short times (Equation 1a–b) exhibits synergy because density is suppressed most by the combination where θ = 1/2, so the plot of Δt(θ) has the convex, U-shaped ‘smile’ shown in blue in Figure 2(b). At later times, but only provided μ>0, the shape of the density profile changes and now density is greatest for the 50-50 combination and lowest for the ‘monotherapies’, where θ = 0 and θ = 1. So the plot of Δt(θ) now exhibits a near-concave, W-shaped ‘frown’ consistent with antagonism having its maximal value at θ = 1/2, as shown in red in Figure 2(b). Density is now maximised where before it was minimised. We call the resulting passage from synergy to antagonism the ‘smile-frown transition’, referring to it on occasion as ‘synergy inversion’ because the convex, synergistic profile is inverted to form a near-concave, antagonistic one; this is a different notion of synergy inversion to the one in [26].
If we set μ = 0, thus preventing the modelled population from adapting to the drug, it then follows that Δt(θ) has a synergistic profile at all times. In this case the 50-50 combination, represented by the value θ = 1/2, is the optimal combination for all times as it minimises population density, irrespective of treatment duration.
We tested the veracity of these theoretical predictions using an evolutionary functional genomics approach that combined evolution experiments using Escherichia coli, a genomic analysis, the genetic manipulation of an identified candidate resistance mechanism and quantitative mathematical modelling. This approach highlights the molecular mechanism that causes the synergy loss predicted by theory, whereas the theory alludes to the generality of the empirical results that we now describe.
The above predictions are best tested in vitro where the drug interactions are well-understood and can be rigorously controlled. We therefore cultured E. coli K12 (MC4100) over a five-day period using a serial dilution protocol and sixteen different combination treatments of erythromycin (ERY, a macrolide) and doxycycline (DOX, a tetracycline), two bacteriostatic translational inhibitors with an established synergy [14]. The bacteria are first cultured for 24 h in liquid growth medium containing antibiotics at concentrations described below and, at the end of the 24 h period, a random sample of the bacteria is transferred using a standard plate replicator to inoculate fresh growth medium. This process is repeated to create a treatment lasting several days.
We began by choosing a pair of normalising, or ‘basal’, antibiotic concentrations, D50 and E50, in such a way that each DOX-only and ERY-only monotherapy achieved a 50% reduction in density when measured at 24 h relative to a zero-drug control (the basal concentrations D50 and E50 are the IC50 values of each drug). Each of the sixteen different treatments may therefore be described by a single pair of concentrations(2)where θ is the relative drug proportion. When combined in a 50-50 ratio at these doses, where θ = 1/2, a 90% reduction in bacterial growth at 24 h is achieved, greater than the 50% reduction achieved by each monotherapy (the data in Figure 3(a) (Day 1) supports this). We implemented 14 different combination treatments and two monotherapies at those basal doses with θ ranging in discrete values from 0 and 1/15 to 14/15 and then 1 (19 replicates per treatment; see Section 3.2 in Text S1).
The fixed drug proportion, θ, that minimises bacterial density from the sixteen implemented and determined empirically by culturing the bacteria for 24 h will be denoted by θsyn in the following. This value between zero and one denotes the maximally synergistic combination treatment obtained after fixing the basal drug concentrations, as shown in Figure 1(b). The time-dependent optimal combination will be denoted θopt (T) (see Materials and Methods) and this value represents the combination of ERY and DOX that minimises density for a treatment of duration T hours. It follows by design that θopt (T) = θsyn if T is small, less than 24 h, say.
After calibrating concentrations D and E so that each drug has equal effect, so θsyn≈1/2 in practise as Figure 4(c) shows, the short-term optimal treatment is a 50-50 combination of both ERY and DOX. As a reflection of this, the day 1 data in Figure 3(a) then shows the 50% growth reduction obtained for each monotherapy, the 90% reduction for the maximally synergistic 50-50 combination in addition to the growth reduction for all the other combinations we tested. We can now test our null hypothesis by asking whether the drug combination that is optimal on day 1, 50-50 by design, is also optimal on subsequent days. Equation 1 makes a clear prediction: the best therapy on day 1 will be the worst later.
The first day's data exhibits synergism with the lowest short-term bacterial densities found for near 50-50 combinations of ERY and DOX, so θsyn≈1/2, this can be seen in Figure 5 (shown in blue). However, the subsequent population dynamics beyond day 1 lead to us to reject H0 for Figure 5 (in red) shows they are consistent with the theory of competitive release and exhibit the smile-frown transition before 36 h have elapsed, as we now explain.
Consistent with the predictions of Equation 1, Figure 4(a) illustrates how the degree of interaction, I(T), defined in Materials and Methods, shifts from synergy (where I(T)<0; t-test, df = 19, t≈−6.13, p<0.0001,) to antagonism (where I(T)>0; t-test, df = 19, t≈6.83, p<0.0001) between 12 h and 36 h. The degree of interaction thereafter remains positive, denoting antagonism, until the end of the experiment. This is shown with more detail in Figure 4(b) where the dynamics of the interaction profile are shown on an hour-by-hour basis; this illustrates that the interaction changes at about 30 h.
Examining the apparent change in drug interaction more closely in Figure 5, at 12 h the interaction profile is synergistic (α-test, α = 0.61±0.05>0, df = 13, t≈11.22, p<10−7, θopt(12h) = 0.49±0.01≈θsyn; see Materials and Methods and Section 4.3 in Text S1 for a description of the α-test) but combination treatments for which θ≈2/3 (estimated robustly using the α-test described in Materials and Methods as 0.65±0.04) yield the highest observed population densities by 36 h. As a result, the optimal combination has changed within two days from a 50-50 combination to an ERY monotherapy because the interaction profile is now antagonistic (α-test, α = −0.44±0.14<0, df = 13, t≈−3.05, p<0.0093, θopt(36h)≈0≠θsyn; Section 4.3 in Text S1).
These data were produced for optical density measures of bacterial growth, but analogous results are obtained using different notions of fitness. Using an area under the curve measure of growth inhibition that accounts for both population sizes and growth rates (Section 4.2 in Text S1) Figure 3(a) shows drug efficacy approaches zero most rapidly for near 50-50 combination treatments. The same figure shows the optimal treatment has shifted in this measure too, to the ERY monotherapy within two days. For completeness, the smile-frown transition is also seen if we use colony-forming units to measure bacterial population densities (Section 7.4 in Text S1).
As a further test for loss of synergy, dose-response checkerboards and isobolograms were produced using bacteria sampled from the highly synergistic θ≈8/15 treatment at the beginning of days one and five, both are shown in Figure 6. The earlier checkerboard is consistent with synergism whereas the latter checkerboard shows a progressing wave of increased resistance, with synergy at higher drug concentrations and a mixed interaction apparent at lower concentrations. Figure 6(a, right) shows isoboles at 60% inhibition that are suggestive of a suppressive interaction by day 4 in which doxycycline reduces the inhibitory effect of erythromycin. The white isobole of 50% inhibition in Figure 6(b) shows a shift from day 1 to 5 that indicates increased resistance of the population to both ERY and DOX (for controls that the antibiotics do not degrade significantly when stored at 4°C for several days see Section 3.2 in Text S1).
Having established the rapid loss of optimality of the most synergistic combination treatment, at which point the latter becomes the worst treatment of all, it is essential we understand the genetic basis of this change. So we first performed a test to determine whether increased drug resistance was the result of epigenetic adaptation (Section 3.2 in Text S1).
Samples each of the initially most synergistic drug treatment and the control treatment without drug were taken from the end of day 5 and cultured without antibiotics for a further 24 h. The resulting populations were then all subjected to the most synergistic drug combination for another 24 h. Consistent with a likely genetic basis to drug-resistance adaptation, samples from the short-term synergistic treatment still displayed greater AUC inhibition when measured relative to the no-drug control (Wilcoxon signed rank test, W = 92, N = 10, p<0.001).
Knowing such rapid adaptation has a genetic basis, our goal was to exploit the resistance mechanism and understand what organismal function, if suitably manipulated, could maintain antibiotic synergy for longer and so ensure the smile-frown transition does not occur so rapidly.
We therefore conducted a whole-genome sequencing study of independent biological replicates of both monotherapies and of the maximally synergistic treatment sampled at the end of day 5. The analysis revealed single nucleotide polymorphisms (SNPs) in most replicates modifying physiology, metabolism and drug resistance, including treatments with SNPs in marRAB and acrR (see Table 1, Figure 7, and Section 5.3 in Text S1). Indeed, the mar regulon is known to control a range of stress-responses in E. coli [27] including the multidrug efflux system acrAB-tolC [28].
Rapid increases in resistance to antibiotics can occur when regions of the genome containing resistance genes are duplicated and whole-genome sequencing was proposed as a method to detect such duplications [29],[30]. Our analysis revealed 90% of the independent replicates in the most synergistic combination treatment had the same 315 Kb fragment duplicated, a region containing several efflux pumps including acr (Table 2, Section 5.4 in Text S1). The duplication was found in monotherapies too, but only in 30–40% of those treatments (3/10 replicates for DOX-only and 2/6 for ERY-only).
The duplication was therefore observed significantly more for the 50-50 combination treatment than in the ERY monotherapy (Fisher's exact test, P<0.035) and the DOX monotherapy (Fisher's exact test, P<0.02). In all 14 replicates where a duplication was detected, it was located between positions 274,201 bp and 589,900 bp. This region contains 293 genes, among which are 12 antibiotic resistance or binding genes, 32 transporter genes and 31 transposon-related genes (Appendix B in Text S1). Cross-resistance to antibiotics not used in the protocol is likely as three known multi-drug efflux systems and ampicillin degradation proteins are encoded within the duplicated region (Section 5.4 in Text S1 and Appendix B in Text S1). Such consistent, parallel evolution towards a 315 Kb duplication in all but one replicate of the 50-50 combination treatment strongly suggests, therefore, that genetic amplification of a multi-drug efflux pump is the adaptation that confers the multi-drug resistance phenotype we observe.
To test the stronger hypothesis that a drug efflux system could be responsible for synergy loss and the smile-frown transition, we first developed a system-specific, physico-genetics theoretical model (detailed in Section 6.4 of Text S1) in which cells may express a gene whose product can pump both antibiotics from the cell with no fitness or ATP cost. We assume the drugs have different affinities for the pump and the model encodes three phenotypes: drug-sensitive cells that do not express the efflux system, less sensitive cells that do and a third phenotype then possesses an additional efflux gene and expresses both. Figure 5 shows that the model successfully captures the first 48 h of data predicting that the rapid inversion of synergy that we observe empirically is consistent with the up-regulation and duplication of efflux genes.
Generalising this mathematical framework, we can show that the short-term optimal combination, represented by θsyn, and the time-dependent optimal combination θopt(T) are close in general for a time that depends on the convexity of the drug interaction profile (Section 8.2 in Text S1). The two quantities are related as follows:(3)where T is treatment duration, ρ is the divergence rate between the optimal treatment and maximal synergy; ρ may be positive or negative depending on how the bacteria adapt to each drug. The times(4)are therefore approximations of the moment at which the optimal protocol is a monotherapy and no longer a combination. Figures 2(b), 3 and 5 all exhibit this phenomenon, but it can be seen most clearly in Figure 4(c) that shows the dynamical path taken by the best and the worst therapies. Analogous to a critical transition, a shift takes place at 30 h of treatment where the 50-50 therapy displaces the DOX monotherapy as the worst treatment. The synergistic treatment never recovers its previously favourable status rather, as Figure 3(b) shows in red, its performance continues to deteriorate exponentially.
The physico-genetics model predicts the drug interaction profile will be robust to changes in the duration of treatment, which can be interpreted as ρ being reduced in magnitude and so synergy maintained, if the efflux system were suppressed (Figure S16 in Text S1). This is analogous to setting μ = 0 in Equation 1 above.
To test this prediction we repeated the original evolutionary protocol using two new E. coli strains: a wild-type strain AG100 and a mutant AG100A(Δacr) [31]; we refer to Section 7 of Text S1 that details the minor differences between the first and now this evolutionary protocol. The latter strain differs from the former through a large deletion in acrAB that renders efflux systems that use the products of this operon, like acrAB-tolC, inoperable. As already observed using the E. coli K12 strain MC4100, AG100 soon exhibited the smile-frown transition, within 48 h according to Figure 8(a). In contrast, the mutant strain AG100A(Δacr) that lacks acrAB continued to exhibit synergy until 72 h according to Figure 8(b), consistent with the prediction.
We now ask whether the synergy loss we observe is contingent on the choice of D50 and E50 as basal drug concentrations. For example, might synergy be maintained for longer if we were to increase the dosage of both drugs? We address this question with the following experiment.
We re-ran the drug-specific mathematical model (Section 6.4 in Text S1) at different dosages and repeated the evolutionary protocol using four different pairs of basal drug concentrations, chosen as follows. By analogy with (3) each new treatment can be represented by a pair of concentrationsEmpirically, we calibrated these four concentration pairs to produce a 40%, 80%, 90% and 95% reduction in growth relative to a zero-drug control by 18 h on day 1 for the 50-50 treatments (ones with θ = 1/2). We then subjected AG100 to treatments at each of the four basal dosages for a duration of five days using the drug proportions θ = 0,1/4,2/4,3/4, and 1.
The prior mathematical model made a quantitative prediction for this new protocol that is depicted in Figure 9(a): the greater the antibiotic dose, the greater the synergy observed on day 1 and the greater the resulting antagonism on day 2 (see also Figure 9(b)). These figures show the model predicts that synergy is maintained from the first day onwards only when the dosages are sufficiently low.
Figure 9(c) shows the results of this experiment are in quantitative agreement with the model. Indeed, the numerical values of day-one synergy and day-two antagonism are positively correlated in both the model and the resulting data (R2 = 0.990, F = 145, p<0.0069) provided the antibiotic dose is sufficiently high in the former. Finally, we observe more rapid selection for resistance at higher doses in the sense that the greater the dose, the sooner the transition to antagonism (Section 7.3 in Text S1).
It is important to state that we, of course, exercise extreme caution when drawing parallels with in vivo infections where the immune response, the highly-organised spatial structure of the host, xenobiotic metabolism and the pharmacokinetics that result may substantially complicate antibiotic interaction dynamics. However, we also argue that in vitro evolutionary studies of bacteria allied to genome-wide analyses and mathematical modelling can play an important role in elucidating how antibiotic interactions change through time precisely because model systems like ours are so simple.
Drug interactions are subtle and synergy can be lost, and inverted, for reasons other than competitive release. Synergy must decay with time because of selection for drug-resistant alleles but it can be inverted when drugs degrade to produce non-antibiotic metabolites [26]. It is known that drug interactions can depend upon population heterogeneities because of differential pump expression between subpopulations [32], but cellular mechanisms not commonly associated with resistance might also force drug interactions to change with time. For example, a theoretical model was used to propose [33] that synergism and antagonism could be found simultaneously in a population of cancer cells due to metabolic adaptation in subpopulations, the so-called Harvey Effect [34]. To our knowledge, this theory has not been tested.
There are parallels with a prior study [14] that used antagonistic and synergistic antibiotic pairs to show that synergistic environments promote resistance more quickly than do antagonistic ones and the analogy of their result in our data is Figure 10. Their core argument, that single drug-resistance mutations have a greater fitness effect in more synergistic environments is applicable to our study and consistent with our findings. Unlike ours, however, that study did not address which treatments lead to the lowest or greatest bacterial loads.
Nothing of the molecular, multi-drug resistance mechanism is encoded within Equation 1 and despite its simplicity, this model may explain other phenomena. This includes the unreliability of antibiotic synergy assays such as checkerboards [35]–[37]. If a drug interaction assay were conducted with resistant cells in the inoculum [32] or if one emerged, irrespective of genetic mechanism, Equation 1 predicts synergy and antagonism could be reported for two replicates of the same checkerboard [37]. Indeed Figure 4(c) illustrates how the change from synergistic to antagonistic interaction can occur quickly and it is only when population density data is sufficiently well-resolved through time that a transition point from one to the other is found.
Our theoretical models are consistent with the smile-frown transition not being specific either to the drugs used or to the bacterium, any multi-drug resistance mechanism inactive in the absence of drugs that confers a fitness advantage in their presence may be sufficient (Section 8 in Text S1). However, while our data establishes that the duplication of a chromosomal multi-drug efflux operon is sufficient to observe the transition, this has been done for one Gram negative bacterial species and one drug pair.
Many questions therefore remain regarding the generality of our observations. Clinically-important pathogens are known to efflux drugs into extracellular space, or the periplasm, thus conferring resistance to a wide range of drugs in many species [38],[39]. As efflux has been observed both in clinical Staphylococcus aureus [40] and Mycobacterium tuberculosis (TB) [41] we ask whether synergy loss or the smile-frown transition might be observed in other bacteria. Relevant to this question is the study [35] of several clinical isolates of methicillin-susceptible and -resistant S. aureus (MSSA and MRSA) in which a combination of vancomycin and rifampin was variously reported as synergistic and antagonistic at 24 h and 48 h, with different interactions reported for both different strains and different drug concentrations. No mechanistic explanation has been attributed to this discrepancy and while this may not be at all related to efflux, the true nature of this important combination remains unclear [42].
What of drug combinations reliant on different mechanisms of synergy [43]? The duplicated genomic region illustrated in Figure 7 contains dacA with β-lactamase activity [44] and three efflux systems in addition to acr. Efflux of fosmidomycin by far [45], of aminoglycosides by emrE and of fluoroquinolones by mdlAB [39], all of which are found in the duplicated region (Table 2), indicates the smile-frown transition may also be relevant to other classes of antibiotics.
And would the transition still be observed if two target-altering, de novo mutations were needed for multi-drug resistance because there were no pre-existing chromosomal resistance mechanism that could be so rapidly duplicated? We have not been able to determine a pair of such mutations and so, by way of a partial response, we compared the duration for which synergy is maintained when an important chromosomally-encoded multi-drug pump is, and is not, present using data from E. coli strains AG100 and AG100A(Δacr). Figure 8(a) shows that synergy is lost to antagonism in the former strain around 35 h but for the latter strain, the interaction only ceases to be significantly synergistic around 72 h, although significant antagonism is not observed thereafter. The latter strain, without acr, does therefore exhibit synergy loss but the smile-frown transition was not observed. However, in this case the interaction converges towards indifference in which one of the combination treatments maximises population densities by day 4 but without the smile-frown transition ever appearing (Section 7.2 in Text S1).
It has been suggested that the treatment of multi-drug resistant TB will be more successful if supplemented with efflux pump inhibitors (EPIs) [39],[46]. The present work suggests that if EPIs are used as an adjuvant to combination therapy they may prove beneficial by maintaining synergy for longer, although we have not conducted a direct test of this hypothesis using an EPI molecule.
We conclude that complementary theoretical and in vitro approaches agree that the optimal way of combining antibiotics depends on the duration of treatment. This could have been deduced from a simple engineering principle that complex adaptive systems cannot be controlled optimally using strategies that are constant through time (Section 8.2 in Text S1). The consequences of this principle for antibiotic combinations are dramatic and cause the emergence of what looks like antagonism from a synergism, rendering the supposed optimal combination the worst treatment of all within a day. So while it is axiomatic in theory [18] and demonstrable empirically [14] that drug resistance rises faster for more synergistic treatments, that the greatest antibiotic potency can also select for the highest bacterial densities has been overlooked.
The protocol is a standard batch-transfer protocol used elsewhere [14] in the context of antibiotic treatments and described in detail in Section 3 of Text S1. Briefly: bacteria are cultured in liquid growth medium for 24 h in the presence and absence of different antibiotics and continually shaken. Optical density measurements are taken continually from where the inhibition due to treatment can be calculated relative to the growth observed in a control cultured without drugs. After each 24 h period has elapsed, the environment is sampled and approximately 1% of biomass transferred to fresh a environment that includes replenished growth medium and drugs. This process was repeated for 5 days.
There are many nonequivalent definitions of antibiotic synergy [8],[17],[47]–[49]. To ensure a precise quantification of drug interactions we use several consistent measures with different granularity derived with Loewe additivity as the key assumption. Suppose bacterial growth is measured over a fixed and short length of time, usually 24 h in the literature, although our measurements will be substantially longer. Population density is denoted by the function B(D,E) where D and E are extra-cellular drug concentrations, the number B(0,0) then represents density in a zero-drug environment. Assume each basal concentration, D and E, have been normalised to equal inhibitory effect, thus B(D,0) = B(0,E) = rB(0,0). The value corresponds to the choice of IC50 for D and E, the concentrations denoted D50 and E50 in the text.
Quantification of the drug interaction begins with i, the interaction profile, where i(θ) = B(θD,(1−θ)E). Following Loewe additivity [8], i is said to be synergistic if, for all θ between zero and one exclusive, the effect of the drugs combined is greater than the sum of effects produced by each drug separately:(5)This definition is described pictorially in Figure 1, Figures 1(b) and 1(d) are particularly relevant. Property (5) holds necessarily if i(θ) is convex (c.f. blue lines in Figures 2(b) and 5). When property (5) does hold it follows that θsyn, the maximally synergistic drug proportion that satisfiesalso satisfies 0<θsyn<1. Drug antagonism is said to occur when the reverse inequality applies in (5), this is necessarily the case if i(θ) is concave. The drug interaction is additive in this context if i(θ) is independent of θ.
Bacterial density is measured empirically over a time period of length T hours, so we now introduce T into the definition of B. Denote density by B(T;D,E) and re-write i as i(θ,T) to account for the change. The time-dependent optimal combination, θopt(T), then satisfies(6)It follows by definition that θopt(T) and θsyn are equal when T = 0 and are therefore also close for small T, Equation 3 describes the rate of divergence between the two.
If we define the dimensionless interaction profilethe degree of interaction, I(T), is given by the mean interaction taken over the relevant drug combinations:Negative I(T) denotes synergy, positive I(T) denotes antagonism.
A measure of the convexity and concavity of i(θ,T) obtained by fitting a quadratic, , can be used to assess the drug interaction. Significant positivity (obtained using a t-test) of α indicates synergy, negativity indicates antagonism; Section 7 in Text S1 gives further information on the use of this test. If the density data is significantly nonlinear as a function of θ, meaning α≠0, the fitted quadratic can be used to robustly estimate the drug proportion that maximises bacterial density at each time. This proportion is given by one of θ = 0,1 or −β/(2α) depending on which value is the lowest of q(0), q(1) or q(−β/(2α)). Provided −β/(2α) lies between 0 and 1, an approximate upper bound on the confidence interval for this optimal value can be found from a t-test that returns confidence intervals for α, β, and γ. Throughout we will refer to the test described in this paragraph as the ‘α-test’ and it is implemented using the regression facilities in the Statistics Toolbox of MATLAB.
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10.1371/journal.pntd.0001242 | Lymphatic Filariasis: A Method to Identify Subclinical Lower Limb Change in PNG Adolescents | Lymphedema related to lymphatic filariasis (LF) is a disabling condition that commonly manifests in adolescence. Fifty-three adolescents, 25 LF infected and 28 LF non-infected, in age and sex-matched groups, using the Binax ICT rapid card test for filarial antigen were recruited to the study. None of the participants had overt signs of lymphedema. Lymphedema assessment measures were used to assess lower limb tissue compressibility (tonometry), limb circumference (tape measure), intra- and extra-cellular fluid distribution (bioimpedance) and joint range of motion (goniometry). The mean tonometric measurements from the left, right, and dominant posterior thighs were significantly larger in participants with LF compared to participants who had tested negative for LF (p = 0.005, p = 0.004, and p = 0.003, respectively) indicating increased tissue compressibility in those adolescents with LF. ROC curve analysis to define optimal cut-off of the tonometry measurements indicated that at 3.5, sensitivity of this potential screening test is 100% (95%-CI = 86.3%, 100%) and specificity is 21.4% (95%-CI = 8.3%, 41.0%). It is proposed that this cut-off can be used to indicate tissue change characteristic of LF in an at-risk population of PNG adolescents. Further longitudinal research is required to establish if all those with tissue change subsequently develop lymphedema. However, thigh tonometry to identify early tissue change in LF positive adolescents may enable early intervention to minimize progression of lymphedema and prioritization of limited resources to those at greatest risk of developing lifetime morbidity.
| The effects of lymphatic filariasis (LF) on the lymphatic system often become apparent during adolescence when the lower limb swells due to lymphedema and males develop hydrocele. Currently there is no simple or mobile field method to identify those at greatest risk of developing lymphedema or those with early subclinical lower limb change. Fifty-three adolescents, 25 LF infected and 28 LF non-infected were recruited to the study. The groups were compared with respect to lower limb tissue compressibility (tonometry), limb circumference (tape measure), intra- and extra-cellular fluid distribution (bioimpedance) and hip, knee and ankle joint range of motion (goniometry). Tonometry, is a simple, inexpensive tool, which measures the distance a plunger will indent the soft tissues. Those adolescents who were LF positive had significantly increased soft tissue compressibility when assessed with tonometry than adolescents who were LF negative. Tonometry has high levels of sensitivity to identify adolescents who test positive to LF. If we are able to identify adolescents before they have overt symptoms, management practices to decrease disease progression can be implemented. This could prevent lifetime morbidity and allow allocation of scarce resources to those identified to be most at risk of developing lymphedema.
| The mosquito-borne parasitic disease lymphatic filariasis (LF) is endemic in around 81 tropical countries, has a global burden of around 120 million cases, and is classified by the World Health organization as the second most common cause of long term disability after mental illness [1]. Three species of filarial parasites cause LF. Wuchereria bancrofti, the cause of Bancroftian filariasis, accounts for 90% of the cases worldwide. Brugian filariasis is caused by Brugia malayi, which is found in eastern Asia, and Brugia timori, which is confined to Timor and adjacent islands. All three species cause similar lymphatic disease but only Bancroftian filariasis causes hydrocele and all are controlled and treated by the same methods [2].
LF has a wide clinical spectrum ranging from debilitating acute bacterial dermatolymphgangioadenitis (ADLA) attacks, covert lymphatic and renal disease, and various degrees of lymphedema, to the terrible disfiguring, and often socially ostracizing, chronic manifestations of hydrocele and elephantiasis [3].
A global program to eliminate LF as a public health problem was introduced in 2002 [1], [3]. It is based upon two “pillars.” Pillar one is the interruption of transmission by the use of community-wide preventative chemotherapy (PCT). This used to be called “mass drug administration”, MDA. The second pillar is the alleviation of suffering in those who already have chronic manifestations of the disease.
Over time filarial infection can lead to the development of chronic lymphedema. Infection often occurs in childhood but the obvious clinical effects of the disease such as filarial lymphedema or hydrocele may not occur until they reach adolescence [4]. Shenoy et al [5], [6] have shown covert abnormalities in the lymphatics with lymphoscintigraphy and worm nests by Doppler ultrasonography in B.malayi LF-infected adolescents as young as three years. Importantly, it has also been shown that these early lymphatic changes can be reversed by rug administration [7].
The mechanisms involved in the development of filarial lymphedema are not fully understood but they are known to be a complex interaction between the parasite and the host's immune system [8], [9].
Filarial lymphedema is painful and debilitating, accompanied by skin changes, decreased joint range of motion and recurrent infections. The presence of childhood filarial lymphedema results in social problems including embarrassment, frequent absence from school and even discontinuation of studies [10]. It is generally accepted that acute dermatolymphangioadenitisis the main risk factor for progression of lymphedema and incidence of these attacks can be greatly reduced by the introduction of a basic “self care” program consisting of daily limb washing, treatment of skin entry lesions, antibiotic treatment of established infections, gentle exercise, appropriate footwear and limb elevation [11]–[13].
Although lymphoscintigraphy is an effective method for detecting covert lymphatic changes, it is invasive in that it requires the injection of a radioactive substance and is not suitable for use in field-based research and the monitoring and evaluation, or “fine tuning” of treatment programs for morbidity control.
This study was undertaken to establish a non-invasive method to identify early, sub-clinical changes of lymphedema secondary to LF in the lower limbs of adolescents in Papua New Guinea (PNG). PNG was chosen because of the high Bancroftian filarial antigen prevalence. There is a national plan for the elimination of LF but PCT has only been started in two out of 19 provinces, and there is currently no morbidity control component in place [2].
Ethics approval was obtained from the PNG Medical Research Advisory Council (MRAC number 08.25) and James Cook University Human Ethics Committee, Townsville, Australia. The proposed study was carefully explained to community leaders, school teachers, and health care workers in English, Hiri Motu and Tok Pisin. Parents provided verbal consent for their adolescents to participate. In this community of low literacy the use of verbal consent is appropriate and was approved by both the PNG Medical Research Advisory Committee and James Cook University Human Ethics Committee.
The research was undertaken at Opau village, in central Province whilst a baseline survey for the PNG national LF program was conducted. Members of the village where inducted into the study based on their willingness to participate after the introduction (tok save). As adolescents (aged 10–21 years) were inducted, their LF status was determined using the Binax ICT rapid card test for filarial antigen [2] and demographic data was collected. These individuals were divided into LF reactive and LF non-reactive groups by the investigators responsible for sampling and testing. Binax ICT testing was conducted according to the manufacturers' instructions. An age and sex matched sub-sample of each group were selected for participation. Verbal consent was sought and obtained from the parents of those inducted. The researcher responsible for all measurements of the adolescents was blinded to the results of the ICT test.
There are many methods available to assess the lymphatic system. It was important that the methods included were inexpensive, portable and easy to apply in the field and therefore usable in the context of PNG. Hence the following methods were included in the study:
Circumferential measures of each lower limb were undertaken using a tape measure following the protocol described by the Australasian Lymphology Association [14]. This provided a gross measure of limb size. Measurements were performed at the metatarsophalangeal joints (MTP) (tape applied around the foot from MTP 1 to MTP5), the foot adjacent to the leg (tape applied around the dorsum and plantar aspect of the foot as close as possible to the leg), and 10 and 15 centimetres above and below the joint line of the knee. The distance from the knee was measured with a rigid ruler from the medial and lateral joint lines of the knee.
Tonometry was used to measure alterations in tissue resistance. The tonometer consists of a plunger device which, when applied to the skin, provides a measure of the ability of the skin and underlying tissue to resist compression. A higher tonometry value indicates greater indentation and decreased tissue resistance. It has been used to investigate post-surgical lymphedema [15] and LF in adults with established stage II and III lymphedema [16].
Tonometry was undertaken using a Flinders Tissue Tonometer (Flinders Medical Centre Biomedical Engineering, Australia) consisting of a central plunger operating through a 6 cm diameter footplate that rests on the skin and applies a load of 200 grams. The degree of penetration of the plunger is measured by a micrometer on a linear scale [15]. The tonometer was calibrated according to the manufacturers' instructions prior to each measurement session. The length of the posterior thigh was measured from the gluteal fold to the posterior knee crease using a tape measure. The value was halved and this mid-point in the centre of the posterior thigh was marked. The same distance from the superior aspect of the patella was marked on the anterior thigh. The length of the calf was measured from the posterior knee crease to the base of the heel. This value was halved and a point marked on the centre of the posterior calf. The tonometer was placed on the skin at each marked point and readings to the nearest millimeter were recorded.
Goniometry was used to measure joint range of motion (ROM). When lymphedema is present and limb size increases the range of movement at joints can become restricted. Goniometry was undertaken for ankle dorsiflexion, knee flexion and hip flexion of each lower limb [17].
Bioimpedance spectroscopy (BIS) measures the opposition or impedance (Z) to the flow of an alternating electrical current passed through the body. The SFB7 used in this study performs measurements at 256 frequencies over the range 3–1000 KHz.. At low frequencies, <20 kHz, the current passes predominantly through the extra-cellular fluid (ECF) while at a high frequencies, >50–100 kHz, it passes through both the intra- cellular fluid (ICF) and the ECF. Thus the ratio of impedances, measured at high and low frequencies, provides an indication of change in fluid distribution between the tissue compartments. In secondary lymphedema, increased volumes of ECF are expected and hence the ratio of ICF to ECF should decrease. Since impedance is inversely related to fluid volume this would be observed as an increase in the impedance ratio Ri∶Re (intracellular impedance: extracellular impedance1).
Evidence exists that bioimpedance spectroscopy can identify a change in fluid distribution in the arms of women at risk of lymphedema following management for breast cancer up to six months earlier than the usual clinical method of circumferential measurement [18] . Bioimpedance instruments are light and portable and may provide a more accessible and reasonable method to identify early accumulation of fluid in the limbs of adolescents.
Tissue bioimpedance was measured for each lower limb using an Impedimed SFB7 bioimpedance spectrometer device (ImpediMed Limited, Unit 1–50 Parker Court, Pinkenba, Qld, 4008 Australia). Adhesive electrodes were attached to the feet and hands of participants and the impedance recorded according to the manufacturer's instructions. All data was collected between the hours of 11am and 3pm over three consecutive days. All measurements were made with the subjects in the same position, supine. There is no reason to anticipate that BIS will perform any differently in a rural/village environment to elsewhere assuming a consistent procedure is used. However, as no reliability studies regarding the use of this equipment in a field environment were available two bioimpedance measures were undertaken for each limb. Bioimpedance values, goniometry and circumferential measures are altered by limb composition hence participants were asked which leg they would kick a ball with to determine limb dominance.
Numerical variables were described using mean values and standard deviations (SD) when found to be approximately normally distributed. Median values and inter-quartile ranges (IQR) were used when the variable was skewed. Participants with and without lymphatic filariasis (LF) were compared using t-tests, Fisher's exact tests, and non-parametric Wilcoxon Mann-Whitney test. Binary logistic regression analyses were conducted to identify associations between lower limb measurements and LF. Demographic and body characteristics were considered as potential confounders. The model was adjusted for a confounder when the estimate changed by about 10% or more. Paired analyses comparing left and right limb were conducted using paired t-tests and paired non-parametric Wilcoxon signed rank tests. Throughout the analysis a significance level of 0.05 was assumed. Statistical analysis was conducted using PASW (version 18 of SPSS; SPSS Inc. IBM; Chicago; Illinois).
In order to assess the reliability of the BIS measures we calculated the concordance correlation coefficient by Lawrence I-Kuei Lin [19]. The concordance correlation coefficient was high with 0.88 (95%-confidence interval 0.82, 0.92) suggesting good reliability of the measurement (Figure 1).
Mean age of the 53 participants was 16.5 years (SD 2.5; range 10 to 21 years) and 54.7% were female. Overall 47.2% tested positive for LF. The mean weight of the participants was 50.6 kg (SD 7.1) and the mean body mass index (BMI) was 19.7 kg/m2 (SD 1.9; range 14.5 to 23.6). Four participants (7.5%) were left leg dominant and one participant (1.9%) would use both legs equally to kick a ball (Table 1). None of the demographic and body characteristics were significantly different for participants with and without LF (Table 1).
The mean tonometric measurements from the left, right, and dominant posterior thighs were significantly higher in participants with LF compared to participants who had tested negative for LF (p = 0.005, p = 0.004, and p = 0.003, respectively; Table 2). Logistic regression showed increasing the tonometric measurement from the dominant posterior thigh by 1 unit increased the odds of having tissue change secondary to LF by 2.2 (95%-CI = (1.2, 4.1); p = 0.014). This result was adjusted for the confounding effects of BMI.
The mean circumferential measurements of the right and dominant thighs, 10 cm proximal to the knee, and of the left thigh, 15 cm proximal to the knee were significantly greater in participants with LF compared to participants who had tested negative for LF (p = 0.038, p = 0.042 and p = 0.043, respectively; Table 2). However when adjusted for the confounding effects of BMI those measurements were no longer significantly associated with LF (p = 0.164, p = 0.189, p = 0.226, respectively).
There were no significant differences found in the impedance ratio of the legs between LF positive and LF negative subjects, irrespective of which leg was compared or of limb dominance (Table 2). However there was a significant difference in the bioimpedance ratios of the dominant and non-dominant legs of the LF negative group. This relationship was not identified in the LF positive group (Table 3) possibly suggesting that BIS is detecting an LF-related change in ECW∶ICW ratios but that this is confounded by limb dominance effects upon the impedance measurements.
These results support the use of tonometry of the dominant posterior thigh to indentify alteration in tissue compressibility in adolescents with sub-clinical lymphedema secondary to LF. Mean and standard deviation values for dominant posterior thigh tonometry in the positive LF group were 5.26 and 0.99 and in the negative LF group were 4.38 and 1.07. Receiver operating characteristics (ROC) curves were plotted to identify the optimal cut-off for these measurements to differentiate between those with and those without altered tissue compressibility related to LF. Power when comparing tonometry of the dominant posterior thigh in adolescents who are LF positive and LF negative was 86.3%.
ROC curve analysis to define optimal cut-off of the tonometry measurements of the dominant posterior thigh to identify early tissue changes is reported in Figure 2 and Table 4.
If we chose as the cut-off the 3.5, then sensitivity of this potential screening test is 100% (95%-CI = 86.3%, 100%) and specificity is 21.4% (95%-CI = 8.3%, 41.0%). All true LF cases are detected as such, but together with 22 false positive cases. The positive predictive value is 53.2%. However the false positive cases could be picked up in a second test.
Digital tonometry identified increased tissue compressibility in the posterior thigh in LF-infected adolescents with no overt signs of secondary lymphedema. As there was no significant concomitant change in BIS values these findings indicate a softening of tissue rather than altered intra- and extra-cellular fluid distribution as the first measurable LF tissue changes. This is in contrast to women who develop secondary lymphedema after breast cancer (BC) where alteration of extra- and intra-cellular fluid distribution is the earliest identifier of lymphatic change [18]. The difference in sensitivity of measurement tools in the LF and BC groups is likely to be related to the cause of lymphatic dysfunction. During intervention for cancer the removal of lymph nodes and direct trauma to the lymphatic tissue (surgical, radiation) are the main contributors to secondary lymphedema. In LF, dilation of the lymphatic vessels in response to the presence of the worm and the host inflammatory response to the living worm and their secreted antigens cause lymphatic damage [20]. Dilation of the lymphatics in this early stage is unlikely to lead to the accumulation of lymph seen in BC-related lymphedema and detected by BIS. Further, when the worm dies symbiotic Wolbachia organisms are released and introduce Wolbachia bacteria to the host which causes an inflammatory response in the lymphatic system [8]. It has recently been reported that living worms may also release bacteria and/or the products of the symbiotic Wolbachia into their host [21]. A study of Rhesus monkeys identified that they mount an antibody response to Wolbachia surface proteins that are temporarily associated with worm death and lymphedema development [8]. Specifically, increased Th1/Th17 responses and decreased regulatory T cells as well as regulation of Toll- and Nod-like receptors have been identified in the pathogenesis of filarial lymphedema [20]. Intra-subject between limb variation in BIS is expected due to limb dominance and consequent variation in limb mass and composition. This was not identified in the LF positive group.
Research is required to determine if increased tissue compressibility is due to fatty infiltration, collagen breakdown, loosening of inter-cellular junctions or simply dilation of lymph vessels.
Two sets of genetic risk factors have been suggested for the development of lymphedema, immune response genes associated with heightened inflammatory responses and lymphatic damage following filarial infection and genes associated with impaired lymphangiogenesis [8].
Thigh measures are likely to show the earliest change due to the heavy worm burden in the groin associated with LF. In adult males the worms have been identified by ultrasound to have a preference for the vessels associated with the spermatic cord [22] while in young boys they are found in the inguinal or other peripheral lymph nodes [8] and in the scrotal lymphatic vessels [23]. It has been suggested therefore that puberty and hormonal change may alter worm infestation. This may have contributed to why BIS did not detect differences in this study. The electrode placement in this study measured the impedance of the whole lower limb. As impedance is inversely proportional to the cross sectional area of the limb this results in the impedance of the whole leg being dominated by that of the smaller cross-sectional area of the lower leg, calf and below. Therefore even if impedance of the thigh is different the sensitivity to detect this difference is decreased when measuring the whole leg. BIS sensitivity should be improved by altering electrode placement and measuring the thigh region only.
Tonometry is simple to learn, non-invasive, portable, does not require electricity or batteries and is relatively inexpensive (<$500AUD). After a short training session (30 minutes), subsequent practice and establishment of intra-measurer reliability it would be suitable for use by community health workers. The training session should include the manufacturers requirements for calibration of the tonometer on a flat surface prior to measurement, placement of the tonometer in contact with the skin surface to be measured in a vertical position and reading of the measurement dial. As each measurement takes less than a minute one tonometer would allow a large number of people to be assessed. Tonometry could be used to screen for onset of tissue change, to monitor tissue change and the effect of interventions in those with established lymphedema. A digital tonometer is being developed that may allow even easier field use.
Tonometry is most reliable when assessed on an even surface. This may account for the difference in findings for the posterior and anterior thigh tonometry measures as when lying in the prone position the posterior thigh provides a more even surface than the anterior thigh in the supine position. There are many factors which contribute to the development of lymphatic change in the LF population, many of which are not well understood. It is not known if all the positive LF adolescents identified in this study will undergo progressive lymphatic change.. Further research is required to track changes in tonometry, circumferential measures and BIS in these participants and better understand the progression of lymphatic and tissue change related to LF.
This study suggests possible cut-off values for tonometry which may indicate tissue change characteristic of LF in an at-risk population of PNG adolescents. However, this should not be generalized to other populations and requires further research to establish its generalisability even within PNG. The cut-off chosen in this analysis results in low specificity and a high number of false positives. However the consequences of being wrongly treated are little in comparison to unidentified and unmanaged lymphedema.
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10.1371/journal.pbio.1002421 | Epidermal Growth Factor Receptor-Dependent Mutual Amplification between Netrin-1 and the Hepatitis C Virus | Hepatitis C virus (HCV) is an oncogenic virus associated with the onset of hepatocellular carcinoma (HCC). The present study investigated the possible link between HCV infection and Netrin-1, a ligand for dependence receptors that sustains tumorigenesis, in particular in inflammation-associated tumors. We show that Netrin-1 expression is significantly elevated in HCV+ liver biopsies compared to hepatitis B virus (HBV+) and uninfected samples. Furthermore, Netrin-1 was upregulated in all histological stages of HCV+ hepatic lesions, from minimal liver fibrosis to cirrhosis and HCC, compared to histologically matched HCV- tissues. Both cirrhosis and HCV contributed to the induction of Netrin-1 expression, whereas anti-HCV treatment resulted in a reduction of Netrin-1 expression. In vitro, HCV increased the level and translation of Netrin-1 in a NS5A-La-related protein 1 (LARP1)-dependent fashion. Knockdown and forced expression experiments identified the receptor uncoordinated receptor-5 (UNC5A) as an antagonist of the Netrin-1 signal, though it did not affect the death of HCV-infected cells. Netrin-1 enhanced infectivity of HCV particles and promoted viral entry by increasing the activation and decreasing the recycling of the epidermal growth factor receptor (EGFR), a protein that is dysregulated in HCC. Netrin-1 and HCV are, therefore, reciprocal inducers in vitro and in patients, as seen from the increase in viral morphogenesis and viral entry, both phenomena converging toward an increase in the level of infectivity of HCV virions. This functional association involving a cancer-related virus and Netrin-1 argues for evaluating the implication of UNC5 receptor ligands in other oncogenic microbial species.
| Viruses and bacteria are implicated in 15%–20% of total cancer occurrences. Hepatitis C virus (HCV) infection is one of the main causative agents of liver cancer. “Dependence receptors” are a class of receptors that auto-activate and trigger apoptosis in the absence of their ligands, and “dependence receptor” ligands such as Netrin-1 are known to be overactivated in cancers, especially in inflammation-driven tumors. In this study, we show that HCV and Netrin-1 are mutual inducers—Netrin-1 expression is increased upon HCV infection and, in turn, the rise in Netrin-1 leads to an increase in the HCV particle infectivity. The effects on HCV infectivity involve the liver cancer-related epidermal growth factor receptor (EGFR), which is known to be a host receptor necessary for HCV entry and which we now show is activated by Netrin-1. Our work, therefore, illustrates a pathogenic positive feedback loop involving HCV, Netrin-1, and EGFR, among other factors, in association with cancer development.
| Cancers triggered by microbial oncogenes account for approximately 16% of cancer occurrences [1]. Hepatitis C virus (HCV) is a major etiologic agent of hepatocellular carcinoma (HCC), the fifth most common cancer worldwide [2]. The epidermal growth factor receptor (EGFR) is a host factor for entry of HCV [3, 4] as well as for the influenza virus [5] and adeno-associated virus 6 [6]. EGFR signaling is involved in HCC development [7] and possibly in the resistance to the HCC drug sorafenib [8].
An interesting advance in developmental biology and oncology in the last decade was the discovery of dependence receptors (DRs) [9–13], a class of receptors that auto-activate and trigger apoptosis in the absence of their ligands. One such ligand is Netrin-1. Netrin-1 is a secreted protein that was initially identified as the canonical soluble partner of the uncoordinated receptor-5 (UNC5) DR family in the field of neuroembryogenesis. It inactivates UNC5-mediated intrinsic signals, including cell death, unlike most ligands that exert positive pharmacology on their cognate receptors. Recent data support the implication of Netrin-1 and its main receptors in epithelial tissues and suggested its role in the morphogenesis of “branched” organs [11]. In cancer, the model of dependence receptors predicts that instead of losing Netrin-1 receptors, a second potential selective advantage for tumor cell survival could be an autocrine expression of the ligand that inhibits this receptor. Accordingly, Netrin-1, a reprogramming modulator [14], is upregulated in several cancer types [13,15–18] as well as in cancer-associated inflammatory diseases such as colitis and Crohn’s disease [13,19,20]—for review, see Paradisi and Mehlen [21]. The inflammatory response associated with several epithelial disorders thus appears to play a key role in Netrin-1 induction. As is the case for most viral infections, chronic hepatitis C also bears an important inflammatory component, thought to strongly participate in the worsening of the liver structure and function, which could ultimately result in cancer promotion within hepatocytic compartments.
Taken together, these data argue in favor of the establishment of a more complete landscape regarding the interplay between inflammation and cancer. Long-term infections involving inflammation-inducing oncogenic viruses may represent potential factors for the regulation of Netrin receptors. To our knowledge, data linking such factors and oncogenic viruses are nonexistent. In addition, neither the regulation of the Netrin-1 transcript nor that of the protein have, so far, been identified in association with HCV via high-throughput studies. We therefore decided to investigate the possible interplay between the DR system and HCV, focusing on Netrin-1.
In this study, we show that Netrin-1 is upregulated by HCV and that it participates in a mutual amplification loop with HCV, in turn leading to an enhancement of viral infectivity. Our results indicate that induction of Netrin-1 by HCV represents an important mechanism by which the virus establishes persistent infection and may contribute to neoplastic transformation.
HCV, along with several other liver conditions, is known to trigger hepatic inflammation. To establish a connection between the expression of Netrin-1 (Uniprot Acc. # O95631) and viral infection of the liver, we first measured the level of Netrin-1 mRNA (GenBank Acc. # NM_004822) in 418 liver biopsies, taken either from virus-free patients (165 samples), from HCV-infected patients (223 samples), or from HBV-infected patients (30 samples) (S1 Table). The latter were included as a positive control for chronic viral infection of the liver, and tissue biopsies revealed an 11-fold increase in the level of Netrin-1 mRNA compared to uninfected controls (Fig 1A). Interestingly, the HCV-infected samples displayed a further 2-fold increase in Netrin-1 transcripts versus HBV+ samples, totaling a 23-fold increase in Netrin-1 mRNA levels compared to the uninfected controls. Moreover, a positive correlation was found between the levels of Netrin-1 mRNA and HCV RNA in those liver biopsies (Fig 1B). Similarly, HCV RNA and Netrin-1 mRNA levels were measured in patients before and after first-time treatment with interferon and ribavirin, two antiviral compounds, in biopsies obtained from 18 HCV+/HBV- patients. Of these, 16 showed a partial treatment response (i.e., presented a decrease in viral load; Fig 1C, left panel), accompanied in all but one with a clear decrease in Netrin-1 mRNA levels (Fig 1D, left panel). The two patients who failed to respond to treatment (Fig 1C, right panel) showed stable or increased levels in Netrin-1 mRNA, which paralleled their stable or increased HCV RNA load (Fig 1D, right panel). These data support the HCV-dependent status of Netrin-1 upregulation in HCV-positive patients.
Chronic HCV infection features gradual worsening of the liver through the replacement of functional hepatocytes by nonfunctional connective tissue, a process called fibrosis. To determine whether an increase in the level of Netrin-1 mRNA resulted in a concurrent increase at the protein level, immunostaining for Netrin-1 and the HCV E2 envelope glycoprotein antigens was performed on liver samples matched for fibrosis score. Netrin-1 was clearly observed in HCV+ samples compared to their uninfected counterparts, and, furthermore, the use of the well-characterized anti-E2 antibody [23] confirmed that only hepatocytes exhibit positive Netrin-1 staining in infected samples (Fig 1E and S1 Supporting Information). The additional comparison of protein levels in HCV- and HCV+ liver tissues, by immunoblotting, corroborated our findings (Fig 1F). Together, these data indicate that hepatocytes of HCV+ patients express increased levels both of Netrin-1 mRNA and protein and further strengthen the likelihood that HCV is a hepatocytic Netrin-1 inducer.
It is well known that Netrin-1 induction can result from inflammation, in particular in the gastrointestinal system [21]. To address the specific role of HCV in our model and distinguish fibrosis-associated Netrin-1 induction from HCV-associated Netrin-1 induction, we plotted Netrin-1 mRNA levels in HCV+ versus HCV- samples against all (F0 to F4/cirrhosis) histological stages. Netrin-1 mRNA had increased significantly by 25-fold (F0), 15-fold (F1), 17-fold (F2), 12-fold (F3), and 4-fold (F4) in all HCV-infected samples compared to their HCV-uninfected counterparts (HCC: 1.4-fold) (Fig 1G). In addition, levels of Netrin-1 mRNA were further elevated (>30-fold) in HCV-infected cirrhotic patients compared to control patients (samples F0–F3). Of note, no association could be observed between non-HCV clinical parameters and levels of Netrin-1 mRNA (S1 Fig; see also S2 Supporting Information for more insight on Netrin-1 transcript levels in patients). Importantly, a comparison of HCV(-) biopsies revealed that HCV-negative cirrhosis (i.e., F4) samples already displayed the highest 4-fold to 12-fold increase in Netrin-1 mRNA compared to all other HCV-negative samples (S2C Supporting Information). Taken together, these data suggest that Netrin-1 expression is induced in patients chronically infected with HCV across all stages of the disease, and that HCV and cirrhosis cooperate for higher Netrin-1 induction.
Next, we investigated whether induction of Netrin-1 by HCV could also be seen in vitro, in a tissue inflammation-free environment. Primary human hepatocytes (PHH) were infected with an HCV japanese fulminant hepatitis 1 (JFH1; genotype 2) selected for its higher rate of propagation in cell cultures [24]. Results revealed a peak in Netrin-1 upregulation (by 5-fold to 20-fold) in the HCV-infected cultures at day two or day three (Fig 2A–2D). This was also confirmed in endogenously infected PHH with wild-type genotype 3 virus (Fig 2E). Our in vitro experiments were then conducted using a known hepatocytic cell line that is also amenable for mechanistic studies, in order to further examine Netrin-1 induction. We infected proliferating and dimethyl sulfoxide (DMSO)-differentiated [25] Huh7.5 cells [26] with an HCV JFH1 isolate bearing three adaptive mutations [27], or with a non-adapted genotype 1 strain [28]. Over the five- to ten-day kinetic follow-up study, HCV induced an 8-fold increase in the levels of Netrin-1 mRNA at day eight post-infection in proliferating cultures infected with an HCV JFH1 isolate (Fig 2F) or with a genotype 1 strain (Fig 2G) and in differentiated cells (Fig 2H). These results indicate that HCV induces Netrin-1 expression in hepatocytes both in vivo and in vitro.
Next, we reasoned that substances other than virions released by HCV-infected cells might contribute to upregulating Netrin-1. To test this possibility, we incubated naïve Huh7.5 cells with conditioned medium obtained from HCV-infected Huh7.5 cultures, which had previously undergone ultracentrifugation to remove virions. The HCV-depleted conditioned medium had little effect on Netrin-1 expression in the recipient cultures (Fig 3A), indicating that the increase in Netrin-1 expression described above was most likely mediated by the virus. Overall, these observations indicate that HCV and Netrin-1 levels are linked in individual patients, as well as across the cohort, and that HCV infection is able to induce Netrin-1 expression in vitro.
Having established that Netrin-1 expression was strongly induced by HCV, we were interested in studying the mechanisms underlying this expression. As is frequently observed in studies on Netrin-1 using cultured non-neural cell lines, Netrin-1 was difficult to detect at the protein level in Huh7.5 cells. In a novel and indirect approach to study Netrin-1 protein production, we investigated the association of Netrin-1 mRNA with endoplasmic reticulum (ER) membrane-bound polysomes, in which the translation of this secreted protein takes place, or with free polysomes, upon infection. The partitioning of the glucuronidase (GUS) and phosphomannomutase 1 (PMM1) mRNAs, which are translated by membrane-bound and free polysomes, respectively, was also assessed as enrichment controls. Our results showed that HCV infection caused a striking enrichment in membrane-bound Netrin-1 mRNA but did not alter the profiles of the enrichment controls (Fig 3B). Taking advantage of this subcellular fractionation approach, we submitted these previously obtained microsomes to Netrin-1 immunoblotting and observed an increase in the levels of Netrin-1 in HCV+ cells (Fig 3C). These observations indicate that HCV increases Netrin-1 translation and support the pattern of increased levels of Netrin-1 protein observed in the HCV+ clinical biopsies.
In order to investigate the HCV-related induction of the Netrin-1 protein, we examined the genetic structure of the Netrin-1 transcript and found that its mRNA has a terminal oligopyrimidine tract (TOP) in its 5’UTR using the RegRNA2 database [29,30]. As reported previously, TOP mRNAs interact with the La-related protein 1 (LARP1; GenBank Acc. # NM_015315; Uniprot Acc. # Q6PKG0) protein during translation [31,32]. We therefore reasoned that Netrin-1 may benefit from such an interaction. Indeed, it is known that the Netrin-1 transcript is a LARP1-binding transcript [30], and our experimental data using the LARP1-binding transcript ribosomal protein S18 (RPS18; GenBank Acc. # NM_022551) as a positive control confirmed this finding in hepatocytic cells using RNA immunoprecipitation (S2 Fig) followed by qPCR (Fig 4A and 4B).
We then searched for a specific HCV factor that could implicate LARP1 in the final Netrin-1 phenotype. We screened for potential interactions between individual HCV proteins and LARP1 using a mammalian cell-based protein-fragment complementation assay (PCA). This technique provides a highly reproducible and specific means of measuring protein interactions, including those involving membrane proteins in a cell model and in subcellular compartments [33,34]. Open reading frames (ORFs) of the ten individual HCV proteins [35] were cloned and recombined into an expression vector containing a fragment of the luciferase reporter (GLuc1-A). These plasmids were expressed in Huh7.5 cells along with a plasmid encoding for the complementary luciferase fragment (GLuc2-A) fused to LARP1. This screen revealed a novel interaction between LARP1 and HCV NS5A (Fig 4C). The apparent affinity of the NS5A-LARP1 binding was comparable to or greater than that of NS5A binding to its known partners VAPA, GRB2, RAF1, PITX1, and TP53 (reviewed in He et al. [36]). In contrast, core and NS5B exhibited weak binding close to the detection threshold, and E1, E2, P7, NS2, NS3 (expressed either individually or with Flag-tagged NS4A), NS4A, and NS4B exhibited an even lower level of binding. Interestingly, NS5A is an ER-bound protein (reviewed, for instance, in reference [36]) and is, therefore, theoretically capable of bringing its interacting partners closer to this subcellular compartment.
In this context, we verified whether HCV infection induced alterations in the expression pattern of LARP1 in infected cells. Since HCV NS5A appeared to bind to LARP1 with the highest affinity, we conducted immunofluorescence assays to confirm this finding. LARP1 signals were strongly reconfigured following HCV infection, adopted a granular pattern at the expense of their initial homogenous staining profile in naïve cells, and concentrated at the vicinity of lipid droplets visible as spheric structures surrounded by LARP1 and NS5A staining (Fig 5A). We confirmed that the HCV NS5A protein colocalized with LARP1 in infected cells using a plot profile assay (Fig 5B). This was further confirmed using calnexin, an independent ER marker. Indeed, LARP1 underwent general relocalization to ER-positive sites (i.e., relevant to translation of secreted proteins) in HCV+ cells, especially at the vicinity of classically core-decorated lipid droplets (Fig 6, zoomed insert). Li colocalization parameters presented in diagram Fig 6A and the coefficient (Fig 6B) between LARP1 and calnexin were significantly upregulated (>2.5-fold) following HCV infection (for more details on Li values, see S1 Text). Representative images and plot profiles (r = 0.16; p = 0.1 in naïve cells versus r = 0.38; p = 0.002 in HCV+ cells) of these colocalization levels are shown in Fig 6C and 6D, respectively. To determine whether LARP1 was localized in translationally active sites within infected cells, we compared LARP1 aggregation sites with puromycin(+) areas using the ribopuromycylation method [37]. Accordingly, LARP1 had significantly accumulated in the cytosol of HCV+ cells (Fig 7). Li diagrams (Fig 7A) and coefficient (Fig 7B) were significantly upregulated (1.3-fold) in HCV+ cells. Corresponding representative images (Fig 7C) and plot profiles (r = 0.31 in naïve cells versus r = 0.82 in HCV+ cells, Fig 7D) are shown. These data (i) show that LARP1 strongly relocates to ER-associated translationally active sites upon HCV infection, which comprise areas adjacent to lipid droplets, and (ii) led us to investigate whether the HCV-induced increase in Netrin-1 translation was, in turn, LARP1-mediated.
LARP1 expression is conditioned by NS5A in infected cells. We reasoned that the siRNA-based modulation of LARP1 expression in HCV+ cells should alter the microsomal levels of Netrin-1 in these cells. To test this hypothesis, we first assessed LARP1 knockdown by immunoblotting (S3 Fig) and subsequently separated the microsomes from the cytosol by performing subcellular fractionation with the HSP60 marker. We then evaluated Netrin-1 levels in both types of samples after modulating the expression of LARP1. In agreement with previous immunofluorescence data, levels of LARP1 and Netrin-1 increased in the microsomal compartment upon HCV infection, while Netrin-1 decreased in this subcellular fraction upon depletion of LARP1 (Fig 8). Therefore, the virus NS5A-mediated relocalization of LARP1 toward ER-associated translationally active sites accounts for the HCV-related increase in Netrin-1. Consistently with the secreted protein status of Netrin-1, this HCV-related increase occurs primarily in the secretory, microsomal machinery of the cell.
These initial findings prompted us to investigate whether Netrin-1 produced by cultured cells was, in turn, able to promote HCV replication and/or propagation. HCV-infected proliferative Huh7.5 cells were transfected with a Netrin-1 expression plasmid. Intracellular HCV RNA, viral RNA release, supernatant infectivity, and virion-specific infectivity were then monitored. The expression vector produced a modified Netrin-1 containing the hemagglutinin (HA) epitope at its carboxyl-terminus, while a plasmid expressing HA-tagged vanilloid receptor (VR1-HA) served as a control. Immunoblotting analyses confirmed expression of both proteins in the transfected cells (Fig 9A). Plasmid-delivered Netrin-1-HA produced a 3-fold increase in the intracellular HCV RNA compared to cells transfected with VR1-HA (Fig 9B) and was accompanied by a 2.5-fold increase in supernatant infectivity, measured using the TCID50 protocol (Fig 9C). Furthermore, overexpression of Netrin-1 resulted in a 2-fold increase in the level of intracellular infectivity (Fig 9D) and likewise increased the infectivity peak of the released virions (Fig 9E). Since LARP1 intriguingly concentrates at the ER and in the proximity of lipid droplets, an important viral budding site [38], upon infection, we tested whether accumulation of Netrin-1 in microsomes could foster increased morphogenesis, through the evaluation of the specific infectivity of virions. This viral parameter is defined by the ratio of biological infectivity values, expressed as TCID50 units, and viral RNA copy numbers of the sample. To achieve this, we plotted TCID50/extracellular HCV RNA ratios against their buoyant densities for Netrin-1- and control VR1-transfected samples. Netrin-1 overexpression caused a 4-fold increase in the specific infectivity of released virions (Fig 9F), suggesting that the protein induces alterations in the virus particle. Netrin-1 also resulted in a viral increase of the poorly infectious, high-density fractions (S4 Fig).
The DR hypothesis states that, ligand withdrawal induces receptor activation for subsequent death signaling. As Netrin-1 is known to promote cell survival [39], we performed a set of experiments resulting in the overexpression or depletion of Netrin-1, to test whether its effect on HCV occurs via a cell death protection-dependent mechanism. We initially performed cell death-related assays on Huh7.5 cells during the entire course of the transfection experiments, and observed that neither caspase-3 activity nor cell proliferation (S5 Fig), known to be beneficial for HCV replication in vitro, were altered by the forced expression of Netrin-1. This suggests that Netrin-1 does not exert its proviral effect through the death-related DR function of its cognate receptors
We then incubated recombinant soluble Netrin-1-Fc on HCV in Huh7.5 cells. In both cell systems, Netrin-1-Fc induced a significant (up to 2-fold) increase in the level of intracellular HCV RNA. TCID50 assays showed that while extracellular release of HCV RNA was not changed, it produced a 2-fold increase in the level of infectivity of the supernatant (S6 Fig). Netrin-1-Fc did not affect caspase-3 activity in Huh7.5 cells. Results of neutral red assays indicated that Netrin-1-Fc did not influence the viability of the Huh7.5 cells over time regardless of their HCV infection status (S7 Fig). Similar results were obtained when using a distinct recombinant soluble Netrin-1, Netrin-1-FLAG (S8 Fig), strengthening our previous findings that Netrin-1 does not promote HCV infection by protecting against cell death.
In turn, we also studied the effect of Netrin-1 depletion on all previously depicted viral parameters. The efficiency of the Netrin-1 siRNA was assessed by qPCR and immunoblotting (Fig 9G). SiRNA-mediated Netrin-1 knockdown was associated with a 3-to-4-fold decrease in the level of intracellular HCV RNA (Fig 9H; see also SI3 for dose-dependence data) and caused a 2-fold decrease in the level of global infectivity of the HCV supernatant (Fig 9I), as well as a decrease in intracellular infectivity (Fig 9J). Infectivity of the released virions was also clearly impaired by Netrin-1 depletion (Fig 9K). Consistently with results generated upon overexpression, Netrin-1-silencing caused a 3-fold decrease in the specific infectivity levels of released virions (Fig 9L), another indication that Netrin-1 induces alterations in the virus particle that are virion-density unrelated (S9 Fig). Finally, Netrin-1-depleted cells were also analyzed for caspase-3 activity and cell viability to rule out the possibility that the positive effects of Netrin-1 on HCV infection were due to protection against cell death. No effect of Netrin-1 depletion on caspase-3 activity (S10A Fig) or cell viability (S10B Fig) was observed. Importantly, in an approach to deplete Netrin-1 in an RNAi-independent fashion, infected Huh7.5 cells were exposed to a recombinant anti-Netrin-1 monoclonal antibody. This treatment resulted in a 3-fold decrease in intracellular HCV RNA (S11A Fig) and a 5-fold decrease in supernatant infectivity (S11B Fig).
Having shown that the modulation of Netrin-1 was capable of influencing specific infectivity levels of the HCV virions, in a context devoid of alterations in cell integrity, we investigated whether Netrin-1 could potentially be a component of the particles, since it also concentrates in the ER. We performed neutralization assays followed by TCID50 quantification. Anti-E2-based neutralization (versus its RO4 isotype) [23] served as a positive control (Fig 10A), while a recombinant form of the DCC (deleted in colorectal cancer) receptor of Netrin-1 (compared with the same heat-inactivated receptor) and two distinct anti-Netrin-1 antibodies (compared with their respective isotypic IgG controls) were used to evaluate the effect of virus production on Huh7.5 cells (Fig 10B). Neutralization decreased the initial level of infectivity by up to 80%, and this inhibition was enhanced by Netrin-1 overexpression in the initial virus-producing cells, suggesting that Netrin-1 participates in HCV infectivity as a candidate part of the viral particle. The effects of Netrin-1 were also tested in Huh7-derived cell lines containing subgenomic HCV replicons [40], a system that does not generate viral particles. Results showed that Netrin-1 did not alter levels of HCV RNA (S12 Fig). These observations confirm that the increase in HCV levels mediated by Netrin-1 occurs at the level of the assembly/morphogenesis, with a specific impact on the level of infectivity of the virions, but show no effect on viral RNA replication.
Netrin-1 exerts most of its known activities by interacting with the DRs DCC and UNC5 [41,42]. In order to identify the receptor transducing the pro-HCV activity of Netrin-1, we quantified the levels of expression of UNC5s and DCC in Huh7.5 and PHH cells, as well as in tissue biopsies. UNC5A (GenBank Acc. # NM_133369), B (GenBank Acc. # NM_170744), and D (GenBank Acc. # NM_080872) transcripts were readily detectable in Huh7.5 cells, while UNC5C (GenBank Acc. # NM_003728) levels remained marginal and DCC (GenBank Acc. # NM_005215) mRNA was neither expressed in Huh7.5 cells nor in PHH. UNC5 profiles were similar in Huh7.5 cells, PHH, and liver tissues (S13 Fig), suggesting that the in vitro setting presented in this study was a representative model for the UNC5 DR profile in patients.
Based on these results, we set out to identify which of the UNC5 receptors was responsible for mediating the effects of Netrin-1 by monitoring HCV in Netrin-1-Fc-treated Huh7.5 cells, which had previously been subjected to siRNA-mediated depletion of each individual UNC5 transcript. Depletion of UNC5A alone induced an up to 4-fold increase in the levels of HCV (Fig 11, left column). RT-qPCR conducted to detect the UNC5 transcripts confirmed the efficacy of the siRNAs (Fig 11, right column). These results were subsequently validated using RNAi-based depletion and plasmid-mediated forced expression approaches of UNC5A (Uniprot Acc. # Q6ZN44) on viral parameters. Indeed, intra/extracellular infectivity parameters increased and decreased by 3-fold to 6-fold upon UNC5A depletion or overexpression, respectively (S14 Fig). These results demonstrate that Netrin-1 exerts its pro-HCV effect via inhibition of the UNC5A receptor that itself decreases Netrin-1’s proviral effect. They also indicate that UNC5A-related functions ultimately condition infectivity of the virus particle, through increased viral propagation inducing enhanced Netrin-1 expression.
The EGFR (GenBank Acc. # K03193; Uniprot Acc. # P00533) is a host receptor necessary for HCV entry [3,4,43] that acts by promoting the formation of the CD81-CLDN1 viral capture complex at the level of the membrane [3]. Since the altered signaling of both Netrin-1 and EGFR plays a widespread role in cancer, we examined whether the two proteins might be functionally connected.
In order to study the response of EGFR concomitantly to Netrin-1 modulation, our experiments were conducted in EGF stimulation synchronized settings. In this context, siRNA-mediated knockdown of Netrin-1 induced a decrease in the level of EGFR at the plasma membrane level, while forced expression of Netrin-1 resulted in an increase in EGFR levels (S15A Fig). Netrin-1 had no effect on the levels of plasma membrane-associated CD81 (Uniprot Acc. # P60033), which is one of the co-receptors of HCV (S15B Fig). Working in similar conditions, we then investigated whether the level of EGFR activation was sensitive to Netrin-1 fluctuations. While knockdown of Netrin-1 caused a decrease in the activation of EGFR, as measured by immunoblotting using an anti-phospho1068 antibody, forced expression of Netrin-1 increased EGFR phosphorylation (S15C Fig), an event necessary for HCV entry [43]. These data were also confirmed in serum-containing conditions (S16 Fig) and indicate that Netrin-1 participates in EGFR activation.
In order to provide a dataset at the functional level regarding the effect of Netrin-1 on the entry of the HCV and the possible role of the EGFR in this process [4], we used the HCV pseudoparticles (HCVpp) system, a pseudotyped HCV glycoprotein-expressing lentiviral tool, widely used for entry quantification assays in hepatitis C research [44]. Experiments were carried out in cells transfected with the Netrin-1 expression plasmid and/or the EGFR siRNA, while cells transfected with the VR1 expression plasmid served as negative controls. SiRNA-mediated EGFR knockdown was tested at the mRNA and protein levels (Fig 12A and 12B). Forced expression of Netrin-1 caused a 2-fold increase in the entry of HCVpp, whereas values were reversed following depletion of the EGFR (Fig 12C). Netrin-1-depletion led to a 3-fold to 4-fold decrease in the entry of HCVpp, a level of inhibition comparable to that observed when cells were transfected with EGFR siRNA (Fig 12D). The entry of positive control lentivirus pseudotyped with VSV-G was insensitive to either treatment. Entry of HCVpp devoid of envelope glycoproteins served as a negative control (Fig 12C–12F). Treatment of cells with the EGFR inhibitor erlotinib interfered with their entry, similarly to interferences observed upon overexpression of Netrin-1, but did not affect the entry of particles carrying the VSV-G phenotype (Fig 12E). Erlotinib cooperated with siRNAs directed against EGFR for further entry inhibition, as previously shown (Fig 12F) [4]. These data support the hypothesis that Netrin-1 heightens the infectivity of an inoculum by increasing the susceptibility of target cells to the entry of HCV.
Immunoblotting experiments conducted to detect the EGFR protein in total cell lysates showed that silencing or overexpression of Netrin-1 did not alter protein levels (S17 Fig). Since, in contrast, Netrin-1 was found to modulate the EGFR at the level of the plasma membrane (S15 and S16 Figs), we verified whether Netrin-1 was able to modulate its recycling upon experimental binding with its cognate ligand EGF. RT-qPCR and immunoblotting results revealed that stimulation of Netrin-1-silenced or Netrin-1-overexpressing Huh7.5 cells with EGF, for 5 or 15 min, affected neither EGFR mRNA nor protein levels (S17A and S17B Fig, respectively). In contrast, results of flow cytometry confirmed the positive effect of Netrin-1 on the levels of cell surface-exposed EGFR (S17C Fig, top panel), while EGF was used as an internalization control (S17C Fig, center and bottom panels). These results indicate that the EGFR is functional in the experimental setting used in the present study, and also that its global expression level is not regulated by Netrin-1.
We then investigated the interplay between HCV infection and Netrin-1 on the internalization of EGFR. HCV-infected cells were transfected with anti-Netrin-1 (siRNA) or a Netrin-1 expressing plasmid. Cells were subsequently serum-starved and incubated with EGF prior to their fixation and indirect immunofluorescence analysis to detect EGFR along with the endosomal marker EEA1 (Uniprot Acc. # Q15075). The EGFR and EEA1 signals were assessed by intensity correlation coefficient-based (ICCB) analyses (Fig 13A), based on the Li coefficient (also see detailed explanation in S3 Supporting Information) [45]. Our results reveal a strong colocalization of EGFR and EEA1 in Netrin-1 depleted cells (i.e., almost all of the pixels have positive staining amplitude values) and inversely display low levels of colocalization in cells overexpressing Netrin-1. The effect of EGF treatment on the internalization of the EGFR in Netrin-1 expression-modulated cells was then assessed by comparing changes in the Li coefficient, which is a measure of the transfer of EGFR to the early endosome. Treatment with EGF, for 5 or 15 min, resulted in an increase in the Li coefficient in the transfected cells. At the 5-min time point, the Li coefficient increased by 35% in cells depleted of Netrin-1, whereas it showed a 39% decrease in cells overexpressing Netrin-1 (Fig 13B). We showed a partial (empty arrows) and total (solid arrows) colocalization of EGFR and EEA1 (Fig 13C) by immunofluorescence in Netrin-1 overexpressing and depleted cells, respectively. The plot profiles of the fluorescence intensities of EEA1 and EGFR signals were measured across a five-micron line located at the tip of each arrowhead. Spearman correlation coefficients between EEA1 and EGFR fluorescence intensities were calculated from these graphs and yielded values of 0.34 and 0.78 for cells transfected with the control siRNA and Netrin-1 siRNA, respectively, and 0.43 and 0.85 for cells transfected with the Netrin-1 plasmid and control plasmid, respectively (Fig 13D). These results suggest that Netrin-1 increases the amount and activation of EGFR at the level of the plasma membrane by impeding the internalization of this receptor. Netrin-1, therefore, fosters persistence of activated EGFR at the cell surface. This phenotypic alteration increases susceptibility of target cells to viral entry and, thus, leads to an even higher level of HCV infectivity than that induced by Netrin-1 at the level of the virions produced by infected cells.
In this study, we establish a causative relationship between a pro-oncogenic viral infection, namely HCV, and Netrin-1, an extensively studied dependence receptor ligand, in the context of cancer development. These observations were made in vitro as well as in chronically infected patients suffering from fibrotic liver disease, cirrhosis, or even HCC.
We report that the major molecular mechanism underlying the increase in Netrin-1 translation upon HCV infection is regulated by the NS5A and LARP1 proteins and that this increase is particularly marked in the microsomal compartments close to viral budding sites. In turn, we show that the rise in Netrin-1 leads to an increase in the level of infectivity of HCV particles, through both its presence on virions and its implication in the formation/morphogenesis of viral particles. Of note, LARP1 was recently identified as having a pivotal role in general [30,31,46,47] and in hepatic [48] carcinogenesis. Its implication in HCC now needs to be addressed in light of our findings regarding its sensitivity to an oncogenic virus, HCV, and its role in regulating Netrin-1.
The pro-viral effect mentioned above was further enhanced by the indirect effect of Netrin-1 on the persistence of EGFR at the surface of target cells, thus increasing their susceptibility to HCV viral entry. Indeed, Claudin-1/CD81 interactions, which enable HCV entry into hepatocytes [49], are mediated by the activation of EGFR [3,43]. As an HCV virion candidate component and an EGFR activator, Netrin-1 is, thereby, prone to favor the contribution of EGFRs to virion transfer from CD81 to CLDN1 at the level of the membrane [3]. EGFR signaling is implicated in HCC [7] and possibly in the resistance to the anti-HCC drug sorafenib [8]. The fact that Netrin-1 fosters HCV entry through fostering EGFR activation is in agreement with previous reports on the involvement of Netrin-1 in cancers [11,41,50], in which dysregulated EGFR expression and signaling play a major role [51].
As for high-throughput approaches and Netrin-1 biology, transcriptomic studies focusing on the regulation of Netrin-1 have so far not been reported. Although previous research has resulted in the identification of cell death-related signatures in HCV+ cells, in vitro, in either human or chimpanzee samples [52–58], most transcriptomic screens performed in the last 15 y on HCV-related samples were conducted using non-tiling arrays or RNAseq procedures. It is, therefore, not entirely surprising that Netrin-1, a difficult-to-target GC-rich transcript, has so far not been identified.
Our data revealed the occurrence of a positive feedback loop, represented here by the reciprocal induction of Netrin-1 and HCV. Such a positive feedback is relatively scarce in biological systems because of the irreversible imbalance this type of dynamics can rapidly produce. For this reason, our findings raise questions regarding the consequences of an increase in Netrin-1 on HCV infection, beyond HCV persistence per se. Although Netrin-1 expression did not confer a pro-survival advantage to cells used in this study, elevated levels of intrahepatic Netrin-1 may enhance the survival of hepatocytes previously altered and/or rendered more resistant to apoptosis by genetic or tissular injuries, such as HCV trans-acting factors or HCV-related chronic liver regeneration. Netrin-1-related enhancement of cell survival in a cytotoxic context has been observed in non-hepatic cancers following chemotherapy [59]. It is likely to be most relevant at the cirrhosis level, a harsh environment for hepatocytic survival, which displays the highest level of Netrin-1 signals, and even more so upon HCV infection. Importantly, previous studies demonstrated that the expression and activation of EGFR and its main dimerization partner HER-3 (ErbB-3) are frequently dysregulated in HCC [60–62]. Other investigations also indicated that EGFR and the dimerization inducer HER-3 may provide compensatory signals for cancer cells to escape targeted therapies in the liver [63]. Hence, our observation that EGFR undergoes functional upregulation by Netrin-1 indicates that it may mediate the potentially deleterious effects of Netrin-1 in liver pathologies. A relevant study on the role of Netrin-1 in fostering liver regeneration upon ischemia/reperfusion in mice livers [64] has been most recently published. We believe this study emphasizes the relevance of our work, since it concatenates the consequences of elevated Netrin-1 levels at the hepatic level with the Netrin-1 inducer status of HCV. Indeed, HCV may induce Netrin-1 for its own replicative benefit in a direct manner, but also in a liver maintenance prospective.
EGFR-mediated interferon resistance has been evidenced in the context of hepatitis C [65]. From the therapy perspective, it is widely accepted that treatments targeting HCV have undergone major improvements in recent years [66], although cirrhotic patients may in some cases remain more exposed to treatment failure than a majority of patients [67,68]. In this context, elevated levels of Netrin-1 in HCV+ cirrhotic patients should orient future research efforts not only in the direction of the onset of HCC but also of Netrin’s potential ability to condition the efficacy of direct-acting antivirals (DAAs) through a dysregulated Netrin-EGFR-endogenous interferon sensitive axis.
In summary, the HCV-Netrin-1 amplification loop studied herein is composed of two molecularly distinct Netrin-1-mediated arms, which converge toward a single phenotype of increased infectivity conferred to HCV particles. The involvement of Netrin-1 in hepatic pathobiology and the role of DR ligands in the persistence of infectious agents associated with cancer deserve further investigation.
Samples and PHHs were used according to the French IRB “Comité de Protection des Personnes (CPP) Sud-Est IV” agreement #11/040, obtained in 2011. Written informed consent was obtained from patients.
PHHs were prepared, grown in William’s E medium, and infected with cell culture-adapted HCV as described previously [24]. The human hepatocyte cell line Huh7.5 was grown in DMEM (Life technologies) and supplemented with 10% fetal bovine serum (FBS; Thermo Scientific) and 1% penicillin-streptomycin (Life technologies). Twenty thousand cells per square centimeter were infected with HCV JFH1 [27] at an MOI of 0.1 (proliferative cells) or 0.05 (differentiated cells). In order to induce differentiation, 2% DMSO (Sigma) was added to the medium. Netrin-1-Fc (125 ng/mL) was obtained from Apotech Corp./Axxora. For Netrin-1 mRNA stability assays, cells were treated with either DRB (25 μg/mL, Sigma-Aldrich) or actinomycin D (5 μg/mL, Sigma-Aldrich) in order to inhibit transcription for the various durations of time prior to total RNA isolation using the Extract-all reagent (Eurobio).
Total RNA was extracted using the Nucleospin RNA/protein kit (Macherey-Nagel) for biopsies and the Extract-all reagent (Eurobio) for cultured cells. One μg of RNA was DNAse I-digested (Promega) and reverse transcribed with 5% DMSO, using the MMLV enzyme, according to the manufacturer’s instructions (Invitrogen). Real-time quantitative RT-PCR was performed on a LightCycler 480 device (Roche) using the iQ™ SYBR®Green Supermix (BIO-RAD). DMSO (10%, Sigma-Aldrich) was added to the PCR reaction for Netrin-1 quantification. All PCR primer sequences and qPCR conditions are reported in the S2 Table.
The partitioning of Netrin-1, GUS, and PMM1 mRNA between the cytosol (free polysomes) and ER membrane-bound polysome compartments was performed as described by Stephens et al. [69]. GUS and PMM1 mRNAs were used to assess the quality of each fraction. GUS mRNA is membrane-enriched [70], whereas PMM1 mRNA is not. Nucleocytoplasmic fractionation was performed as described previously [71].
Two hundred thousand cells in ten-square-centimeter wells were transfected either with 2.5 μg Netrin-1 or with the neuronal vanilloid receptor VR1 expressing plasmid, or with the F-Luc-bearing Netrin-1 promoter reporter construct described previously [20] using the TransIT-LT1 reagent (Mirus Bio), following the manufacturer’s instructions.
ORF encoding LARP1 was picked from the Human ORFeome library (Open biosystems) and recombined into pGLuc vector (for luciferase tagging) [33] by gateway technology (Invitrogen). ORFs encoding HCV proteins were amplified from described vectors [72] and recombined into pGLuc vector and/or pFlag vector. Sequences of relevant primers are available upon request.
As described [33,34], combinations of plasmids encoding prey (A) and bait (B) proteins, each fused to a fragment of the Gaussia princeps luciferase protein (GLuc1 and GLuc2) or control vectors, were cotransfected into Huh-7.5 cells plated in 96-well plates in triplicates. At 24 hr post-transfection, cells were lysed and subjected to luciferase assays (Promega). Results were expressed as normalized luminescence ratios (NLR): the average luminescence signal in cells transfected with GLuc1-A and GLuc2-B divided by the average signal in wells transfected with GLuc1-A and an empty GLuc2 vector and those transfected with GLuc2-B and an empty GLuc1 vector. We benchmarked the sensitivity and accuracy of this screen by including a random reference set (RRS) composed of 53 noninteracting human protein pairs and a set of host factors known to interact with various HCV proteins [33].
Cell culture supernatants collected at the indicated time points were loaded on top of 10%–60% sucrose gradients and centrifuged at 38,000 rpm at 4°C for 16 h in a SW41 rotor (Beckman). A total of 12 fractions (1 ml each) were collected and their densities were determined with a refractometer (Euromex). HCV RNA in each fraction (150 μl) was extracted with the Nucleospin RNA Virus kit (Macherey-Nagel) and quantified by RT-qPCR. The infectivity of HCV virions in each fraction was determined on Huh7.5 cells using the TCID50 protocol [73] as described below.
Twenty thousand cells per square centimeter were transfected with various concentrations (12.5, 25, and 50 nM final concentration) of a nontargeting control siRNA, Netrin-1 siRNA, LARP1 siRNA, or EGFR siRNA (Sigma-Aldrich) using Lipofectamine 2000 (Invitrogen), according to the manufacturer’s instructions. siRNAs sequences are listed in S3 Table.
The level of infectivity of the HCV produced in cell culture was measured following the TCID50 protocol outlined by Lindenbach [73]. A human HCV antiserum at a 1/500 dilution (initially validated against the anti-HCV capsid C7/50 clone (Abcam) in a double immunofluorescence assay) and a goat anti-human Alexa 488 secondary antibody (Invitrogen) were used, at a concentration of 1 μg/mL. Cells were counterstained with DAPI and examined with a Nikon TE-2000E epifluorescence microscope. Titers were calculated using the Reed and Muench method [73].
RIP-Chip was performed as described in Keene et al. [74], using control IgG and anti-LARP1 antibodies (Novus Biologicals) and protein G magnetic beads (Millipore) prior to RNA extraction and RT-qPCR. For direct or indirect protein immunoprecipitation, Huh7.5 cells were lysed in RIPA extraction buffer. Lysates were incubated with anti-LARP1 antibody at 4°C for 1 h. Protein G magnetic beads were then added to the antibody/lysate mixture and incubated for 45 min before immunoblotting.
Caspase-3 activity assays were performed using the Caspase 3/CPP32 Colorimetric Assay Kit, according to manufacturer’s instructions (Gentaur Biovision). The cell proliferation assay was performed using neutral red uptake as described by Repetto et al. [75].
Formalin-fixed, paraffin-embedded liver samples were sectioned at a thickness of 4 μm. After deparaffinisation and rehydration, tissue sections were unmasked in citrate buffer (pH 9) in a 96°C water bath for 50 min. To block endogenous peroxidases, slides were incubated in 5% hydrogen peroxide in sterile water. Slides were then incubated at room temperature for 1 h with a polyclonal goat antibody recognizing human Netrin-1 (R&D Systems), diluted 1/800 in an antibody diluent solution (Dako). For HCV immunostaining, slides were incubated overnight at 4°C with a human anti-HCV E2 antibody [23] diluted 1/50. After three washes in PBS, slides were incubated with a biotinylated secondary antibody bound to a streptavidin peroxidase conjugate (Lsab+ kit, Dako). Netrin-1 and HCV staining were contextualized by DAB staining. Nuclei were counterstained with hemalun.
HCVpp and their VSV-G and Env-negative controls were produced as described previously [44]. A construct package containing the Renilla luciferase gene under the control of the cytomegalovirus (CMV) promoter (pMLVΨCMV-Luc) was used as a reporter. Huh7.5 cells transduced with virions containing the luciferase transgene were lysed 3 d post-infection, and luciferase activity was monitored by using the Renilla luciferase assay system according to the manufacturer’s instructions (Promega).
Huh7.5 cells were detached with Versene buffer, washed in PBS and centrifuged at 1 200 r.p.m for 5 min. Cells were then stained with an EGFR antibody (Calbiochem) at a 1/1000 dilution and a mouse anti-human Alexa 488-conjugated secondary antibody (Invitrogen). EGFR membrane expression was analyzed using a FACscalibur (BD).
Immunoblotting was performed using standard protocols with antibodies against the HA-tag (Sigma-Aldrich), actin (Sigma-Aldrich), total EGFR (Millipore), phosphorylated (position 1068) EGFR (Cell Signaling), Netrin-1 (R&D System), core (Abcam), HSP60 (Bd Biosciences), and LARP1 (Novus).
Cells were fixed in 4% paraformaldehyde for 10 min and permeabilized for 30 min in 0.15% Triton-X100 in PBS + 3% BSA. Incubation with primary antibodies was performed for 2 hrs in 0.15% Triton-X100 in PBS + 3% BSA at room temperature. dsRNA, EGFR, and EEA1 immunolocalization was performed using anti-dsRNA J2 (Scicons) [76], anti-EEA1, anti-Calnexin, anti-Puromycin (BD Biosciences), anti-LARP1 (Novus Biologicals), anti-NS5A 9E10 (gift from C.Rice), and anti-EGFR (Millipore) antibodies (2 μg/mL). For puromycylation assays, 200 μM emetin (Sigma) and 90 μM puromycin (Sigma) were incubated on cells for 10 min before harvest. Samples were incubated with Alexa-488-goat anti-mouse and Alexa-594-goat anti-rabbit antibodies (Invitrogen, 1 μg/mL) for 1 h at room temperature. Nuclei were counterstained with Hoechst 33342. Images were acquired using a Leica SP5X confocal microscope equipped with LAS AF software. Subsequent analyses were performed using the JACop ImageJ colocalization plugin (http://rsb.info.nih.gov/ij/plugins/track/jacop.html) and its plot profile function.
Naïve Huh7.5 cells were transfected with Netrin-1 or VR1-expressing plasmids and infected with HCV for 4 d. The supernatant was first clarified at 8,000 g for 15 min, then ultracentrifugated on a 20% sucrose / 1X TNE cushion for 4 h, minimizing the carryover of soluble, non-viral material. The pelleted virions were collected in fresh culture medium and incubated with two distinct anti-Netrin-1 antibodies (clones #2F5 from Netris Pharma and #AF1009 from R&D Systems) or DCC-Fc (recombinant receptor of Netrin-1 from R&D Systems) overnight, at a concentration of 10 μg/ml. To quantify the level of viral infectivity, we performed a TCID50 assay following the TCID50 protocol. Naïve Huh7.5 cells were seeded in a 96-well plate at a density of 20,000 cells per square centimeter and infected the day after with the neutralized virions at serial dilutions of 10−1 to 10−8 (one dilution per line). Hence, each biological sample was processed in the context of 12 technical replicates. Immunofluorescence staining was performed 3 d post-infection using a human HCV antiserum at a 1/500 dilution and a goat anti-human Alexa 488 secondary antibody at a concentration of 1 μg/mL. Positive wells were counted with a Nikon TE-2000E epifluorescence microscope and titers were calculated using the adapted Reed and Muench method [77] as considered by Lindenbach [73].
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10.1371/journal.ppat.1003598 | Activation of Ran GTPase by a Legionella Effector Promotes Microtubule Polymerization, Pathogen Vacuole Motility and Infection | The causative agent of Legionnaires' disease, Legionella pneumophila, uses the Icm/Dot type IV secretion system (T4SS) to form in phagocytes a distinct “Legionella-containing vacuole” (LCV), which intercepts endosomal and secretory vesicle trafficking. Proteomics revealed the presence of the small GTPase Ran and its effector RanBP1 on purified LCVs. Here we validate that Ran and RanBP1 localize to LCVs and promote intracellular growth of L. pneumophila. Moreover, the L. pneumophila protein LegG1, which contains putative RCC1 Ran guanine nucleotide exchange factor (GEF) domains, accumulates on LCVs in an Icm/Dot-dependent manner. L. pneumophila wild-type bacteria, but not strains lacking LegG1 or a functional Icm/Dot T4SS, activate Ran on LCVs, while purified LegG1 produces active Ran(GTP) in cell lysates. L. pneumophila lacking legG1 is compromised for intracellular growth in macrophages and amoebae, yet is as cytotoxic as the wild-type strain. A downstream effect of LegG1 is to stabilize microtubules, as revealed by conventional and stimulated emission depletion (STED) fluorescence microscopy, subcellular fractionation and Western blot, or by microbial microinjection through the T3SS of a Yersinia strain lacking endogenous effectors. Real-time fluorescence imaging indicates that LCVs harboring wild-type L. pneumophila rapidly move along microtubules, while LCVs harboring ΔlegG1 mutant bacteria are stalled. Together, our results demonstrate that Ran activation and RanBP1 promote LCV formation, and the Icm/Dot substrate LegG1 functions as a bacterial Ran activator, which localizes to LCVs and promotes microtubule stabilization, LCV motility as well as intracellular replication of L. pneumophila.
| Legionella pneumophila is an environmental bacterium that grows within free-living amoebae and, upon inhalation, in human lung macrophages, thus causing the severe pneumonia Legionnaires' disease. Within amoebae or macrophages the bacteria form a distinct membrane-bound replication niche, the “Legionella-containing vacuole” (LCV). To this end, L. pneumophila injects via a dedicated secretion apparatus about 300 different “effector” proteins directly into host cells, where they interfere with cellular processes. LCV formation is poorly understood, and the function and targets of most bacterial effector proteins are unknown. In this study, we characterize an L. pneumophila effector protein that activates the small host GTPase Ran, which is essential for crucial cellular processes, such as spindle assembly and cytokinesis, nucleo-cytoplasmic transport, as well as nuclear envelope formation. We discovered that Ran promotes intracellular replication of L. pneumophila and its activation on the LCV membrane by LegG1 causes the polymerization of microtubules, along which cellular vesicles as well as LCVs move within cells. Our study defines a novel strategy how pathogenic bacteria subvert host processes to promote intracellular survival and replication.
| The amoebae-resistant environmental bacterium Legionella pneumophila is the causative agent of a severe pneumonia termed Legionnaires' disease [1], [2]. In free-living amoebae as well as in macrophages of the innate immune system, L. pneumophila employs an apparently conserved mechanism to form a replication-permissive membrane-bound compartment, the “Legionella-containing vacuole” (LCV) [3], [4], [5]. LCVs avoid fusion with bactericidal lysosomes, and instead interact in a bi-phasic process with early secretory vesicles budding from endoplasmic reticulum (ER) exit sites and with the ER. Microtubules play a role in the initial trafficking events of LCVs, prior to the acquisition of the early secretory vesicle marker GFP-HDEL and the resident ER marker calnexin-GFP [6]. A proteomics analysis of purified intact LCVs revealed more than 560 host proteins, including α- and β-tubulin, as well as a number of small GTPases and GTPase-interacting factors [7]. In addition to Arf1 and Rab GTPases implicated in the secretory and endosomal vesicle trafficking pathways, Ran and its effector Ran binding protein 1 (RanBP1) were identified in this study as LCV host components.
The small GTPase Ran is implicated in a variety of cellular processes, such as nuclear pore translocation [8], or mitotic spindle assembly and post-mitotic nuclear envelope formation [9], [10]. Furthermore, Ran plays an important role in cytoplasmic events involving non-centrosomal microtubules, e.g. endocytic receptor trafficking and retrograde signaling along microtubules in nerve axons [11]. Ran can be activated by a nuclear (or in mitotic cells: chromatin-bound) Ran guanine nucleotide exchange factor (GEF) termed regulator of chromosome condensation 1 (RCC1) [12]. Ran(GTP) is inactivated by the cytoplasmic Ran GTPase-activating protein 1 (RanGAP1) in concert with RanBP1 harboring a Ran(GTP)-binding domain [11].
The common mechanism of Ran activity involves sequestration of a transport complex compound by Ran(GTP), which is liberated upon GTP hydrolysis. Thus the displacement of Ran leads to the assembly of functional transport complexes and process activation [13]. A prominent example of this mechanism is the direct binding of Ran(GTP) to β-importin, preventing the formation (or leading to disassembly) of cargo transport complexes during nucleo-cytoplasmic transport, axonal retrograde signaling and post-mitotic nuclear membrane reconstitution. In addition, Ran has been implicated in endocytic receptor trafficking [14] and cytoplasmic organization of non-centrosomal microtubules [15]. A role for Ran in pathogen vacuole formation has not yet been described.
The formation of the Legionella-containing pathogen vacuole requires the Icm/Dot type IV secretion system (T4SS), which translocates at least 275 different “effector proteins” into eukaryotic cells [4], [16], [17]. The function of most Icm/Dot substrates is unknown, yet recent studies by several groups shed light on the intricate manner by which some effector proteins modulate small host GTPases. To this end, L. pneumophila produces an Arf1 GEF termed RalF [18] and devotes as many as six different translocated effectors to subvert the function of Rab1 [5]. SidM (alias DrrA) functions as a Rab1 GEF and guanine dissociation inhibitor (GDI) displacement factor (GDF) [19], [20], [21], [22], while LepB deactivates Rab1 through its Rab1 GAP activity [23]. Interestingly, SidM also acts as an adenylyl transferase by covalently attaching AMP to Rab1 [24], [25], and AnkX attaches a phosphocholine moiety to Rab1 [25], [26]. The covalent adenylylation or phosphocholination modifications are reversible, and the corresponding deadenylylation or dephosphocholination reactions are catalyzed by the effector proteins SidD [27], [28] or Lem3 [29], [30], respectively. Finally, the Icm/Dot substrate LidA supports the GEF activity of SidM [20] and binds with immense affinity to activated Rab1 [31]. SidM, but not SidD or RalF, anchors to the LCV membrane by binding with high affinity to the phosphoinositide (PI) lipid phosphatidylinositol-4-phosphate (PtdIns(4)P) [32], [33], [34].
Bacterial proteins targeting the small GTPase Ran have not been characterized, yet the Icm/Dot-translocated L. pneumophila protein LegG1 (Legionella eukaryotic gene G1; lpg1976) shows amino acid sequence homology to the Ran GEF RCC1 [35], [36], [37]. LegG1 (alias PieG) is encoded in the Pie (Plasticity island of effectors) gene cluster and localizes to small vesicle-like structures in eukaryotic cells upon ectopic production [37]. LegG1/PieG contains a C-terminal CAAX tetrapeptide motif, which is lipidated by the host prenylation machinery to facilitate targeting of the bacterial protein to host membranes [38]. Mutation of the conserved cysteine to serine, as well as treatment with the isoprenoid biosynthesis inhibitor mevastatin or with a geranylgeranyltransferase inhibitor abolished membrane localization of ectopically produced LegG1, suggesting that prenylation is the major if not sole membrane-targeting determinant [38]. The function of LegG1 in L. pneumophila-infected host cells is so far unknown. Here, we demonstrate that LegG1 acts as a Ran activator in infected cells and cell lysates. Moreover, the activation of Ran on LCVs promotes microtubule stabilization, LCV motility and intracellular replication of L. pneumophila.
The small GTPase Ran and its effector RanBP1 have been identified on purified LCVs by proteomic analysis [7], [39]. To directly investigate the presence of these proteins on LCVs by fluorescence microscopy, Dictyostelium discoideum producing the corresponding GFP fusion proteins was infected with red fluorescent L. pneumophila. Ran was found to localize to the LCV membrane in D. discoideum infected with L. pneumophila wild-type or ΔlegG1 but not with ΔicmT mutant bacteria (Figure 1A). Moreover, RanBP1 localized to LCVs harboring wild-type L. pneumophila (see below). These results confirm the proteomic data and show that Ran and RanBP1 localize to LCVs in an Icm/Dot-dependent manner.
To assess whether Ran or RanBP1 play a role for intracellular replication of L. pneumophila we depleted the proteins by RNA interference. To this end, A549 lung epithelial cells were treated with siRNA oligonucleotides targeting Ran, RanBP1 or, as a positive control, Arf1, and intracellular replication of L. pneumophila was monitored over 24 h (Figure 1B). Upon depletion of either Ran or RanBP1 the number of intracellular L. pneumophila was reduced two-fold, indicating that the small GTPase as well as its effector RanBP1 are required for efficient intracellular growth of L. pneumophila. As expected, the depletion of Arf1 also reduced intracellular growth of L. pneumophila, albeit less efficiently than depletion of Ran or RanBP1. The treatment with siRNA oligonucleotides efficiently depleted Ran or RanBP1, yet had no effect on A549 cell viability (Figure S1). Therefore, the depletion of Ran or RanBP1 impedes the intracellular replication of L. pneumophila without dramatically affecting the host cell physiology.
The legG1 gene is conserved among the L. pneumophila strains sequenced to date (Philadelphia-1, Paris, Lens, Corby, Alcoy, 130b/AA100, Lorraine, HL06041035), but apparently not present in other Legionella spp. LegG1 is translocated by the Icm/Dot T4SS as a TEM-β-lactamase fusion protein into J774 or RAW264.7 macrophages ([36]; Figure S2), or as an adenylate cyclase fusion protein into CHO cells [37]. Upon infection of D. discoideum producing the ER/LCV marker calnexin-GFP with red fluorescent L. pneumophila producing M45-tagged LegG1, the effector protein accumulated in an Icm/Dot-dependent manner on the LCV membrane (Figure 1C). Under these conditions, approximately 60–70% of the LCVs scored positive for M45-LegG1. In summary, Ran, RanBP1 and LegG1 all accumulate on the LCV membrane in an Icm/Dot-dependent manner.
To analyze the function of LegG1 genetically, an L. pneumophila strain lacking legG1 (ΔlegG1, Table S1) was constructed by deleting the gene from the chromosome by double homologous recombination. LCVs harboring ΔlegG1 mutant bacteria stained for the GTPase Ran faintly but to the same extent as wild-type L. pneumophila (Figure 1A), suggesting that LegG1 is dispensable for the recruitment of the small GTPase to the pathogen vacuole. In contrast, however, significantly fewer LCVs containing ΔlegG1 acquired detectable levels of the Ran effector RanBP1 compared to wild-type L. pneumophila (Figure 2A). Less than 50% of LCVs containing ΔlegG1 stained positive for RanBP1 compared to wild-type LCVs, and the phenotype of the ΔlegG1 mutant strain was fully complemented by expressing plasmid-encoded M45-legG1. This finding indicates that LegG1 promotes the accumulation of RanBP1 on LCVs and thus activates Ran on LCV membranes.
To determine more directly whether L. pneumophila activates Ran and whether LegG1 plays a role in this process, we assayed the production of activated Ran in pulldown experiments using an antibody specifically recognizing Ran(GTP), as detailed in the Materials and Methods section. To this end, RAW264.7 macrophages were infected with L. pneumophila wild-type, ΔicmT or ΔlegG1, or with ΔlegG1 expressing M45-legG1. The infected macrophages were lysed and Ran(GTP) was immuno-precipitated and visualized by Western blot using an anti-Ran antibody. Ran(GTP) was detected following infection with L. pneumophila wild-type but not with ΔicmT or ΔlegG1, and the phenotype of the ΔlegG1 mutant strain was complemented by providing legG1 on a plasmid (Figure 2B). Therefore, LegG1 is required to catalyze the activation of Ran by L. pneumophila.
A similar pulldown experiment was performed by adding purified His6-LegG1 to lysates of A549 cells (Figure 2C). Under these conditions, Ran(GTP) was produced in cell lysates upon addition of native (but not heat-inactivated) LegG1 in presence of GTP (but not GDP). Moreover, purified LegG1-His6 but not the mutant protein LegG1N223A-His6 also activated Ran in cell lysates (Fig. S3C).
Using purified N- or C-terminally His-tagged LegG1 fusion constructs, we also attempted to directly measure Ran GEF activity in vitro with fluorescent mantGDP (2′/3′-O-(N-methyl-anthraniloyl)-guanosine-5′-diphosphate). As a positive control, the human Ran GEF RCC1 significantly stimulated mantGDP-release from Ran(mantGDP) in the presence of excess GTP as indicated by a rapid decrease in mantGDP-fluorescence. However, under various conditions tested the purified LegG1 fusion constructs did not show Ran GEF activity in vitro even at elevated concentrations (Fig. S3; data not shown). In summary, these results demonstrate that L. pneumophila activates Ran in infected protozoan and mammalian host cells by means of a translocated effector and that LegG1 is required to activate Ran.
L. pneumophila lacking legG1 grew in broth at the same rate as the isogenic wild-type strain (data not shown). Yet, the ΔlegG1 strain was slightly impaired for intracellular replication in RAW264.7 macrophages (Figure 3A) and Acanthamoeba castellanii (Figure S4A), but not in D. discoideum (Figure S4B). Upon co-infection of ΔlegG1 with wild-type L. pneumophila at a 1∶1 ratio, the mutant strain was efficiently out-competed by wild-type bacteria and eradicated within 6 days (Figure 3B). Thus the Ran activator LegG1 is essential for competition against wild-type L. pneumophila upon co-infection of amoebae. In contrast, the Rab1 GEF SidM was not required for competition under the same conditions, since a mutant strain lacking sidM did not show a competition defect (Figure S5).
The ΔlegG1 mutant strain was as cytotoxic for RAW264.7 macrophages as wild-type bacteria, since macrophages infected with either strain were vacuolized to the same extent (Figure 3C), and the percentage of cells permeable for propidium iodide was similar (Figure 3D). However, the overproduction of LegG1 reduced cytotoxicity significantly. The reduction of cytotoxicity was specific for LegG1, as overproduction of other effector proteins such as SidC or SidM significantly increased cytotoxicity (Figure S6A). Cytotoxicity reduction by LegG1 did not seem to be due to an impairment of type IV secretion, since translocation of the Icm/Dot substrate SidC was not affected (Figure S6B). Furthermore, L. pneumophila lacking or overexpressing legG1 was taken up with the same efficiency as wild-type bacteria by amoebae (Figure S7). The uptake of L. pneumophila is promoted by the Icm/Dot T4SS [40], and therefore, this finding also indicates that the overproduction of LegG1 does not simply obstruct the T4SS.
To test further effects of LegG1 on host cells, we assessed the fragmentation of the Golgi apparatus by L. pneumophila by using transmission electron microscopy (Fig. 3E). Upon infection of RAW264.7 macrophages with wild-type L. pneumophila the Golgi cisternae were almost completely disrupted, while in cells infected with a ΔicmT mutant strain the Golgi was preserved to an extent similar to uninfected cells. In macrophages infected with L. pneumophila ΔlegG1 the Golgi cisternae were conserved to an intermediate degree and appeared shorter than in uninfected cells.
Finally, in absence of legG1, the same number of LCVs accumulated the ER/LCV marker calnexin, suggesting that LegG1 does not affect the fusion of the pathogen vacuole with the ER (Figure S7C). Together, these results indicate that LegG1 is an Icm/Dot-translocated L. pneumophila virulence factor that localizes to the LCV membrane, is dispensable for bacterial uptake, and promotes intracellular replication in protozoan and mammalian phagocytes.
Ran controls a number of cellular processes, some of which involve microtubule assembly and microtubule-dependent trafficking processes [11], [13]. To study a possible role of microtubule polymerization and LegG1 for intracellular replication of L. pneumophila, macrophages were treated with the microtubule-depolymerizing agent nocodazole, and the fate of GFP-producing wild-type, ΔicmT or ΔlegG1 mutant bacteria was monitored (Figure 3F). The growth rate of wild-type L. pneumophila in nocodazole-treated cells was somewhat lower compared to control cells treated with DMSO only. However, the difference in the growth rate of the ΔlegG1 strain in nocodazole-treated cells compared to control cells was much more pronounced, indicating that in the absence of the Ran activator LegG1 the depolymerization of microtubules is more deleterious for intracellular bacterial growth. ΔicmT mutant bacteria were unable to grow and were killed to the same extent in macrophages treated with nocodazole. These results suggest that the depolymerization of microtubules by nocodazole and the absence of the Ran activator LegG1 synergistically compromise intracellular growth of L. pneumophila.
Next, we used confocal laser scanning fluorescence microscopy to test whether Ran activation by LegG1 promotes microtubule polymerization in L. pneumophila-infected phagocytes. For this purpose, a D. discoideum strain producing GFP-α-tubulin was infected with red fluorescent L. pneumophila wild-type, ΔicmT, ΔlegG1 or ΔlegG1 expressing M45-legG1 (Figure 4A). In amoebae infected with wild-type L. pneumophila microtubules were polymerized to a greater extent compared with cells infected with the ΔlegG1 or ΔicmT mutant strains. More than 50% of the cells infected with ΔlegG1 showed a less dense microtubule network, and the phenotype was complemented by the legG1 gene. In D. discoideum infected with the complemented strain, the majority of microtubules emanated from the centrosome and reached the cell cortex, similar to amoebae infected with wild-type L. pneumophila.
Similarly, in RAW264.7 macrophages infected with red fluorescent L. pneumophila wild-type, microtubules were polymerized to a greater extent, compared to cells infected with the ΔlegG1 mutant strain (Figure 4B). Approximately 50% of the cells infected with ΔlegG1 showed a less dense microtubule network, and again the phenotype was complemented upon providing the legG1 gene on a plasmid. In macrophages infected with ΔicmT, microtubules were polymerized to a similar extent as in cells infected with wild-type L. pneumophila. Uninfected macrophages treated with taxol or nocodazole served as controls for microtubule polymerization or depolymerization, respectively.
The effect of the above L. pneumophila strains on microtubule polymerization in macrophages was further analyzed by stimulated emission depletion (STED) microscopy. This super-resolution immuno-fluorescence microscopy analysis confirmed that microtubules were polymerized to a greater extent in macrophages infected with green-fluorescent wild-type L. pneumophila or ΔicmT, compared to cells infected with ΔlegG1, and the phenotype was complemented by providing the legG1 gene on a plasmid (Figure 4C). Moreover, a high magnification inspection of the microtubule network in the vicinity of LCVs revealed an α-tubulin accumulation on 11% or 49% of LCVs harboring either wild-type L. pneumophila or the complemented ΔlegG1 strain, but not on LCVs harboring ΔicmT or ΔlegG1 mutant bacteria (Figure 4C, inset; Figure 4D).
The amount of polymerized or non-polymerized microtubules in L. pneumophila-infected macrophages was also assessed by Western blot using an anti-tubulin antibody (Figure 4E). Lysates of infected macrophages were subjected to a low and a high speed centrifugation step, and the quantity of microtubules in the pellet and the supernatant was compared. This approach revealed that macrophages infected with L. pneumophila lacking legG1 contained smaller amounts of polymerized microtubules compared to cells infected with wild-type bacteria, and the phenotype was complemented by overexpression of legG1. In summary, different approaches revealed that L. pneumophila promotes microtubule polymerization in amoebae and macrophages in a LegG1-dependent manner to efficiently replicate intracellularly.
As an alternative approach to analyze the effect of a single effector protein, LegG1, on host cells, we delivered the effector into host cells by microbial microinjection using the Yersinia enterocolitica strain WA (pT3SS). This Yersinia “toolbox” strain produces the Ysc type III secretion system (T3SS), yet lacks all endogenous T3SS effectors [41], [42]. N-terminal fragments of the Y. enterocolitica RhoG/Rac1 GAP YopE (YopE1–53, YopE1–138) are not cytotoxic but mediate secretion and translocation of hybrid proteins through the T3SS. Fusions of YopE1–53 or YopE1–138 with LegG1 or SidM were produced and secreted into the bacterial supernatant via the T3SS upon calcium depletion with 5 mM EGTA (Figure 5A). Moreover, dependent on the T3SS and sensitive to the protonophore CCCP (carbonyl cyanide m-chlorophenyl-hydrazone) heterologously produced YopE1–53-LegG1 was translocated into HeLa cells by Y. enterocolitica WA (pT3SS) (Figure 5B).
To analyze the translocation of heterologously produced effector fusion proteins, HeLa cells were infected for 2 h with Y. enterocolitica WA (pT3SS) producing YopE1–138-LegG1, YopE1–138-SidM or YopE, and the morphology and microtubules were analyzed by immuno-fluorescence microscopy (Figure 5C). Uninfected cells or cells infected with WA (pT3SS) producing YopE1–138-LegG1 showed a similar morphology and microtubule network. In contrast, cells infected with WA (pT3SS) producing YopE1–138-SidM or full length Y. enterocolitica YopE, a RhoG/Rac1 GAP [43], rounded up and the microtubules disintegrated. Uninfected HeLa cells treated with 30 µM taxol or nocodazole served as controls.
In order to visualize more subtle effects of LegG1 on the microtubule network, HeLa cells were pretreated for 1 h with 1 µM nocodazole prior to infection for 2 h with Y. enterocolitica WA (pT3SS) producing YopE1–53 or YopE1–53-LegG1 (Figure 5D). Immuno-fluorescence microscopy indicated that under these conditions, cells infected with Y. enterocolitica producing YopE1–53-LegG1 contained a denser microtubule network, compared with cells infected with bacteria producing YopE1–53 or uninfected cells. Throughout the cell body, the injection of YopE1–53-LegG1 caused the formation of a larger number of microtubule bundles, which emanated from the peri-nuclear region and radiated towards the cell cortex.
Microtubule polymerization triggered by Y. enterocolitica WA (pT3SS) producing YopE1–53 or YopE1–53-LegG1 was also quantified by Western blot using an anti-α-tubulin antibody (Figure 5E). This approach confirmed that LegG1 injected into HeLa cells significantly increased the amount of insoluble tubulin in the pellet and thus caused microtubule polymerization. Taken together, these results indicated that heterologously produced LegG1 delivered into eukaryotic cells via microbial microinjection promotes polymerization of microtubules.
Finally, to assess whether LegG1 requires the Ran GTPase to exert its effect on microtubule polymerization, A549 cells (Fig. S1) were treated with siRNA oligonucleotides silencing Ran for 2 days, followed by 1 µM nocodazole for 1 h and infection with Y. enterocolitica WA (pT3SS) producing YopE1–53 or YopE1–53-LegG1 for 2 h (Figure 5F). Under these conditions, LegG1 triggered microtubule condensation only in cells treated with AllStars negative control siRNA, but not in cells depleted for Ran. While 83% of the control cells infected with Y. enterocolitica producing YopE1–53-LegG1 contained a dense tubulin network, only 45% of the Ran-depleted cells did so. This experiment indicates that Ran is essential for LegG1 to promote microtubule polymerization.
Shortly after formation, LCVs rapidly move within D. discoideum cells, and the pathogen vacuoles are transported along microtubules [6]. The dynamics of LCVs harboring either L. pneumophila wild-type (Movie S1) or ΔlegG1 mutant bacteria (Movie S2) was assessed by real-time confocal laser scanning fluorescence microscopy using calnexin-GFP-producing D. discoideum and DsRed-producing L. pneumophila. Two hours post infection the LCV motility was recorded for 5 min with images taken every 15 s (Figure 6A). While LCVs harboring wild-type L. pneumophila were very motile and rapidly moved along microtubules, LCVs harboring ΔlegG1 mutant bacteria were drastically slowed down and hardly moved. The velocity of LCVs was quantified by tracking the migration distance of LCVs over time. These experiments revealed that LCVs harboring wild-type L. pneumophila moved with a speed of about 40 nm/s within the D. discoideum cells, while LCVs harboring ΔlegG1 mutant bacteria moved with a 3–4 times lower speed and were almost stalled (Figure 6B).
In this work, we demonstrate that the small GTPase Ran, its effector RanBP1 and the Icm/Dot substrate LegG1 localize to LCVs and promote intracellular replication of L. pneumophila. Moreover, the legG1 gene is required for Ran activation in infected macrophages, and purified LegG1 protein functions as a Ran activator. Finally, activation of Ran on LCVs promotes microtubule stabilization, LCV motility and intracellular growth of L. pneumophila. LegG1 represents the first prokaryotic Ran activator characterized. LegG1 may activate Ran either directly or indirectly. Direct activation might occur through GEF activity, similar to the activity of the eukaryotic Ran GEFs RCC1 and RanBP10. However, in a nucleotide exchange assay containing purified human Ran loaded with fluorescent mantGDP, excess GTP and His-tagged LegG1, the bacterial effector did not show GEF activity in vitro (Figure S3). Under the same conditions purified RCC1 efficiently catalyzed nucleotide exchange. Importantly, LegG1 harbors three RCC1 domains, while the human RCC1 GEF harbors as many as seven of these domains forming a seven-bladed propeller structure (Figure S8). Given the structural differences between LegG1 and RCC1, the former might promote the activation of Ran not by GEF activity. Possibly, LegG1 stabilizes activated Ran by binding to Ran(GTP), or the bacterial effector functions indirectly as a Ran GAP inhibitor, thus preventing the inactivation of Ran. Finally, the level of Ran(GTP) can also be modulated by nucleotide release proteins such as Mog1 [44], which might be targeted by LegG1.
The Ran GEF RCC1 is chromatin-bound and nuclear in interphase cells, or chromosome-associated in mitotic or post-mitotic cells. Regardless of the cell cycle phase, RCC1 produces a gradient of activated Ran originating from cellular DNA [10], [13]. In contrast, the cytoplasmic Ran GEF RanBP10 has been shown to directly bind tubulin and activate Ran [15], and therefore might provide a scaffold for cytosolic Ran activation and microtubule polymerization. Analogously to RanBP10, LegG1 localizes to the cytosol by accumulating on the cytosolic face of the LCV in L. pneumophila-infected cells (Figure 1C). Furthermore, LegG1 co-localizes with (but does not disrupt) the Golgi apparatus upon ectopic production in mammalian cells [37], [38]. Thus, LegG1 likely regulates in a spatiotemporal manner the production of a Ran(GTP) gradient originating from (a) subcellular membrane-bound compartment(s). While LegG1 localizes to the LCV membrane, the effector might not only act as a Ran activator in cis (on LCVs) but also in trans (in a distance from LCVs) to promote the formation of a replication-permissive compartment and/or to affect other cellular processes regulated by Ran. In this context it is interesting to note that the L. pneumophila Icm/Dot substrate RomA (Regulator of methylation A) is targeted to the host cell nucleus, where the methyltransferase modifies chromatin and gene expression by producing a novel histone mark [45]. It will be interesting to assess, whether by activating Ran LegG1 regulates nucleo-cytoplasmic transport, and thereby, the activity of RomA.
Membrane localization of LegG1 is modulated by prenylation (likely geranyl-geranylation) of a C-terminal CAAX motif [38], implying that the bacterial Ran activator subverts essential host lipidation machinery to direct its subcellular localization. Prenylation might contribute to the specific subcellular distribution among different membranous compartments and thus play an important role for the function of LegG1. Instead of the prenylation machinery, the L. pneumophila Rab1 GEF SidM exploits the PI lipid metabolism of the host cell and specifically binds PtdIns(4)P to anchor to the LCV membrane [32], [46]. Thus, L. pneumophila employs two different but analogous strategies to exploit host lipids as membrane anchors for bacterial GEF effector proteins targeting distinct eukaryotic small GTPases.
Ran activation in mitotic or post-mitotic cells controls the assembly of microtubule spindles and the reconstitution of the nuclear membrane envelope, a process which requires vesicle trafficking, recruitment and fusion [10], [13]. Similarly, Ran activation by translocated LegG1 positively regulates microtubule polymerization (Figure 4, 5), and LCV motility (Figure 6). Microtubule-dependent motility of LCVs might reposition the pathogen vacuole in the infected phagocyte and serve to localize the vacuole in the vicinity of interacting compartments such as the ER. Alternatively or in addition, LegG1-dependent microtubule polymerization might promote vesicle trafficking processes in a distance from the pathogen vacuole, in order to promote fusion and fission events of vesicles communicating with the vacuole. Thus, LegG1-dependent microtubule polymerization likely plays a crucial role in defining the membrane dynamics and equilibrium of LCVs. In any case, the LegG1-catalyzed microtubule dynamics are essential for L. pneumophila infection, as in absence of LegG1, Ran or RanBP1 intracellular bacterial growth is compromised (Figure 1B, 3A, 3B).
Ran accumulates on LCVs in an Icm/Dot-dependent manner (Figure 1A). Yet, LegG1 apparently does not affect the recruitment of Ran to LCVs, and it is unknown which protein or lipid receptor(s) on LCVs the Ran GTPase binds to. In contrast, LegG1 promotes the activation of Ran on LCVs, since in absence of legG1 less RanBP1 accumulates on LCVs in D. discoideum, and Ran(GTP) production is reduced in infected macrophages (Figure 2). In amoebae or macrophages infected with the ΔlegG1 mutant strain, active Ran is still detectable. This residual Ran GEF activity might be caused by eukaryotic GEFs or by L. pneumophila Ran activators other than LegG1. A possible candidate is the Icm/Dot substrate PpgA (Lpg2224), which shares 16% identity and 25.4% similarity with LegG1 and is also predicted to contain RCC1 domains [37].
In order to observe effects of LegG1 on host cells without potential interference by other L. pneumophila effector proteins, we also performed microbial microinjection using the T3SS competent Y. enterocolitica “toolbox” strain WA (pT3SS), which lacks all endogenous type III-secreted effector proteins. The L. pneumophila T4SS substrates LegG1 and SidM were secreted and translocated into HeLa cells as N-terminal fusion proteins with either the YopE1–53 or the YopE1–138 secretion/translocation signal attached (Figure 5). Thus the folding state of these relatively small (31.2 kDa or 73.4 kDa) T4SS substrates is compatible with translocation through a T3SS. LegG1 promoted the polymerization of microtubules in HeLa cells, and therefore, the effector adopted a functional conformation in the target cell. In contrast, the 105 kDa T4SS substrate SidC was produced but neither secreted nor translocated by Y. enterocolitica WA (pT3SS), while its 20 kDa PtdIns(4)P-binding domain SidCP4C [47], [48] was produced and secreted upon calcium depletion (data not shown). Thus, the Y. enterocolitica T3SS apparently does not transport substrates exceeding a certain size or, more likely, the 90 kDa C-terminal domain of the T4SS substrate SidC adopts a folding state in Y. enterocolitica that is not compatible with type III secretion. Yet, in principle microbial microinjection by the Yersinia “toolbox” strain is suitable to functionally deliver into host cells not only T3SS substrates but also heterologous T4SS substrates.
In summary, we document here a characterization of the first bacterial Ran activator, L. pneumophila LegG1. This finding paves the way for the future analysis of the signal transduction pathways activated by Ran(GTP) in L. pneumophila-infected phagocytes, which are implicated in microtubule polymerization, LCV motility and intracellular bacterial replication.
Bacteria and cells are listed in Table S1. L. pneumophila strains were grown for 3 days on CYE agar plates containing charcoal yeast extract, buffered with N-(2-acetamido)-2-amino-ethanesulfonic acid (ACES). Liquid cultures were inoculated in AYE medium at an OD600 of 0.1 and grown at 37°C to an OD600 of 3.0 (21–22 h). Chloramphenicol (Cam; 5 µg/ml) and IPTG (1 mM) were added when needed.
Murine RAW264.7, as well as human HeLa and A549 lung epithelial carcinoma cells were cultivated in RPMI 1640 medium amended with 10% heat-inactivated fetal bovine serum and 1% glutamine (all from Life Technology). D. discoideum strains (Table S1) were grown and transfected by electroporation as described [47], [49], and A. castellanii (ATCC 30234) was propagated as described [50].
The infection of phagocytes by L. pneumophila wild-type, ΔicmT or ΔlegG1 mutant strains producing GFP was analyzed as described using A. castellanii, D. discoideum or murine RAW264.7 macrophages as host cells [32], [47], [48], [50], [51]. Briefly, the phagocytes were infected with L. pneumophila grown for 21–22 h in AYE broth (MOI 1–50), the infection was synchronized by centrifugation (450×g, 10 min, RT), and the infected phagocytes were incubated at 37°C, 30°C or 25°C (D. discoideum) for the time indicated.
All plasmids and oligonucleotides used are listed in Table S1 or Table S2, respectively. DNA manipulations were performed according to standard protocols, and plasmids were isolated using commercially available kits from Macherey-Nagel. All PCR fragments were sequenced.
The chromosomal deletion of legG1 was performed as described [50]: 800 bp upstream and downstream fragments of legG1 (lpg1976) were amplified by PCR using the primer pairs oSU94/oSU95 and oSU96/oSU97, respectively, and chromosomal L. pneumophila DNA as a template. Both fragments were inserted by a four way ligation into a pGEM-T easy vector with a KanR cassette in-between using BamHI sites and adenosine overhangs, yielding plasmid pSU1. Clones were analyzed by restriction digestion and sequencing. The KanR cassette flanked by upstream and downstream fragments was transferred into the pLAW344 suicide plasmid using NotI, yielding plasmid pSU2. L. pneumophila JR32 was transformed by electroporation with pSU2 and selected for KanR/SucR and CamS colonies. Positive clones were tested by PCR, using the primers oSU94, oSU97, oKan3′ and oKan5′, and by sequencing.
Translational M45-fusion proteins of legG1 were constructed by PCR amplification using the primer pairs: oCR158/oCR160 or oER3/oER7 and chromosomal DNA of L. pneumophila JR32 as a template. The fragments were cut with the appropriate restriction enzymes and inserted into pMMB207-RBS-C-M45 (pCR33), pMMB207-C-RBS-gfp-RBS (pCR76), pMMB207-C-RBS-DsRed-RBS (pCR77), yielding the plasmids pSU19, pER4 and pER5, respectively. The plasmids pSU17 and pSU26 (encoding RanA-GFP or RanBP1-GFP) were constructed by PCR-amplification of the corresponding genes using the oligonucleotides listed in Table S2. D. discoideum cDNA was used as a template, cut with NsiI and inserted into pSW102 cut with the same restriction enzyme.
Plasmids encoding LegG1-His6 (pER2) or His6-LegG1 (pER3) were constructed using the primers oER4/oER6 or oCR158/oER05, respectively, and chromosomal DNA from L. pneumophila strain JR32 as a template. The PCR fragment was cut with NcoI/SalI or with BamHI/SalI and cloned into the vector pET-28a. Plasmid pER35 encoding LegG1N223A-His6 was obtained with the QuickChange protocol using the primers oER22 and oER23, and plasmid pER2 as a template.
To construct plasmids encoding YopE1–53 (5.5 kDa) or YopE1–138 (14.8 kDa) fusion proteins produced in Y. enterocolitica, the legG1 gene was amplified by PCR using the oligonucleotides oER158/oER3 and chromosomal DNA of L. pneumophila JR32 as template. The PCR fragment was cut with BamHI/SalI and cloned into pCJYE53-G3 or pCJYE138-G3 cut with the same enzymes, yielding pGP3 and pGP4, respectively. The genes sidM, sidC or sidC_P4C were released from plasmid pEB189, pCR6 or pHP56, respectively, by digestion with BamHI/SalI and cloned into pCJYE53-G3 or pCJYE138-G3 cut with the same enzymes. To liberate gfp from pCJYE53-G3 or pCJYE138-G3, the plasmids were cut with BamHI/SalI, filled in with Klenow polymerase and re-ligated.
To determine Icm/Dot-dependent translocation into host cells of LegG1, 5×105/ml RAW264.7 macrophages were seeded onto 96-well plates in a final volume of 100 µl/well and incubated at 37°C overnight. The macrophages were infected with L. pneumophila (MOI 20, 1 h, 37°C) producing TEM β-lactamase fusion proteins (kindly provided by X. Charpentier), grown for 21 h in AYE supplemented with 0.5 mM IPTG (isopropyl-β-D-thiogalactopyranoside). 20 µl of 6-fold CCF4/AM substrate (Invitrogen) was added to each well. After 90 min incubation, the fluorescence was measured with a fluorescence plate reader (FluoStar Optima, BMG Labtech) using an excitation wave length of 410 nm, and an emission of 450 nm or 520 nm, respectively. The Icm/Dot substrate LepA served as a positive control and the cytoplasmic protein FabI as a negative control.
The constructs pER2 or pER3 were transformed into E. coli BL21(DE3) grown aerobically in LB medium at 37°C and induced with 1 mM IPTG during exponential growth for 4 h at 30°C. Alternatively, pER2 was transformed into E. coli BL21-CodonPlus (DE3)-RIL and grown in 5 l LB medium at 37°C to an OD600 of 0.5 to 0.7 before expression was induced with 0.5 mM IPTG at 20°C overnight. Cells were harvested by centrifugation (7'000×g, 30 min, 4°C), suspended and homogenized in lysis buffer (50 mM Tris/HCl, pH 7.5, 10% glycerol, 10 mM β-mercaptoethanol (βME), 0.5 mM PMSF, 30 ng ml−1 DNase), disrupted at 10,000 psi by a French Press, and the suspension was cleared by centrifugation (11'000×g, 30 min, 4°C).
His6-tagged LegG1 was purified by affinity chromatography using nickel-nitrilotriacetic acid-agarose (Qiagen) equilibrated with buffer E (50 mM Tris/HCl, pH 8.0, 10% glycerol, 10 mM βME, 10 mM imidazole), eluted with buffer E containing 250 mM imidazole and dialyzed overnight against elution buffer lacking imidazole (4°C). Some preparations of His6-LegG1 were further purified by gel filtration (Superdex 75 16/600; GE Healthcare, Munich, Germany) using a buffer containing 20 mM HEPES pH 7.5, 50 mM NaCl, 1 mM MgCl2, and 2 mM DTE (dithioerythritol).
For the GEF assay Ran GTPase was preparatively loaded with fluorescent mantGDP (2′/3′-O-(N-methyl-anthraniloyl)-guanosine-5′-diphosphate). To this end, purified human Ran was incubated for 2 h RT in the presence of EDTA in five times molar excess over MgCl2 and mantGDP in five times molar excess over Ran protein. Unbound nucleotides were removed by buffer exchange using a NAP-5 column (GE Healthcare, Munich, Germany) eluting the protein with exchange buffer (20 mM HEPES pH 7.5, 50 mM NaCl, 2 mM DTE, 1 mM MgCl2, 1 µM mantGDP). Collected fractions containing the protein were pooled and concentrated using a Spin-X UF 500 (Corning, Munich, Germany) concentrator.
The GEF-assay was performed in 1 ml fluorescence buffer (20 mM HEPES pH 7.5, 50 mM NaCl, 2 mM MgCl2, 2 mM DTE, 1 µM GTP) at 25°C using a fluorescence spectrometer (Fluoromax-4, Horiba Jobin Yvon). The excitation and emission wavelength of mantGDP is 345 nm and 440 nm, respectively. The release of mantGDP was monitored via the change in mant-fluorescence after addition of recombinant LegG1 or RCC1, respectively, in the presence of 100 µM GTP. The fluorescence traces have been corrected for dilution.
Ran(GTP) was identified in cell lysates by pulldown experiments using reagents from New East Biosciences. To this end, RAW264.7 macrophages in T75 tissue culture flasks were infected (MOI 20, 1 h), lysed with lysis buffer (50 mM Tris-HCl, pH 8.0, 130 mM NaCl, 10 mM MgCl2, 1 mM EDTA, 1% Triton X-100) containing a protease inhibitor tablet (complete; Roche) and incubated with an anti-Ran(GTP) antibody (1∶2000, New East Biosciences) together with protein A/G-agarose beads for 2 h. The beads were then washed 4 times with lysis buffer, subjected to SDS-PAGE and analyzed by Western blot using an anti-Ran antibody (1∶1000, Abcam). As positive and negative controls, uninfected cells were treated with γ-S-GTP or GDP in presence of 20 mM EDTA before incubation with the anti-Ran(GTP) antibody. Alternatively, A549 cells were washed three times with cold PBS before lysis, incubated for 30 min at 30°C with γ-S-GTP (100 µM) or GDP (1 mM), together with purified His6-LegG1, heat-inactivated His6-LegG1, LegG-His6, LegG1_N223A-His6, or human RCC1, and Ran(GTP) was immuno-precipitated as described above.
To separate polymerized and soluble tubulin, RAW264.7 macrophages seeded in 6-well plates were infected with L. pneumophila strains (MOI 10, 4 h), lysed with microtubule stabilization buffer (0.1 M PIPES, pH 7.6, 2 M glycerol, 5 mM MgCl2, 2 mM EGTA, 0.5% Triton X-100, 4 µM taxol, protease inhibitors) and centrifuged at low speed (240×g, 10 min, RT) and high speed (20'000×g, 10 min). The protein concentration of each fraction was determined with Bradford reagent, and identical concentrations of each sample were separated by SDS-PAGE. Anti-α-tubulin (1∶6000, Abcam) and anti-actin (1∶500, Abcam) antibodies were used for Western blots.
For uptake experiments A. castellanii, D. discoideum or RAW264.7 macrophages infected in 24-well plates (MOI 50, 1 h) were washed, detached by scraping and analyzed by flow cytometry. To quantify uptake, an uptake index was defined as the product of the number of cells above the gate threshold and the fluorescence intensity of the cells. Equal fluorescence intensities of different L. pneumophila strains were checked by a plate reader.
To determine cytotoxicity of different L. pneumophila strains, 2.5×105 RAW264.7 macrophages were seeded onto a 24-well plate. The cells were infected with L. pneumophila (MOI 10, 4 h), and a bacterial input control was plated on CYE plates. After the infection, the medium was collected and replaced with PBS. To detach the infected macrophages, the plate was shaken vigorously at 1400 rpm for one minute on an Eppendorf plate incubator. The supernatant containing detached cells was combined with the stored media and centrifuged (240×g, 10 min). The cells were suspended in 0.5 ml PBS containing 1 µg/µl propidium iodide (15 min, 25°C, in the dark) and quantified by flow cytometry.
To analyze intracellular replication of L. pneumophila, exponentially growing D. discoideum were washed with Sörensen phosphate buffer (2 mM Na2HPO4, 15 mM KH2PO4, pH 6.0) containing 50 µM CaCl2 (SorC), seeded into a 96-well plate (1×105 cells/ml MB medium) and allowed to adhere for 1–2 h. RAW264.7 macrophages were seeded (1×105 cells/ml RPMI medium) one day before infection. L. pneumophila grown for 21 h in AYE broth was diluted in MB or RPMI and used for infection (MOI 1). After centrifugation, the infected phagocytes were incubated at 30°C (A. castellanii) or 37°C (macrophages), a bacterial input control was plated, and samples were taken at the time points indicated (t0 = 10 min post infection).
Single round intracellular growth of GFP-producing L. pneumophila was assayed in A. castellanii or RAW264.7 macrophages as described [52]. Fresh medium was added to the cultures two days before the experiment. One day before the experiment, the cells were suspended and seeded into a black 96-well clear bottom plate (Perkin-Elmer) at a density of 2×104 (amoebae) or 8×104 (macrophages) cells/well and allowed to adhere overnight. Overnight cultures of L. pneumophila harboring pNT28 (GFP) were grown in AYE/Cam to an OD600 of 3.0 (∼2×109 bacteria/ml) and diluted to 8×106 bacteria/ml in LoFlo low fluorescence medium (Formedium). The cells were infected (MOI 20) with 100 µl of diluted L. pneumophila, centrifuged and incubated at 30°C (amoebae) or 37°C (macrophages) for 48 h or longer. Nocodazole was added directly to the infection at the concentrations indicated. The GFP fluorescence was quantified at multiple time points using a plate reader (FluoStar Optima, BMG Labtech). To correlate fluorescence readings with bacterial viability, the cells were lysed at set time points using 0.8% saponin, dilutions were plated on CYE plates, and CFU were counted.
For the competition assays, A. castellanii (2×104 per well, 96-well plate) in Ac buffer was co-infected (MOI 0.01) each with wild-type L. pneumophila and the Kan-resistant mutant strain to be tested. The infected amoebae were grown for 21 days at 37°C. Every third day the supernatant was combined with amoebae lysed with 0.8% saponin, diluted 1∶1000, and fresh amoebae were infected (50 µl homogenate per 200 µl amoebae culture volume). Aliquots were plated on CYE agar plates containing Kan (10 µg/ml) or not to determine CFU.
For the RNA interference experiments, A549 cells were grown in 96-well plates and treated for 2 days with a final concentration of 10 nM of the siRNA oligonucleotides indicated (Table S3). To this end, the siRNA stock (10 µM) was diluted 1∶15 in RNAse-free water, and 3 µl of diluted siRNA was added per well. Allstars siRNA (Qiagen) was used as a negative control. Subsequently, 24.25 µl RPMI medium without FCS was mixed with 0.75 µl HiPerFect transfection reagent (Qiagen), added to the well, mixed and incubated for 5–10 min at RT. In the meantime, cells were diluted in RPMI medium with 10% FCS, 175 µl of the diluted cells (2×104 cells) were added on top of each siRNA-HiPerFect transfection complex and incubated for 48 h. The cells were then infected (MOI 10) with GFP-expressing L. pneumophila wild-type grown for 21 h, diluted in RPMI, centrifuged and incubated for 1 h. The infected cells were washed 3 times with pre-warmed medium containing 10% FCS and incubated for 24 h (well plate was kept moist with water in extra wells). To determine intracellular growth of L. pneumophila, GFP-fluorescence was measured using a plate reader (FluoStar Optima, BMG Labtech).
For microbial microinjection the Y. enterocolitica strain WA (pT3SS) was used [41]. This Yersinia “toolbox” strain harbors a mini pYV virulence plasmid encoding the Ysc T3SS and the YadA adhesin, yet lacks all endogenous type III-secreted effectors [42], [53]. The T3SS recognizes an N-terminal secretion/translocation signal of Yersinia effectors. In particular, N-terminal fragments of the RhoG/Rac1 GAP YopE (YopE1–53, YopE1–138) are not cytotoxic but sufficient to mediate secretion and translocation of hybrid proteins. Fusion proteins composed of YopE1–53 or YopE1–138 and L. pneumophila LegG1, SidM, SidC or its PtdIns(4)P-binding domain SidCP4C were produced in Y. enterocolitica WA (pT3SS) and analyzed by Western blot using a polyclonal anti-YopE antibody (1∶5000; gift from J. Heesemann). The production of YopE served as a positive control.
T3SS-dependent protein secretion was triggered by calcium depletion with EGTA essentially as described [53]. Briefly, Y. enterocolitica cultures grown overnight in BHI broth at 27°C were diluted 1∶20 in 10 ml BHI and grown at 37°C for another 1.5 h, followed by the addition of 10 ml of a solution containing 15 mM MgCl2, 5 mM EGTA and 0.2% glucose for 2 h (final OD600 0.6–0.7). Subsequently, the cells were centrifuged (5000×g, 10 min), and the pellet was washed with PBS and suspended in 500 µl SDS PAGE sample buffer. The supernatant was sterile filtered, precipitated with TCA (10% v/v, 1 h, on ice) and centrifuged (20'000×g, 30 min, 4°C). The resulting pellet was suspended in 3 ml cold acetone (15 min, −20°C), centrifuged (20'000×g, 5 min, 4°C), washed once with each 1 ml cold acetone, and suspended in 50 µl SDS PAGE sample buffer. YopE and YopE fusion proteins were visualized by Western blot using an anti-YopE antibody.
Protein translocation into HeLa cells was determined by subcellular fractionation essentially as described [53], [54]. Y. enterocolitica producing YopE1–53 or YopE1–53-LegG1 were grown over night in BHI at 27°C, diluted 1∶20 into fresh media, grown for another 2 h at 37°C and used to infect HeLa cells (MOI 10) seeded at a density of 2×107 in T75 tissue culture flasks the day before the experiment. Alternatively, the cells were infected with L. pneumophila (MOI 100) as described above. Two hours post infection the HeLa cells were washed several times with PBS and lysed with 1% digitonin. This treatment selectively lysed the host cells, while the bacteria remained intact. After centrifugation, proteins in the pellet (intact bacteria, debris) and in the supernatant (translocated bacterial proteins, soluble host proteins) were precipitated with methanol/chloroform, and YopE1–53, YopE1–53-LegG1 or SidC was visualized by Western blot using anti-YopE (1∶5000) or anti-SidC (1∶1000) antibodies. As controls, HeLa cells alone were used or bacteria treated with 1% digitonin, 1% SDS or 50 µM of the protonophore CCCP, a potent inhibitor of bacterial T3SSs [55], [56].
To analyze protein translocation by fluorescence microscopy, HeLa cells in 24-well plates containing sterile cover slips were infected (MOI 10, 2 h) with Y. enterocolitica producing YopE1–138-LegG1, YopE1–138-SidM or YopE. Uninfected cells or cells treated with 30 µM taxol or nocodazole served as controls. Alternatively, HeLa or A549 cells were treated with 1 µM nocodazole (1 h) and left uninfected or were subsequently infected (MOI 10, 2 h) with Y. enterocolitica producing YopE1–53 or YopE1–53-LegG1. Where indicated A549 cells were treated with siRNA two days before infection as described above. The cells were immuno-stained for α-tubulin (1∶600; Abcam) and YopE (1∶5000), and nuclei were labeled with DAPI (1 µg/ml).
Fluorescence microscopy of D. discoideum producing GFP fusion proteins was performed as described [47], [48], [51]. Briefly, exponentially growing cells were seeded on sterile coverslips in 24-well plates at 2.5×105 per well in 1 ml HL5 medium and let grow over night. L. pneumophila cultures grown for 21–22 h in AYE liquid medium were diluted in HL5 medium and used for infection (MOI 50). The infection was synchronized by centrifugation, and the infected cells were incubated at 25°C for the time indicated and fixed on cover slips.
To obtain homogenates, infected amoebae were washed with cold SorC one hour post infection, suspended in homogenization buffer (20 mM HEPES, 250 mM sucrose, 0.5 mM EGTA, pH 7.2) [57] and lysed by seven passages through a ball homogenizer (Isobiotech) using an exclusion size of 8 µm. The homogenate was centrifuged onto coverslips coated with poly-L-lysine, fixed with 4% paraformaldehyde (PFA) for 30 min at 4°C and blocked with 1% BSA in SorC for 30 min.
The coverslips containing homogenates were incubated for 1 h at RT on parafilm with 40 µl of primary antibody diluted in blocking buffer (affinity purified rabbit anti-SidC (1∶100) [48]; anti-M45 1∶300, Genovac AG) and washed 3 times. Appropriate secondary antibodies were diluted 1∶200 in blocking buffer and incubated for 1 h at RT. Finally, the coverslips were washed and mounted using Vectashield (Vector Laboratories) supplemented with 1 µg/ml DAPI to stain DNA. The samples were viewed with a Leica TCS SP5 confocal microscope (HCX PL APO CS, objective 63×/1.4-0.60 oil; Leica Microsystems, Mannheim, Germany).
Microtubule cytoskeleton analysis was performed with RAW264.7 macrophages or D. discoideum infected with DsRed- or GFP-producing L. pneumophila (MOI 10, 4 h, 37°C). Subsequently, the infected phagocytes were washed once with Brb80, and fixed (50% Brb80, 0.1% Triton X-100, 0.5% glutaraldehyde) for 5 min. After washing with SorC, samples were blocked with 1 mg/ml sodium borohydrate in SorC for 10 min. The samples were stained with the anti-α-tubulin antibody WA3 (gift from M. Schleicher) and anti-SidC (1∶100) on parafilm for 1 h. Appropriate secondary antibodies were used at a dilution of 1∶200 (confocal microscopy) or 1∶400 (STED microscopy). 10 cells per coded sample were assessed for the degree of tubulin polymerization.
For real-time fluorescence microscopy exponentially growing D. discoideum amoebae producing calnexin-GFP were seeded in life-cell imaging dishes (Ibidi) in LoFlo medium containing G418 (20 µg/ml) to a total cell number of 5×105 cells one day prior to the experiment. Before infection with L. pneumophila strains producing DsRed (MOI 10), the cells were washed with LoFlo. Ascorbic acid was added to the cells (20 mg/ml). After two hours incubation at 25°C with no centrifugation, an agar overlay was prepared and the infected cells were observed (recorded) for 5 min. Images were taken every 15 s.
Stimulated emission depletion (STED) microscopy was employed for sub-diffraction resolution fluorescence imaging on a custom-made in-house setup. The system's basic principle is described elsewhere [58]. The system used is capable of acquiring one channel with confocal and two channels with STED resolution quasi-simultaneously. For imaging GFP-producing Legionella and Atto 655-coupled anti-α-tubulin, we used excitation/emission wavelengths of 488±3 nm/520±14 nm and 637±5 nm/685±20 nm, respectively. The STED wavelength for Atto 655 was 750±10 nm. Beam powers for acquisition were 0.5–4.5 µW, and 7 µW for GFP and Atto 655, respectively, as measured in front of the objective. STED beam powers were 1.4 mW for Atto 655. To reduce crosstalk, pulses for various channels were separated in time by varying optical path lengths. A home-built electronic gating device transmitted detector signals occurring at the correct time to the acquisition hardware, and rejected crosstalk signals occurring at other times. Dichroics and filters were purchased from AHF, Tübingen. The supercontinuum laser source was a SC450-PP-HE system running at 1 MHz, manufactured by Fianium Ltd, Southampton. For beam-scanning, we used a YANUS IV scan head from Till Photonics, Munich. The objective was a Leica 100×/1.4.
STED image processing: For acquiring images, movies comprised of 5–20 frames were acquired at an exposure time per pixel of 20–50 µs. All frames were then added up, and a non-local means algorithm (Buades et al., 2005) was applied via a matlab script (Peyré et al., 2007) with algorithm parameters being radius of search window: 1, radius of similarity window: 10, degree of filtering: 10.
For transmission electron microscopy cells were fixed by adding 2% glutaraldehyde (GA) in 100 mM PIPES, pH 7.0, to the culture medium at a 1∶1 volume ratio. After 5 min, the supernatant was discarded, replaced with fresh 1% GA in the same PIPES buffer and incubated overnight at RT. For the subsequent epoxy resin embedding, either (i) cells were scraped, spun down and re-suspended in non-supplemented PIPES buffer, or (ii) the fixative of adherent cells was exchanged with non-supplemented PIPES buffer.
For epoxy resin embedding, the suspended or adherent cells were washed two times with 100 mM cacodylate buffer, post-fixed with 2% OsO4 solution containing 1.5% potassium ferricyanide for 1 hour, and stained en block with 1.5% aqueous uranyl acetate for 30 min. The suspended cells were then dehydrated using a graded ethanol series and propylene oxide, and embedded in epoxy resin (Sigma-Aldrich; St. Louis/MO, USA). The cell monolayer was dehydrated using a graded ethanol series and detached from the bottom of the culture dish by dissolving the plastic with propylene oxide followed by rapid, vigorous pipetting. The cells were then washed four times with propylene oxide to remove the remains of dissolved plastic and thereafter embedded in epoxy resin. Ultrathin sections of 60–70 nm were cut with an ultramicrotome (Leica Ultracut UCT), stained with 0.2% lead citrate (Taab; Berks, England) in 0.1 M NaOH for 20 s and examined in a Philips CM100 transmission electron microscope.
For the stereological analysis of Golgi cisternae, two whole sections from two blocks of each sample were systematically sampled at 900× magnification in order to estimate the cytoplasmic area (volume). Within these micrographs all areas containing identifiable Golgi cisternae were selected and imaged at 8.900× magnification. To analyze the micrographs, a stereological test grid with horizontal and vertical lines was used. The total volume of the cytoplasm, which represented a reference space, was estimated by counting the number of test points over the cytoplasm. In order to estimate the total length of the Golgi cisternal membrane, the number of intersections of all identifiable Golgi membrane with horizontal test lines was counted. In addition, the number of Golgi cisternae - defined as an elongated enclosed membrane profile with a length minimum twice its breadth - was counted in these images. From these values the ratios of the total number of intersections (length) of Golgi cisternal membranes to the total volume of cytoplasm (relative surface density of cisternae) was estimated, as was the relative number of cisternae per cytoplasmic volume for the different conditions. At least 80 cell profiles were analyzed for each sample, and two independent experiments were performed. Within each experiment indices of the estimated values were calculated as ratios compared to the wild-type sample.
Supporting information includes eight figures (Figure S1, S2, S3, S4, S5, S6, S7, S8), three tables (Table S1, S2, S3) and two movies (Movie S1, S2) and can be found with this article online.
The GenBank (http://www.ncbi.nlm.nih.gov/Genbank) accession numbers for the proteins discussed in this paper are L. pneumophila Icm/Dot T4SS (Y15044), LegG1/Lpg1976 (YP_095992), SidC (AY504673), D. discoideum calnexin (AF073837), human Ran GTPase (CAG29343) and RanBP1 (CAG30442).
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10.1371/journal.pgen.1001074 | Disease-Associated Mutations That Alter the RNA Structural Ensemble | Genome-wide association studies (GWAS) often identify disease-associated mutations in intergenic and non-coding regions of the genome. Given the high percentage of the human genome that is transcribed, we postulate that for some observed associations the disease phenotype is caused by a structural rearrangement in a regulatory region of the RNA transcript. To identify such mutations, we have performed a genome-wide analysis of all known disease-associated Single Nucleotide Polymorphisms (SNPs) from the Human Gene Mutation Database (HGMD) that map to the untranslated regions (UTRs) of a gene. Rather than using minimum free energy approaches (e.g. mFold), we use a partition function calculation that takes into consideration the ensemble of possible RNA conformations for a given sequence. We identified in the human genome disease-associated SNPs that significantly alter the global conformation of the UTR to which they map. For six disease-states (Hyperferritinemia Cataract Syndrome, β-Thalassemia, Cartilage-Hair Hypoplasia, Retinoblastoma, Chronic Obstructive Pulmonary Disease (COPD), and Hypertension), we identified multiple SNPs in UTRs that alter the mRNA structural ensemble of the associated genes. Using a Boltzmann sampling procedure for sub-optimal RNA structures, we are able to characterize and visualize the nature of the conformational changes induced by the disease-associated mutations in the structural ensemble. We observe in several cases (specifically the 5′ UTRs of FTL and RB1) SNP–induced conformational changes analogous to those observed in bacterial regulatory Riboswitches when specific ligands bind. We propose that the UTR and SNP combinations we identify constitute a “RiboSNitch,” that is a regulatory RNA in which a specific SNP has a structural consequence that results in a disease phenotype. Our SNPfold algorithm can help identify RiboSNitches by leveraging GWAS data and an analysis of the mRNA structural ensemble.
| Genome-wide association studies identify mutations in the human genome that correlate with a particular disease. It is common to find mutations associated with disease in the non-coding region of the genome. These non-coding mutations are more difficult to interpret at a molecular level, because they do not affect the protein sequence. In this study, we analyze disease-associated mutations in non-coding regions of our genome in the context of their structural effect on the message of genetic information in our cells, Ribonucleic Acid (RNA). We focus in particular on the regulatory parts of our genes known as untranslated regions. We find that certain disease-associated mutations in these regulatory untranslated regions have a significant effect on the structure of the RNA message. We call these elements “RiboSNitches,” because they act like switches turning on and off genes, but are caused by Single Nucleotide Polymorphisms (SNPs), which are single point mutations in our genome. The RiboSNitches we identify are potentially a new class of pharmaceutical targets, as it is possible to change the structure of RNA with small drug-like molecules.
| Genome-Wide Association Studies (GWAS) pinpoint mutations associated to a disease state with single nucleotide precision [1]–[4]. In some cases, the molecular cause of the disease is evident from the mutation data alone. For example, if the mutation results in a premature stop codon, the production of a truncated protein is the cause for the disease [5]. In a majority of cases, however, it is difficult to identify the molecular cause of the disease from the GWAS data alone [3], [6]–[11]. This is especially true when associations are identified in non-coding and intergenic regions of the genome [10], [11]. Since a majority of the human genome is non-coding and intergenic, it is not surprising that many GWAS studies are finding disease associations in such regions [12]–[14]. In this study we aim to evaluate the role of mutation induced structural changes in regulatory RNAs of the human genome and their consequence on the disease state.
The central role of RNA as a major regulator of genetic networks in the cell is now well established [15]. Furthermore, it is estimated that up to 95% of the human genome is transcribed, suggesting that a majority of mutations are transferred to the transcriptome [1]. This study focuses on the potential structural consequences of disease-associated mutations on the RNA transcriptome, in particular single nucleotide polymorphisms (SNPs) in the 5′ and 3′ UTRs of genes. UTRs are the regulatory elements of genes, acting as controllers of translation and RNA decay, as well as targets for RNA interference (RNAi) [16]–[18]. Since UTRs are readily transcribed, play a central role in post-transcriptional regulation, and are integral to the mature mRNA, they present an ideal starting point for studying the potential structure/function relationships of disease-associated mutations on the transcriptome.
Unlike highly structured RNAs such as self splicing introns [19], Riboswitches [20], and the Ribosome [21], the UTRs of mRNAs are not generally evolved to adopt single, well-defined structures. Instead they adopt an ensemble of conformations best described by a partition function, which is defined as the probabilities of all possible base-pairs [22]–[24]. Most mutations in an RNA only have local effects on the structural ensemble. A small subset of mutations, however, have a large and global effect [22]. If a disease-associated mutation belongs to the latter, it can suggest a role for RNA structure in the molecular mechanism of the disease. We make several assumptions in this study, which will be borne out by the data presented below. These assumptions are:
In this study we investigate known disease associated SNPs that map to non-coding UTR regions of the human genome with respect to their effect on the ensemble RNA structure. We identify disease states in which the associated SNPs significantly alter the RNA structural ensemble of the UTR. This analysis provides insight into the potential molecular causes of several genetic disorders including Hyperferritinemia-cataract syndrome [26], β-Thalassemia [27], [28], and Chronic Obstructive Pulmonary Disease (COPD) [29], [30]. More importantly, our analysis reveals the extent to which SNPs affect RNA structure, and the nature of those effects in disease-states.
We first consider the C33G SNP in the 5′ UTR of the HBB (β-globin) gene, which is associated with β-Thalassemia [31], [32] to illustrate the basic premise of our methodology. The SNP is not located near any transcription, translation start or stop sites (Figure 1A). A recent study demonstrated that the C33G mutation (replacing C33 with a G) has a negligible effect on mRNA transcriptional levels [33]. A possible cause for the disease state is therefore a conformational change in the RNA structure. In Figure 1B, we show the result of a partition function calculation for the wild-type (non-diseased) “C” allele of the UTR. Unlike traditional Minimum Free Energy calculations (MFE) that predict a single low energy structure of the RNA, the partition function computes the probability of pairing for all possible base-pairs including potential pseudoknots [22]–[24]. The partition function therefore is a representation of the RNA structural ensemble, i.e. all possible RNA structures [22]. Since whole UTRs are generally not evolved to adopt a single well defined structure, the partition function illustrated in Figure 1B is a more accurate representation of the RNA's structural ensemble than the single structure obtained by traditional MFE computations such as mFold [23].
We choose to highlight the HBB 5′ UTR and the C33G SNP associated with β-Thalassemia [31], [32] because of the difference in the partition functions illustrated in Figure 1B and 1C. The partition function calculation using the mutant sequence (replacing C33 with a G) is dramatically altered by this single SNP, suggesting a significant change in the overall structural ensemble of the UTR RNA. In Figure 1D, we compute the base accessibility (i.e. the probability of the base being paired) by summing the base-pair probabilities down the columns of the partition function. When we compare the base-pairing probabilities for the wild type (C33 non-diseased allele, black line) with the disease-associated mutation (G33, red line), we see that specific bases show large changes in nucleotide accessibility while others remain unaffected by this mutation.
For the purposes of this study, we are particularly interested in identifying disease-associated SNPs like C33G in the HBB 5′ UTR that have a significant effect on the RNA structural ensemble as defined by the partition function calculation. We quantify the overall structural effect of a mutation on an RNA by computing the Pearson correlation coefficient between the wild-type and diseased base-pair probabilities (black and red lines, Figure 1D). For the C33G mutant we determine a WT/mutation correlation coefficient of 0.797 (Table 1). This simple calculation allows us to quantitatively describe the overall rearrangement in the structural ensemble of the RNA caused by the disease-associated mutation.
The Pearson correlation coefficient as computed above provides a quantitative measure of the overall change in the partition function caused by a mutation. However, based on this single calculation, it is difficult to determine the significance of the structural change. We compute Pearson correlation coefficients for all 150 possible single nucleotide mutations (the HBB 5′ UTR is 50 nucleotides in length) and illustrate their values as a heat map in Figure 2A. This result illustrates that a majority of mutations in the HBB 5′ UTR only have small effects (Pearson correlation coefficient >0.95) on the structural ensemble. To better illustrate this point, we plot in Figure 2B a histogram of Pearson correlation coefficients for all single nucleotide mutations of HBB.
The distribution of Pearson correlation coefficients is dependent on both the sequence and its length. This is illustrated in Figure 2C where we plot the distribution of Pearson correlation coefficients for the 1599 mutations in the 5′ UTR of SERPINA1 (serpin peptidase inhibitor, clade A (α-1 antiproteinase, antitrypsin), member 1, which is 533 nucleotides in length), where the C116U SNP is associated with COPD [34]. The two distributions are clearly different and these results suggest a straightforward approach for comparing the extent of conformational change caused by a SNP in an RNA. The C33G mutation in the HBB 5′ UTR has the sixth lowest correlation coefficient out of the 150 possible mutations and we therefore compute a p-value of 6/150 = 0.04 for this SNP (Table 1). Similarly, the C116U mutation in the 5′ UTR of SERPINA1 results in a Pearson correlation coefficient of 0.664 and this yields a p-value of 21/1599 = 0.013. This simple calculation allows us to compare the effects on SNPs on different UTRs and thus rank order the disease-associated SNPs in the Human genome with respect to the significance of the structural rearrangement they induce.
We analyzed a total of 514 disease-associated SNPS in 350 UTRs and non-coding RNAs from the HGMD (Human Gene Mutation Database) [35], [36]. HGMD is a curated database that records the results of published GWAS and other disease association studies [35]. This database is unique in that it provides flanking sequence for a majority of its entries, allowing us to automatically validate the location of SNPs within UTRs using the latest human genome annotations [37], [38]. Of the 350 RNAs we analyzed, 206 were 5′ UTRs, 132 were 3′ UTRs and 12 were non-coding RNAs. The SNPs we analyzed map only to the untranslated regions of mature mRNA and are at least 10 nt away from any transcription or translation start or stop sites. Furthermore, the HGMD annotation stores SNPs associated with alternative splicing in a separate table, which we did not include in our analysis. Our data therefore represents a comprehensive subset of known disease-associated mutations within mRNA UTRs that are not expected to directly affect splicing, translation or transcription through sequence variation. We chose to perform our analysis on this particular subset of disease-associated SNPs to maximize our chances of finding disease states where RNA structural rearrangements are likely to be causative in the association. We map in Figure S5 all SNPs in strong LD (Linkage Disequilibrium, R2>0.9) for common variants identified in Table 1.
Our results are presented in Table 1 and in Table S1. We report on all the disease-associated SNPs that alter RNA structure with a p-value<0.1. We therefore report the top 10 percent of disease-associated SNPs in regulatory non-coding RNA that alter their RNA structural ensemble within the human genome. The disease-states reported in Table 1 are particularly interesting to this study, as they potentially offer mechanistic insight into how RNA structural rearrangement can affect gene regulation and lead to disease. We begin our analysis of SNP induced RNA conformational change by considering the four SNPs associated with Hyperferritinemia Cataract Syndrome listed in Table 1.
We identify four SNPs in the 5′ UTR of the FTL (ferritin light chain) gene that significantly affect the RNA structural ensemble (Table 1) and that are associated with Hyperferritinemia Cataract Syndrome. The FTL gene encodes the Ferritin light chain protein, and deregulation of this gene leads to the disease phenotype [39]. Recent studies on the regulation of FTL have revealed an Iron Response Element (IRE) in the 5′ UTR to which a regulatory Iron Response Protein (IRP) binds [26], [39]. The IRE is an RNA hairpin and mutations in the 5′ UTR disrupt the structure of the IRE and thus alter the binding affinity of the IRP, leading to aberrant FTL regulation [26]. This type of regulatory system is precisely what we aim to identify with our genomic analysis.
One limitation of the partition function representation (Figure 1B, for example) is in the visualization and interpretation of the structural ensemble change induced by mutation. UTRs generally do not adopt single well-defined structures and classic representations of RNA structure (commonly referred to as “airport terminal diagrams”) cannot accurately be used to visualize overall changes in the ensemble. An alternative visualization of the structural ensemble is illustrated in Figure 3A for the wild-type (non-diseased) FTL 5′ UTR. We carried out a Boltzmann sampling of RNA structures using the sFold procedure [40], [41] and generated an ensemble of 5000 alternative RNA structures from the wild-type and mutant sequences. We then perform principal component analysis (PCA) on the full ensemble of structures. The ensemble of structures that belong to a particular sequence (wild-type or a specific mutant) were then projected onto the first two principle components as shown in Figure 3. This allows us to visualize the structural heterogeneity in the ensemble of structures for a sequence, keeping in mind that two points that are close together in our projection diagram indicate the two corresponding structures are similar in structural space.
For the FTL wild-type sequence we find that a majority of our sampled structures are grouped in a single cluster in the right center quadrant of the PCA graph. Representative structures for the three main structural clusters identified for FTL are illustrated in the Figure 3A insets as linear diagrams. We clearly see the formation of the IRE in the representative structure (red), indicating that a majority (97%) of wild-type RNAs adopt this structure. It is when we perform the same Boltzmann sampling procedure for the four diseased SNP sequences that we are able to visualize the nature of the structural ensemble change caused by these disease-associated mutations.
In Figure 3B–3E we project Boltzmann sampled structures onto the same principle components as those used in Figure 3A for the four Hyperferritinemia Cataract Syndrome associated SNP sequences. This analysis immediately reveals the nature of the structural change that putatively is the cause of the disease phenotype. The U22G and A56U mutations result in all three structural clusters populated (Figure 3B and 3C) while the C10U and C14G mutations selectively populate one of the mutant clusters (Figure 3D and 3E). In all cases, we find that the disease-associated mutations populate alternative conformations where the IRE is not formed. For FTL, the non-diseased UTR adopts a compact structural ensemble where the IRE is formed, while the diseased-associated SNPs shift the ensemble to include a significant number of structures where the IRE is disrupted in favor of long-range base pairs. In Table 2, we compute the relative population of the three clusters for the wild type and mutant sequences and find that all four disease-associated mutations significantly reduce the percentage of structures containing an IRE. Nonetheless, we see that no single mutation completely abolishes the cluster with the IRE, suggesting a shift in the relative populations of each conformation.
The four SNPs we identify in the 5′ UTR of FTL as having a large effect on its structural ensemble are a subset of the 30 SNPs associated with Hyperferritinemia Cataract Syndrome reported in HGMD. Since HGMD is based on existing published literature, one can assume that these 30 SNPs represent only a subset of all mutations that can cause the Hyperferritinemia phenotype. A majority (28) of the known SNPs associated with Hyperferritinemia Cataract Syndrome occur in the 5′ UTR of FTL, suggesting that the UTR is central in the regulation of the gene. The four mutations we identify using our partition function calculation and correlation analysis (which we will now refer to as the SNPFold algorithm) identify SNPs that have a major effect on the RNA structural ensemble. By design, SNPFold identifies the SNPs that alter the global structural ensemble of the RNA, and will not identify SNPs that have only local structural effects on the RNA. It is clear, however, that a global effect on the RNA structural ensemble is not a prerequisite for disease association. Clearly, multiple molecular mechanisms can cause the same phenotype; in the case of Hyperferritnaemia Cataract Syndrome any mutation that either directly or indirectly affects the IRE and its ability to bind the corresponding Iron Response Protein (IRP) can result in the phenotype.
In the supplement (Figure S2) we illustrate a natural extension of the SNPFold algorithm for analyzing multiple disease-associated SNPs. We average the change in base-pair probability for each nucleotide and for all Hyperferritinemia Cataract Syndrome associated SNPs. This global analysis of the effects of SNPs on the RNA structure clearly identifies the IRE in the 5′ UTR, which is where on average, the largest changes in base-pair probability are observed. As more associated genotypic information becomes available, it is likely that it will be possible to use this data to identify other RNA structural elements within the transcriptome.
Our analysis of the effects of disease-associated human genetic variation on mRNA and regulatory non-coding RNAs reveals the extent to which specific SNPs affect the RNA structural ensemble. The SNPfold algorithm we propose is unique in that it takes into account the effects of mutation on the ensemble of possible RNA structures, and not just a single minimum free energy structure. UTRs are not evolved to adopt a single, well-defined structure (unlike catalytic RNAs, for example [42]) but will rather adopt a large ensemble of structures [43]. We find that a majority of mutations have small, local effects on the structural ensemble (Figure 2), while certain specific mutations can profoundly alter it. In Figure S3, we compare the performance of MFE (mFold) algorithms to the partition function approach we used and show that our approach is far less sensitive to mutation. We identified those disease-associated mutations in human UTRs that have a large effect on the RNA structural ensemble and report them here.
We identified a broad range of disease phenotypes that are associated with SNPs that alter the RNA structural ensemble. For all the disease states presented in Table 1, the mRNA is either hypothesized or has been shown to play a causal role in the association. In certain cases, assays have already been carried out to show that the SNP causes a change in translation efficiency [26], [39], and/or mRNA stability [44], [45]. We also identified the mRNAs in which RIP-chip [46] experiments measured an interaction with an RNA binding protein (Table 1). We find that several RNA binding proteins including ELAVL1 (embryonic lethal, abnormal vision, Drosophila)-like 1), PABPC1 (Polyadenylate-binding protein 1), and IGFBP2 (insulin-like growth factor binding protein 2) are found to co-IP with our mRNAs of interest (Table 1 and Table S1). This suggests that the SNP induced structural changes could affect protein binding for the mRNAs identified in Table 1. Furthermore, our analysis of pre-mRNAs (Table S2) suggests that the conformational changes induced by SNPs are most significant in the mature mRNA. Finally, analysis of eQTL (expression Quantitative Trace Locus, Table S3) data reveals that for all but two of the common SNPs we identified in our RNA structural analysis, there is no measured effect on transcriptional levels [47].
To ascertain the relationship between our predicted changes in base-pairing probability and RNA functional elements we performed additional analyses reported in the supplement (Figure S4). We find that predicted changes in base-pairing probability overlap significantly with known RNA functional elements including IREs, IRES (Internal Ribosome Entry Sites), uORFs (upstream Open Reading Frames), PAS's (Polyadenylation Sites), TOPs (Terminal Oligopyrimidine tracts), MBEs (Musashi Binding Elements), K-Boxes and GY-Boxes. The IRES is an alternate translation initiation site that allows the ribosome to bind the mRNA in a 5′ cap independent manner [48]. uORFs are found upstream of the normal ORF and lower the translation of the main ORF, and in some cases lead to the production of a short regulatory transcript [49], [50]. A PAS is a variable AU-rich sequence that is essential for the recruitment of the polyadenylation machinery needed to add the polyA tail to a given RNA [51]. TOP elements tag the mRNA for growth associated translational repression [52]. MBEs recruit and bind the Musashi protein, an evolutionarily conserved RNA-binding protein known to have the ability to regulate mRNA translation [53]. K-Boxes and GY-Boxes are conserved negative regulators, acting as binding platforms for the 5′ seed regions of miRNAs [54], [55]. We therefore observe SNP induced changes in base-pairing probability in a majority of the RNA functional elements in our UTRs of interest. For each of these elements, accessibility is key to function, and the base-pairing probability changes we predict affect accessibility.
We performed a complete analysis of the structural changes caused by disease associated mutations in the 5′ UTR of FTL, because it is already established that an IRE is present in the UTR and is responsible for regulating FTL [26], [39]. Our structural analysis of the FTL 5′ UTR (Figure 3) begins to reveal the molecular complexity of disease caused by mRNA structural rearrangement. We see in Figure 3 that no single SNP has the exact same effect on the structural ensemble. Nonetheless, the structural changes observed are limited in the case of this phenotype to three major structural clusters. Mutations shift the equilibrium between the different structural clusters. However, all structures sampled when projected in principal component space fall into these same clusters. A different behavior is observed in the 5′ UTR of RB1 (retinoblastoma 1), where the two disease-associated SNPs we identified also significantly repartition the structural ensemble (Figure S1). In this case, the disease-associated SNPs have the opposite effect to that observed in the FTL 5′ UTR. For the RB1 5′ UTR, the Retinoblastoma associated SNPs collapse the structural ensemble from three clusters to one.
Structural rearrangement of a UTR as a post-transcriptional regulatory mechanism is common in bacterial Riboswitches [16], [20]. In this case, the binding of a small molecule, in general a metabolite, changes the secondary structure of the RNA so as to promote or inhibit Ribosomal binding and gene translation [16]. It is therefore not surprising that certain specific mutations can have profound structural consequences on a human UTR. The UTRs and their associated SNPs we report here are in fact a type of “RiboSNitch,” that is a molecular switch that is activated by SNP. Unlike the Riboswitch, however, a RiboSNitch results in a permanent change in regulation and thus leads to the disease phenotype. RiboSNitches represent a novel therapeutic target, since small molecules can repartition the RNA structural ensemble.
The U310A and U336A mutations in the 5′UTR of CPB2 are particularly noteworthy. CPB2 codes for the Thrombin-Activable Fibrinolysis Inhibitor (TAFI) [45]. An activated form of TAFI is known to slow down Fibrinolysis [44]. Mutations that alter the expression level of this protein are associated with various thrombotic disorders, including ischemic stroke [56]. Results from mRNA decay assays show the presence of these SNPs result in an mRNA with an altered stability [45]. Our results suggest that the associated SNPs significantly alter the RNA conformational ensemble of the TAFI 5′ UTR and that this could affect RNA decay. Therefore, conformational change is also a likely determinant of mRNA stability which indirectly controls protein expression.
Low-cost whole genome sequencing, SNP microarrays specifically focused on non-coding regions of the genome, and greater phenotypic information available through electronic medical records will necessarily yield new phenotypic associations in the non-coding regions of the genome. The SNPfold algorithm provides a novel approach to gain structural insight into the structural consequences of mutations on a transcript. We therefore developed a web server (http://cloud.wadsworth.org/snpfold) that reproduces the computational functionality we describe in this manuscript. In particular our web server allows the simultaneous analysis of multiple SNPs. This computational tool will provide the GWAS community with a simple way to quantitatively evaluate the effects of SNPs (and other mutations) on the RNA structural ensemble.
The Human Genetic Mutations Database (http://www.hgmd.cf.ac.uk/) was utilized [35], [36] as a primary source of genotype/phenotype associations in our study. The professional version of the database, obtainable through a yearly subscription fee, contains the “prom” table. The 2009.1 version of HGMD that we utilized contains 1459 entries in the prom table. Each entry contains DNA sequences that flank the disease associated SNP. These flanking sequences were mapped to the human reference genome, in order to determine the genomic coordinates of the corresponding SNPs [37]. 1385 mutations from this table were successfully mapped to some specific coordinate within a specified chromosome.
Once the coordinates of the SNPs in the table were obtained, the ‘refgene’ table from the hg18 build of the Human genome [38] was used to identify SNPs that map on a UTR of a gene. For a given gene transcript, the corresponding chromosome and strand are provided, as well as coordinates of the transcription and translation start/stop sites, and the exon start/stop sites. SNPs whose coordinates map between the transcription start/translation stop sites or the translation stop/transcription stop sites were classified as mapping onto a UTR region. SNPs that either mapped onto intronic regions of UTRs (not between an exon start and stop coordinate) or were less than 10 nucleotides away from either end of the UTR were excluded from our analysis.
The gene coordinates in ‘refgene’ were used to extract UTR sequences for a given disease associated UTR SNP in ‘prom’. For this, full sequences for each chromosome in the human reference genome were required. We used UCSC genome build hg18 [37]. If the gene was on the ‘minus’ strand, we used the reverse complement of the extracted sequence, as the human reference genome consists entirely of sequence from the ‘plus’ strand. Using the mapped coordinates for each UTR SNP, two different UTR sequences were produced: the wild type sequence, and the sequence containing the disease-associated SNP. It should also be noted that the UTR sequences produced were from the mature transcripts, and are fully spliced.
The SNPfold algorithm that was developed utilizes the RNA partition function calculations implemented in RNAfold [57], [58]. The algorithm requires an input of two different RNA strands that are identical in length. For the analysis of any RNA SNP, the wild type RNA sequence and the RNA sequence containing the disease associated SNP of interest was obtained as previously described. The sum of the columns of each partition function was used to compute the Pearson Correlation coefficient for each WT/SNP pair.
To normalize for sequence length, we computed a non-parametric p-value for a given correlation coefficient. This value represents the likelihood of a random mutation in the RNA of interest producing the same or lower correlation coefficient. For a sequence of length n all possible 3n mutations are computed and the mutation of interest ranked compared to all the other possible mutations. The non-parametric p-value was then estimated as the rank of the mutation of interest divided by 3n.
The structures for the Principal Component analysis were generated using the statistical sampling algorithm in the sFold software [40]. The structures were then parameterized into a vector of ones and zeros (with one representing the base being paired). A sample of 1000 structures from each mutant and WT sequence was randomly selected and used to generate the basis vectors of the principle component analysis. The two firsty basis vectors representing the variances in the data were used to project the 5000 structures from each sequence onto the same principle components. The resultant data took the form of a 2D scatterplot. The linear structure diagrams for the wild type were generated using the VARNA software [59].
A search for known RNA regulatory motifs was carried out in every UTR reported in Table 1 and Table S1. The UTRscan algorithm (which searches a user-submitted RNA sequence for known UTR motifs listed in the UTRsite database) was utilized [60], [61]. In 3′ UTRs, an additional search for miRNA binding sites was conducted using RegRNA which predicts splicing sites and miRNA binding sites in mRNA sequences [62].
RIP-Chip Data obtained from Scott Tenenbaum (UAlbany) was analyzed in the context of the mRNAs reported in Table 1 and Table S1 [46]. The data included analyses of RNA transcript coprecipitation with three different RNA-binding proteins (Elavl1, Pabpc1, and Igfbp2) in two different cell lines (Gm12878 and K562). p-values (−log10) above 1.3 were deemed statistically significant for RNA binding, and are reported in Table 1 and Table S1.
We searched dbSNP to identify common variants (SNPs) with accession IDs (rs numbers) from Table 1 and Table S1. For the mRNAs in which we identified common variants, LD data from HapMap was downloaded [63] and reported above a significant (R2>0.9) threshold. eQTL data from [64] was queried using the common dbSNP IDs.
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10.1371/journal.pntd.0003756 | Prioritising Infectious Disease Mapping | Increasing volumes of data and computational capacity afford unprecedented opportunities to scale up infectious disease (ID) mapping for public health uses. Whilst a large number of IDs show global spatial variation, comprehensive knowledge of these geographic patterns is poor. Here we use an objective method to prioritise mapping efforts to begin to address the large deficit in global disease maps currently available.
Automation of ID mapping requires bespoke methodological adjustments tailored to the epidemiological characteristics of different types of diseases. Diseases were therefore grouped into 33 clusters based upon taxonomic divisions and shared epidemiological characteristics. Disability-adjusted life years, derived from the Global Burden of Disease 2013 study, were used as a globally consistent metric of disease burden. A review of global health stakeholders, existing literature and national health priorities was undertaken to assess relative interest in the diseases. The clusters were ranked by combining both metrics, which identified 44 diseases of main concern within 15 principle clusters. Whilst malaria, HIV and tuberculosis were the highest priority due to their considerable burden, the high priority clusters were dominated by neglected tropical diseases and vector-borne parasites.
A quantitative, easily-updated and flexible framework for prioritising diseases is presented here. The study identifies a possible future strategy for those diseases where significant knowledge gaps remain, as well as recognising those where global mapping programs have already made significant progress. For many conditions, potential shared epidemiological information has yet to be exploited.
| Maps have long been used to not only visualise, but also to inform infectious disease control efforts, identify and predict areas of greatest risk of specific diseases, and better understand the epidemiology of disease over various spatial scales. In spite of the utilities of such outputs, globally comprehensive maps have been produced for only a handful of infectious diseases. Due to limited resources, it is necessary to define a framework to prioritise which diseases to consider mapping globally. This paper outlines a framework which compares each disease’s global burden with its associated interest from the policy community in a data-driven manner which can be used to determine the relative priority of each condition. Malaria, HIV and TB are, unsurprisingly, ranked highest due to their considerable health burden, while the other priority diseases are dominated by neglected tropical diseases and vector-borne diseases. For some conditions, global mapping efforts are already in place, however, for many neglected conditions there still remains a need for high resolution spatial surveys.
| Maps provide an essential evidence-base to support progress towards global health commitments [1]. For example, they provide important baseline estimates of disease limits [2–7], transmission [8–10] and clinical burden [11–14]; underpin surveillance systems and outbreak tracking [15,16]; help target resource allocation from the macro- [17,18] through the meso- [19–22] to the micro-scale [23]; and inform international travel guidelines [24–26]. Significant developments in mapping techniques have occurred over the last few decades, particularly through the use of species distribution models and model-based geostatistics [1,27]. Similarly, disease data has become more widespread and easier to share [28]. Despite these advances however, a recent review of 355 clinically-significant infectious diseases (IDs) indicated that of the 174 IDs for which an opportunity for mapping was identified, only 4% had been comprehensively mapped [1]. For many of these conditions, there is a significant shortfall between existing maps and what can be achieved with contemporary methods and datasets.
Traditional mapmaking has focussed on a vertical, single-species approach, requiring highly labour intensive, and therefore expensive, manual data identification and assembly [13,21,29–31]. The present era of open-access big data, high computational capacity, and rapid software development offers new opportunities for scaling-up the spatial mapping of IDs, primarily through the automation of data gathering and geopositioning but ultimately also to mapping. The Atlas of Baseline Risk Assessment for Infectious Disease (abbreviated ABRAID, as in, “to awake”) is a developing software platform designed to exploit this opportunity and has the ambition to produce continuously updated maps for 174 IDs globally [28]. Realising automation of data retrieval and positioning at this scale is a practically non-trivial but conceptually simple, logistic scaling exercise. In order to automate mapping for each ID so that it is continuously updated and improved as new information becomes available, the spatial inference methods used need to be tailored to each unique ID epidemiology [28]. In some cases this will require disease-specific methodological developments. This requires substantial investment, so an objective and systematic approach is required to determine the order in which IDs are to be mapped.
The first stage in this process is to organise all IDs using a schema based upon shared biological and epidemiological traits; for example, “the mosquito-borne arboviruses”. Such groups will likely have similar mapping requirements, enabling synergies in data collation, covariate selection, increased efficiency (i.e. in software development), and more robust validation of outputs [32,33]. We refer to these disease groups as “mapping clusters” and they form the basic architecture of the prioritisation process.
To rationally prioritise mapping of these conditions, the diseases within each mapping cluster were evaluated based upon their global burden (both morbidity and mortality), as well as the disease’s importance amongst public health stakeholders. Data inputs are quantitative in nature and reliant on either independently derived data or data sourced from entire communities rather than selected expert individuals. Therefore, this proposed framework is unaffected by much of the subjectivity associated with other prioritisation studies, and also provides a platform for rapidly incorporating changes to existing diseases, as well as emerging novel public health threats. The prioritisation exercise helps to guide the order in which diseases are mapped to best support public health priorities; we argue that all relevant diseases can and should eventually be mapped.
A comprehensive atlas of IDs is of central importance in providing geographical context to the understanding of tropical disease and global health [34–36]. Moreover, as the atlas becomes more complete the overlay of maps will provide opportunities for investigating patterns of global disease diversity [37,38] and the process of disease emergence [39].
In order to generate disease prioritisation standards, diseases with shared taxonomy and transmission characteristics were grouped together to create clusters. Diseases within each cluster were evaluated based upon two factors reflecting their importance from a public health perspective: (a) the global burden of the disease and (b) the current public health focus on the condition. Both metrics were assessed simultaneously in order to rank the clusters, and specific diseases were then identified for prioritisation.
This study aimed to be comprehensive in its scope of IDs. All diseases identified in a previous review as meriting mapping were included [1]. This earlier study categorised 355 diseases into five classes: Option 1, indicating that the disease was unsuitable for occurrence based mapping methods; Option 2, mapping the observed occurrence of the disease; Option 3, mapping the maximum potential range of the disease using knowledge of vector, intermediate host and reservoir species; Option 4, using niche mapping methods such as boosted regression trees; and Option 5, where sufficient data exist to allow for global maps of variation in prevalence of infection and/or disease. Option 1 diseases included those that showed no sustained spatial variation in occurrence (i.e. had a cosmopolitan distribution) and had insufficient evidence to allow for the global mapping of variation in prevalence using advanced statistical methods such as model-based geostatistics. In cases where such information does exist, these diseases were promoted to Option 5 status. Revisions to the Hay et al. (2013) paper have led to the inclusion of tuberculosis, ascariasis, trichuriasis and trachoma—all previously listed as Option 1—as Option 5 diseases. Further revisions included the exclusion of New and Old World Spotted Fever Rickettsiosis and New and Old World Phlebovirus because their constituent diseases were included. In addition, Plasmodium knowlesi was included due to the increasing appreciation of its significance to human health in Southeast Asia [40,41]. The new revised total of diseases that warrant mapping was therefore 176. Those diseases not considered for mapping due to Option 1 classification are outlined and justified in the Supporting Information.
Diseases were grouped into clusters based on characteristics relevant to spatial epidemiology. Diseases were placed in the same cluster if they had the potential to mutually reinforce each other in terms of data assembly, mapping requirements and cross-validation of data by comparison of outputs. Clustering classifications were therefore based on the key factors influencing the approach taken for mapping.
At the coarsest level, pathogens were grouped by agent type (virus/bacteria/fungus/other) and the larger agent groupings were split into specific phyla (e.g. Nematoda and Platyhelminths) [42]. These relatively coarse groups reflect fundamental differences in life histories and epidemiology as well as the most basic taxonomic divisions. Within these broad groupings, the mode of transmission was used to create the final disease clusters. This is an important factor when mapping IDs, as the mode of transmission has a large influence on which abiotic correlates are relevant to the mapping process. For instance, the transmission limits of vector-borne diseases are restricted in part by the environmental suitability for the vector species in question, thus diseases spread by similar vectors will share covariates [43]. Similarly, sexually-transmitted diseases are likely to share mapping methods linked to human distribution and behaviour, whilst pathogens spread by water contact would share common traits linked to the environment; these groupings can therefore be logically considered together within a mapping framework. The mode of transmission classifications are defined in the previous publication [1].
The burden of each disease was assessed using the disability-adjusted life year (DALY) estimates from the 2013 Global Burden of Disease Study (GBD 2013) [44,45]. DALYs quantify both morbidity and mortality attributed to each disease and therefore better capture the total impact of a disease than do clinical cases or mortality alone [44,45]. The GBD’s systematic approach across a wide spectrum of diseases provides an extremely valuable resource from which to compare the relative impact of diseases on human health.
Wherever possible, direct links were made between the GBD estimates and diseases in the mapping list. The GBD disease categories, which are based upon the International Classification of Diseases and Related Health Problems (ICD-10) [46], do not always specify particular infectious agents, but rather focus on the clinical symptoms of infection, or non-specified disease groups. These aggregated DALY estimates had to be split across the relevant causative diseases in the mapping list, therefore the Hay et al. (2013) study was reconciled with the ICD-10 codes and then GBD categories in order to disaggregate the broader classifications such as “other diarrheal diseases” and “other neglected tropical diseases”. The full process is outlined in the associated Supporting Information, S1 Text.
Overall, 11 of the 176 mapping diseases could not be reconciled to the GBD categories. Some were not considered due to having unknown pathogenic agents (e.g. tropical sprue) and others were very rare and fell into ICD-10 categories that were assigned over various groupings (e.g. pentastomiasis). These diseases were allocated a nominal DALY of 100; this value, while arbitrary, is low enough to avoid skewing the analysis. For each cluster, the total DALYs for all diseases was calculated and contributed to the final analysis.
Of equal importance is the need to produce maps for those diseases where there is the greatest demand, whether from international organisations or from local public health authorities. Measuring this factor was achieved by surveying a representative subset of potential end-users, to identify which diseases have been prioritised by major public health stakeholders: state-funded public health agencies, private companies (e.g. vaccine developers), political bodies, non-governmental advocates and practitioners, as well as the scientific research community. For each disease, the final policy score was the sum of three component scores: public health, stakeholder interest, status as a notifiable disease, and h-index.
Cases from the different categories of public health stakeholders were included to capture the spectrum of interest groups (see S1 Text for full listing). Each organisation’s mission statement and project pages were reviewed to identify the diseases contained in their public health portfolio. Depending on the type of stakeholder, this would indicate that the organisation would, for example, dedicate funding and effort towards control of that disease, advocate for the disease to governments or public health agencies, or dedicate research funding to the disease. Each disease was allocated one point per stakeholder reporting an interest in it. An inclusive approach was followed, whereby diseases were considered to be of interest to a stakeholder, irrespective of any hierarchy within the agency’s prioritisation system.
Another point was allocated to diseases which were notifiable to national disease control agencies. In order to mitigate spatial bias in the notifiable disease listed by different agencies, a search for countries which had readily-accessible and clearly defined domestic policy relating to named pathogens was performed, and one country from each of the main GBD defined regions was selected: USA (High Income), Brazil (Latin America and the Caribbean), Zambia (sub-Saharan Africa), United Arab Emirates (North Africa and the Middle East), India (South Asia), Malaysia (South East Asia, East Asia and Oceania) and Croatia (Central and Eastern Europe and Central Asia). Interest in these diseases at a domestic level suggests that there will be interest in maps of these diseases, as demonstrated by the presence of subscription-only online databases of maps including GIDEON [47] and the rapid expansion of real-time maps to which physicians are encouraged to contribute [15,28].
Academic output, a proxy of funder agency awards, but also of high-quality data availability [1], was quantified based on the h-index of each disease [48], as reported by Scopus [49]. More commonly used to assess a scientist’s productivity and impact, the h-index is used here to quantify the level of active interest across the academic community in each disease [50]. The h-index is the number of published papers (referring to a particular disease) that have been cited by at least as many other papers. In other words, an h-index of 7 signifies that 7 published papers including that disease name have been cited at least 7 times. For each disease in turn, Scopus citation numbers were generated for all publications referring to the disease (document search for "Disease Name" in "Article Title, Abstract, Keywords"). This Scopus search generates a Citation Tracker file showing the number of citations to each publication referring to the "Disease Name". Diseases were then categorised according to their h-index. Those for which there was evidence of very high scientific output scored 2 (h-index >100), those with intermediate h-index (>50–100) scored 1.5, while diseases with h-index of <50 scored 1.
The diseases classified as Option 4 (use niche modelling methods) and Option 5 (model prevalence or incidence) have the most epidemiological data available and have the greatest potential to benefit from a dedicated mapping exercise, but also require the most resources. Option 2 and 3 diseases are data-poor and both require mapping of occurrence data only [1], and therefore are significantly less time-intensive to map, limited to more simplistic analyses, than those diseases categorised as Option 4 and 5. Option 3 disease mapping relates potential transmission limits to aspects of vector biology. In cases where Option 4 and 5 diseases also have the same vector, the Option 3 disease will be considered as part of mapping these complementary diseases; where this is not the case, a disease’s transmission limits can be assessed through a mixture of literature surveys and occurrence data overlap. Option 4 and 5 diseases within the disease clusters were therefore prioritised and for each cluster, the average policy score for the Option 4 and 5 diseases was calculated and contributed to the final analysis. These diseases should be the primary focus of future cartographic efforts as these require the most attention and bespoke inputs to be generated.
The final step in the process was to combine these assessments to produce a ranking of disease clusters and therefore recommend diseases to prioritise for mapping. Each cluster was plotted on a graph based on its total DALYs and the average policy priority of its Option 4 and 5 diseases. Option 2 and 3 diseases were included in the cluster DALY scores in order to reflect the relative importance that each cluster represented in terms of burden of disease. One cluster may consist of a large number of minor diseases which, as a collective grouping, represent a significant problem—by retaining the DALY score, this burden is reflected, With the policy priority score however, the opposite is the case; inclusion of multiple low scoring diseases would down-weight the cluster as a whole. In scenarios where clusters consist of a diverse grouping of pathogens, averaging policy score across all conditions misrepresents those with a high policy priority and therefore masks these diseases in comparison to clusters that only consist of those diseases with high policy priority scores.
Each cluster was then evaluated based upon its distance from a hypothetical cluster which had the highest DALYs (i.e. that of HIV) and the highest policy score (i.e. that of Malaria) relative to a line drawn from this cluster to the origin; those closer to this hypothetical cluster, along this axis, were prioritised higher. As a result, the relative influence of burden and policy priority could be considered both simultaneously and independently. Within each cluster, the diseases to be prioritised (i.e. Option 4 or 5) were then reported (Table 1). The code to replicate this methodology is freely available from: https://github.com/SEEG-Oxford/prioritisation.
The 176 diseases identified as having a rationale for mapping were organised into 33 clusters, based upon the biological and taxonomic classifications of the causative pathogen, modes of transmission and the mapping method recommended in a previous review [1] (Fig 1). Seven of these clusters included only a single disease due to their unique transmission within their broader taxonomic grouping (HIV, poliomyelitis, avian influenza, pythiosis, South American bartonellosis, tuberculosis and babesiosis). Conversely, the mosquito-borne arbovirus cluster was the largest cluster, consisting of 26 diseases, many of which have the potential to benefit from modelled maps.
Fig 2 brings together the two indices selected to prioritise diseases for mapping—disability adjusted life-year (DALY) burden and relative stakeholder interest. These plots demonstrate that the HIV, malaria and tuberculosis clusters are exceptional in representing an overwhelming share of DALY burden [51] and being of highest priority to the global health community with their placement in the top right quadrant of the graph. These three clusters contain five individual diseases that are a mapping priority, malaria (Plasmodium falciparum, P. vivax, and P. knowlesi), HIV and tuberculosis. Table 1 shows the top 15 disease clusters (i.e. those in the top right of Fig 2), representing 44 individual diseases, with their associated scores. Fig 3 demonstrates that there exists a group of approximately 45 diseases that are the collective focus of public health agencies. The 44 diseases prioritised by this study include all those diseases that represent a significant cartographic challenge (i.e. those diseases requiring either species distribution modelling approaches to produce occurrence maps or model-based geostatistics to produce prevalence maps, n = 33) identified by these public health agencies, save rabies and avian influenza. The clusters are ranked in order, whilst the diseases within each cluster are alphabetical and should be considered equal on the basis of this prioritisation.
The top ten priority clusters account for over 92% of all DALYs for those IDs which require mapping (i.e. the 176 IDs identified); if this is expanded to the top 15 clusters containing 44 diseases to map, this value increases to 95% (Fig 4). Within these 44 diseases, 19 of the 29 neglected tropical diseases (NTD) highlighted by the WHO are represented. Within the top ten prioritised clusters, 14 individual diseases relate to these same NTDs [52,53]. The top 15 prioritised clusters include some diseases, such as the picornaviridae (polio), that have a low DALY burden but a high public health ranking because they are high on the eradication agenda.
It was possible to establish a direct correspondence with GBD estimates for 34 of the 176 diseases with a strong rationale for mapping as listed by Hay et al. (2013) [44,45]. DALY estimates were allocated to a further 132 diseases by linking diseases with ICD-10 codes [46] and their respective GBD category definitions. Whilst these burden values are not accurate absolute values, and should not be interpreted as such, this DALY allocation does allow relative burdens to be determined. The remaining 11 diseases were given the baseline DALY allocation of 100, a value not intended to represent an estimate of the “true” DALYs associated with these diseases, but rather to distinguish them from diseases which were considered to cause a major burden in the GBD analysis. It is safe to assume that if such diseases were not assigned a specific GBD classification, their global impact on mortality and morbidity is relatively small.
In total, the 176 diseases with a strong rationale for mapping [1] represent over 230 million DALYs, approximately 10% of the global DALY burden and 47% of the global ID DALY burden. At the cluster level, HIV, malaria and tuberculosis represent 80% of the overall mapping-disease DALY burden (Fig 5A). Apart from these three conditions, the only other IDs in the top 50 highest DALYs globally are not currently recommended for mapping because they do not show spatial variation in their occurrence and have insufficient data to map variation in disease prevalence with model-based geostatistical analyses. The high-burden diseases not currently considered for mapping include respiratory diseases, meningitis, and many diarrhoeal infections. Alternative approaches to mapping broader symptom groupings (severe pneumonia, severe diarrhoea and severe febrile illnesses) and then differentiating constituent disease components, are being developed. Together, this would map 80% of all DALYs caused by communicable diseases.
A higher resolution focus on the clusters excluding HIV, malaria and tuberculosis (Fig 5B) shows that over 60% of DALYs associated with the 176 IDs are accounted for by the other top ten prioritised clusters; approximately three quarters of the remaining DALYs are accounted for when the remaining prioritised clusters of diseases are included.
The treemap in Fig 5C displays the repartition of interest from the global health community across the clusters. Interest was scored in terms of: 1) the stated priorities of a survey of assorted public health stakeholders who are expected to be end-users of the maps, 2) status as a notifiable disease, and 3) prominence in the academic literature.
A total of 20 diverse stakeholders were surveyed. This was found to be a sufficiently large number to sample based on an analysis similar to a species accumulation curve that demonstrates the diminishing returns from increasing sampling effort [54]. The number of new diseases reported levelled off at around 15 organisations sampled (Fig 3) and so the 20 organisations used for this analysis was sufficient to capture the diseases of public health priority. Of the 176 diseases recommended for mapping [1], 24% were prioritised by at least one public health agency, and 55% were notifiable to at least one of the national disease control agencies.
Of those diseases that represent the greatest cartographic challenge, all were prioritised by at least one public health agency and two thirds were notifiable diseases. Of the 176 diseases, thirty diseases (17%) had an h-index [48] above 100 (with HIV having the highest h-index of 461), while 64% of the diseases had an h-index of 50 or less. Of the occurrence mapping and prevalence mapping diseases, 30% had an h-index above 100 and only 37% had an h-index of 50 or less.
Unlike the DALY burden, which was allocated at the disease level (S1 Text), the stated priority diseases were often grouped to the cluster level by the surveyed stakeholders. For instance, rather than specifying “Plasmodium vivax” or “visceral leishmaniasis” as a focus, “malaria” and “leishmaniasis” would be more commonly stated targets. Each component disease of these clusters would therefore be allocated a point, meaning that the number of component diseases in each cluster strongly inflated the overall interest score allocated at the cluster aggregate. Interest scores were calibrated in the final prioritisation assessment to the number of diseases classified as occurrence or prevalence mapping within each cluster (i.e. those requiring the more advanced geostatistical techniques, see Methods for more details), so as to avoid being unduly skewed by the size of the cluster.
Overall, malaria, HIV and tuberculosis were the leading clusters of interest, with scores of 11.8, 11 and 11, respectively. A further seven clusters received repeated interest, including food-borne/water-borne bacteria (score = 8) and water-borne trematodes (7.7), trypanosomiasis (7.5), filariasis (6.2), picornaviridae (6), avian contact viruses (6), soil-transmitted helminths (5.3) and leishmaniasis (5.2) all scoring highly, indicating their importance to the public health community. These scores are relative and intended to reveal general trends across the clusters rather than quantitatively reflect the weighting that any one institution places on a particular disease.
A review of all clinically significant IDs identified 176 with a strong rationale for mapping, of which only 4% have been adequately mapped [1]. The current study was undertaken to define a ruleset for determining which diseases, from a cartographic and public health perspective, should be prioritised when sequentially addressing this shortfall. Diseases were clustered together based upon shared characteristics (such as basic taxonomic division and mode of transmission) in order to consider together those diseases that would synergise operationally in terms of data collection, covariate selection and methodology used. Given the large number of diseases identified, prioritisation is necessary; we addressed this by evaluating both within the context of disease burden as well as considering the diseases’ influence within public health organisations and the wider academic community. It is important to stress that the study was focussed on priorities for mapping, and was not a general prioritisation of IDs; this is particularly important to emphasise given that a number of high-burden diseases, including meningitis, pneumonia and some diarrhoeal diseases, were not included in the list of 176 diseases [1,44,45].
Malaria is the infectious disease for which the most detailed and robust global risk maps exist [13,29]. The work of the Malaria Atlas Project [33,55] along with a proliferation of national and local-scale studies [56] has established a mature and sophisticated methodological approach centred on the use of model-based geostatistics to generate continuous surfaces of risk. This has been possible, in part, due to the long history of population-based malaria infection prevalence surveys where researchers and control programmes have used microscopy or rapid diagnostic tests to establish the proportion of randomly sampled individuals testing positive for malaria parasitaemia [30,57]. Crucial for geospatial mapping, such data are increasingly georeferenced with a latitude and longitude for each observation established via gazetteer methods (recorded location names linked to digital atlases) or directly using Global Positioning System (GPS) technology at the time of survey [58,59].
The high prioritisation of HIV and tuberculosis shown in the current study brings into sharp focus the need for similar mapping activities to be established for HIV and tuberculosis. All three diseases have an established history of routine and survey-based data collection that, in comparison to many other diseases, is of relatively high quality and consistency, laying the foundation for similar statistical mapping approaches to those used for malaria to be applied. A cornerstone of HIV surveillance over the last several decades has been routine blood testing for HIV infection in mothers attending sentinel antenatal clinics. Such data provide rich longitudinal observations of prevalence in this demographic group and the potential exists to combine these with cross-sectional data from nationally representative household surveys [60] to generate optimal space-time models of the changing geographical pattern of infection across individual countries. Unlike HIV and malaria, population-based tuberculosis prevalence testing is not currently included as part of the major international survey programmes [58,61]. However, such surveys (reporting on the prevalence of bacteriologically-confirmed pulmonary tuberculosis) have been undertaken in a number of high-burden countries in recent years, with many more planned in the near future [62]. In a similar way to HIV, the prospect exists of a mapping methodology that could combine survey-based data with the rich health-system based data on new case notifications and other metrics, leveraging the respective strengths of community- and facility-based data. A longer-term goal must be the development of a data assimilation and modelling architecture for all three of these major global diseases to support robust and regularly updated global maps detailing their joint distribution and its evolution though time which can be used to assess the impact of control and international financing efforts [18].
The current analysis identifies a number of different NTDs as priority diseases for mapping, a finding which is consistent with the emphasis given to mapping by the global NTD community in order to geographically target NTDs interventions [63,64]. Specifically, for those NTDs where morbidity control is the goal, including soil-transmitted helminths (STH) and schistosomiasis, interventions are most cost-effective when they are targeted to areas of highest transmission [21]. For those NTDs which are identified for elimination, such as onchocerciasis and lymphatic filariasis, it is essential to know where transmission occurs and when it has been successfully halted following control measures. As a consequence of these operational requirements, large-scale mapping initiatives are underway for each of the main NTDs (Table 1). A challenge for mapping the NTDs, and indeed for mapping many IDs, is the need to continually update maps in order to help track the progress in control. As interventions reduce transmission levels and therefore distributions become more focalised, the need for mapping will only increase.
Unsurprisingly, the top 44 diseases for prioritisation are dominated by those with the highest global burden. However, certain clusters stand out as having high public health attention without a high burden, particularly the picornaviridae cluster and its constituent disease, polio. Although cases are now restricted to a few hundred each year, polio has been identified as an eradication target and is a high priority for many public health stakeholders despite recent obstacles in the eradication schedule [65,66]. In these eradication and elimination scenarios, the role of mapping changes subtly to both identifying areas where cases continue to occur, and in highlighting potential future risks and improving surveillance [67]. Following a similar logic, diseases such as dracunculiasis, African trypanosomiasis and onchocerciasis, in spite of relatively low burdens, remain high policy priorities due to elimination efforts in various parts of the globe [68,69]. These examples demonstrate the utility of the approach used in this study of using assessments of the public health burden as well as metrics of public health attention.
The disease prioritisation methodology used here differs from existing approaches, such as the “Delphi panel method”, in that it does not include a panel of experts scoring various criteria associated with the diseases being considered [70–74]. In contrast, this study uses a simplified methodology, placing importance in reproducibility and flexibility, using clearly defined rules to assess available evidence and remove potentially subjective expert-opinion. The methods employed are reliant on independent, third party information, and are assessed in a consistent manner, which can easily respond to changes either in burden or public health focus. The relative importance of these diseases will most likely change over time, so an approach that can easily accommodate this is preferable. Burden estimation using the GBD is crucial, since it is the leading globally consistent measure by which to compare these various diseases and the effects of their many different clinical manifestations. Any global assessment of 301 causes of mortality and morbidity, and associated sequelae, will be subject to the limitations of data availability and epidemiological understanding as well as model assumptions and implementation [53,75,76], and will require frequent updates in a rapidly changing world. The technique presented here has the advantage of being rapidly updateable, and we will reproduce these numbers with each new iteration of the GBD project. As a consequence, public health authorities can also easily create bespoke prioritisation lists based upon a selection of disease inclusion criteria (such as those endemic to their particular country or region). This can more easily be achieved with the availability of sub-national estimates of disease burden from the GBD study. Country specific estimates of the interest scores can also be generated with greater specificity, and can therefore avoid some of the potential biases resulting from the use of other countries as representatives of each GBD region used in this study.
Additional factors that may influence the disease priority, such as potential economic impact [77–79], were not used in this analysis because insufficient information was available to include these metrics. The methodology outlined above benefits from two metrics that can be applied globally to quantify DALYs and public health priority. As and when measures of additional disease impacts become available, they can and should be incorporated into assessments such as this.
The study also identifies some high DALY groupings that do not have high-level policy interest. Three groupings (Tick-borne (Bacterial), Tick-borne (Viral) and Mammal contact (Viral)) have a cumulative high DALY burden, but relatively low policy rankings and therefore are just outside the top 15 cluster listing. This may reflect the large number of diverse pathogens that make up these groupings, many of which are relatively restricted in distribution and hence would not commonly be prioritised by globally focussed organisations. That said, the high DALY value indicates that these diseases are of international interest, particularly when secondary human-to-human transmission is a possibility such as with Lassa fever and Crimean Congo Haemorrhagic Fever [80]. These conditions further advocate the utility of regional and national level priority estimates.
The exclusion of diseases not suited for occurrence based mapping, and therefore omitted from the prioritisation process (so called Option 1 diseases [1]), is entirely based on cartographic considerations. Some of these diseases are inherently linked to human-to-human interactions, others are endogenous in origin, with the pathogen essentially ubiquitous amongst humans and only occasionally causing opportunist infections in certain scenarios, whilst some have the potential to cause infection anywhere across the globe due to the cosmopolitan distribution of their sources of infection, whether they be environmental or human based. Many of these diseases can vary spatially, as evidenced by the African meningitis belt, although such variation, when considered relative to the rest of the world, is due to differences in prevalence or intensity, not presence or absence. Occurrence based mapping methods, such as boosted regression trees, rely on binary presence/absence data. For conditions such as the common cold, diphtheria or respiratory syncytial virus, which have the potential to occur across the globe, these mapping techniques are ineffective. It is only through using more advanced methods, such as model-based geostatistics, that maps analysing the variation in intensity of these diseases can be produced. The limitation of this methodology is the amount of prevalence survey data required, which for many diseases is not comprehensive or detailed enough to allow for global analyses. Basic human related covariates, such as population density, urban extent profiles and national vaccination statistics can be used to explain a degree of the global variation in these diseases, but fall short of the wealth of information that can be derived from comprehensive global prevalence datasets, such as those available for malaria. As we continue to explore additional data avenues, there will be an increasing number of diseases where such data become available.
The disease prioritisation outlined in this study offers a logical framework for proceeding with disease mapping, which reinforces the necessity of existing programmes and identifies those diseases to focus on next (Table 1). Diseases which will form the initial focus of future study comprise both those with the highest-burden and those of greatest concern to the global health community. The initial top-priority diseases include a range of disease agents and transmission routes, and therefore present a variety of challenges for mapping. The prioritisation and clustering of these diseases presents a clear plan of action designed to maximise the effectiveness and value of future cartographic efforts.
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10.1371/journal.ppat.1006965 | Contributions from the silent majority dominate dengue virus transmission | Despite estimates that, each year, as many as 300 million dengue virus (DENV) infections result in either no perceptible symptoms (asymptomatic) or symptoms that are sufficiently mild to go undetected by surveillance systems (inapparent), it has been assumed that these infections contribute little to onward transmission. However, recent blood-feeding experiments with Aedes aegypti mosquitoes showed that people with asymptomatic and pre-symptomatic DENV infections are capable of infecting mosquitoes. To place those findings into context, we used models of within-host viral dynamics and human demographic projections to (1) quantify the net infectiousness of individuals across the spectrum of DENV infection severity and (2) estimate the fraction of transmission attributable to people with different severities of disease. Our results indicate that net infectiousness of people with asymptomatic infections is 80% (median) that of people with apparent or inapparent symptomatic infections (95% credible interval (CI): 0–146%). Due to their numerical prominence in the infectious reservoir, clinically inapparent infections in total could account for 84% (CI: 82–86%) of DENV transmission. Of infections that ultimately result in any level of symptoms, we estimate that 24% (95% CI: 0–79%) of onward transmission results from mosquitoes biting individuals during the pre-symptomatic phase of their infection. Only 1% (95% CI: 0.8–1.1%) of DENV transmission is attributable to people with clinically detected infections after they have developed symptoms. These findings emphasize the need to (1) reorient current practices for outbreak response to adoption of pre-emptive strategies that account for contributions of undetected infections and (2) apply methodologies that account for undetected infections in surveillance programs, when assessing intervention impact, and when modeling mosquito-borne virus transmission.
| Most dengue virus infections result in either no perceptible symptoms or symptoms that are so mild that they go undetected by surveillance systems. It is unclear how much these infections contribute to the overall transmission and burden of dengue. At an individual level, we show that people with asymptomatic infections are approximately 80% as infectious to mosquitoes as their symptomatic counterparts. At a population level, we show that approximately 88% of infections result from people who display no apparent symptoms at the time of transmission. These results suggest that individuals undetected by surveillance systems may be the primary reservoir of dengue virus transmission and that policy for dengue control and prevention must be revised accordingly.
| Though often assumed benign, it is increasingly recognized that for many pathogens, clinically inapparent infections can represent a sizeable portion of the infectious reservoir [1–3] and contribute substantially to pathogen transmission [4]. Understanding the contribution to transmission from people with inapparent infections is fundamental for inferring drivers of transmission [3], estimating the timing and scope of outbreaks [5], planning and monitoring control efforts [6], and assessing the feasibility of pathogen elimination [1,6].
Of the 390 million dengue virus (DENV) infections that occur globally each year, an estimated 300 million do not result in symptoms severe enough for a person to seek treatment [7,8], meaning that they likely go undetected by most surveillance systems. The four closely related DENV1-4 serotypes are transmitted predominantly by Aedes aegypti mosquitoes [9], and infection with one serotype is believed to be followed by short-term, heterologous cross-immunity and life-long homologous immunity [10]. Based on observed positive correlations between DENV viremia and disease severity [11–14], it has been assumed that the 300 million annual clinically inapparent infections contribute little to onward transmission because their viremia levels are too low to efficiently infect mosquitoes. On the other hand, high sero-conversion rates coinciding with few reported cases in some areas suggest that inapparent infections may contribute appreciably to silent DENV transmission [15]. Furthermore, recent blood-feeding experiments with Ae. aegypti mosquitoes demonstrated that people with asymptomatic and pre-symptomatic DENV infections are indeed capable of infecting mosquitoes [16].
Although these indications of a possible role of inapparent infections in DENV transmission have become more evident, the proportion of overall transmission for which they are responsible is unknown. This potentially significant unknown has important implications for policy given the difficulty of identifying people with inapparent infections, which may be a critical technical limitation in the event that they contribute appreciably to transmission.
We addressed this question by estimating the net infectiousness (NI) of DENV-infected individuals with different clinical manifestations, including asymptomatic infections, and quantifying the relative contributions of these clinically distinct classes to the overall force of infection (FoI) of DENV. Our approach involved three distinct steps (Fig 1). First, we estimated the net infectiousness of different classes of DENV infections by combining class-specific estimates of DENV viremia trajectories and class-specific estimates of the relationship between DENV viremia and infectiousness to mosquitoes. Second, we estimated the proportion of infections of each class by first quantifying the proportion of primary, secondary, and post-secondary DENV infections in a population with a given level of transmission intensity. We then translated those proportions of primary, secondary, and post-secondary infections into proportions of each infection class based on estimates of those relationships from the literature. Third, we used a novel formulation of DENV force of infection to combine estimates of individual-level net infectiousness of each infection class and estimates of the proportion of each infection class in a hypothetical population to estimate the proportional contribution of each infection class to overall DENV force of infection.
We distinguished four classes of infections (Fig 2). We referred to the first class as “asymptomatic” (As), which we defined as people having absolutely no perceptible symptoms at any point during their infection [16]. The remaining people with symptomatic (S) infections were divided into: (1) inapparent symptomatic (IS), people whose symptoms are sufficiently mild to not disrupt their daily routine and thus do not prompt healthcare seeking [7,16]; and (2) apparent symptomatic (AS) individuals, whose clinical presentation does disrupt their daily routine according to the WHO definition of “at least fever and two dengue symptoms” [17]. Detected apparent symptomatic individuals (DAS) are identified by health surveillance systems if they seek clinical consultation and are diagnosed as a confirmed dengue case. Others remain undetected (UAS) (Fig 2).
We quantified differences in the net infectiousness to mosquitoes of individuals with As, IS, and AS infections. We modeled viral transmission dynamics of each of these infection types by using a mechanistic model of the within-host dynamics of DENV fitted to plasma viral titers over time for patients with S infections [18] and adjusted these trajectories using an empirically informed distribution of correction ratios to model trajectories of As infections [16]. Secondary (2°) infections were parameterized to exhibit faster cell entry and accelerated clearance of viral particles than primary (1°) infections, consistent with theory for antibody-dependent enhancement and increased activation of the immune system [18]. This resulted in a shorter duration of detectable, and potentially infectious, viremia [19–21]. Post-2° infections were excluded from consideration for our primary analysis due to a combination of low risk of apparent disease and high rates of cross-reactive antibodies for this group [22]. Nonetheless, because there is no conclusive evidence about viremia of post-2° infections [22], we performed a sensitivity analysis in which an upper limit for post-2° viremia was assumed similar to that of 2° infections given that the dynamics of both are limited by varying degrees of immunity (S1 Fig). Next, we applied logistic regression models [16] to infer infectiousness to mosquitoes from human viral titers (S1 and S4 Tables, S2 Text) (see Materials and Methods for details).
The median NI to Ae. aegypti of As infections was lower than that of S infections, but of similar magnitude (1°: 87%, 95% CI: 0–151%; 2°: 74%, 95% CI: 0–137%). The median NI of 1° infections was greater than that of corresponding 2° infections (As: 140%, 95% CI: 111–275%; S: 120%, 95% CI: 100–235%) (Fig 3). Approximately one quarter of the NI of S infections occurred before symptom onset (1°: 21%, 95% CI: 1–56%, 2°: 27%, 95% CI: 0–97%). By calculating the probability that a random draw from the NI distribution of one infection class was lower than a random draw from another class (Pr), we confirmed that As infections are more likely to be less infectious than S infections (1°: Pr = 0.55; 2°: Pr = 0.58) and 2° infections are more likely to be less infectious than 1° infections (As: Pr = 0.56; S: Pr = 0.62) (S2 Table). There was wide variability in the NI of As infections, however, with both two-fold lesser or greater infectiousness compared to S infections appearing probable (lesser, 1°: 0.38, 2°: 0.42; greater, 1°: 0.16, 2°: 0.20). Overall, 1° As infections were not significantly less infectious than 2° S infections (Wilcoxon rank sum test, p = 0.97).
Next, we estimated the proportion of each infection class in a hypothetical population and derived each class’ relative contribution to FoI, the rate at which susceptible people become infected. We assumed an equal probability of being bitten by Ae. aegypti across infection classes. To quantify the proportion of people with As, IS, UAS, and DAS infections (and thus the pool of individuals who could potentially give rise to new infections among susceptibles), we used estimates from a recent meta-analysis on published (As+IS):AS ratios for primary and secondary infections (1°: 82%, 95% CI: 81–84; 2°: 76%, 95% CI: 74–78) [23]. Post-2° infections were assessed separately, using studies compiled by [7]. A beta-binomial model of these proportions was supported over a binomial model (deviance information criterion (DIC) binomial: 163, beta-binomial: 34). We estimated the (As+IS):AS ratio among post-2° infections to be higher than that of 1° and 2° infections (86%, 95% CI: 63–97). For all pre-exposure histories we adopted an As:(IS+AS) ratio of 9.2% (95% CI: 4.4–14.0%), which reflects the proportion of asymptomatic infections detected among cluster participants in [16] and a detection rate of AS infections of 8% (95% CI: 5–15%) [24] (see sensitivity analysis for an assessment of the full range of As:IS:AS ratios).
The proportion of individuals with a given pre-exposure history (e.g., no previous DENV exposure, prior exposure to one serotype), or with temporary heterotypic immunity or permanent homotypic immunity, depends on the age distribution and the history of local transmission intensity [25]. We considered scenarios for our hypothetical populations with demographic characteristics of Brazil and Thailand, respectively, and simulated pre-exposure history by age across values of time-averaged FoI [26] (Figs 4 and S2). Combining the aforementioned inapparent ratios with realistic demographic scenarios in a location with a given overall, time-averaged FoI, we derived formulas
10.1371/journal.ppat.1006965.g004
Fig 4
Pre-exposure history (A and C) and infection class (B and D) stratification by age and for FoI values of 0.01 (top) and 0.1 (bottom).
An individuals’ susceptibility to infection and clinical outcome depend on pre-exposure history. Serohistory by age (A and C) is estimated using a system of ordinary differential equations with state variables denoting the proportion of the population pre-exposed to 0–4 serotypes. Transition to pre-exposure state i occurs at rate iFoI. Individuals entering a new pre-exposure state have temporary heterologous immunity (gray) to all serotypes before later becoming susceptible again to each serotype to which they do not have a history of exposure. After four infections with four different serotypes, individuals are assumed fully immune (black) to all serotypes.
FoIAs=bma∑i=1pNIAsiσRAsiga+∑i=1pNIAsiσRAsie−gn,FoIAs+IS+UAS+DAS=bma∑i=1pNIAsiσRAsi+∑i=1pNIISiσRISi+∑i=1pNIUASiσRUASi+∑i=1pNIDASiσRDASiga+∑i=1pNIAsiσRAsi+∑i=1pNIISiσRISi+∑i=1pNIUASiσRUASi+∑i=1pNIDASiσRDASie−gn,
(1)
for the FoI attributable to As infections that can be divided by the overall FoI as FoIAs / FoIAs+IS+UAS+DAS to quantify the proportional contribution to FoI from the As class. The same approach can be applied to derive the proportional contribution to FoI of any other infection class. The parameters in Eq (1) are defined in S1 Table and account for different aspects of local dengue epidemiology and the aforementioned descriptions of differential net infectiousness and prevalence of As, IS, and AS infections with distinct pre-exposure histories (see Materials and Methods for additional details).
Based on our metric of relative FoI, we estimated that 88% (95% CI: 77–92%) of human DENV infections are attributable to individuals that do not present with apparent symptoms at the time when they are bitten by a susceptible mosquito (i.e., As, IS, and pre-symptomatic AS) (Figs 5 and S3 for Thailand). We estimated that As and IS infections could together be responsible for causing 84% (95% CI: 82–86%) of all human DENV infections, reflecting a near one-to-one relationship with their representation in the population. Of the remaining 16% (95% CI: 14–18%) of infections, 76% (95% CI: 21–100%) are attributable to bites by mosquitoes on people whose infection eventually becomes apparent, and thus potentially detectable, after onset of symptoms. At a detection rate of 8% [24], an estimated 1.3% (95% CI: 1.1–1.4%) of total infections result from infected individuals after they are detected by surveillance systems (0.8%, 95% CI: 0.7–0.9%; 2.5%, 95% CI: 2.1–2.7% at detection rates of 5% and 15% [24], respectively).
Data on viremia and infectiousness in asymptomatic, pre-symptomatic, and post-2° infections are sparse [16], resulting in substantial uncertainty around our estimates. We performed a variance-based sensitivity analysis [27] to identify the primary sources of uncertainty for estimating net infectiousness and the proportion of net infectiousness occurring prior to symptom onset. Limited data on the viremia-to-infectiousness relationship of asymptomatic infections drove the majority of uncertainty in estimates of their net infectiousness, whereas uncertainty in time until symptom onset was responsible for the majority of uncertainty in estimates of the proportion of net infectiousness prior to symptom onset (S4 Fig).
The estimated contributions to FoI by each infection class were robust across different transmission settings (Fig 5) and in settings where DENV is newly emerging (S5 Fig), but not when allowing for contributions to transmission from post-2° infections (S1 Fig). Under the assumption that the net infectiousness of post-2° infections is equivalent to that of 2° infections, we estimated the contribution of inapparent infections to be up to 11% (95% CI: 10–13%) higher than if post-2° infections had not been assumed to contribute to transmission (S1 Fig). This increase resulted from the relatively high proportion of IS infections among post-2° infections, who made up a larger proportion of the infectious reservoir in more intense transmission settings. Under the assumption that IS infections are more similar in their infectiousness to As than to AS infections, the estimated contribution of inapparent infections was reduced from 84% to 73% (95% CI: 0–91%), reflecting a lower bound on this assumption. The impact of accounting for the differential viral trajectories of severe dengue cases [18] was minor due to their small numerical prominence [28], but their inclusion did increase the contribution of post-symptomatic DAS infections from 1.0% (95% CI: 0.8–1.1%) to 2.1% (95% CI: 0.8–3.6).
As:IS:UAS:DAS ratios can fluctuate in space and time; for instance, as a result of serotype differences in virulence [7,23]. The contribution of inapparent (As+IS) infections across the spectrum of As:IS:AS ratios is driven by the ratio of As to IS infections, with the total contribution of As+IS infections generally being similar to their numerical prominence in the infected population (Fig 6). Although variability in As:IS:AS ratios likely does drive some variability in the contribution of As+IS infections, our analysis suggests that their contribution is unlikely to be less than 81% of their numerical prominence (Fig 6).
Combined with recent empirical findings [16,18], our modeling analysis suggests that inapparent infections contribute appreciably to DENV transmission and its disease burden via infection of others who develop clinical symptoms. In large part, our results stem from the large numerical prominence of As and IS DENV infections and the finding that differences in the viremia profiles of As and S infections are insufficient to result in considerable differences in net infectiousness. Moreover, our finding that approximately one quarter of an individual’s infectiousness occurs prior to symptom onset supports the hypothesis that a large proportion of human-to-mosquito transmission is silent; i.e., it happens when there is no detectable illness [15,16].
The substantial role that inapparent infections play during dengue epidemics may result in more rapid transmission and geographic spread [3] and, as a result, more widespread transmission prior to case-driven outbreak detection and onset of control efforts [29]. The considerable potential for pre-symptomatic transmission further impedes the use of case data to predict whether an outbreak will occur and, if it does, what its final size will be [30]. Despite increasing recognition of the importance of silent transmission to the epidemiology of a variety of pathogens, it is rarely taken into account in dengue models [31–34].
Our results also highlight the potential significance of a key uncertainty in projections of the population-level impact of the recently licensed Dengvaxia vaccine, which is thought to protect some vaccine recipients against apparent disease but not infection [35]. Although spared from disease, our results imply that breakthrough infections in these individuals could appreciably contribute to transmission and, therefore, limit the indirect effects of vaccination [31,36]. If individuals with asymptomatic or inapparent infections are as infectious as their apparent symptomatic counterparts, positive indirect effects of vaccine implementation in highly endemic settings would be reduced [31,32]. Conversely, adverse effects of vaccination in low-transmission settings, due to higher rates of severe disease in DENV-naïve vaccine recipients [37], would be offset to some degree if inapparent infections are important contributors to transmission given that this would increase the projected indirect effects of vaccination in those settings [31,32].
The contribution of silent infections to transmission helps explain the difficulty of carrying out control efforts in response to reported cases, particularly in intense transmission settings [29]. That approach could be further impeded by substantial inter-individual variability in pre-symptomatic and asymptomatic infectiousness [29]. If some people with silent infections are substantially more infectious than those with apparent infections, a significant portion of DENV transmission clusters would not be detected or contained by traditional surveillance efforts. Additional data are required to determine to what extent the observed variability in infectiousness results from uncertainty in the data or variability among individuals [33,38]. Improving strategies with enhanced potential to prevent infections, silent or otherwise, requires a deeper understanding of the spatial and temporal scales of transmission [39].
Our results underscore the need to resolve substantial uncertainties about within-host DENV dynamics, in particular with respect to viremia and infectiousness of asymptomatic, pre-symptomatic, and post-2° infections. An improved understanding of differences in viremia profiles between 1° and 2° infections, and the mechanisms underlying those processes requires more detailed immunological and viremia titer data, including quantifications of infectious particles, particularly from earlier stages of the infection [38,40]. Similarly, a greater understanding of the factors underlying differences in viral trajectories [38,41,42], infectiousness [41–43], and disease rates [23] between serotypes is needed to gain better, more context-specific estimates of the contributions of silent transmission to DENV transmission. Further, uncertainty about the net infectiousness of post-2° infections limits understanding of dengue dynamics overall [44] and the contribution of silent transmission in particular. Acquiring this kind of empirical data is complicated by the difficulty of identifying people in these classes when they are viremic and the absence of diagnostic tools that reliably distinguish 2° from post-2° infections [45]. Moreover, people with symptomatic infections may experience impaired mobility [46] or otherwise modified interactions with mosquitoes, the impacts of which on net transmission are presently not well understood. Resolving these uncertainties and identifying effective strategies for mitigating the contributions of all infections, apparent or not, to DENV transmission will require comprehensive studies that combine field work and modeling to address the coupled nature of multiple transmission heterogeneities [33] (see S1 Text for a comprehensive discussion of additional limitations and research needs).
Heterogeneous infectiousness coupled with heterogeneity in disease symptoms is common across a wide range of pathogens [47]. A more thorough, quantitative understanding of the factors underlying these heterogeneities and their role in pathogen transmission is critical for a more complete understanding of pathogen dynamics and, in turn, enhanced disease prevention [48]. Our results advance understanding of these heterogeneities for dengue, showing that they are significant at both individual and population levels. In particular, our findings suggest that failing to acknowledge these coupled heterogeneities may significantly handicap understanding of DENV transmission dynamics, the effectiveness of dengue outbreak response efforts, and the evaluation of novel interventions for dengue prevention and control. These results show that additional emphasis on model-based approaches that bridge individual-level infection and population-level transmission processes have the potential to provide valuable insights for infectious disease prevention and control and for the identification of high-priority needs for future data collection.
We modeled viremia trajectories (log10 cDNA copies/mL of plasma) using a model [18] of virus and immune dynamics with four state variables—uninfected target cells (x), infected targets cells (y), free viral particles (v), and a clearing immune response (z)—according to
dxdt=A−γx−βxvdydt=βxv−δy−αzydvdt=ωy−κvdzdt=ηyz.
(2)
The parameter A denotes the daily production rate of target cells, which die at rate γ and become infected proportional to the concentration of free viral particles at rate β, assuming random mixing. Infected cells die at rate δ and are cleared of infection at a rate proportional to the size of the immune response and the removal rate α. Free viral particles are produced by infected cells at rate ω and are cleared at rate κ. The immune response grows proportional to the number of infected cells at rate η. This model was fitted to individual plasma viral titers from primary (1°) and secondary (2°) apparent symptomatic (AS) DENV-1 infections [21] using Markov chain Monte Carlo methods [18]. We used n = 3,000 random samples from the joint posteriors from that analysis to model viremia trajectories for 1° and 2° AS infections. Inapparent symptomatic (IS) infections are assumed to have similar viremia as AS infections. To adapt this approach to model viremia trajectories of As infections, we relied on the observation by Duong et al. [16] that log10 viral titers of As infections were lower on average than those of S infections (76%, CI: 63–88%). To account for uncertainty in this relationship, we took the ratio of 3,000 random samples from the normal distributions of symptomatic (mean: 6.27 +/- SE 0.14 log10 cDNA copies/mL) and asymptomatic observed viremia (mean: 4.75 +/- SE 0.39 log10 cDNA copies/mL) and reduced each viremia trajectories of AS infections by a random draw from the distribution of fractions to approximate the trajectories of As infections. In doing so, we assumed the duration of viremia to be similar in As and S infections on the basis of limited data from the study performed by Duong et al. [16], which revealed no detectable differences in the duration of viremia between A and S infections.
To describe the probability of infecting a mosquito given an individual’s viremia (V), we used logistic regression models
F(V)=11+e−(β0+β1V),
(3)
where β0 and β1 denote the logistic intercept and the slope coefficient for plasma viremia (log10 cDNA copies per mL), respectively [49] (see S2 Text for fits to alternative functional forms). We fitted this relationship to data from DENV-infected symptomatic and asymptomatic individuals (S4 Table) and found these relationships to be significantly different across infection classes (i.e., asymptomatic, pre-symptomatic, and post-symptomatic) but not with respect to serotype or pre-exposure history (i.e., primary vs. post-primary infection) [16] (See S2 Text for a formal comparison with other studies on DENV infectiousness [41,42]). Pre-symptomatic individuals become symptomatic after their intrinsic incubation period (IIP) is over, at which time they become subject to a significantly different relationship between viremia and infectiousness (S1 Table). Each of 3,000 samples of symptomatic viremia trajectories had a corresponding duration of the IIP as informed by the posterior distributions derived in [18]. These were paired within a given realization of the regression model with coefficients randomly drawn from the multivariate normal distribution of best fit parameters (S1 Table), so as to explicitly account for uncertainty in these estimates resulting from limited sample sizes in this study, in particular for As infections [16]. The pre-symptomatic parameterization was used before the IIP concludes, and the post-symptomatic parameterization was used afterwards. For the infectiousness of asymptomatic infections, the viremia-infectiousness relationship remained the same over the course of the infection, because the concepts of IIP and onset of symptoms do not apply to asymptomatic infections. To summarize the extent of infectiousness of an individual over the entire course of their infection, we defined net infectiousness as the integral of an infectiousness curve over time
NI=∫F(V(t))dt.
(4)
This quantity NI is proportional to the expected number of mosquitoes infected by a human infection assuming that biting occurs at a constant rate over the course of the human infection. By extension, the ratio of the net infectiousness of two individuals with two different types of infections is identical to the ratio of the expected number of mosquitoes infected by people with those respective types of infections. Given that we interpret the end of the intrinsic incubation period (IIP) as the beginning of the symptomatic phase of the infection, we also used this distinction to estimate the proportion of infectiousness that occurs prior to symptom onset (NIPreS)
NIPreS=∫0IIPF(V(t))dtNI,
(5)
and likewise for the proportion after symptom onset
NIPostS=∫IIP∞F(V(t))dtNI.
(6)
We calculated the proportion of the population previously exposed to 0 to 4 serotypes as a function of the population’s age distribution and the time-averaged, serotype-specific FoI to which the population is subject. The time-averaged FoI metric (defined below) that we used was assumed to be constant with respect to virus serotype and space. Although DENV FoI is known to exhibit substantial variation with respect to these factors [26,50], we simplified this aspect of our analysis to reflect the average across a wide geographic area or across many realizations of a complex temporal pattern of transmission.
Consistent with these assumptions and with the further assumption of FoI acting as a constant hazard, we represented the proportion pre-exposed to i = 0…4 serotypes at age a as ei(a). After acquiring infection at rate (4-i)FoI, an individual has temporary heterologous immunity to all serotypes for a period of average duration σ-1. The probability that an individual of age a has temporary heterologous immunity after exposure with i serotypes is represented by ri (a). Individuals permanently retain immunity to serotypes to which they were previously exposed; i.e., permanent homologous immunity. The dynamics of these classes with respect to age follow
de0da=−4 FoI e0drida|i=1…4=(4−(i−1)) FoI e(i−1)−σrideida|i=1…4=σri−(4−i) FoI ei.
(7)
Accounting for the proportion of the population in each age group p(a), the population-wide proportion pre-exposed to i serotypes is
Ei=∑a(p(a)ei(a)),
(8)
where p(a) was informed by national age distributions from Brazil and Thailand [51], as representative examples of two DENV-endemic regions.
As+IS:AS ratios for primary and secondary infections were derived from a recent meta-analysis using cohort studies with laboratory-confirmed infections [23]. In brief, a literature search was performed for dengue cohort studies that 1) reported the number of (As+IS) and AS infections and 2) provided lab confirmation of apparent infections [22,52–59] (S3 Table). For a specific time and place j, observed infections with pre-exposure to i serotypes (Oi) were assumed to result from a binomial distribution Oi,j(As+IS)∼Binomial(Ni,ζiλj), where Ni is the number of subjects pre-exposed to i serotypes, ζthe (As+IS):AS ratio, and λ the location- and time-specific force of infection. Similarly, the observed AS infections were assumed to result from Oi,j(AS)∼Binomial(Ni,(1−ζi)λj).The models were fit to the data in a Bayesian framework with the R package RStan [60]. We assessed observations on post-2° infections [22,28], as collated by [7] in a similar fashion and assumed the number of As+IS infections to have arisen from a binomial distribution O≥2(As+IS)∼Binomial(OA+IS+OAS,ξ≥2). To account for intrinsic variability in the rates, similar models assuming a beta-binomial distribution were assessed and compared to the binomial model using DIC [61]. Given the scarcity of data and the fact that these estimates comprise only a portion of our analysis, we did not estimate λi as was done in [23]. Had we done so, we would expect our estimates of that quantity to be similar.
To calculate the proportions of infected people who have previously been exposed to zero or one serotype and who experience either an asymptomatic (A) or symptomatic (S) infection, we used Ei and our estimates of As:S ratios for a given pre-exposure history (θi= 9.2% for 1° and 2° infections, other options are assessed in Fig 6), resulting in
Pr(As)=∑i=12EiθiPr(S)=∑i=12Ei(1−θi).
(9)
Similarly, the proportion of infections found to be inapparent symptomatic (IS) or apparent symptomatic (AS) follows from the (As+IS):AS ratio, ζ, as
Pr(As+IS)=∑i=12ζiEiPr(AS)=∑i=12(1−ζi)EiPr(IS)=Pr(As+IS)−Pr(As).
(10)
Classical epidemiological theory for mosquito-borne pathogen transmission posits that FoI—i.e., the rate at which susceptible individuals become infected—is a function of a number of factors, including infection prevalence among hosts (X) and the infectiousness of infected hosts [62]. On a per capita basis, FoI = bmaY in Ross-Macdonald models, where b is the probability that a susceptible host becomes infected after being bitten by an infectious vector, m is the ratio of vectors to hosts, a is the daily rate of at which a vector bites, and Y is the infection prevalence among vectors. The latter depends further on the daily vector mortality rate g, the incubation period n in the vector, human infection prevalence X, and the probability c that a susceptible mosquito becomes infected upon biting an infectious host [62].
To account for the population stratification that is necessary for our analysis, we derived a formula for the FoI that is more generalizable than the classic formula in that it allows for distinct contributions to FoI from different host groups (i.e., As, IS, UAS, and DAS infections with different pre-exposure histories). Specifically, each host group differed in its infectiousness and its overall prevalence in the population. By separating contributions from different host groups to mosquito infectiousness and, in turn, to the FoI on susceptible hosts, we calculated the proportional contribution of people with As infections to FoI by calculating the ratio between the FoI resulting from As infections alone and the total FoI, and similarly for IS, UAS, and DAS infections.
Differentiating between S and As infections, we can describe FoI as
FoIAs=bmaacAsXAsg+acAsXAse−gn,FoIS+As=bmaa(cAs+ρcS)XAsg+a(cAs+ρcS)XAse−gn,
(11)
where ρ is the prevalence of S infections relative to As. The contribution of As infections follows from dividing the quantities in (11) as
FoIAsFoIS+As=(1+ρcScAs)ga+cAsXAsga+(cAs+ρcS)XAs,
(12)
which demonstrates the robustness of our results to different values of n, b, and m.
Under a Ross-Macdonald formulation, the time-invariant infection probability c relates to net infectiousness (NI) according to NI=c/ι, where ι−1 represents the average duration of the period of infectiousness. The prevalence Xi is proportional to the proportion of the population that has temporary heterologous immunity, Ri, according to Xi=Riσ/ι.The quantity Ri is defined from Eq (7) according to Ri=ri(a)p(a). It follows that
cXi=NIσRi.
(13)
Substituting Eq (13) into Eq (11) to further stratify infection classes and pre-exposure histories gives Eq (1) from the Results section, with p = 2 when assessing the impact of 1° and 2° infections on transmission and p = 4 when assessing the impact of 1°, 2°, and post-2° infections on transmission. To account for uncertainty in this metric, the relative FoI was calculated for n = 3,000 random samples from the distributions of NI for each infection class.
We performed a sensitivity analysis to assess the impact of uncertainty in As:IS:AS ratios among 1° and 2° infections on the relative contributions to the overall FoI (Fig 6). Additionally, we examined what the contribution of inapparent infections to the FoI would be under the assumption that post-2° infections are similarly infectious as 2° infections and using estimates from the meta-analysis presented in S3 Table to estimate the proportion of post-2° infections to be apparent. This and the assumption we effectively made in our primary analysis—i.e., that post-2° infections make no contribution to transmission—represent two different extremes and therefore provide bounds on the potential sensitivity of our results to alternative assumptions about the contributions to transmission from post-2° infections. Similarly, we addressed uncertainty around the net infectiousness of IS infections by first assuming their net infectiousness to be similar to As infections rather than AS infections, as in the core analysis. We then examined the impact of severe AS infections by adopting fitted dengue hemorrhagic fever (DHF) viremia trajectories [18], DHF rates among S infections of 0.8% and 3% for primary and post-primary infections, respectively [28], and the assumption that all DHF cases are detected. The latter assumption provides an upper bound for the potential contribution of DAS infections for a given detection rate.
To assess the impact of the three main sources of uncertainty in deriving two outcome variables (Q), the net infectiousness and the proportion of infectiousness prior to symptoms, we performed a variance-based sensitivity analysis [27]. The variance for both variables was measured under four different scenarios: 1) with all sources of uncertainty and 2–4) with all sources of uncertainty except one. The contribution to the total variance is expressed by the total-effect index
Ti=1−VarX~i(Q)Var(Q).
(14)
Here, X denotes a vector of uncertain model inputs and ~i denotes that uncertainty around all inputs was included with the exclusion of i. For input i, we used the mean of the uncertainty distribution for the quantity of interest. The three main sources of uncertainty are quantities describing temporal patterns of viremia, the relationship between viremia and infectiousness, and the duration of the intrinsic incubation period (IIP).
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10.1371/journal.ppat.1005220 | Calcium Regulation of Hemorrhagic Fever Virus Budding: Mechanistic Implications for Host-Oriented Therapeutic Intervention | Hemorrhagic fever viruses, including the filoviruses (Ebola and Marburg) and arenaviruses (Lassa and Junín viruses), are serious human pathogens for which there are currently no FDA approved therapeutics or vaccines. Importantly, transmission of these viruses, and specifically late steps of budding, critically depend upon host cell machinery. Consequently, strategies which target these mechanisms represent potential targets for broad spectrum host oriented therapeutics. An important cellular signal implicated previously in EBOV budding is calcium. Indeed, host cell calcium signals are increasingly being recognized to play a role in steps of entry, replication, and transmission for a range of viruses, but if and how filoviruses and arenaviruses mobilize calcium and the precise stage of virus transmission regulated by calcium have not been defined. Here we demonstrate that expression of matrix proteins from both filoviruses and arenaviruses triggers an increase in host cytoplasmic Ca2+ concentration by a mechanism that requires host Orai1 channels. Furthermore, we demonstrate that Orai1 regulates both VLP and infectious filovirus and arenavirus production and spread. Notably, suppression of the protein that triggers Orai activation (Stromal Interaction Molecule 1, STIM1) and genetic inactivation or pharmacological blockade of Orai1 channels inhibits VLP and infectious virus egress. These findings are highly significant as they expand our understanding of host mechanisms that may broadly control enveloped RNA virus budding, and they establish Orai and STIM1 as novel targets for broad-spectrum host-oriented therapeutics to combat these emerging BSL-4 pathogens and potentially other enveloped RNA viruses that bud via similar mechanisms.
| Filoviruses (Ebola and Marburg viruses) and arenaviruses (Lassa and Junín viruses) are high-priority pathogens that hijack host proteins and pathways to complete their replication cycles and spread from cell to cell. Here we provide genetic and pharmacological evidence to demonstrate that the host calcium channel protein Orai1 and ER calcium sensor protein STIM1 regulate efficient budding and spread of BSL-4 pathogens Ebola, Marburg, Lassa, and Junín viruses. Our findings are of broad significance as they provide new mechanistic insight into fundamental, immutable, and conserved mechanisms of hemorrhagic fever virus pathogenesis. Moreover, this strategy of targeting highly conserved host cellular protein(s) and mechanisms required by these viruses to complete their life cycle should elicit minimal drug resistance.
| There is an urgent and unmet need for safe and effective therapeutics against high priority pathogens, including filoviruses (Ebola and Marburg) and arenaviruses (Lassa fever and Junín), which can cause fatal infections in humans. We and others have established that enveloped RNA viruses, including hemorrhagic fever viruses, exhibit a common requirement for host pathways, most notably ESCRT pathway functions, for efficient budding [1–7]. Indeed as host dependent budding mechanisms are highly conserved within and sometimes across virus families, they represent innovative and immutable antiviral targets for inhibiting virus transmission and disease progression [8–11]. Importantly, high mutation rates of RNA viruses in general are a factor in their ability to develop resistance to therapeutics that target specific viral proteins or functions [3, 12–23]. Consequently, strategies that target specific host mechanisms required by viruses should reduce the development of resistance.
As a number of these host mechanisms, including steps in ESCRT protein function, are targets of calcium regulation, the focus of this study was to determine whether and how hemorrhagic fever viruses mobilize calcium in host cells and whether calcium so mobilized regulates virus budding. Here we reveal a novel and fundamental requirement for host STIM1- and Orai-mediated Ca2+ entry that regulates late steps of filovirus and arenavirus egress from mammalian cells. Orai activation is typically linked to either tyrosine kinase or G-protein coupled receptors that activate phospholipase C (PLC) and generate diacylglycerol and inositol 1,4,5-triphoshate (IP3) from membrane phospholipids. IP3 activates receptor/channels on the endoplasmic reticulum (ER) to allow Ca2+ to exit from the ER. The subsequent drop in ER Ca2+ below the KD (400–600μM, [24]) for the N-terminal EF hands of the ER membrane-resident protein STIM1 initiates a conformational change that promotes STIM1 oligomerization and localization to ER regions adjacent to the plasma membrane. At the plasma membrane, STIM1 interacts with and activates Calcium-Release Activated Calcium (CRAC) channels through which extracellular Ca2+ enters the cell (reviewed in [25]). CRAC channels are encoded by the Orai family of proteins (Orai1, 2, & 3; [26–28]) that provide a pathway for sustained extracellular Ca2+ entry to regulate a range of cell functions including gene expression, subcellular trafficking, and the regulation of cell shape and motility [29–31].
Herein, we demonstrate that both filovirus (VP40) and arenavirus (Z) matrix proteins trigger Orai dependent Ca2+ entry in mammalian cells. In addition, suppression of STIM1 expression and genetic inactivation or pharmacological blockade of Orai inhibits Ebolavirus (EBOV), Marburgvirus (MARV), Lassa Virus (LASV), and Junín Virus (JUNV) VLP and infectious virion production and transmission in cell culture. Together, these data establish a novel and critical role for STIM1- and Orai-mediated Ca2+ entry in late steps of hemorrhagic fever virus egress and establish STIM1 and Orai inhibitors as potential broad-spectrum anti-viral targets for regulation of these and possibly other enveloped RNA viruses that bud by similar mechanisms.
While we previously implicated Ca2+ in EBOV VP40-dependent VLP generation [32] our initial objective here was to understand if and how hemorrhagic fever virus matrix proteins trigger a change in cytosolic calcium in host cells. To do this we measured intracellular calcium in cells during an extended time course of EBOV and MARV VP40 (eVP40 and mVP40, respectively) and JUNV Z matrix protein-mediated VLP production. Calcium levels (R-GECO-1 fluorescence, [33]) measured in HEK293T cells under physiological conditions for 18–24 hours revealed that eVP40, mVP40, and JUNV Z protein expression each induced a time-dependent increase in Ca2+ concentration (Fig 1, blue), while the GFP-vector backbone induced a negligible Ca2+ increase that plateaued at a low amplitude or declined to baseline levels (Fig 1, magenta). To probe the role of Orai1 in these responses we performed identical measurements in an HEK293T line that stably expresses a dominant negative mutant Orai1 having a glutamic acid (E) to alanine (A) substitution in its ion selectivity filter (E106A). Incorporation of even a single Orai1 E106A subunit into endogenous WT Orai channels exerts a dominant negative block of its Ca2+ permeation [34]. Importantly, both WT and E106A Orai HEK293T cells exhibited a similar transient Ca2+ elevation following treatment with the membrane permeant SERCA pump inhibitor thapsigargin in Ca2+ free bath solution, indicating that ER stores were intact in E106A Orai1 expressing HEK293T (S1 Fig). The absence of a secondary increase in cytoplasmic Ca2+ (S1 Fig, left panel) following reperfusion of HEK293T Orai1 E106A cells with Ca2+-containing Ringers solution (S1 Fig, right panel) verified the Orai permeation defect of this line. Significantly, in permeation defective Orai1 E106A cells neither eVP40, mVP40, JUNV Z protein (Fig 1, yellow), nor GFP vector (Fig 1, orange) triggered any change in cytoplasmic Ca2+ levels indicating that Ca2+ elevations initiated by expression of EBOV, MARV, and JUNV matrix proteins required and resulted from Ca2+ entry though Orai channels. Consistent with these results from Orai E106A HEK293T cells and specifically the role of Orai, the Orai inhibitor Synta66 similarly blocked the eVP40-mediated increase in cytoplasmic Ca2+ (Fig 1, lower right panel) in WT HEK293T cells.
Given the ability of EBOV, MARV, and JUNV matrix proteins to initiate an Orai-dependent Ca2+ signal in HEK293T cells, we assessed the role of Orai1-mediated calcium signals in eVP40, mVP40, LASV Z or JUNV Z mediated VLP production in WT and Orai1 E106A HEK293T cells. Consistent with a role for Ca2+ entry via Orai1 in VLP production, we found that Orai1 E106A cells did not support efficient filovirus or arenavirus VLP production (Fig 2). Indeed, levels of eVP40 VLPs from Orai1 E106A cells were ~50 fold lower than that from WT cells (Fig 2A, VLPs). Similarly, production of mVP40 VLPs exhibited an even greater dependence on Orai1-mediated calcium entry (Fig 2B, VLPs), as mVP40 VLPs from Orai1 E106A HEK293T cells were ~100 fold lower than that from WT cells (Fig 2B). Orai similarly regulated JUNV Z (Fig 2C) and LASV Z (Fig 2D) VLP production as both JUNV Z and LASV Z protein-mediated VLP production from E106A cells was ~100 fold lower than that from WT HEK293T cells. In all instances, cellular levels of VP40 or Z were similar in WT and E106A cells, indicating no general requirement for Orai1-mediated Ca2+ entry in viral protein expression (Fig 2A–2D; Cells). Together, these data point to a conserved and selective role for Orai-mediated Ca2+ entry in hemorrhagic fever virus budding.
Implicit in this common critical role for Orai1-mediated Ca2+ entry in EBOV, MARV, JUNV, and LASV VLP production is an upstream requirement for STIM1, the only known trigger for Orai activation in mammalian cells. STIM1 is a single pass ER membrane protein whose activity is regulated by ER Ca2+ binding. Ca2+ dissociation from STIM1 following a decrease in ER concentration triggers a N-terminal conformational change that initiates its multimerization and relocalization within the ER membrane to domains juxtaposed to the plasma membrane [35–37]. The resulting subplasmalemmal STIM1 clusters physically activate Orai channels to allow extracellular Ca2+ to enter the cell [25]. Using eVP40 VLP budding as our model, we probed the role of STIM1 in VLP formation by assessing VLP production from STIM1 suppressed HEK293T cells. eVP40 VLP budding from STIM1 suppressed cells was reduced by approximately 10 fold relative to that from cells receiving random siRNAs or no siRNA (Fig 3A), and the loss of STIM1 had no effect on cellular expression of eVP40 or actin (Fig 3A; Cells). To further confirm this requirement for STIM1 in VLP formation, we utilized a bicistronic vector to suppress endogenous STIM1 (by targeting the 5’ UTR) and rescued its expression with exogenous human STIM1 translated from a shRNA resistant cDNA (shSTIM1-STIM1 plasmid) (Fig 3B). HEK293T WT cells expressing a fixed amount of eVP40 were transfected with increasing amounts of the shSTIM1-suppression vector or empty vector (Fig 3B). While cellular eVP40 expression levels were equivalent under all conditions (Fig 3B, Cells), progressive suppression of STIM1 expression led to a dose-dependent decrease in eVP40 VLP production (Fig 3B, VLPs, middle panel). Importantly, STIM1 re-expression in suppressed cells fully rescued eVP40 VLP production across all levels of STIM1 suppression (Fig 3B, VLPs, bottom panel). Similar to results with STIM1 shRNA, budding of eVP40 VLPs was significantly reduced (Fig 3C, VLP) following siRNA mediated STIM1 suppression (>90%, Fig 3C, middle panel); and over-expression of exogenous STIM1 restored eVP40 VLP production (Fig 3C). Taken together with results from experiments performed on E106A HEK293T cells, these data definitively establish a role for STIM1/Orai dependent Ca2+ signals in regulation of VLP egress.
Genetic approaches outlined above to modulate STIM1 expression and Orai1 permeation establish a novel and common critical role for STIM1 and Orai1 in filovirus and arenavirus budding. Given the broad utility of targeting ion channels to regulate a range of cell physiological functions, we asked whether pharmacological blockade of Orai might represent an effective strategy for regulating filovirus and arenavirus budding. Although high affinity Orai1 blockers for in vivo applications are not available at present, we tested several commercially available inhibitors including Synta66 and 2-APB, both of which inhibit Orai-mediated Ca2+ entry in HEK293T cells at micromolar levels (10–50 μM) [38, 39] without impacting calcium release from the ER (S2 Fig). Both 2-APB and Synta66 inhibited eVP40- (Fig 4A and 4B) and mVP40-induced (Fig 4C and 4D) VLP production with identical potency as inhibition of Orai-mediated calcium entry, and neither drug affected cellular expression of VP40 or actin (Fig 4A–4D). A concentration of 2-APB that fully blocks Orai1 channels (50 μM) [38] inhibited eVP40 VLP production (Fig 4A, right panel) by ~5 fold and mVP40 VLP production (Fig 4C, right panel) by ~50 fold. Likewise, Synta66 (50μM) substantially inhibited eVP40 (~5 fold) and mVP40 VLP (~10 fold) production (Fig 4B and 4D) with no effect on steady state levels of cellular VP40 or actin and without altering membrane localization of viral proteins (S3 Fig). Importantly, as neither 2-APB (Fig 4E) nor Synta66 (Fig 4F) exerted cytotoxic effects on cells under conditions of these measurements (cell viability, cellular production of VP40, or VP40 membrane localization, Fig 4 and S3 Fig), their anti-budding activity can be attributed to inhibition of Orai-mediated Ca2+ entry. Finally, an additional Orai selective inhibitor (RO2959 [40]), which recently became commercially available, also blocks eVP40 VLP formation (~10-fold) with a potency that parallels its inhibition of calcium permeation of the channel (S4 Fig, IC50 = ~2.5μM). Thus, the sensitivity of budding to three chemically distinct Orai inhibitors, at the same concentration that blocks calcium permeation of Orai, further substantiates the critical role of Orai-mediated Ca2+ entry in VLP production.
We next sought to validate VLP findings by examining the effect of the Orai1 inhibitors Synta66 and 2-APB on budding of the live attenuated Candid-1 JUNV vaccine strain [41, 42]. Briefly, VeroE6 cells infected with live attenuated Candid-1 JUNV were cultured in the absence or presence of Orai inhibitors, and infectious virions produced from these cells were quantified in a focus forming assay (Fig 5). Enumeration of JUNV foci revealed a statistically significant, dose-dependent reduction in JUNV virus production following treatment with Synta66 (Fig 5A) or 2-APB (Fig 5B). Moreover, neither compound affected the viability of cells cultured under conditions mimicking those used for JUNV infection experiments (Fig 5A and 5B, right panels), nor affected the synthesis of JUNV GP in infected VeroE6 cells at any concentration tested (Fig 5A and 5B, Western blots). Together, these findings corroborate results of VLP budding assays (Fig 2) and demonstrate that Orai1-mediated calcium entry is required for efficient budding of infectious JUNV.
Based on the general requirement we identify for Orai channels in filovirus and arenavirus VLP production and JUNV (Candid-1) budding, we next sought to determine whether Orai channels regulate spread of infectious pathogenic strains of EBOV, MARV, LASV, and JUNV. We first examined viral spread, an indicator of efficient viral budding, in HEK293T cells that constitutively express the dominant negative permeation defective variant of Orai1 (E106A) used in VLP assays described above (Fig 2). These cells were infected at a low multiplicity of infection (MOI), which resulted in the infection of approximately 2–5% of the cells. Cells were then incubated for a period of time that equates to several rounds of viral replication, allowing us to assess viral spread. We observed that the percent of Orai1 E106A expressing cells infected with live BSL-4 variants of EBOV, MARV, JUNV, or LASV was significantly lower than Orai WT cells infected with the same viruses (Fig 6). These results are consistent with a role for Orai in the spread of infectious filoviruses and arenaviruses.
We next assessed the effect of the Orai blocker Synta66 on the spread of these BSL-4 pathogens, because it is a more consistent Orai blocker than 2-APB. Viral spread was assessed by infecting HeLa cells with LASV, JUNV, MARV, or EBOV at a low MOI and then treating with vehicle or Synta66 at the indicated concentrations beginning 1 hour post infection and for the duration of experiments. Seventy two (LASV, JUNV) or 96 (MARV, EBOV) hours post infection we quantified the percentage of cells infected with virus.
For each virus, we observed a significant Synta66 dose-dependent decrease in the percentage of cells infected (Fig 7A). Consistent with inhibition of viral spread, we also observed a general decrease in the number and size of infected cell clusters with increasing Synta66 concentration (Fig 7B). Similar to the more potent inhibition by Synta66 of mVP40- versus eVP40-mediated VLP production (Fig 4), Synta66 also exerted more potent inhibition of live MARV than EBOV. Interestingly, the spread of both arenaviruses was more sensitive to Orai inhibition than either filovirus (Fig 7A). In general, cultures treated with higher concentrations of Synta66 and for a prolonged period of time (72–96 hours) contained fewer cells than vehicle control treated cultures as measured by the number of nuclei (Fig 7B). For this reason, we normalized the results as the percent of cells infected at the time of analysis for each condition. The decrease in cell numbers, however, does not appear to reflect toxicity (see Figs 4F and 5A). Indeed, as HeLa cells autonomously divide, fewer cells more likely reflects an effect of prolonged Synta66 treatment on cell proliferation. Nonetheless, we evaluated Synta66 induced toxicity by two separate methodologies. Cell-titer Glo “viability” measurements revealed that prolonged Synta66 produced a dose-dependent decrease in ATP (S5 Fig) that is attributed to a decrease in the overall number of cells in cultures and not an effect of Synta66 on cell viability (see Fig 7B). We then utilized an Alamar Blue assay to assess the metabolic health of Synta66 treated cells. Indeed, cellular oxidation-reduction potential of Synta66 treated and vehicle control treated cells were equivalent, confirming comparable metabolic activity (S5 Fig). Thus, while prolonged Synta66 treatment resulted in lower overall cell numbers, those cells that are present are metabolically healthy and are fully capable of producing virus.
We next sought to definitively establish that the effect of Synta66 on virus spread is due to inhibition of virus egress and not entry. We first pretreated HeLa cells with Synta66 and then infected with a high MOI of LASV, JUNV, MARV, or EBOV. Cells were then fixed after only 2–3 viral replication cycles. Infecting with a high MOI and fixing soon after infection ensured that the extent of infection minimally involves spread between cells and rather reflects the extent of primary infection. Under these high MOI conditions, we observed relatively little effect of Synta66 on infection levels with only modest inhibition of infection evident at high Synta66 concentrations (S6 Fig). Further confirmation that Synta66 blocks egress of live filoviruses and arenaviruses was obtained by assessing the amount of virus released into culture supernatants. Supernatants were collected between 48 and 96 hours post-infection with JUNV, LASV, MARV, or EBOV from Synta66 or vehicle treated HeLa cells. Consistent with all of our VLP (Figs 2–4) and live virus data (Figs 5, 6, 7A and 7B) we found that Synta66 (30μM) significantly reduced the titer of infectious Lassa, Junín, Marburg, and Ebola virion particles in culture supernatants (Fig 8). Taken together, this data provide a clear and comprehensive demonstration that Synta66 treatment significantly impairs authentic filovirus and arenavirus budding and release from infected cells.
In summary, our results clearly establish that 1) Orai1-mediated Ca2+ entry is a critical virus-triggered host signal that regulates filovirus and arenavirus budding, and 2) STIM1 and Orai1 represent novel targets for broad-spectrum control of these emerging and often fatal viruses. Indeed, the conserved role for Orai mediated calcium entry among these four hemorrhagic fever viruses raises the interesting possibility that Orai inhibitors may have general utility for broad spectrum control of these and other enveloped RNA viruses that bud by similar Ca2+ dependent mechanisms.
The recent catastrophic outbreak of EBOV in West Africa highlights the need to develop therapeutics for EBOV and other hemorrhagic fever viruses. Indeed, much progress has been made toward the development of candidate vaccines and therapies against EBOV that are currently in clinical trials. Nevertheless, it is critically important that we improve our understanding of the mechanisms of hemorrhagic fever virus pathogenesis not only to identify novel viral targets, but also to identify host targets and common mechanisms that these viruses require for completion of their life cycles as these could lead to the development of broad spectrum host oriented therapeutics.
A key advantage of therapeutics that target conserved host pathways required broadly by families of viruses for transmission is the potential for broad spectrum efficacy compared with drugs that target strain specific viral targets. Moreover, host targets should be essentially immutable and thereby insensitive to selective pressures that normally allow pathogens to develop drug resistance [3, 12–23]. Here we focused on the second messenger Ca2+ and the host proteins responsible for its mobilization and asked whether calcium signals within host cells orchestrate virus assembly and budding.
While calcium has been implicated generally in EBOV and HIV-1 budding [32, 43, 44], previous efforts have not addressed if and how matrix proteins encoded by filoviruses or arenaviruses might trigger changes in Ca2+ concentration in host cells. Herein, we demonstrate for the first time that the filovirus matrix protein VP40 and JUNV Z protein trigger STIM1/Orai activation and that the resulting influx of extracellular Ca2+ controls both VLP formation and production of infectious filovirus and arenavirus progeny. Moreover, using Orai channel inhibitors, Orai permeation defective lines, and by suppressing STIM1 expression, we establish STIM1 and Orai as effective host targets for pharmacological regulation of virus egress. It should be noted; however, that we cannot rule out a role for other Orai isoforms (Orai2 and Orai3) in to the residual budding observed for live virus from E106A or Synta66 treated cells.
While we have established a critical role for Orai-mediated calcium entry in budding of hemorrhagic fever viruses, the mechanism by which Ca2+ does so remains an important question and the focus of ongoing efforts. Indeed, a number of critical steps implicated in efficient budding of enveloped RNA viruses have been linked to cellular Ca2+ signals, including the activation and localization of specific ESCRT components. Although not absolutely required, the ESCRT pathway has been shown to play a key role in efficient budding of a plethora of RNA viruses including filoviruses, arenaviruses, rhabdoviruses, and retroviruses [5]. It is tempting to speculate that the observed calcium regulation of budding described here may be linked mechanistically to the role of ESCRT during virus egress. For example, the structure, activation, and interactions of ESCRT-related proteins such as Tsg101, Nedd4, and Alix have been shown to be regulated in part by calcium [44–47]. Additionally, given that Ca2+ control of membrane repair reflects ESCRT induced shedding of damaged membrane [48], one might also speculate that Ca2+ dependent mechanisms are similarly triggered by insertion of viral proteins in the plasma membrane. Studies underway are thus focused on determining whether Ca2+ controls budding through regulation of ESCRT pathway function.
STIM1 and Orai1 mediated Ca2+ signals have been implicated in distinct steps of the life cycle of other viruses including the replication of Rotaviruses, which are non-enveloped RNA viruses that do not bud from the plasma membrane. Constitutive STIM1 (and Orai1) activation observed in rotavirus-infected cells reflects an effect of its nonstructural protein 4 (NSP4) on endoplasmic reticulum Ca2+ permeability [49]. Indeed, ongoing efforts within our group to understand the mechanisms by which hemorrhagic fever virus matrix proteins trigger STIM1/Orai activation include testing whether VP40 might likewise trigger Ca2+ leak from the ER by inhibiting SERCA pump activity. Furthermore, Ca2+ influx also seems to regulate entry of West Nile virus [50, 51], Coxsackievirus [52, 53], Hepatitis B virus [54], and Epstein Barr virus [55, 56].
Recently it was shown that subunits of a functionally distinct family of voltage-gated calcium channels (VDCCs) also play a role in JUNV and Mouse Mammary Tumor pseudovirus entry and infection [57] and that the VDCC blockers nifedipine and verapamil suppressed host cell entry by these viruses. Surprisingly; however, in this instance the involvement of VDCC subunits seemed to be distinct from any role in regulating Ca2+ levels. How VDCC inhibitors might operate independently of any action on VDCC Ca2+ permeation is unclear, but could reflect the promiscuous affinity of VDCC inhibitors for channels including voltage-gated potassium (Kv) channels. Indeed, verapamil and nifedipine also block voltage-gated potassium channels that set the membrane potential of non-excitable cells [58, 59]. Depolarization of the plasma membrane as a result of Kv channel blockade could indirectly block calcium entry by dissipating the electrical driving force (membrane potential) required for calcium permeation of Orai [60–62].
While these studies cumulatively point to additional roles for Orai1-mediated and independent Ca2+ influx in steps of infection and replication used by a range of disparate viruses, these roles are distinct from the selective requirement we identify for Orai-dependent calcium entry in budding of filoviruses and arenaviruses. However, Orai might represent a conserved target for regulating budding of additional enveloped RNA viruses, including retroviruses such as HIV-1, which buds by similar mechanisms. Indeed, similar to hemorrhagic fever viruses, the HIV-1 matrix protein Gag directs HIV-1 budding in part, via well-characterized L-domain interactions with ESCRT proteins, and Gag mediated VLP formation also exhibits dependence on Ca2+ regulation [43]. Further study is needed to fully assess the role for calcium in the HIV-1 lifecycle because, unlike filoviruses and arenaviruses, Gag-mediated VLP production was found to be insensitive to high concentrations of 2-APB (up to 200uM) that fully block Ca2+ permeation of Orai channels [44].
In conclusion, we provide the first direct evidence that host Ca2+ signals, triggered by virus activation of STIM1 and Orai, are among key host mechanisms that orchestrate late steps of filovirus and arenavirus assembly and budding. Importantly, from a therapeutic perspective, Orai channels are ubiquitously expressed and like ion channels in general, they represent pharmacologically accessible (cell surface) therapeutic targets. While Orai1 inhibitors by themselves appear to have broad spectrum efficacy, an exciting possibility raised by our results is that drug cocktails formulated to target sequential steps in the virus life cycle, including entry, L-domain/host interactions, and other steps involved in budding, could produce enhanced potency, coverage and efficacy over approaches targeting any one host dependent step in the virus life cycle. Thus, while other calcium channel modulators identified may have distinct targets and even calcium independent effects, they may synergize with Orai1, and also L-domain inhibitors we’ve described previously that block VP40 and Z protein L-domain interactions with host Nedd4 and Tsg101 [42, 63].
Finally, the ability of certain individuals to survive hemorrhagic fever virus infection seems to reflect their capacity to mount a robust anti-viral immune response. In the context of the severity and the acute nature of these viral diseases, the impact of side effects and even minor effects on cell proliferation that might be associated with long term administration of Orai inhibitors that would be required for immune suppression and immune modulation, may be tolerable in the context of infection with these highly pathogenic and often fatal viruses. Indeed, there is no evidence from murine models that the loss of STIM or Orai activity or function would affect antigen induced lymphocyte activation required for an antiviral immune response [64, 65]. Thus our prediction is that administration of Orai1 or STIM1 inhibitors, or cocktails that could also include L-domain inhibitors, would slow or dampen virus transmission within and between individuals, and thereby could provide infected individuals additional time needed to mount a protective adaptive immune response. Although Synta66 and the more potent compound RO2959 are no longer being developed as therapeutics, several smaller pharmaceutical companies and academic groups persist in efforts to develop potent Orai1 inhibitors to suppress the pathogenesis of chronic immune-mediated and inflammatory diseases. If and when these become available, direct inhibition of enveloped RNA virus budding from host cells and transmission between individuals may represent an entirely novel use for these channel blockers.
HEK293T, HeLa, and VeroE6 cells were maintained in Dulbecco’s modified Eagle’s medium (DMEM) supplemented with 10% fetal calf serum (FCS), penicillin (100 U/ml)/streptomycin (100μg/ml) at 37°C in a humidified 5% CO2 incubator. The stable HEK293T Orai1 E106A mutant-expressing cell line was maintained in DMEM with 10% FCS, penicillin (100 U/ml)/streptomycin (100μg/ml) in the presence of 500 μg/ml of G418. HeLa cells were maintained in Minimal Essential Medium (MEM) with 5% fetal bovine serum, penicillin (100 U/ml)/streptomycin (100μg/ml) at 37°C in a humidified 5% CO2 incubator. The pCAGGS based plasmids expressing EBOV VP40, MARV VP40, JUNV Z, LASV Z, and GFP-eVP40 have been described previously [42, 63, 66]. mVP40 and JUNZ Z protein are flag tagged while eVP40 was detected using an anti-eVP40 polyclonal antibody previously described [67]. mVP40 and JUNV Z protein were detected with an anti-flag monoclonal antibody (Sigma-Aldrich), and STIM1 was detected with a rabbit anti-STIM1 specific polyclonal antibody (gift of Dan Billadeau, Mayo Clinic). 2-aminoethoxy diphenyl borate (2-APB, Sigma Aldrich), Synta66, and RO2959 (Glixx Labs, Southborough, MA) were freshly prepared from stock solutions in DMSO.
Cell viability in VLP budding and live virus infection assays was examined using an MTT assay (Amresco). 5×103 HEK293T or VeroE6 cells were plated in collagen-coated 96-well tissue culture plates in triplicate. Cells were transfected with empty vector using Lipofectamine for 6 hours, and incubated in serum-free or 2% FCS in phenol-red-free OPTI-MEM in the presence of Synta66 or 2-APB at the indicated concentrations for 20 hours, which mimics the transfection and treatment conditions for VP40 VLP budding. 20μl of MTT solution (5mg/ml in PBS) was added into each well and cells were incubated for 3.5 hours. Media was discarded and 150 μl DMSO was added. Absorbance was determined by spectrophotometry using a wavelength of 590 nm. For experiments with BSL-4 variants of filoviruses and arenaviruses, HeLa cells were seeded in 96 well plates ~24 hours prior to addition of Synta66 or vehicle control at indicated concentrations. Cells were incubated for 72 or 96 hour at 37°C in a humidified 5% CO2 incubator and viability was assessed using either CellTiter-Glo assay (Promega), or AlamarBlue assay (Life Technologies), in accordance with manufacturer’s instructions.
HEK 293T Orai1-wild type and Orai1-E106A mutant cells (kind gift from Dr. Jonathan Soboloff, Temple University) were plated at 5x105 cells/well in a 2-chambered Lab-Tek II Chambered #1.5 slide (Nunc, Rochester, NY) and grown overnight at 37°C, 5% CO2 in Dulbecco’s modified Eagle’s Medium supplemented with 4.5g/L glucose, L-glutamine, sodium pyruvate (Mediatech, Inc., Manassas, VA), 10% FBS (Gibco), and 1% Penicillin/Streptomycin (Gibco). Cells were transfected with 1μg of R-GECO-1 plasmid (Addgene, Cambridge, MA) using Lipofectamine 2000 reagent (Invitrogen) according to manufacturer’s instructions in phenol-red-free OPTI-MEM. The next day, cells were transfected with GFP-eVP40 fusion plasmid (GFP-eVP40) using Lipofectamine 2000 reagent (Invitrogen) according to manufacturer’s instructions in phenol-red-free OPTI-MEM. Six hours post transfection, fresh phenol-red-free OPTI-MEM was added to the wells, and cells were imaged at 37°C and 5% CO2 in a custom environmental chamber for the duration of the imaging on a Leica DMI4000 with Yokagawa CSU-X1 Spinning Disk Microscope with a 20X dry objective. Cellular R-GECO-1 fluorescence was imaged every 4 seconds for 1 minute periods, repeated every 10 minutes over 18 hours with a Hamamatsu 16-bit cooled EMCCD camera. Imaging and data analysis were performed using the Metamorph 7.6 imaging suite. Normalized fluorescence intensity (F/F0, where F0 is calculated as the average fluorescence intensity for the initial 1 minute interval) was calculated for each region of interest (ROI) in the time-series.
WT HEK293T or HEK293T E106A cells were seeded in collagen-coated six-well plates and transfected with 0.5μg of the indicated expression plasmids using Lipofectamine (Invitrogen) and the protocol of the supplier. At 6 hours post-transfection, cells were incubated in serum-free OPTI-MEM media or 2% FCS DMEM for 20–24 hours. Cells were incubated with vehicle (DMSO) alone, Synta66, or 2-APB at the indicated concentrations. Culture medium was harvested and centrifuged at 2500 rpm for 10 minutes to remove cellular debris, layered over a 20% sucrose cushion in STE buffer and centrifuged at 220,000xG for 2 hours at 4°C. The VP40 VLP-containing pellet was suspended in 50 μl of STE buffer at 4°C overnight. Cells were lysed in RIPA buffer as described above. Viral proteins in VLPs and cell lysates was detected by SDS-PAGE and Western-blot using primary rabbit anti-VP40 antibody for Ebola VP40, mouse anti-flag antibody for Marburg VP40, or mouse anti-HA antibody for LASV and JUNV Z followed by an appropriate HRP-conjugated secondary antibody.
Human STIM1 siRNAs were purchased from Dharmacon SMARTpools. ON-TARGETplus STIM1 siRNA (Thermo SCIENTIFIC) is a mixture of 4 siRNAs to specifically silence the target gene. HEK293T cells in OPTI-MEM media in collagen-coated six-well plate were transfected twice with either control siRNA or STIM1 siRNA at a final concentration of 200nM using Lipofectamine (Invitrogen) at 2-day intervals. The final transfection included both siRNAs and 0.5μg of Ebola VP40 expression plasmid. VLPs and cell lysates were harvested at 48 hours following the last transfection as described above. VP40 protein levels in VLPs and VP40 and STIM1 levels in cell extracts were analyzed by Western-blot with rabbit anti-VP40 antibody, or rabbit anti-STIM1 antibody, followed by anti-rabbit IgG HRP-conjugated secondary antibody. STIM1 suppression and rescue was accomplished using bicistronic vectors developed in house. STIM1 shRNA generated against the 5’ untranslated region of human STIM1 was expressed using the H1 promoter and human STIM1 cDNA expressed from a CMV promoter in the same construct. VLP samples and cell lysates were harvested at 48 hours after last transfection and analyzed by Western-blot.
The Candid-1 vaccine strain of JUNV was kindly provided by Robert B. Tesh (U.T.M.B., Galveston, TX) via Susan R. Ross (Univ. of Penn., Philadelphia, PA), and was propagated in VeroE6 cells as described previously [41]. For JUNV infection, VeroE6 cells were infected with JUNV (Candid-1) at an MOI of 0.02 for 42 hours at 37°C. Supernatants were removed and the cells were washed 3X with 1X PBS. Cells were then treated with DMSO alone, or the indicated concentrations of Synta66 for an additional 30 hours. Virions were harvested from the supernatant samples as described above for VLPs, and then used to infect fresh monolayers of VeroE6 cells for 48 hours for quantification of all foci detected in 6-well plates using fluorescence microscopy as described previously [42].
For all experiments using authentic viruses, Ebola virus (Kikwit isolate), Marburg virus (Ci67 isolate), Lassa virus (Josiah isolate), and Junin virus (Espindola isolate) were used. Wild-type HEK293T cells and Orai1 E106A mutant-expressing HEK293T cells, seeded in 96 well black plates (Corning BioCoat Cellware, Collagen Type I), were incubated with EBOV (MOI = 0.5), MARV (MOI = 0.1), JUNV (MOI = 0.1), or LASV (MOI = 0.05) in a Biosafety Level 4 laboratory located at USAMRIID. Following 1 hour absorption, virus inoculum was removed and growth media was added. Cells were then incubated at 37°C, 5% CO2, 80% humidity for 72 (LASV) or 96 (EBOV, MARV, JUNV) hours, at which time the cells were washed once with PBS and submerged in 10% formalin prior to removal from the BSL4 laboratory. Formalin was removed and cells were washed 3 times with PBS. For LASV infection only, cells were treated with 300mM NaOH for 20 minutes at room temperature prior to the blocking step. Cells were blocked by adding 3% BSA/PBS to each well and incubating at 37°C for 2 hours. EBOV GP-specific mAb KZ52 (kind gift from Kartik Chandran, Albert Einstein College of Medicine, Bronx, NY), MARV GP-specific mAb 9G4 (USAMRIID), LASV GP-specific mAb 52-161-6 (USAMRIID), and JUNV GP-specific mAb GD01-AG02 (BEI Resources), diluted in 3% BSA/PBS, were added to appropriate wells containing infected cells and incubated at room temperature for 2 hours. Cells were washed 3 times with PBS prior to addition of goat anti-mouse or goat anti-human IgG-AlexaFluor 594 (Invitrogen, Molecular Probes) secondary antibody. Following 1 hour incubation with secondary antibody, cells were washed 3 times prior to addition of Hoechst 33342 (Invitrogen, Molecular Probes) diluted in PBS. Cells were imaged and percent of virus infected cells calculated using the Operetta High Content Imaging System (PerkinElmer) and Harmony High Content Imaging and Analysis Software (PerkinElmer). Statistical significance between wild-type cells and Orai1 E106A mutant cells was determined using student t test, two-tailed.
For immunofluorescence based assays, HeLa cells, seeded in 96 well black plates (Greiner Bio-One Cellcoat), were incubated with EBOV (MOI 0.1), MARV (MOI 0.1), LASV (MOI 0.01), or JUNV (MOI 0.1) for 1 hour at 37°C, 5% CO2, 80% humidity. Virus inoculum was removed and cells were washed once with PBS. Synta66 was diluted in HeLa cell culture media at indicated concentrations and added to cells. An equivalent percentage of DMSO in HeLa media served as the vehicle control. Cells were then incubated at 37°C, 5% CO2, 80% humidity for 72 (LASV and JUNV) or 96 (EBOV and MARV) hours at which time, the cells were washed once with PBS and submerged in 10% formalin prior to removal from the BSL4 laboratory. As described above, virus specific antibodies were added to appropriate wells containing infected cells and samples processed as previously described, except that goat anti-mouse or goat anti-human IgG-AlexaFluor 488 (Invitrogen, Molecular Probes) was used as the secondary antibody. Statistical significance was determined by two way ANOVA with Bonferroni multiple comparisons relative to vehicle control treated cells. For viral titer analysis, HeLa cells were seeded in 6 well plates ~24 hours prior to infection with LASV (MOI = 0.01), JUNV (MOI = 0.1), MARV (MOI = 0.1), or EBOV (MOI = 0.1). One hour after infection, cells were treated with Synta66 or vehicle control at indicated concentrations. Culture supernatants were collected at 48 (MARV), 72 (LASV, JUNV) or 96 (EBOV) hours and cell debris removed by centrifugation. Viral titers of clarified supernatants were determined by routine plaque assay as previously described [68–70]. All data is a graphical representation of at least two independent, replicate experiments. Statistical significance of log transformed data was determined by two way ANOVA with Bonferroni multiple comparisons relative to vehicle control treated cells.
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10.1371/journal.pntd.0001482 | Clinical and Virological Study of Dengue Cases and the Members of Their Households: The Multinational DENFRAME Project | Dengue has emerged as the most important vector-borne viral disease in tropical areas. Evaluations of the burden and severity of dengue disease have been hindered by the frequent lack of laboratory confirmation and strong selection bias toward more severe cases.
A multinational, prospective clinical study was carried out in South-East Asia (SEA) and Latin America (LA), to ascertain the proportion of inapparent dengue infections in households of febrile dengue cases, and to compare clinical data and biological markers from subjects with various dengue disease patterns. Dengue infection was laboratory-confirmed during the acute phase, by virus isolation and detection of the genome. The four participating reference laboratories used standardized methods.
Among 215 febrile dengue subjects—114 in SEA and 101 in LA—28 (13.0%) were diagnosed with severe dengue (from SEA only) using the WHO definition. Household investigations were carried out for 177 febrile subjects. Among household members at the time of the first home visit, 39 acute dengue infections were detected of which 29 were inapparent. A further 62 dengue cases were classified at early convalescent phase. Therefore, 101 dengue infections were found among the 408 household members. Adding these together with the 177 Dengue Index Cases, the overall proportion of dengue infections among the study participants was estimated at 47.5% (278/585; 95% CI 43.5–51.6). Lymphocyte counts and detection of the NS1 antigen differed significantly between inapparent and symptomatic dengue subjects; among inapparent cases lymphocyte counts were normal and only 20% were positive for NS1 antigen. Primary dengue infection and a specific dengue virus serotype were not associated with symptomatic dengue infection.
Household investigation demonstrated a high proportion of household members positive for dengue infection, including a number of inapparent cases, the frequency of which was higher in SEA than in LA.
| Dengue is the most important mosquito-borne viral disease in humans. This disease is now endemic in more than 100 countries and threatens more than 2.5 billion people living in tropical countries. It currently affects about 50 to 100 million people each year. It causes a wide range of symptoms, from an inapparent to mild dengue fever, to severe forms, including dengue hemorrhagic fever. Currently no specific vaccine or antiviral drugs are available. We carried out a prospective clinical study in South-East Asia and Latin America, of virologically confirmed dengue-infected patients attending the hospital, and members of their households. Among 215 febrile dengue subjects, 177 agreed to household investigation. Based on our data, we estimated the proportion of dengue-infected household members to be about 45%. At the time of the home visit, almost three quarters of (29/39) presented an inapparent dengue infection. The proportion of inapparent dengue infection was higher in South-East Asia than in Latin America. These findings confirm the complexity of dengue disease in humans and the need to strengthen multidisciplinary research efforts to improve our understanding of virus transmission and host responses to dengue virus in various human populations.
| Dengue is the most important mosquito-borne viral disease of humans. The disease is now endemic in more than 100 countries and threatens more than 2.5 billion people. It currently affects about 50 to 100 million people each year [1]. Dengue viruses (DENV) are enveloped, single-stranded positive-sense RNA viruses (family Flaviviridae, genus Flavivirus). There are four types of DENV: DENV-1, DENV-2, DENV-3 and DENV-4. Dengue virus infection induces life-long protective immunity to the homologous serotype, but confers only partial and transient protection against subsequent infections with any of the other three serotypes [2]. The disease spectrum ranges from inapparent infection or mild dengue fever [3], probably the most common form, to a potentially severe form of dengue characterized by plasma leakage and hemorrhage, known as severe dengue. Uncommonly, severe dengue may manifest as hepatitis, encephalopathy or rhabdomyolysis [2], [4]–[7]. About 500,000 people are estimated to have severe dengue and about 25,000, mostly children, die from it each year [8]. The underlying causes determining the outcome of DENV infection remain unknown. Although previous exposure, viral strain and human host genetic polymorphisms also influence the clinical outcome of DENV infection, we still know little about the complex interplay between host and pathogen in the pathogenesis of dengue [9]–[12].
Inapparent infections have largely been detected retrospectively through serology. The uses of genome detection or virus isolation have enabled detection of inapparent infections in cluster studies designed to detect natural infections in the community [13], [14]. The present study was designed to identify symptomatic and inapparent dengue-infected subjects in genetically-related individuals living in the same household, in line with the main aim of the DENFRAME project which is to explore the influence of human genetic variants and their functional roles in the pathogenesis of dengue disease in humans. We based the identification of dengue-infected subjects upon virological techniques, namely virus isolation and detection of the genome. We also took this opportunity to evaluate prospectively a commercial NS1 capture assay [15], [16] that could potentially be implemented in laboratories for the diagnosis of acute dengue [17]–[19].
A multinational, prospective study was conducted in South-East Asia (Cambodia and Vietnam) and Latin America (Brazil and French Guiana). We used virological techniques to identify dengue patients diagnosed at the acute phase of disease among the patients presenting with dengue-like illness. We then performed a household investigation, comparing clinical data and biological markers from subjects with a broad range of dengue disease patterns, including inapparent dengue cases that are rarely captured in clinical studies. This clinical study's aims were: (i) to estimate the proportion of inapparent dengue infections among members of the households of laboratory-confirmed symptomatic dengue cases, (ii) to calculate the proportion of dengue-infected subjects at the time of the household investigation, and (iii) to compare clinical and biological data from inapparent and symptomatic dengue-infected subjects.
Five institutions were involved in this study during the recruitment period: Instituto Evandro Chagas (IEC) in Belém (Pará state, Brazil), Institut Pasteur du Cambodge (IPC) in Phnom Penh (Cambodia), Institut Pasteur de la Guyane (IPG) in Cayenne (French Guiana) and Institut Pasteur de Ho Chi Minh Ville (IPHCM) in Vietnam were responsible for the recruitment of patients and virological analyses; the Institut Pasteur (IP) in Paris (France) designed the study and was responsible for central monitoring and data analysis.
As shown in the two maps (Figure 1), volunteers were recruited at four clinical sites: Vinh Thuan District Hospital (Vietnam), Kampong Cham Referral Hospital (Cambodia), the IPG in Cayenne (French Guiana) and public outpatient and emergency rooms managed by the Belém Health Secretariat in the districts of Guamá, Marco, Marambaia and Sacramenta, and the outpatient unit of the IEC (Brazil). The virology laboratories of the four institutions responsible for recruitment are all National Reference Centers (NRC) for Arboviruses (IEC is also a WHO collaborative center). These laboratories carried out virological, NS1 antigen (Platelia Dengue NS1 Antigen, Bio-Rad, Marnes La Coquette, France), and serological techniques.
We recruited subjects with acute dengue-like illness at the study sites. These subjects were identified by the treating physicians and were included if they satisfied the following criteria: (i) aged over 24 months; (ii) oral temperature >38°C and onset of symptoms within the last 72 h; and (iii) presenting with at least one clinical manifestation suggestive of dengue-like illness: severe headache, retro-orbital pain, myalgia, joint pain, rash or any bleeding symptom. Furthermore, for inclusion in the second step of the study, the subject had to come from a familial household containing more than two people during the seven days preceding illness. We first identified the dengue-infected subjects (referred to in this study as Dengue Index Cases or DIC) and non-dengue-infected subjects (defined as Non-Dengue Cases - NDC) on the basis of virological results from an acute sample (see below). We then recruited individuals from the households of the DIC. We thus constituted three groups of participants: 1) DIC, 2) household members (HHM), and 3) NDC not related to the DIC. For all groups (DIC, HHM and NDC), we applied the same exclusion criteria: women who were pregnant or breastfeeding, individuals with a focal source of infection (e.g. otitis media, pneumonia, meningitis), patients presenting with a known chronic illness, and patients with malaria. Moreover, to ensure the feasibility of this study, each study site was asked to target a convenient sample of 50 households and to recruit subjects from July 2006 to June 2007 in line with the approval granted by the Institutional Review Board and the timing of the dengue season at each site.
Participants were examined during sequential visits, as shown in the study design charts (Figure 2). At each visit, data were collected with a standardized questionnaire. Severe dengue cases were classified, according to WHO recommendations on the basis of the clinical data. Biological data were also recorded at the sequential visits [2]. Blood samples were collected during the visits and were rapidly processed by the laboratories of each of the recruiting sites, for dengue diagnosis and biological testing. Blood sample volume was adapted for children weighing less than 20 kg.
Paired blood samples were collected for subjects presenting dengue-like illness to allow classification as DIC or NDC: during the acute phase (Visit 1) and during the convalescence phase (Visit 4: 15 to 21 days after the onset of fever). Blood samples were taken from hospitalized DIC within 24 hours of defervescence (Visit 3). HHM were visited at home for blood collection within 24 to 72 hours of DIC identification (Home Visit 1). For practical and logistical reasons this delay of up to 72 hours was unavoidable. HHM were supplied with a monitoring diary card and a thermometer, to enable them to follow their temperature over a 7-day period. For HHM with a positive diagnosis of dengue or with an onset of fever during the seven days of monitoring, a second visit with blood collection for dengue diagnosis was organized (Home Visit 2). Blood analyses included virological and serological dengue diagnosis, complete blood count, transaminases and bilirubin levels. Finally, the data were coded and entered into the computer via a secure website specifically developed with the PHP/MySQL system.
All serum samples collected at Visit 1 or at Home Visit 1 or Home Visit 2 were tested: (i) for acute dengue diagnosis, defined as positive virus isolation on mosquito cells [20] and/or positive viral RNA detection by reverse transcriptase-polymerase chain reaction (RT-PCR) [21], and (ii) for the diagnosis of early convalescent dengue cases based on a standardized DENV IgM capture enzyme-linked immunosorbent assay (MAC-ELISA) [22], and DENV IgG detection by indirect ELISA (in-house protocol developed by each NRC for Arboviruses). NS1 antigen detection was also performed.
Only subjects with febrile dengue infection diagnosis were classified as DIC. Subjects in the early stage of dengue convalescence at Visit 1 (i.e. positive NS1 antigen detection with concomitant DENV IgM detection, or isolated DENV IgM detection with no positive viral tests) were not classified as DIC; we did not perform a household investigation for them. For the classification of dengue-infected HHM at Home Visit 1, we included both HHM with an acute (febrile or inapparent) dengue infection diagnosis and HHM with isolated DENV IgM detection, presumably related to an infection preceding that of the DIC (i.e in the early convalescence phase). During the 7-day period of home monitoring, several new febrile cases of dengue-infected HHM were also confirmed through Home Visit 2.
We were unable to use the DENV IgM/IgG ratio to distinguish between primary and secondary dengue infections, due to a lack of standardization of DENV IgG tests among laboratories [23]. We therefore established two groups of dengue-infected participants, based on the presence or absence of DENV IgG during the acute phase of the disease. In this study, we considered the presence of DENV IgG in the acute phase of the study to be suggestive of previous dengue infection. All sera were also checked for DENV IgM and IgG at Visit 4. Finally, if all these dengue tests were negative, participants were classified as NDC.
The study was approved by the Institutional Review Board of the Institut Pasteur and by the ethics committees of each of the countries concerned. It was conducted in accordance with the Declaration of Helsinki, and the participants or the parents of minors participating in the study gave written informed consent before inclusion. The clinical protocol, the questionnaires, the standard operating procedures and informed consent forms were adapted and translated for each clinical site. All the documentation was accessible through a dedicated website with a specific login access (www.denframe.org). The centralized electronic database was based at the Institut Pasteur in Paris and registered with the Commission Nationale de l'Informatique et des Libertés (CNIL) in France.
We present here the data from all four study sites in Latin America and South-East Asia. DIC are described according to region, disease severity, DENV type, age group and IgG status. We estimated the proportion of inapparent dengue infections among HHM, and we calculated the proportions of dengue-infected subjects among household subjects, in total and according to the IgG status at the time of household investigation. We compared clinical data and biological markers between inapparent dengue-infected subjects, symptomatic dengue-infected subjects, and non-dengue-infected participants at the time of the household investigation. We created binary variables to evaluate the potential effect of DENV infection on biological markers (hematocrit, platelets, neutrophils, lymphocytes, monocytes, ASAT, ALAT, bilirubin). For lymphocytes and neutrophils, we used a threshold of 2×109/l. We used chi-squared or Fisher's exact tests to compare categorical variables between symptomatic cases, inapparent dengue-infected cases and non-dengue-infected subjects among HHM. Univariate and multivariable logistic regression models were used to assess the effect of covariates on the odds ratios (OR) of symptomatic dengue-infected cases, inapparent dengue-infected cases, and non-dengue-infected subjects among HHM. For the multivariable logistic regression models including data from household members, we used two-stage hierarchical regression models taking into account the family household structure [24]. Potential confounders with a P value of less than 0.20 in univariate analysis were retained for the final multivariable analyses. STATA version 10.0 (Stata Corp., College Station, TX, USA) and a significance level of 5% were used for all statistical analyses.
Flowcharts for the recruitment of participants at each step are shown in Figure 3.
We screened 473 febrile subjects for dengue infection. Thirty (6.3%) had at least one criterion for non inclusion in the study at presentation; the remaining 443 (93.7%) were included in the study. We identified 215 (48.5%) of these 443 subjects as DIC, 21 (4.7%) as dengue convalescent cases, 187 (42.2%) as NDC, and 20 (4.5%) could not be classified because some biological markers were lacking. Recruitment levels during the study period were very low in French Guiana (9 DIC and 24 NDC), whereas there had been a large number of dengue cases during the rainy season of the previous year [25]. For the 215 subjects classified as DIC, 149 (69.3%) were positive by genome detection and viral isolation, 43 (20.0%) were positive by genome detection only, 15 (7.0%) were positive by viral isolation only, and a very few subjects (n = 8, 3.7%) were ultimately classified as DIC by the virologists, based on positive NS1 detection, clinical data and serological results (negative IgM at Visit 1 followed by seroconversion IgM at convalescent phase).
The proportions of subjects classified as either NDC or DIC differed between Latin America and South-East Asia: 69.5% (130/187) of the total NDC in the study, and 47.0% (101/215) of the DIC, were recruited in Latin America whereas 30.5% (57/187) of the NDC and 53.0% (114/215) of the DIC were recruited in South-East Asia (P<10−4) (Figure 3A). In other words, in Latin America, in two thirds of subjects presenting with dengue-like illness, the cause was not related to dengue infection. Given the inclusion criteria, the dengue-like illness symptoms were not different between NDC and DIC (data not shown). However, all biological variables, including counts of platelets, lymphocytes and neutrophils, were significantly lower, whereas hematocrit and liver enzyme levels were higher in the DIC group than in the NDC group (data not shown).
Table 1 shows the distribution of DIC by region and according to IgG status at Visit 1 as a function of DENV type and age group. The proportions of severe dengue and dengue fever cases with DENV IgG (suggestive of previous DENV infection) and without DENV IgG in the acute phase were similar (Table 1): 15 (55.6%) severe dengue cases tested negative for DENV IgG and 12 (44.4%) tested positive for DENV IgG, versus 49 (31.8%) and 105 (68.2%) of the subjects with non severe disease, respectively (P = 0.017). DENV-1, -2 and -3 were found with similar frequencies in South-East Asia, whereas DENV-3 predominated in Latin America. Fifteen of the severe dengue cases reported in South-East Asia were infected with DENV-2 (53.6%; 15/28). Interestingly, seven severe dengue cases positive for DENV-2 virus and negative for DENV IgG in the acute phase but with subsequent DENV IgM and IgG seroconversion were identified. This serological pattern suggests that these patients had primary DENV infection. Two DIC in Vietnam were reported with co-detection of multiple DENV strains by RT-PCR: DENV-2/DENV-1 and DENV-4/DENV-2 respectively; the viral cultures were negative for both subjects. Only the first virus detected was considered for further statistical analysis (DENV-2 and DENV-4, respectively).
According to the WHO criteria, twenty-eight (13.0%) subjects were classified as severe dengue (based on severe plasma leakage and/or severe hemorrhages and/or severe organ impairment). All these cases were from clinical sites in South-East Asia (25 in Vietnam and 3 in Cambodia, as presented in Table S1). At visit 1, presentation with the following combination of features was significantly associated with the occurrence of severe dengue in this population: being male, over the age of seven years, with no retro-orbital pain but with bleeding, low monocyte count, normal liver enzyme levels and DENV-2 type infection.
For 163 (75.8%) DIC, data were available for all the biological markers at visits 1 and 4 (Figure 3A). All these markers had returned to normal levels by visit 4, and all participants, including the 28 severe dengue cases displayed clinical recovery from dengue disease (data not shown).
Agreement for household investigations was obtained from 177 (82.3%) DIC, corresponding to a total of 651 household members. We compared the distribution of the covariates (as listed in Table S1) between the 38 DIC with no familial investigation and the 177 DIC who underwent familial investigation; no significant differences were found in the distribution of the covariates between these two groups (data not shown). All 28 patients with severe dengue infection underwent household investigation. In total, 141 (21.7%) of the 651 household members refused to participate in the study. We therefore screened 510 participants, 497 (97.5%) of whom were eligible for the study. All but one of these 497 household members were genetically related to the DIC. Eighty-four were not classifiable due to the lack of some biological results. Full assessment of DENV infection was carried out according to the study protocol for the remaining 413 of these subjects (Figure 3B) during Home Visit 1.
At the time of the household investigation (Home Visit 1), 39 subjects were identified as being in the acute phase of dengue infection: 29 (74.4%) cases were inapparent and 10 (25.6%) had symptomatic dengue infection. An additional 62 subjects were classified as being in the early phase of convalescence from dengue infection. The remaining 312 subjects were considered as non-dengue-infected at the time of Home Visit 1 (Figure 3B); however, five of them developed some clinical symptoms of dengue fever and were laboratory-confirmed as having acute dengue infection during the 7-day home monitoring. We excluded them (n = 5) from the remaining analysis (n = 312 subjects with 7-day home monitoring) that thus included 307 subjects (Figure 3B). It should be noted that a second home visit and blood sampling was not possible, for ethical and logistical reasons, for HHM without any clinical symptoms after the 7-day home monitoring. Hence, among the 307 remaining subjects, some may have had an inapparent dengue infection after Home Visit 1. Therefore, we considered that at least 101 (39 acute or 62 early convalescent) dengue infections were found amongst 408 HHM (24.8%; 95% confidence interval (CI): 20.6–28.9) at the time of Home Visit 1 (Figure 3B). Thus, adding together the 177 DIC and the 101 DENV-infected HHM, the overall proportion for dengue among the study participants was estimated at 47.5% (278/585; 95% CI: 43.5–51.6) (Figure 3B). We have also estimated these proportions according to the IgG status (Table 2) at the time of Home Visit 1 (excluding the 5 subjects with known symptomatic infection – 3 were IgG positive and 2 were IgG negative). Among the 585 subjects, 6 had missing IgG data. Among 425 subjects with positive IgG, the estimated proportion of dengue-infected subjects was 43.8% (186/425; 95% CI: 39.0–48.5) and, among the 154 with negative IgG, this estimated proportion was 57.1% (88/154; 95% CI: 49.3–65.0).
In 101 (57.1%) households, there was only one dengue-infected case. For the 76 (42.9%) households with at least two dengue-infected cases, DENV type had been determined for all subjects in 29 households. Nine (31.0%) households were found to have two different DENV types circulating during the same time period: DENV-1 & DENV-3 (n = 2 in Brazil, n = 4 in Cambodia), DENV-1 & DENV-2 (n = 1 in Vietnam), and DENV-2 & DENV-3 (n = 2 in Vietnam).
Hematologic and hepatic biological markers observed among non-dengue-infected cases (n = 307), inapparent dengue-infected cases (n = 29), and symptomatic dengue-infected subjects (n = 192) are described in Table S2. Tables 3 & 4 show comparisons between non-dengue-infected and inapparent dengue-infected cases, and symptomatic and inapparent dengue-infected subjects, respectively, among the household subjects. Table S3 presents the main characteristics of subjects with acute dengue infection compared to non-dengue-infected subjects among the household subjects. In the comparisons between non-dengue-infected and inapparent dengue-infected subjects, taking into account potential confounders, only neutrophil and monocyte levels differed significantly whereas presence of IgG at Visit 1 was almost significant with the non-dengue-infected group. The comparison between symptomatic and inapparent dengue-infected subjects (Table 4) showed significant difference between groups for lymphocyte counts and positive NS1 antigen detection. In this analysis, no significant difference was found for DENV types identified or IgG detection during the acute phase.
Several previous epidemiological studies have focused on school-based surveillance aiming at improving dengue-vector control measures [3], [14], studying the dynamics of patterns of dengue transmission [26]–[28] or describing a model that takes into account the role of human movement in the transmission dynamics of vector-borne pathogens [29]. Earlier cluster investigation methods were designed as an alternative approach to the commonly used prospective cohort study method for investigating the natural history of dengue virus infection in South-East Asia and Latin America [13], [30]. Although different study designs have demonstrated the feasibility of identification of inapparent dengue cases, it remains difficult to recruit these subjects. We designed our study to include family household investigation in order to identify a group of inapparent dengue-infected subjects and to compare them with symptomatic dengue-infected and non-dengue-infected subjects living in the same family household. The study design was based on family household recruitment specifically in order to collect data and biological samples, and to study secondarily the host susceptibility to dengue infection and disease. Unlike studies based on cohorts from hospital referrals, this multi-country study captured dengue cases ranging from inapparent infections, through mild disease to severe dengue fever, using definitions of clinical cases and diagnostic methodology standardized across the four sites. The period of inclusion, from July 2006 to June 2007, spanned the dengue season at each site, although incidence of dengue was low that year in French Guiana.
The main objective of this study was to identify dengue infections and particularly inapparent infections among dengue patients' household family members in South-East Asia and Latin America. Based on our data, we estimated the proportion to be about 45% among those participating in the household study. Most of the dengue cases studied had symptomatic infections, covering the spectrum of disease from dengue fever to severe dengue cases. We also identified inapparent infections in the population. We observed dengue-infected subjects classified as DIC and some of their HHM without acute dengue infection but with a positive IgM detection, suggesting an early convalescent phase after dengue infection with no clinical symptoms. In this study we identified 29 inapparent dengue infections but we believe this number underestimates the proportion of inapparent dengue cases because we were not able to take blood samples from non-symptomatic subjects at Home Visit 2.
We postulated that dengue is transmitted to members of the DIC's family household during the period of the index subject's infection, and thus designed our study to detect inapparent dengue infections with a home visit organized shortly after identification of DIC. Obviously, we cannot confirm whether the index subject's DIC was always the source of infection in other family members, but we can postulate that a non-hospitalized DIC who remains at home during acute illness represents a potential source of DENV transmission to Aedes. According to our study design, clustering of cases within a household could be the result of a single or very few infected mosquitoes biting different household members during a short period of time, perhaps within a single gonotrophic cycle as previously suggested [14], [31]. This is also consistent with a previous observation that over periods from 1 to 3 days, dengue cases were clustered within short distances, i.e., within a household [32]. No mosquito captures were, however, conducted in our study to identify DENV-positive Aedes mosquitoes. DENV sequencing would help resolve the extent of localized transmission.
We characterized subjects with acute dengue infection using virus isolation and detection of the genome. We also used NS1 antigen detection, a more recently recognised diagnostic tool. As for many tropical infectious diseases, there is an urgent need for validated diagnostic tools for dengue. In parallel with the virological techniques, we evaluated detection of the NS1 antigen with the Platelia Dengue NS1 Ag test. In this study, this test was found to have good sensitivity (83.6%; 95% CI: 78.5–88.6) and specificity (98.9%; 95% CI: 96.6–99.9) in both Asia and Latin America, as reported in previous studies [17], [33], [34]. A recent multi-country study observed unequal sensitivity between geographical regions that remains unexplained, suggesting further assessments are needed [35]. The use of viral detection antigen is particularly useful during the first five days of illness with NS1 assays that are significantly more sensitive for primary than secondary dengue [18], [34], [36]. However, NS1 antigen could be detected in only 20% of inapparent DENV-infection. This finding suggests that NS1 antigen may have a role in dengue disease pathogenesis and also indicates that this test cannot be relied upon for detection of inapparent dengue infection.
By comparing HHM not infected with dengue with those presenting with inapparent dengue infection, we showed that neutrophil and monocyte counts were early indirect biological markers of dengue infection, whereas platelet counts and the frequency of IgG detection at the first visit did not differ between the two groups (Table 3). A comparison of inapparent dengue-infected HHM with symptomatic dengue-infected subjects showed that lymphocyte counts and detection of the NS1 antigen differed significantly between these two groups (Table 4). Moreover, the NS1 antigen was detected during the acute phase in most of the dengue cases tested, and the sensitivity of this test was even higher in severe dengue cases (26/28, Table S1), possibly reflecting higher viral loads. These findings may indirectly reflect the progression of the immune response to DENV, leading in some cases to severe acute lymphopenia and a lack of virological control, with high rates of NS1 antigen circulation in the blood that may be correlated with high-level or prolonged viremia [7], [36]. Severe dengue cases were also more likely to be male, to have lower monocyte counts or normal liver enzyme levels, and to be infected with DENV-2, although quantitative RT-PCR did no permit study of the magnitude of the viremia. We showed that half of the severe dengue cases had not previously been infected with DENV, as confirmed by the occurrence of DENV IgG seroconversion during convalescent phase [7]. In all dengue-infected subjects, including inapparent, we observed a decrease in neutrophil and monocyte counts. On one hand, it may suggest a direct effect of dengue illness on hematopoiesis, although such an effect is in conflict with data reported elsewhere in the literature [37]. On the other hand, DENV is detected in peripheral monocytes during acute disease, and the infection of monocytes leads to cytokine production, suggesting that virus-monocyte interactions are relevant to pathogenesis [38]–[40]. Moreover, DENV can induce apoptosis in monocytes, and this may lead to decreases in the number of these cells in severe dengue cases [41].
In this study we only observed severe dengue cases in South-East Asia. Disease severity and pathogenesis remain largely unexplained and certainly related to complex interactions of several factors, including virus strain, immune response to previous dengue infection and host genetic background. The introduction of the Asian 1 DENV-2 genotype into the Americas in the 1980s led to the emergence of severe dengue cases on this continent. Following this introduction a new genotype emerged, named Asian/American DENV-2 genotype [42]–[44]. During the study period, this Asian/American genotype was circulating in French Guiana (Philippe Dussart, personal data) and probably in the north of Brazil, however DENV-2 did not cause an outbreak and we did not report any severe dengue case among Brazilian subjects.
Two constraints of the study design deserve mention. All methods (biological markers, virological testing, NS1 antigen detection and IgM serology) were standardized across the four reference laboratories, with the exception of the IgG ELISA. As a consequence, we were unable to calculate the IgM/IgG ratio [45], [46]. However, as the intention was to include dengue cases during the acute phase of infection, this ratio was not a crucial endpoint for the study. Another constraint of this study was that we did not include infants and children below 24 months of age in the DENFRAME project. However, several previous reports already provide insight into the epidemiology of dengue in this specific population [47]–[50].
These findings confirm the complexity of dengue disease in humans and the need to strengthen multidisciplinary research efforts to improve our understanding not only of virus transmission but also host responses to DENV in various human populations. It will therefore be interesting, based on clinical data and biological samples collected in this study, to further evaluate the host susceptibility to dengue infection and disease using family-based association analyses. Moreover, we think that technological transfer of standardized diagnostic methods in laboratories based in tropical countries is essential if we are to estimate disease burden and to optimize vector control interventions. Together with improvements in clinical care for dengue patients and better understanding of dengue pathogenesis, the development of a preventive vaccine and antiviral drugs would complete the arsenal of weapons for combating dengue worldwide.
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10.1371/journal.pntd.0006671 | Direct nucleic acid analysis of mosquitoes for high fidelity species identification and detection of Wolbachia using a cellphone | Manipulation of natural mosquito populations using the endosymbiotic bacteria Wolbachia is being investigated as a novel strategy to reduce the burden of mosquito-borne viruses. To evaluate the efficacy of these interventions, it will be critical to determine Wolbachia infection frequencies in Aedes aegypti mosquito populations. However, current diagnostic tools are not well-suited to fit this need. Morphological methods cannot identify Wolbachia, immunoassays often suffer from low sensitivity and poor throughput, while PCR and spectroscopy require complex instruments and technical expertise, which restrict their use to centralized laboratories. To address this unmet need, we have used loop-mediated isothermal amplification (LAMP) and oligonucleotide strand displacement (OSD) probes to create a one-pot sample-to-answer nucleic acid diagnostic platform for vector and symbiont surveillance. LAMP-OSD assays can directly amplify target nucleic acids from macerated mosquitoes without requiring nucleic acid purification and yield specific single endpoint yes/no fluorescence signals that are observable to eye or by cellphone camera. We demonstrate cellphone-imaged LAMP-OSD tests for two targets, the Aedes aegypti cytochrome oxidase I (coi) gene and the Wolbachia surface protein (wsp) gene, and show a limit of detection of 4 and 40 target DNA copies, respectively. In a blinded test of 90 field-caught mosquitoes, the coi LAMP-OSD assay demonstrated 98% specificity and 97% sensitivity in identifying Ae. aegypti mosquitoes even after 3 weeks of storage without desiccant at 37°C. Similarly, the wsp LAMP-OSD assay readily identified the wAlbB Wolbachia strain in field-collected Aedes albopictus mosquitoes without generating any false positive signals. Modest technology requirements, minimal execution steps, simple binary readout, and robust accuracy make the LAMP-OSD-to-cellphone assay platform well suited for field vector surveillance in austere or resource-limited conditions.
| Mosquitoes spread many human pathogens and novel approaches are required to reduce the burden of mosquito-borne disease. One promising approach is transferring Wolbachia into Aedes aegypti mosquitoes where it blocks transmission of arboviruses like dengue, Zika and Yellow fever viruses and spreads through mosquito populations. For effective evaluation of this approach, regular surveillance of Wolbachia infections in Ae. aegypti is required. However, current diagnostic tools, such as real time polymerase chain reaction, are not well suited to support these critical surveillance needs in resource poor settings due to their dependence on expensive instruments and technical expertise. To fill this need we developed a simple, robust and inexpensive assay to identify Ae. aegypti mosquitoes and Wolbachia using our unique one-pot assay platform, LAMP-OSD, which uses loop-mediated isothermal amplification to amplify nucleic acid targets at a single temperature. Unlike other LAMP-based tests, our assays assure accuracy by coupling amplification with novel nucleic acid strand displacement (OSD) probes that hybridize to specific sequences in LAMP amplification products and thereby generate simple yes/no readout of fluorescence readable by human eye and by off-the-shelf cellphones. To facilitate field use, we developed our assays so they are compatible with crushed mosquito homogenate as the template, meaning no nucleic acid extraction is required. In blinded tests using field collected mosquitoes, LAMP-OSD-cellphone tests performed robustly to identify 29 of 30 Ae. aegypti even after 3 weeks of storage at 37°C while producing only one false positive out of 60 non-specific mosquitoes. Similarly, our assay could identify Wolbachia in field-caught Aedes albopictus without producing any false positives. Our easy to use and easy to interpret assays should facilitate widespread field mosquito surveillance with minimal instrumentation and high accuracy.
| Mosquitoes are vectors that can transmit an array of pathogens that often cause devastating human diseases [1]. Traditionally considered a problem for tropical regions, mosquitoes are increasingly becoming a global public health challenge [2, 3] due to a changing global environment, urbanization, increases in the global movement of populations, and the emergence of insecticide resistance [4]. Estimates suggest nearly half the world’s population is at risk for mosquito-borne diseases [5, 6], and as such, there is an urgent need for novel approaches to reduce the burden of disease.
One biocontrol countermeasure gaining traction for mosquito control is the release of Wolbachia-infected mosquitoes [7–9]. Wolbachia is a maternally-transmitted endosymbiont that can rapidly become established in the natural mosquito populations and can inhibit a variety of pathogens, including arboviruses, malaria parasites, and filarial nematodes [10–15]. Wolbachia control strategies are currently being deployed into the field to alter the capacity of Aedes aegypti to transmit arboviruses or to suppress mosquito populations [16–18]. Surveillance of transinfected mosquitoes as well as natural vector populations is crucial to evaluate the efficacy of these interventions [19]. However, most current screening methods rely on PCR, which is expensive and relies on laboratory facilities. In addition to screening for Wolbachia infection, it would also be desirable to identify the host mosquito species using these assays since different mosquito species differ in their ability to transmit pathogens [20]. Knowledge of vector species, and prevalence and stability of Wolbachia is essential for effective vector control and pre-emption of disease outbreaks with public health measures [21].
Unfortunately, mosquitoes are most commonly identified using morphological taxonomic keys. This process can be tedious, and requires highly trained personnel and undamaged mosquitoes. Alternative morphological methods such as the identification of morphometric wing characters [22] are low throughput and require microscopes and complex imaging instruments. Moreover, traditional morphological-based approaches cannot detect associated symbionts or pathogens. These limitations restrict widespread accessibility and necessitate sample preservation and transport. On the other end of the spectrum, immunoassay-based tools for identifying pathogen-infected mosquitoes, such as VecTest dipsticks (Medical Analysis Systems Inc.), are portable and inexpensive. However, these tests have poor sensitivity [23–25] and are not necessarily available for distinguishing mosquito species or identifying Wolbachia endosymbionts.
Nucleic acid tests can provide the necessary sensitivity and versatility for identifying both Wolbachia and mosquito species. However, since molecular testing is currently heavily reliant on PCR [26–28], opportunities for field-based determinations are limited, leading to significant delays and gaps in actionable surveillance. To support widespread vector surveillance inexpensive, portable, nucleic acid diagnostic platforms are needed that rapidly produce accurate results without requiring complex procedures, instruments, and laboratory infrastructure. In this regard, isothermal nucleic acid amplification assays such as loop-mediated isothermal amplification (LAMP) have begun to be employed because they do not require complex thermocycling instruments [29–31]. However, although LAMP can rival PCR for sensitivity it often produces spurious amplicons, which in turn lead to false positive readouts with non-specific reporters such as Mg2+ precipitation or fluorescent dye intercalation [32–34].
To mitigate the spurious signals that arise with LAMP, we have previously applied principles that were developed for nucleic acid strand exchange circuits [35–38] to the design of short hemiduplex oligonucleotide strand displacement (OSD) probes for LAMP [39]. The single stranded ‘toehold’ regions of OSD probes bind to LAMP amplicon loop sequences, and then signal via strand exchange [40] that leads to separation of a fluorophore and quencher [39]. OSDs are the functional equivalents of TaqMan probes and can specifically report single or multiplex LAMP amplicons without interference from non-specific nucleic acids or inhibitors [39, 41]. OSDs significantly enhance the diagnostic applicability of LAMP, allowing it to match the allelic specificity of real-time PCR. Recently, we engineered these molecular innovations to function fluently in one-pot LAMP-OSD reactions that can directly amplify a few tens to hundreds of copies of DNA and RNA analytes from minimally processed specimens and produce sequence-specific fluorescence signals that are easily observable by the human eye or (more importantly) by unmodified cellphone cameras [42]. The fluorescence endpoints that are produced can be used for yes/no determinations of the presence of an analyte, and also estimation of analyte copies on an order of magnitude scale [42, 43].
Here, we have adapted our smartphone-read one-pot LAMP-OSD system to directly amplify target nucleic acids from crudely macerated mosquitoes and to sequence-specifically report both mosquito and symbiont amplicons as visually readable fluorescence. In particular, we have developed two LAMP-OSD assays–one targeting the Ae. aegypti cytochrome oxidase I gene (coi), and the other the Wolbachia wAlbB surface protein (wsp) gene. Using a blinded set of field-caught mosquitoes, we demonstrate the exquisite sensitivity and specificity of our LAMP-OSD platform for identifying mosquito species and detecting Wolbachia infections.
All chemicals were of analytical grade and were purchased from Sigma-Aldrich (St. Louis, MO, U.S.A.) unless otherwise indicated. All enzymes and related buffers were purchased from New England Biolabs (NEB, Ipswich, MA) unless otherwise indicated. All oligonucleotides and gene blocks (summarized in S1 Table) were obtained from Integrated DNA Technologies (IDT, Coralville, IA, U.S.A.).
Ae. aegypti coi LAMP-OSD target sequence was amplified by PCR from mosquito genomic DNA. LAMP-OSD target region of the Wolbachia wAlbB wsp gene was purchased as a gBlock fragment. Both amplification targets were cloned into the pCR2.1-TOPO vector (Fisher Scientific, Hampton, NH) by Gibson assembly according to manufacturer’s (NEB) instructions [44]. Cloned plasmids were selected and maintained in an E. coli Top10 strain. Plasmid minipreps were prepared from these strains using the Qiagen miniprep kit (Qiagen, Valencia, CA, USA). All target inserts were verified by sequencing at the Institute of Cellular and Molecular Biology Core DNA Sequencing Facility.
Wolbachia wAlbB and wPip strain wsp genes and Ae. aegypti coi gene sequences were obtained from NCBI GenBank. Consensus signature sequences were derived following MUSCLE (MUltiple Sequence Comparison by Log-Expectation) alignment analysis of each gene set. Target specificity of these signature sequences was evaluated by comparing them to respective wsp or coi gene sets from phylogenetically-related strains and species such as Wolbachia wMel and Ae. albopictus, respectively. Both MUSCLE alignment as well as NCBI BLAST [45, 46] analysis were used for this in silico specificity analysis.
The Primer Explorer v5 primer design software (Eiken Chemical Co., Japan) was used for generating several potential LAMP primer sets composed of the outer primers F3 and B3 and the inner primers FIP and BIP. Primer design was constrained to include at least a 40 bp gap between the F1 and F2 or between the B1 and B2 priming sites. Primer specificity for targeted sequence and a corresponding lack of significant cross-reactivity to other nucleic acids of human, vector or pathogenic origin were further assessed using NCBI BLAST.
These primer sets were functionally tested in LAMP assays using zero to several hundred copies of purified plasmids as templates. Amplification kinetics were measured in real time using the fluorogenic intercalating dye Evagreen and the LightCycler 96 real-time PCR machine (Roche). The fastest primer sets that detected the fewest template copies with negligible spurious reactivity in the absence of templates were selected for further assay development.
Fluorogenic OSD probes were then designed to undergo toehold-mediated strand exchange with these Ae. aegypti coi and the Wolbachia wsp LAMP amplicons. Of the two target derived loop regions (between the F1c and F2, and the B1c and B2 primer binding sites) the regions between F1c and F2 were chosen as wsp and coi OSD binding regions (S1 Fig). Fluorogenic OSD probes were designed using the NUPACK software and our previously described engineering principles [39]. Briefly the hemiduplex OSDs were designed to display 11–12 nucleotide long single-stranded toeholds on the longer, fluorophore-labeled strands. All free 3’-OH ends were blocked with inverted dT to prevent extension by DNA polymerase.
Single loop primers were designed to bind the second loop region (between B1c and B2 primer binding sites) of the wsp and coi LAMP amplicons and accelerate LAMP amplification.
LAMP assays were assembled in a total volume of 25 μl of 1X Isothermal buffer (NEB; 20 mM Tris-HCl, 10 mM (NH4)2SO4, 50 mM KCl, 2 mM MgSO4, 0.1% Tween 20, pH 8.8 at 25°C). The buffer was supplemented with 0.4 mM dNTPs, 0.8 M betaine, 2 mM additional MgCl2, 1.6 μM each of FIP and BIP, 0.8 μM of loop primer, 0.4 μM each of F3 and B3 primers, and 16 units of Bst 2.0 DNA polymerase. Plasmid DNA templates were serially diluted in TE buffer (10 mM Tris-HCl, pH 7.5:0.1 mM EDTA, pH 8.0) immediately prior to use. Zero to several hundred copies of synthetic plasmid or gBlock templates were added to the LAMP reaction mixes followed by 90 min of incubation at 65°C.
1X EvaGreen (Biotium, Hayward, CA, USA) was included in LAMP assays that were then analyzed using the LightCycler 96 real-time PCR machine (Roche, Basel, Switzerland). Reactions were subjected to 45 cycles of two-step incubations–step 1:150 sec at 65°C, step 2: 30 sec at 65°C. EvaGreen signal was measured in the FAM channel during step 2 of each cycle. Subsequently, amplicons were subjected to a melt analysis by incubation at 65°C for 1 min followed by incremental rise in temperature to 97°C. Amplicon melting was monitored by measuring fluorescence at the rate of 10 readings per°C change in temperature. The resulting data was analyzed using the LightCycler 96 analysis software to measure Cq values for amplification and amplicon melting temperatures.
LAMP reactions monitored in real time using OSD probes were assembled and analyzed as above with the following changes. First, OSD probes were prepared by annealing 1 μM of the fluorophore-labeled OSD strand with 5 μM of the quencher-labeled strand in 1X Isothermal buffer. Annealing was performed by denaturing the oligonucleotide mix at 95°C for 1 min followed by slow cooling at the rate of 0.1°C/s to 25°C. Excess annealed probe was stored at -20°C. Annealed OSD probes were added to the LAMP reactions at a final concentration of 100 nM of the fluorophore-bearing strand.
LAMP-OSD assays intended for visual readout and smartphone imaging were assembled in 0.2 ml optically clear thin-walled tubes with low auto-fluorescence (Axygen, Union City, CA, USA). Following 90 min of amplification at 65°C, LAMP-OSD reactions were incubated at 95°C for 1 min followed by immediate transfer to room temperature and fluorescence imaging. Images were acquired using an unmodified iPhone 6 and an UltraSlim-LED transilluminator (Syngene, Frederick, MD, USA). In some experiments, our previously described in-house 3D-printed imaging device [42] was used for LAMP-OSD fluorescence visualization and smartphone imaging. Briefly, this device uses Super Bright Blue 5 mm light emitting diodes (LED) (Adafruit, New York, NY, USA) to excite OSD fluorescence. Two cut-to-fit layers of inexpensive >500 nm bandpass orange lighting gel sheets (Lee Filters, Burbank, CA, USA) on the observation window filter the OSD fluorescence for observation and imaging.
Ae. aegypti, Ae. albopictus, Culex tarsalis, Cx. quinquefasciatus (Houston), and Cx. quinquefasciatus (Salvador) mosquitoes were reared under conventional conditions in the insectary at the University of Texas medical Branch, Galveston, TX, USA. Four to seven day old mosquitoes were collected and immediately frozen for shipment, storage and subsequent testing. To obtain blood fed insects, Aedes mosquitoes were starved for a period of 24 hours then offered a sheep blood meal (Colorado Serum Company, Denver, CO, USA) using a hemotek membrane system (Hemotek). Unfed mosquitoes were separated and mosquitoes that were engorged were collected 24 hours post feeding and processed in the same manner as unfed mosquitoes. For field collections, female mosquitoes were trapped using Fay prince trap (John W. Hock) baited with CO2 in Galveston, Texas. 90 mosquitoes were morphologically identified and sorted into three blinded groups that were stored at -20°C, 4°C and 37°C, respectively for up to 3 weeks prior to LAMP-OSD analysis.
For LAMP-OSD analysis individual mosquitoes were prepared either in 1 cc syringes or in 1.5 ml microcentrifuge tubes as follows. In-syringe preparation: The plunger was removed from a 1 cc syringe and a 0.5 μM pore size 1/8th inch diameter frit (catalog # 59037, Sigma-Aldrich, St. Louis, MO, USA) was placed inside the syringe. A single mosquito was placed on top of the frit and macerated thoroughly using the syringe plunger. 100 μl of water was aspirated into the syringe to fully re-suspend the macerated mosquito prior to evicting this mosquito-containing water from the syringe into a microcentrifuge collection tube. A 2 μl aliquot of this sample was directly tested by LAMP-OSD assays. In-tube preparation: A single mosquito was placed in a 1.5 ml microcentrifuge tube and manually macerated using a disposable micropestle (Fisherbrand RNase-Free Disposable Pellet Pestles, Cat # 12-141-364, Fisher Scientific, Hampton, NH, USA). Each macerated mosquito was resuspended in 100 μl water. A 2 μl 1:10 diluted aliquot of this mosquito sample was directly assessed by LAMP-OSD analysis.
For LAMP-OSD analysis of pools of mosquitoes 25, 50, or 100 mosquitoes were placed in 1.7 ml microcentrifuge tubes and processed by the ‘in-tube’ method. Briefly, the mosquito pools were macerated manually using a disposable micropestle. The macerated pools of 25, 50, or 100 mosquitoes were re-suspended in 250 μl, 500 μl, or 1000 μl of water, respectively. 2 μl aliquots of 1:10 and 1:100 dilutions in water of these mosquito pool macerates were then assayed by LAMP-OSD assays. Mosquito pools tested included Pool A (99 un-infected Ae. aegypti); Pool B (99 un-infected Ae. aegypti and one Wolbachia-infected Ae. albopictus); Pool C (49 un-infected Ae. aegypti and one Wolbachia-infected Ae. albopictus); and Pool D (24 un-infected Ae. aegypti and one Wolbachia-infected Ae. albopictus).
The paired results of morphological identification and LAMP-OSD analysis were compared using 2x2 contingency tables. Sensitivity or true positive rate was calculated by using the formula TP/(TP+FN) where TP are true positive samples, and FN are false negative samples. Specificity or true negative rate was calculated using the formula TN/(TN+FP) where TN are true negative samples and FP are false positive samples.
LAMP uses two inner (FIP and BIP) and two outer (F3 and B3) primers specific to six consecutive target sequences (B3, B2, B1, F1c, F2c and F3c) (S1 Fig) [47]. Bst DNA polymerase extends these primers by strand displacement DNA synthesis to form 109 to 1010 copies of concatemerized amplicons with loops between the F1 and F2, B1 and B2, F1c and F2c, and B1c and B2c regions. We use an additional fifth primer that binds to one of these loop regions and accelerates amplification [41]. OSD probes, with blocked 3’-ends that prevents spurious signaling from polymerase-mediated extension, hybridize to the second loop region (S1 Fig).
To enable molecular identification of Ae. aegypti mosquitoes, we designed a smartphone-imaged LAMP-OSD assay to amplify and detect a signature sequence in the mitochondrial cytochrome c oxidase I (coi) gene. Each cell has multiple mitochondria and hence several hundred copies of the coi gene, which should enable detection from a very small amount of sample. Moreover, mitochondrial coi gene sequences are commonly used as barcodes for molecular identification of animal species including distinction of mosquito species; our chosen coi signature sequence was assigned to Ae. aegypti when queried against the Barcode of Life Data Systems (BOLD; http://www.boldsystems.org/index.php) coi signature sequence database [48–51].
We developed a second visually read LAMP-OSD assay targeting the Wolbachia surface protein (wsp) gene to identify Wolbachia-infected insects. The wsp gene is widely used as a marker for strain typing and screening for infected insect vectors [28, 52]. We engineered our wsp LAMP outer and inner primers to be complementary to, and hence amplify, two Wolbachia strains, the wAlbB and the closely related wPip (S2 Fig). We deliberately designed our assay to detect both strains in order to ensure that we could assess field-collected mosquitoes irrespective of temporal and spatial variation in relative abundance of wAlbB-infected Ae. albopictus and wPip-infected Cx. quinquefasciatus mosquitoes in our collection area [53, 54]. Significant nucleic acid sequence variation should prevent amplification of the wsp gene from all other Wolbachia groups (S2 Fig). To enable transduction of both wAlbB and wPip wsp LAMP amplicons to visible fluorescence we designed an OSD probe that is specific to an identical loop sequence present in both amplicons.
With a single endpoint visual ‘yes/no’ readout of OSD fluorescence (either directly observed or imaged using cellphone camera), the Ae. aegypti coi LAMP-OSD assay could reliably identify the presence of as few as 4 copies of synthetic target DNA (Fig 1). Similarly, the cellphone-imaged wsp LAMP-OSD assay produced bright visible fluorescence when presented with only 40 copies of its target wsp sequences while remaining dark in the presence of synthetic wAlbA, wAus, wMors, and wAna wsp templates (Fig 1 and S2 Fig). In the absence of target DNA, neither assay generated spurious signal.
Our next goal was to demonstrate the ability of these LAMP-OSD assays to detect naturally occurring target sequences in mosquitoes. At the same time, we wanted to ensure that minimally processed samples would be compatible with our detection platform in order to facilitate rapid in-field vector testing with fewest instruments and user-required steps.
Therefore, as an initial approach, we developed the ‘in-syringe’ method for rapid sample preparation wherein individual mosquitoes were crushed inside 1 cc syringes using the syringe plunger as a pestle. A small chromatography column frit placed inside the syringe served as a pedestal that aided maceration and removed larger particulates when the macerated sample was re-suspended in water and recovered. Small portions (up to 8% of a LAMP-OSD reaction) of these macerated samples were added directly to LAMP-OSD reactions, which were then incubated for 90 min at 65°C to initiate and sustain amplification.
Endpoint visual examination of these assays for the presence or absence of OSD fluorescence revealed that our visually read LAMP-OSD system is compatible with direct analysis of crudely processed mosquitoes (Fig 2). The coi LAMP-OSD assay generated bright fluorescence readily distinguishable from sample auto-fluorescence when seeded with crudely prepared Ae. aegypti mosquitoes. In contrast, closely related Ae. albopictus failed to instigate false positive signal. Similarly, the wsp LAMP-OSD assay generated bright fluorescence in response to Ae. albopictus and Cx. quinquefasciatus mosquitoes, which are naturally infected with wAlbB and wPip Wolbachia, respectively, but remained negative in the presence of unrelated Wolbachia wMel and uninfected mosquitoes (Fig 2 and S3 Fig).
These results indicate that the Wolbachia wsp and Ae. aegypti coi LAMP-OSD assays are able to specifically amplify and signal the presence of their target DNA directly from crudely crushed mosquito samples without requiring any extraction and purification of nucleic acids. Furthermore, the large burden of non-specific nucleic acids as well as other molecular and macroscopic components present in a crude mosquito sample did not compromise signal accuracy. We also confirmed the absence of significant inhibition of amplification and signaling by recapitulating the detection limit of synthetic DNA targets in a background of crude non-specific mosquito sample. The coi and wsp LAMP-OSD assays could detect 4 and 40 target copies, respectively, even in the presence of 8% reaction volume of crude mosquito sample (S4 Fig).
Mosquitoes feeding on blood meals have been reported to engorge on 1 nL to as much as 6 μL of blood [55]. It is conceivable that the blood meal might confound visual LAMP-OSD fluorescence analysis by contributing auto-fluorescence. To ascertain compatibility of visually read LAMP-OSD with direct analysis of crudely prepared blood-engorged mosquitoes, we challenged both coi and wsp LAMP-OSD assays with crude in-syringe preparations of blood engorged Ae. aegypti and Ae. albopictus mosquitoes. Mosquitoes that had recently consumed a blood meal could be directly analyzed by visual LAMP-OSD without diminution of signal to noise ratio (Fig 3).
To validate assay performance under more rigorous conditions, we challenged the LAMP-OSD system with a blinded set of 90 field-caught mosquitoes comprised of Ae. aegypti, Ae. albopictus, and Ochlerotatus species. The mosquitoes were divided into three groups of 30 individuals that were stored without desiccant at -20°C, 4°C, or 37°C for 1, 2, or 3 weeks prior to testing. To reduce mosquito processing cost, footprint, and time for this large study, we further simplified sample preparation requirements by optimizing the “in-tube” mosquito preparation method wherein each mosquito was crushed with a micropestle directly in a microcentrifuge tube followed by resuspension in water and introduction in a LAMP-OSD reaction.
The visually read coi LAMP-OSD assay demonstrated an overall sensitivity (true positive rate) of 97% and specificity (true negative rate) of 98% when compared to morphological typing of field-caught mosquito species (Figs 4, S5, S6 and S7). On closer inspection of the data, it is evident that even after three weeks of mosquito collection and storage at temperatures as high as 37°C the coi LAMP-OSD assay was correctly able to identify 29 out of 30 Ae. aegypti mosquitoes. The single mosquito that the LAMP-OSD assay failed to identify had been stored at 37°C for a week prior to testing. We ruled out lack of amplifiable nucleic acids or their incompatibility with coi LAMP primers and OSD probe by PCR amplifying the relevant coi LAMP target and verifying its sequence.
Of the 60 non-Ae. aegypti mosquitoes analyzed by coi LAMP-OSD, only one mosquito generated a false positive signal. Sequence analysis of its coi gene ruled out mis-firing of the coi LAMP-OSD assay. It is possible that this LAMP assay was inadvertently contaminated with a small amount of a pre-formed Ae. aegypti amplicon.
The Wolbachia wAlbB/wPip wsp LAMP-OSD assay did not generate a positive signal from any non-Ae. albopictus mosquito. This is expected since natural populations of Ae. aegypti and most Ochlerotatus species are not infected with Wolbachia [56, 57]. However, ability of the wsp assay to identify Wolbachia infection was influenced by the storage temperature of mosquitoes. The wsp LAMP assay could readily identify Wolbachia infection in 3 out of 4 Ae. albopictus mosquitoes stored at -20°C for as long as 3 weeks. PCR analysis of the wsp-negative mosquito using previously described primers (81F and 691R) and protocols [28] did not produce amplicons suggesting that this individual was likely uninfected or had Wolbachia levels below the levels detectable by PCR. As the storage temperature was increased the frequency of Wolbachia detection dropped. While 40% of Ae. albopictus mosquitoes stored at 4°C gave a positive wsp LAMP-OSD signal, none of the Ae. albopictus mosquitoes kept at 37°C for even as little as 1 week were wsp positive. All mosquitoes that failed to generate a signal by LAMP also failed to produce wsp PCR amplicons. Since, 95–99% of Ae. albopictus mosquitoes in the wild are typically found to be infected with Wolbachia [58], these results are suggestive of nucleic acid deterioration in mosquitoes upon storage at high temperature.
For time- and cost-efficient mosquito surveillance high-throughput analysis of pooled mosquitoes rather than individual insects is often necessary [59, 60]. To determine the utility of our assay platform for analyzing mosquito pools we created four sample pools comprising Wolbachia-infected Ae. albopictus and un-infected Ae. aegypti mosquitoes–Pool A: 99 Ae. aegypti, Pool B: 1 Ae. albopictus and 99 Ae. aegypti, Pool C: 1 Ae. albopictus and 49 Ae. aegypti, and Pool D: 1 Ae. albopictus and 24 Ae. aegypti. Entire pools were subjected to ‘in-tube’ crude sample preparation followed directly by LAMP-OSD analysis for Wolbachia wsp. Our data demonstrate that LAMP-OSD could readily detect the presence of a single Wolbachia-infected mosquito in pools of 25, 50, and 100 mosquitoes (Fig 5). No false signals were produced by pools of 99 Wolbachia-free Ae. aegypti mosquitoes. These results suggest that our rapid sample preparation method and smartphone-read LAMP-OSD assays can be used for accurate analysis of both individual and pooled mosquitoes.
Mosquito control strategies that rely on the introduction of Wolbachia are now being deployed around the world [7–9], and the surveillance of efficacy and spread require agile, field-based methods for both mosquito and symbiont detection. Unfortunately, currently available tools for mosquito diagnostics have several shortcomings. Morphological identification methods are inherently low throughput, require extensive technical expertise, and cannot also readily identify pathogens or biocontrol agents such as Wolbachia. Spectroscopy, such as near infrared spectroscopy [61] and Fourier transform infrared spectroscopy [62], allow identification of mosquito species, Wolbachia, and pathogens, but require expensive instruments and expertise that are generally incompatible with low-resource settings. Immunoassays can detect pathogen-carrying vectors but are insensitive and cannot also identify vector species. Nucleic acid amplification methods could potentially look at both vector and symbiont sequences, but are heavily reliant on PCR with the ensuing encumbrances of expensive instruments and trained operators, again precluding widespread use.
Isothermal nucleic acid amplification assays would facilitate field-based vector monitoring, but most reported approaches rely on nucleic acid purification and non-specific readout, and thus suffer from laborious setup and the risk of false positives [63–65]. Probe-read isothermal methods such as the recombinase polymerase assay (RPA) are more reliable but still require expensive and proprietary reaction formulations and probes, which limits their flexibility and versatility in assay engineering. Furthermore, most RPA applications for vector diagnostics [66] also depend on extensive sample processing and nucleic acid purification prior to amplification.
These drawbacks led us to develop a simpler, more robust field-deployable assay based on loop-mediated isothermal amplification (LAMP) that can identify both mosquito species and specific Wolbachia strains by direct analysis of crudely macerated individual or pooled mosquitoes.
While LAMP assays have previously been developed for Wolbachia detection by targeting the 16S rRNA gene for amplification [63], these assays required extraction and purification of DNA prior to assay, and used non-specific readouts that were highly prone to false positive signals. To overcome these barriers to the use of LAMP, we have previously adopted methods that originated in the field of nucleic acid computation: the use of strand exchange reactions that initiate at complementary single-stranded ‘toeholds’ and progresses via branch migration. The base-pairing predictability and programmability of strand exchange kinetics promotes the construction of exquisitely sequence-specific oligonucleotide strand displacement (OSD) probes for LAMP amplicons (S1 Fig), thereby greatly reducing the detection of non-specific amplification background [39, 41, 42]. For instance, we recorded positive coi LAMP-OSD signals from field-caught Ae. aegypti mosquitos but did not detect signal from closely related Ae. albopictus and Ochlerotatus species mosquitoes. Similarly, the wsp assay detected wAlbB and wPip, as expected, but not wMel, wMors, wAna, wAus, or wAlbA Wolbachia.
Strand exchange circuits have the additional advantage that they can be used to embed algorithms and act as ‘matter computers’ [35–37, 67]. For example, strand exchange transducers can logically integrate multiple analytes; transform nucleic acids to glucose and human chorionic gonadotrophin (hCG); adapt readout to beads, paperfluidics, glucose meters, pregnancy test strips, and cellphones, and allow target copy number estimation using a single endpoint yes/no readout of presence or absence of signal above an adjustable threshold [68–71]. In the current instance, we deliberately designed our wsp LAMP-OSD assay to ‘compute’ the presence of both wAlbB and wPip in order to increase our odds of finding infected field-caught mosquitoes. However, the dependence of strand exchange efficiency on toehold binding strength [39] can be exploited to engineer yes/no distinctions between strain-specific single nucleotide polymorphisms [39], and the same dual wsp assay could be rendered strain-specific by simply substituting an OSD reporter specific to an alternate polymorphic loop sequence (S2 Fig). This might be advantageous for strain discrimination if double infections were released during vector control measures [72, 73].
By using our one-pot LAMP-OSD assay, macerated mosquito homogenates could be directly analyzed and ‘yes/no’ visual readouts could be quickly ascertained with a cell phone in the field without the requirement for laboratory equipment or technically training. Moreover, since our assays can accurately analyze mosquitoes several days after capture–the coi LAMP-OSD assay could for example identify mosquitoes after 3 weeks at 37°C without desiccant–mosquitoes from remote collection outposts can potentially be analyzed even after delayed retrieval. We are currently automating the assays and workflow on low-cost modular paper and plastic devices that will not only further streamline diagnostic application, especially for high-throughput analysis, but will also provide biohazard and aerosol containment by restricting mosquito maceration and molecular assay in sealed chambers. The flexibility of assay timing is further accommodated by the fact that lyophilized LAMP-OSD reaction mixes can be stored and deployed without cold chain [42].
Our sample preparation and assay workflow not only simplify application of molecular diagnostics for surveillance but should also reduce operational costs by eliminating the need for nucleic acid extraction and complex instruments for assay incubation and readout. Market cost of LAMP-OSD assay reagents is ~$1.5/reaction. We are in the process of significantly reducing this cost by substituting commercially sourced purified Bst 2.0 DNA polymerase with our recently developed ‘cellular reagents’ as a cheaper alternative [74]. Open system TaqMan qPCR assays have been reported to also cost $1.51/reaction [75]. Our re-usable in-house 3D-printed device [42] for fluorescence visualization costs <$5 in parts to build and enables ‘instrument-free’ readout of visual signal at no additional cost. Fluorescence signal may also be captured for posterity using unmodified standalone camera or any camera cellphone including low cost (<$200) models [42]. In contrast, lab-based qPCR testing is estimated to require ~$30,000 in startup investment and ~$700 in annual maintenance [75].
These combined features make our assay platform the best tool to date for expanding vector surveillance to resource poor settings [76], especially in that the ease of use should allow minimally trained citizen scientists to participate in otherwise sophisticated public health monitoring operations in the field.
A few caveats must be considered for any nucleic acid based test including qPCR and LAMP-OSD. First, without prior knowledge of integration sites, nucleic acid amplification tests will be unable to differentiate wsp target sequences derived from infectious Wolbachia bacterial genomes versus Wolbachia DNA integrated in nuclear genomes of hosts such as Drosophila ananassae [77]. However, other PCR-based assays also face this challenge. Second, nucleic acid amplification may not be significantly diminished by the presence of one or two mismatches between a primer and its target binding site [41, 78]. While, ability to generate a positive signal from very closely related target variants that differ by only one or two nucleotides is desirable, it is important for a nucleic acid test to distinguish polymorphic strains. Unlike two-primer systems such as qPCR, use of at least six primer binding sites during LAMP allows LAMP primers to scan a three times larger target sequence space for distinguishable polymorphisms. As a result, LAMP assays are more likely to distinguish unexpected polymorphic strains due to the likelihood of encountering mismatches in multiple primers that would in turn lead to diminution of amplification and false positive signals.
The development efforts that we have put into LAMP-OSD should now allow it to be generalized to screening other microbes or mosquito phenotypes in field settings. For example, LAMP-based assays have been developed to identify pathogens transmitted by mosquitoes and insecticide resistance alleles [79–82], but these rely on purified nucleic acid as templates and non-specific readout whereas our method functions with mosquito homogenate and ensures accuracy using unique sequence-specific strand exchange probes. In addition, this technology could be used to identify gut microbes and insect-specific viruses associated with mosquitoes, which is of growing interest given that it is becoming clear that the microbiome can shape vector competence for human pathogens [83–85]. Overall, we have demonstrated a versatile nucleic acid diagnostic platform for rapid and accurate analyses of both insect vectors and symbionts, and that can now be further configured for additional applications.
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10.1371/journal.pgen.1000363 | hnRNP I Inhibits Notch Signaling and Regulates Intestinal Epithelial Homeostasis in the Zebrafish | Regulated intestinal stem cell proliferation and differentiation are required for normal intestinal homeostasis and repair after injury. The Notch signaling pathway plays fundamental roles in the intestinal epithelium. Despite the fact that Notch signaling maintains intestinal stem cells in a proliferative state and promotes absorptive cell differentiation in most species, it remains largely unclear how Notch signaling itself is precisely controlled during intestinal homeostasis. We characterized the intestinal phenotypes of brom bones, a zebrafish mutant carrying a nonsense mutation in hnRNP I. We found that the brom bones mutant displays a number of intestinal defects, including compromised secretory goblet cell differentiation, hyperproliferation, and enhanced apoptosis. These phenotypes are accompanied by a markedly elevated Notch signaling activity in the intestinal epithelium. When overexpressed, hnRNP I destabilizes the Notch intracellular domain (NICD) and inhibits Notch signaling. This activity of hnRNP I is conserved from zebrafish to human. In addition, our biochemistry experiments demonstrate that the effect of hnRNP I on NICD turnover requires the C-terminal portion of the RAM domain of NICD. Our results demonstrate that hnRNP I is an evolutionarily conserved Notch inhibitor and plays an essential role in intestinal homeostasis.
| Many gastrointestinal diseases are characterized by unbalanced proliferation and differentiation of intestinal epithelial cells. Accumulating evidence implicates the Notch pathway as a fundamental regulator of intestinal epithelial proliferation and differentiation. Deregulation of Notch causes intestinal defects, such as abnormal intestinal cell lineage development and uncontrolled intestinal cell growth. Thus, a more comprehensive understanding of mechanisms by which the Notch pathway is regulated in intestinal epithelial cells will provide fundamental insights into human intestinal diseases. We report here that mutation in hnRNP I elevates Notch signaling in the adult zebrafish intestine and causes abnormal intestinal epithelial cell lineage development and uncontrolled intestinal cell growth. We provide evidence that overexpression of hnRNP I promotes the degradation of Notch intracellular domain (NICD) and inhibits Notch signaling. Our results provide the first evidence that hnRNP I plays critical roles in intestinal homeostasis.
| The intestinal epithelium undergoes rapid cell turnover. Renewal of the intestinal epithelium relies on intestinal stem cells in the crypts of Lieberkuhn that are distributed circumferentially around the base of finger-like intestinal villi. New intestinal epithelial cells are continuously produced by stem cells in the crypt and migrate along the crypt-villi axis. During migration, intestinal epithelial cells exit mitotic cell cycle and differentiate. This replaces the cell loss at the tips of villi. Intestinal villi are composed of two differentiated post-mitotic cell lineages: absorptive cells (or enterocytes) and secretory cells, including goblet cells, enteroendocrine cells, and Paneth cells in mammals [1]. Deregulation of intestinal cell proliferation and differentiation impairs the renewal of the intestinal epithelium and causes digestive diseases.
Several signaling pathways are involved in the renewal of the intestinal epithelium [2],[3]. Among these is the Notch pathway, a highly conserved signaling pathway that also regulates many other stem cell lineages during embryonic development and adult tissue homeostasis [3],[4]. Notch signaling is triggered by the interaction between Notch and its ligands Delta/Jagged. Upon ligand binding, Notch undergoes sequential proteolytic cleavages, leading to the release of the Notch intracellular domain (NICD). Subsequently, NICD translocates into the nucleus, where it binds to the transcription factor, CSL (also known as RBP-J in mice, CBF-1 in human, Suppressor of Hairless (Su(H)) in Drosophila, LAG-1 in C. elegans). This converts CSL from a transcriptional repressor into a transcriptional activator and activates the transcription of Notch target genes [5],[6].
The Notch pathway is active in intestinal stem cells, as judged by the restricted expression of the Notch pathway components and Notch target genes in the crypts [7]–[9]. When Notch signaling is overactivated, it expands the intestinal stem cell population and compromises secretory cell differentiation, without affecting absorptive cell differentiation [10],[11]. Conversely, inhibition of the Notch pathway results in an overproduction of secretory cells at the expense of both stem cells in the crypts and absorptive cells [9],[12]. Consistently, ablation of Notch target genes impairs intestinal epithelial homeostasis [7],[13],[14]. It is widely believed that Notch signaling maintains intestinal stem cells in a proliferative state and promotes the absorptive cell fate determination in vertebrate intestine. However, it remains largely unclear how Notch signaling is precisely regulated in the intestinal epithelium.
Heterogeneous nuclear ribonucleoprotein (hnRNP) family RNA binding proteins have been implicated in various aspects of RNA metabolism in a range of biological processes [15]. Among these is hnRNP I (also known as polypyrimidine tract-binding protein, PTB), which regulates tissue specific mRNA alternative splicing [16], mRNA stability [17], localization[18], and translation [19]. Interfering with hnRNP I impairs Xenopus skin development [20], Drosophila spermatogenesis [21], and Drosophila wing development [22]. Like many hnRNP family members, hnRNP I is expressed in the intestine [23]. Yet the function of hnRNP I in the intestine has not been reported. Here we provide the evidence that hnRNP I is an evolutionarily conserved Notch inhibitor and plays a critical role in the intestinal epithelial cell lineage development.
hnRNP I RNA binding protein is composed of four RNA-recognition motifs (RRMs), a nuclear localization signal, and a nuclear export signal [24],[25]. All four RRMs are involved in RNA binding [26]–[31]. We have identified a zebrafish mutant brom bones [32], which carries a nonsense mutation in hnRNP I gene. The mutation occurs in the middle of the second RRM (Wenyan Mei and Mary C. Mullins, unpublished data). The truncated protein lacks 60% of amino acid residues and only contains the nuclear localization/export signals, the first RRM, and the N-terminal portion of the second RRM. As expected, the RNA binding activity of the mutant protein is severely reduced (data not shown).
Homozygous brom bones mutants (hereafter referred to as brom bones) are viable. However, a fraction (9 out of 27) of aged brom bones fish (>9 months) showed bigger abdomens (Figure 1A, arrow) when compared to their age-matched heterozygous and wild-type sibling fish (0 out of 89). We dissected the intestine from a brom bones homozygous fish with the big abdomen and examined its anatomy. As shown in Figure 1B, the intestine from a wild-type adult fish has a tube-like shape and can be divided into anterior, mid and posterior segments based on the height of the intestinal fold and the distribution of differentiated intestinal epithelial cell types [33]. Sparse fecal material can be found occasionally in all segments of the intestinal tubes in wild-type fish (not shown). In striking contrast, the intestine of the brom bones mutant with the “big abdomen” phenotype is full of fecal material (Figure 1C). The food wastes can be found in all three intestinal segments, with the exception of the very anterior portion of the intestinal bulb (Figure 1C, double arrows). It appears that brom bones mutants have difficulties in compacting food wastes into feces and expelling it through the anus. These affected brom bones fish usually die shortly after the appearance of the big abdomen phenotype. Interestingly, we have noticed that the big abdomen phenotype is genetic background-dependent. The phenotype is very severe on the AB and Tubingen background, but less prominent on the WIK background.
To characterize phenotypic defects in the brom bones intestine, we carried out histological analysis. As shown in Figure 1D, the organization of the wild-type adult fish intestine is very similar to that of neonatal mammals. Villi of the intestinal epithelium are organized into ordered periodic protrusions, which vary in width and lack crypts of Lieberkuhn (Figure 1D and 1I and [12],[33],[34]). A number of goblet-like cells, characteristic of large apical mucin filled area, can be easily identified along the villous epithelium (pointed by arrows in Figure 1D). In addition, a muscular layer lies immediately beneath the base of villi. Little if any abnormalities can be detected in the intestinal epithelium of brom bones heterozygous fish (Figure 1E and 1J). brom bones homozygous mutants, however, display abnormal intestinal epithelium. The most severe phenotype was observed in mutants with the big abdomen phenotype. In these fish, both the intestinal epithelium and the underlying smooth muscle layer undergo severe degeneration (Figure 1H). We also analyzed homozygous mutants lacking the big abdomen phenotype. While 17% of mutants (4 out of 23) display normal intestinal epithelium architectures (not shown), 83% of mutants (19 out of 23) show a remarkable decrease in the number of goblet-like cells in both the anterior and the mid segments of their intestines (Figure 1F, 1G, and 1K). In addition to the decrease in the number of goblet-like cells, 17% of mutants (4 out of 23) display a more severe phenotype. The intestinal villi of these fish appear very wide and contain an excessive number of intestinal epithelial cells (Figure 1G). Lamina propria, which separates the epithelium from the underlying smooth muscle layers, is not visible. In contrast to the remarkable decrease in the number of goblet-like cells, the columnar-shaped enterocytes in brom bones mutants are indistinguishable from those in the wild-type intestine. These results suggest that hnRNP I, which is mutated in brom bones, is required for maintaining a normal intestinal architecture in adult zebrafish.
Because the phenotypes in the anterior and the mid segments of the brom bones intestine are very similar, we chose the anterior segment of the intestine for detailed analysis.
The above histological analysis indicates that the number of goblet-like cells is reduced in the brom bones intestine, suggesting that cell fate determination may be altered in the brom bones intestinal epithelium. To determine whether this is the case, we examined the expression of goblet cell and enterocyte markers in the control and brom bones intestines.
The function of intestinal goblet cells is to secrete mucus. To identify goblet cells, we performed Alcian blue-periodic acid Schiff (AB-PAS) histochemical staining, a method specific for detecting mucin [35]. Aged-matched brom bones heterozygous intestines were used as controls (Figure 2A). In the control intestines, 4.4% of intestinal epithelial cells are goblet cells (n = 7 brom bones heterozygous fish) (Figure 2A and 2E). In contrast, only 1.3% of cells in the brom bones intestinal epithelium are goblet cells (n = 9 brom bones homozygous fish) (Figure 2B and 2E). Intestinal alkaline phosphatase (AP) is a specific marker for the brush border of enterocytes [36],[37]. We thus examined the enzymatic activity of AP to identify enterocytes. As pointed by the arrows in Figure 2C and D, the enzymatic activity of AP in the intestinal epithelium of brom bones (100%, n = 20 brom bones homozygous fish) was indistinguishable from that in controls. We conclude that hnRNP I is required for intestinal goblet cell differentiation.
Altered intestinal lineage development is often accompanied by modification in cell proliferation or cell survival. To determine whether cell proliferation and cell survival are altered in the brom bones intestine, we examined the expression of a cell proliferation marker, proliferating cell nuclear antigen (PCNA) [12],34 and a cell apoptosis marker, active caspase 3 [9].
In the control intestine, cells positive for PCNA staining are mainly located in the intervillus pocket (Figure 3A, double arrows), which is functionally equivalent to the crypt of the mammalian intestine [12],[34],[35]. In contrast, PCNA-positive cells are not only located in the intervillus pockets, but also extended distally onto the intestinal villus in brom bones (Figure 3B, arrowheads). The percentage of PCNA-positive cells relative to the total intestinal epithelial cells is markedly increased in the brom bones homozygous intestine (50.5%, n = 8 brom bones homozygous fish) when compared to that in controls (33.0%, n = 6 brom bones heterozygous fish) (Figure 3E). In addition to enhanced cell proliferation, most brom bones intestines (75%, n = 12 brom bones homozygous fish) exhibit a significant increase in the number of caspase 3-positive cells when compared to the control intestines (Figure 3C and 3D). Thus, loss of hnRNP I enhances cell proliferation and apoptosis in the zebrafish intestinal epithelium.
The abnormalities observed in the brom bones intestine, including a decrease in the number of goblet cells, an increase in the levels of cell proliferation and cell apoptosis, resemble the remarkable intestinal phenotypes observed in mice with elevated Notch signaling [10],[11]. Interestingly, Dansereau et al reported that hnRNP I inhibits Notch signaling during Drosophila wing development [22]. Thus, we went to determine whether Notch signaling is elevated in the brom bones intestine.
First, the expression of Hes1, a direct target of Notch signaling, was examined by immunostaining. A small number of Hes1-positive cells were detected in the intervillus pocket in the control intestinal epithelium (Figure 4A, arrowheads). The number of Hes1-positive cells in the brom bones intestine, however, is dramatically increased. These Hes1-positive cells are located not only in the intervillus pocket, but also distally onto the intestinal villus in brom bones mutants (Figure 4B, arrowheads). This phenotype was observed in the majority of brom bones mutants analyzed (83%, n = 12 mutant fish). We also examined the expression of her6 and her9, two zebrafish homologs of mammalian hes1 [38], in the intestine by real-time PCR. Four controls and four brom bones intestines were analyzed. Consistent with the partially penetrant intestinal phenotypes described above, the expression of her6 and her9 is dramatically increased in two mutants, moderately increased in one mutant, and remains relatively normal in one mutant (Figure 4F and 4G). Thus, mutation in hnRNP I results in an increase in Notch target gene expression in the intestinal epithelium.
Next, we examined the level of NICD in the brom bones intestinal epithelium using an antibody specific for the active form of Notch [39]–[42]. In the control intestine, 4.0% of intestinal epithelial cells were positive for NICD staining (n = 7 brom bones heterozygous fish, Figure 4E). These NICD-positive cells were mainly restricted in the intervillus pocket (Figure 4C, arrows). Intriguingly, the percentage of NICD-positive cells was dramatically increased in the brom bones intestine (11.7%, n = 10 brom bones homozygous fish, Figure 4E). These NICD-positive cells were detected not only in the intervillus pocket, but also more distally on the villus (Figure 4D, arrows), resembling the distribution of Hes1-positive cells in the mutant intestine. Indeed, using double staining, we found that NICD staining completely overlaps with Hes1 staining (Figure S1), suggesting that the increased Notch target gene expression in the brom bones intestine was triggered by the excessive NICD. Taken together, we conclude that Notch signaling activity is elevated in the brom bones intestinal epithelium.
brom bones carries a nonsense mutation in hnRNP I gene, leading to a big truncation of the hnRNP I protein. The observation that Notch signaling is enhanced in the brom bones intestine strongly suggests that, like in Drosophila [22], hnRNP I inhibits the Notch signaling in vertebrates. To further test this possibility, we investigated the function of hnRNP I in Xenopus embryos, a vertebrate model widely used for studying Notch signaling.
Ectopic activation of the Notch pathway in neuralized Xenopus animal caps induces the expression of esr1, a Xenopus homolog of mammalian hes1/5 [43]. We took advantage of this assay and performed an epistasis analysis. To initiate signaling from different levels of the Notch pathway, we overexpressed NotchΔE (2 ng), NICD (1 ng), and Su(H)Ank (0.5 ng). NotchΔE lacks the extracellular domain of Notch. It can be converted into NICD and activate Notch target genes in the presence of an active g-secretase [44]. Su(H)Ank is a constitutively active form of Su(H), which functions independent of NICD [43]. As expected, overexpression of NotchΔE, or NICD, or Su(H)Ank induced esr1 expression in neuralized animal caps. Co-expression of zebrafish hnRNP I (1 ng) reduced the expression esr1 induced by NotchΔE and NICD, but not that by Su(H)Ank (Figure 5A). In contrast, co-expression of brb (1 ng), the mutated form of hnRNP I in brom bones, failed to block the expression of esr1 induced by NICD in Xenopus animal caps (Figure 5A). This demonstrates that hnRNP I functions negatively in the Notch pathway upstream of Su(H).
To further understand the mechanism through which hnRNP I inhibits the Notch pathway, we asked whether overexpression of hnRNP I alters the level of NICD. A GFP-tagged NICD (NICD-GFP, 1 ng) was injected into Xenopus embryos alone, or together with zebrafish hnRNP I (zhnRNP I ) (1 ng). As a control, we also co-injected NICD-GFP with brb (1 ng). When embryos reached the tailbud stage, green fluorescence signals, which represent the expression of NICD-GFP protein, were detected in embryos injected with NICD-GFP or NICD-GFP/brb (Figure 5B). Embryos injected with NICD-GFP/hnRNP I either lacked green fluorescence completely (Figure 5B), or only exhibited a very weak level of green fluorescence (not shown). In fact, the level of NICD-GFP was reduced by hnRNP Is from other vertebrates as well, including human (hhnRNP I, 1 ng), mouse (mhnRNP I, 1 ng), and Xenopus (xhnRNP I, 1 ng) (Figure 5B). To confirm this observation, we prepared protein extracts from injected embryos and performed western blot using an anti-GFP antibody. As shown in Figure 5C, overexpression of hnRNP I, but not brb, reduced the level of NICD-GFP. These results, therefore, demonstrate that hnRNP I is an evolutionarily conserved inhibitor of the Notch pathway.
NICD contains several domains, including a RAM domain, six cdc10/Ankyrin repeats, a transcriptional transactivation domain (TAD), and a C-terminal PEST domain [5]. To further understand the mechanism through which hnRNP I inhibits Notch signaling, we went to determine which domain of NICD mediates the inhibitory effect of hnRNP I on NICD. Several deletion constructs, including ΔPEST, ΔC, ΔRAM, and RAM were generated (Figure 6A). When expressed in Xenopus embryos, the expression levels of NICD, ΔPEST, ΔC, and RAM were significantly decreased by the co-expression of hnRNP I (Figure 6A). Notably, all these constructs contain the RAM domain. In contrast, the level of ΔRAM, which lacks the RAM domain, was not sensitive to hnRNP I overexpression (Figure 6A). This indicates that the RAM domain mediates the inhibitory effect of hnRNP I on NICD.
To further map the hnRNP I responsive motif within the RAM domain, we generated Myc-GFP-RAM, myc-GFP-RAMN, and myc-GFP-RAMC, which contain the entire RAM, or the N-terminal, or the C-terminal region of RAM, respectively (Figure 6B). When expressed in Xenopus embryos, the expression levels of myc-GFP-RAM and myc-GFP-RAMC were decreased by hnRNP I, whereas overexpression of hnRNP I had no effect on the level of myc-GFP-RAMN (Figure 6B). Thus, the C-terminal portion of the RAM domain mediates the inhibitory effect of hnRNP I on NICD.
The above results raise the possibility that hnRNP I promotes the turnover of NICD protein. To determine whether this is the case, we took advantage of RAMC, the minimal motif that mediates the inhibitory effect of hnRNP I on NICD. We first expressed and purified a GST-tagged RAMC protein (GST-RAMC) from bacteria. This GST-RAMC protein was injected into Xenopus embryos either alone, or together with hnRNP I RNA (2 ng). Injected embryos were harvested at the tailbud stage and the levels of GST-RAMC were determined by Western blot. As shown in Figure 6C, overexpression of hnRNP I, indeed, decreased the level of GST-RAMC protein in embryos. This demonstrates that hnRNP I promotes the turnover of RAMC and indicates that hnRNP I destabilizes NICD protein.
Recent studies have highlighted the fundamental roles of Notch signaling in many stem cell lineages [4]. In the intestinal epithelium, the Notch cascade is critical for the proliferation of intestinal stem cells and promotes absorptive cell differentiation. Ectopic activation of Notch signaling enhances proliferation of intestinal stem cells [10],[11], and impairs secretory cell lineage development, without preventing absorptive cell differentiation [9]–[12],[45]. Despite the emerging role of Notch signaling in intestinal homeostasis, it is not clear how Notch signaling is precisely controlled during this process.
Here, we report that hnRNP I is an essential inhibitor of Notch signaling and plays critical roles in the intestinal epithelium. We show that brom bones, which is deficient in hnRNP I, displays intestinal defects strikingly similar to phenotypes observed in mice with elevated Notch signaling. Indeed, in the brom bones intestine, the amount of NICD, a hallmark of activated Notch signaling, is dramatically increased. This is accompanied by markedly enhanced Notch target gene expression. NICD/Hes1-positive cells, which are located in the intervillus pocket in wild-type intestine, were detected more distally on villi in the brom bones intestine. This suggests that under physiological conditions, hnRNP I is responsible for turning off Notch signaling when newly derived intestinal epithelial cells migrate along the crypt-villus axis. In agreement with this hypothesis, overexpression of hnRNP I inhibits esr1 expression induced by NotchΔE and NICD in Xenopus animal caps. Furthermore, we show that hnRNP I from human, mice, Xenopus and zebrafish are all capable of promoting NICD turnover. Interestingly, a previous study in Drosophila has indicated that loss of Hephaestus/hnRNP I resulted in stabilization of liberated NICD without affecting the full length Notch in the wing disc [22]. It appears that hnRNP I is an evolutionarily conserved inhibitor of the Notch pathway.
The Notch pathway is essential for embryonic development. Why does mutation in hnRNP I, a negative regulator of Notch signaling, fail to affect embryogenesis in brom bones? Searching the zebrafish genome reveals the existence of two closely related hnRNP I genes. The first one is located on chromosome 2 and is mutated in brom bones. The second one is located on chromosome 11. These two hnRNP I proteins share 84% identity and likely function redundantly. Both hnRNP I genes are expressed during early embryonic development and in all adult tissues analyzed (our unpublished data). Thus, it is reasonable to consider brom bones as a partial loss-of-function mutant. It is likely that in brom bones mutants, the total activity of hnRNP I falls below a critical threshold essential for the intestinal homeostasis but not for the embryogenesis. As a consequence, Notch signaling becomes up-regulated in intestinal epithelial cells and the intestinal homeostasis is disrupted.
Interestingly, a recent report showed that depletion of hnRNP I by morpholino injection induced skin defects during Xenopus embryonic development [20]. Given the important roles of Notch signaling in the skin development [46], it is tempting to speculate that hnRNP I inhibits Notch signaling in this developmental process as well. Nevertheless, further studies are needed to determine if hnRNP I-dependent NICD turnover has a broad impact on embryonic development and adult tissue homeostasis, or whether its function is restricted to only a few lineages.
Mechanistically, how does hnRNP I regulate NICD turnover? It has been reported that interaction between NICD and its co-activators Mastermind and Ski-interacting protein, promotes the phosphorylation of NICD by cyclin-dependent kinase-8 (CDK8). Phosphorylated NICD in turn interacts with the SEL10 E3 ubiquitin ligase through its C-terminal PEST domain, leading to the ubiquitination and proteasome-dependent degradation of NICD [47]–[49]. In addition, the Itch/NEDD4/Su(dx) family of HECT domain E3 ligases can ubiquitinate and target NICD for proteasome-dependent degradation through the RAM-Ankyrin repeat region of the NICD [50]. In Xenopus embryos, NRARP (Notch regulated akyrin repeat protein) can form a complex with NICD/Su(H) and promote NICD degradation as well. In this case, NRARP interacts with the Ankyrin repeats of NICD [51]. Results from our deletion assay demonstrate that RAMC, a small motif located in the C-terminus of the RAM domain, mediates hnRNP I-induced NICD turnover. Since the PEST domain and the Ankyrin repeats are not required for hnRNP I-induced NICD turnover, it is unlikely that hnRNP I promotes NICD turnover through the CDK8/SEL10 pathway or NRARP. Currently, it remains unclear if hnRNP I induces NICD degradation through the Itch/NEDD4/Su(dx) cascade, or whether hnRNP I promotes NICD turnover through a novel mechanism. Nevertheless, results presented here allow us to propose a working model. We hypothesize that through its effect on a yet unknown RNA(s), hnRNP I regulates the interaction between NICD and a RAMC binding factor, and promotes the degradation of NICD. This RAMC binding factor itself could be a direct target of hnRNP I. Alternatively, hnRNP I may regulate the production or activity of this RAMC binding factor through an indirect mechanism. It is of interest to identify this RAMC binding factor and investigate the mechanism through which hnRNP I turns off the Notch pathway.
In summary, we show for the first time that the hnRNP-dependent NICD turnover is an evolutionarily conserved inhibitory mechanism for turning off the Notch pathway. Our work demonstrates a novel function of hnRNP I in intestinal epithelial homeostasis by regulation of cell proliferation and lineage development.
The use of animals in this research was approved by the Research Institute at Nationwide Children's Hospital animal care and use committee (protocol #02904AR for zebrafish and 04104AR for Xenopus). Zebrafish breeding were done as described previously [52],[53]. The brom bones mutant [32] was maintained in the AB and Tubingen backgrounds or AB and Tubingen and WIK background. For genotyping, genomic DNA was isolated from tail fin [54] and amplified with primers 5′-GCTTAACATTAAACAGTCTTTAGATCGA-3′ and 5′-CTTATCATTGTTGTACTTAACATTCAGG-3′. PCR products were further digested with Cla I to detect a restriction fragment length polymorphism generated by the brom bones mutation. All mutant samples shown in this paper were randomly collected from brom bones mutants that lack the big abdomen phenotype, with the exception of Figure 1C and 1H, which were chosen to show the intestinal morphology of a mutant with the big abdomen phenotype.
Xenopus embryos were obtained as described [55]. Microinjection and animal cap assays were performed as described [56]. The dosage of RNA for microinjection is indicated in the text or figure legends.
The intestines were isolated, fixed, paraffin-embedded, and sectioned according to standard protocols. Intestine sections (5–14 µm) were processed for Hematoxylin and Eosin staining or for immunostaining. Immunohistochemistry was performed with R.T.U. vectastain kit (Vector Laboratories) with DAB substrate or AEC substrate. In some experiments, sections were counterstained lightly with Hematoxylin afterwards. Primary antibodies are: mouse anti-PCNA (Sigma, P8825), rabbit anti-active caspase3 (Sigma, C8487), rabbit anti-cleaved Notch1 (Cell signaling, 2421S), and goat anti-Hes1 (Santa Cruz, sc-13842). Secondary antibodies for immunofluorescence are goat anti-rabbit AlexaFluor 488 or 594 and donkey anti-goat AlexaFluor 594 (Invitrogen). For Hes1 and NICD double immunostaining, sections were incubated with anti-Hes1 and anti-NICD antibodies first, then wash with PBS, followed by incubation with the secondary antibody donkey anti-goat AlexaFluor 594. After thoroughly PBS washes, sections were then incubated with the secondary antibody goat anti-rabbit AlexaFluor 488. Goblet cell secreted mucins were identified by sequentially incubating deparaffinized sections in pH 2.5 alcian blue (1 hour), periodic acid (7 minutes) and Schiff's reagent (10 minutes). After the staining, acidic mucins are stained “blue” and neutral mucins are stained red. The enzymatic activity of intestinal AP at enterocytes-brush border was detected with bromochloroindoyl phosphate/nitro blue tetrazolium. Sections were counterstained lightly with Nuclear Fast Red. Images were taken from a dissection or a Compound microscope (Leica) with digital camera or a Zeiss LSM510 confocal microscope and processed using Adobe Photoshop.
NotchΔE, NICD [44],[57], and Su(H)Ank [43] were described. NICD-GFP and Notch deletion constructs were generated by standard cloning strategies. ΔC (V1744-T2128), ΔRAM (C1861-T2128), RAM (V1744-C1861), RAMN (V1744-G1793), and RAMC (N1789-C1861) were PCR amplified from mouse Notch1 and cloned into pCS2-MT or pCS2-MT-EGFP. pCS2-zhnRNP I and pCS2-brb were generated by RT-PCR, using cDNAs derived from wild-type and brom bones fish. A pGEX6P1-RAMC was constructed for expressing the GST-RAMC protein in bacteria. A pCS2-xhnRNP I containing the full length hnRNP I was obtained by screening a Xenopus oocyte cDNA library. Mouse hnRNP I (IMAGE: 30439895) and human hnRNP I (IMAGE: 3863892) were purchased from ATCC.
RNAs for microinjection were synthesized using the mMESSAGE mMACHINE kit (Ambion). Notch deletion constructs were linearized with AseI and transcribed with SP6 RNA polymerase. hnRNP I constructs were linearized with Not I and transcribed with SP6 RNA polymerase.
We followed the standard protocol to express and purify GST-RAMC protein. Briefly, BL21 bacteria containing the pGEX6P1-RAMC were induced by IPTG for 4 hours. Lysate was prepared and incubated with glutathione-agarose beads. Beads were washed 4 times with the lysis buffer (50 mM Tris, pH 8.0, 125 mM NaCl, 1 mM EDTA, 1% Triton X-100, protease inhibitor cocktail (Sigma), twice with 50 mM Tris, pH 8.0, and then eluted with glutathione containing Tris buffer. Purified GST-RAMC protein was stored at −20°C.
RNA extraction and RT-PCR were performed according to standard protocols. PCR primers are: Xenopus esr1: 5′-ACAAGCAGGAACCCAATGTCA-3′ and 5′-GCCAGAGCTGATTGTTTGGAG-3′; zebrafish odc: 5′-CTGCTGTTCGAGAACATGGG-3′ and 5′-CTGCTACAGCACTTGAGTCG-3′; zebrafish her6: 5′- CAAATGACCGCTGCCCTAAAC-3′ and 5′-TGACTGAAGGATGGATGAGGAGG-3′; and zebrafish her9: 5′-CCAGCGTTTGCTTCTGCTACAAC-3′ and 5′-GCTCATTGCTTTCTGCTCCG-3′.
We used the NP-40 buffer (50 mM Tris, pH 8.0, 125 mM NaCl, 1 mM EDTA, 1% NP-40, protease inhibitor cocktail (Sigma) to extract proteins from embryos. Generally, 15 embryos were homogenized in 300 ml cold lysis buffer. Protein lysates were cleared by spinning the samples twice at 4°C. Subsequently, samples were separated on SDS-PAGE and analyzed by Western blotting as described [58]. Antibodies were anti-Myc (9E10, Sigma, 1∶1,000), anti-β-tubulin (mAb, Sigma, 1∶5,000), anti-GST (mAb, Santa Cruz, 1∶500), anti-GFP (mAb, Sigma, 1∶1,000), and HRP-linked donkey anti-mouse IgG (G&E, 1∶5,000).
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10.1371/journal.ppat.1000322 | Functional Analysis of the Leading Malaria Vaccine Candidate AMA-1 Reveals an Essential Role for the Cytoplasmic Domain in the Invasion Process | A key process in the lifecycle of the malaria parasite Plasmodium falciparum is the fast invasion of human erythrocytes. Entry into the host cell requires the apical membrane antigen 1 (AMA-1), a type I transmembrane protein located in the micronemes of the merozoite. Although AMA-1 is evolving into the leading blood-stage malaria vaccine candidate, its precise role in invasion is still unclear. We investigate AMA-1 function using live video microscopy in the absence and presence of an AMA-1 inhibitory peptide. This data reveals a crucial function of AMA-1 during the primary contact period upstream of the entry process at around the time of moving junction formation. We generate a Plasmodium falciparum cell line that expresses a functional GFP-tagged AMA-1. This allows the visualization of the dynamics of AMA-1 in live parasites. We functionally validate the ectopically expressed AMA-1 by establishing a complementation assay based on strain-specific inhibition. This method provides the basis for the functional analysis of essential genes that are refractory to any genetic manipulation. Using the complementation assay, we show that the cytoplasmic domain of AMA-1 is not required for correct trafficking and surface translocation but is essential for AMA-1 function. Although this function can be mimicked by the highly conserved cytoplasmic domains of P. vivax and P. berghei, the exchange with the heterologous domain of the microneme protein EBA-175 or the rhoptry protein Rh2b leads to a loss of function. We identify several residues in the cytoplasmic tail that are essential for AMA-1 function. We validate this data using additional transgenic parasite lines expressing AMA-1 mutants with TY1 epitopes. We show that the cytoplasmic domain of AMA-1 is phosphorylated. Mutational analysis suggests an important role for the phosphorylation in the invasion process, which might translate into novel therapeutic strategies.
| Malaria is one of the most lethal parasitic diseases worldwide, causing more than 1 million fatalities per annum. Drug resistance is widespread, and a vaccine is not available. One of the leading blood stage vaccine candidates is the apical membrane antigen 1 (AMA-1), which is well-conserved among apicomplexan parasites. Although this protein plays an essential role in the invasion of human red blood cells, little is known about the molecular mechanisms underlying its function. In this study we use live video microscopy in the presence and absence of an invasion-inhibiting molecule to investigate the function of AMA-1 in real time. We establish a complementation assay based on strain-specific AMA-1 inhibition that allows functional characterization of this protein on a molecular level. Using this approach, we provide evidence that the intracellular cytoplasmic domain of AMA-1 is directly involved in the invasion process and that this domain is phosphorylated. We conclude that the phosphorylation of AMA-1 might represent an attractive target for novel therapeutic drug strategies independent of the polymorphic extracellular domain.
| Invasion of red blood cells (RBCs) is one of the critical points in the erythrocytic life cycle of the malaria parasite Plasmodium falciparum. The invasive form of the parasite, the merozoite, harbours a set of specialized secretory organelles, in particular two varieties called rhoptries and micronemes, which house key proteins involved in the invasion process. Upon invasion, these proteins are released either onto the surface of the invading merozoite or into the intercellular matrix, where they mediate host-cell recognition, receptor binding, active invasion and the formation of the parasitophorous vacuole. Some of these proteins, like the apical membrane antigen-1 (AMA-1, PF11_0344), are primary targets for vaccine development since antibodies directed against these proteins can prevent invasion (reviewed in [1]). In P. falciparum, the full length AMA-1 protein is a 83 kD type I transmembrane protein stored in the micronemes [2],[3]. Upon egress of the merozoite from an infected erythrocyte, AMA-1 is translocated onto the merozoite surface where it is concentrated at the apical pole [4]. This translocation process is accompanied by N-terminal cleavage of a prodomain, which results in a 66 kDa protein that is itself further cleaved during invasion, releasing a 48 and a 44 kDa isoform into the extracellular environment [5],[6].
AMA-1 plays an essential role in the invasion process [7],[8] and is well conserved between apicomplexan parasites. However, its biological function is still unknown. Several studies have implicated Plasmodium AMA-1 function in erythrocyte binding [9],[10] and in reorientation of merozoites on the surface of RBCs [11]. More insights were gained in the related apicomplexan parasite Toxoplasma gondii, where it was shown that AMA-1 is involved in the regulation of rhoptry secretion and in mediating the formation of the moving junction, an area of intimate contact between the invading parasite and the host cell membrane [12]–[14]. However, precisely how AMA-1 mediates these multiple tasks in the extremely rapid invasion process remains a mystery.
There is significant polymorphism among ama-1 alleles in P. falciparum field isolates [15],[16]. This diversity represents a major hurdle for the development of an AMA-1-based vaccine, as human driven immune selection leads to diversification of ama-1 alleles [16],[17]. Significantly, it is known that invasion-inhibitory antibodies against the AMA-1 type of one parasite strain have no or significantly less efficacy against other parasite strains [17]. Residues responsible for this antigenic escape mechanism have been mapped [18] and co-crystallization studies reveal a hydrophobic cleft in the AMA-1 ectodomain as one of the binding sites for inhibitory antibodies [19]. This functional inactivation of AMA-1 can be mimicked by small peptides [20].
Detailed functional analysis of essential proteins like AMA-1 is hampered in P. falciparum by the limited availability of reverse genetic tools like RNAi or inducible gene knock-out systems [21]. Here we have generated a GFP-tagged full-length AMA-1 cell line that allows visualization of the surface translocation and assessment of the membrane mobility of this key protein in live parasites. Furthermore, we have established a complementation assay based on strain-specific inhibition to functionally characterize the crucial sequence determinants of AMA-1.
It has been previously shown that AMA-1 is a microneme protein that is processed and translocated onto the surface of merozoites around the time of schizont rupture [6]. In order to functionally analyze AMA-1, full length AMA-1-GFP chimeras derived from either a 3D7 or W2mef background were episomally expressed under the AMA-1 promoter [22] in 3D7 parasites resulting in two parasite strains: AMA-13D7-GFP and AMA-1W2-GFP (Figure 1A). Both chimeras are expressed and correctly processed as shown by Western blot analysis (Figure 1B and 1C). The expression and processing of endogenous AMA-1 is not affected by the ectopic expression of the transgenes (Figure 1C). The 3D7 specific, monoclonal antibody 1F9 exclusively recognizes AMA-13D7-GFP but not AMA-1W2-GFP as shown in Figure 1D [23]. In order to confirm correct localization of the AMA-1-GFP fusion, we localized the protein in unfixed parasites (Figure 1E) and colocalized AMA-1W2-GFP with the endogenous protein in fixed parasites using the 3D7 specific monoclonal antibody (Figure 1F). The distribution of AMA-1-GFP is identical to endogenous AMA-1. The GFP-fusion protein is localized at the apical end of forming merozoites (s) and is distributed onto the surface in free merozoites (m). To visualize AMA-1 dynamics during schizont rupture and merozoite release, video fluorescence microscopy was undertaken using the AMA-1-GFP parasite lines (Figure 1G and Video S1 and Figure S1). Strong apical GFP fluorescence with some minor peripheral merozoite surface staining was observed prior to schizont rupture (Figure 1G, left) and immediately after merozoite release (Figure 1G, right). This AMA-1-GFP distribution is indistinguishable from AMA-1 in wild type parasites (Figure 1H) and in agreement with previously published studies of AMA-1 localization [24]–[26]. Over time, the initially apical AMA-1 becomes equally distributed over the periphery in free merozoites as shown in Figure 1E and 1F. To further analyze the mobility of peripheral AMA-1-GFP we used fluorescence recovery after photobleaching (FRAP) analysis of merozoites with predominantly peripheral GFP distribution (Figure S2). The qualitative analysis of the FRAP data revealed a high mobile fraction (89%+/−4%) which shows that most AMA-1 is not associated with an immobile entity and is freely diffusing (Figure S2) as it was previously suggested [26].
To test the functional capacity of the episomally expressed AMA-1-GFP fusion protein, the endogenous AMA-1 of 3D7 parasites was functionally inactivated using a strain specific inhibitory peptide called R1 [20]. This peptide appears to bind to a hydrophobic pocket within the AMA-1 ectodomain [20]. In the presence of this peptide, invasion of 3D7 but not W2mef can be completely inhibited (Figure 2A). Ectopic expression of AMA-1W2-GFP in 3D7 parasites restored up to 50% of the invasion capability compared to W2mef wild type parasites, while AMA-13D7-GFP did not compensate the inhibitory effect of the peptide (Figure 2A).
To reveal the consequences of functional inactivation of AMA-1 during invasion, parasites were observed by live video microscopy (Figure 2B and 2C and Videos S2, S3, S4, S5, S6, and S7, and Figures S3, S4, S5, and S6). As previously shown in P. knowlesi and P. falciparum [27]–[29] merozoites attach, reorientate (Figure 2B, black arrowheads) and subsequently invade the RBC (Figure 2B, red arrowheads, and Videos S2 and S3 and Figure S3). The primary attachment to the RBC surface is accompanied by oscillatory deformation of the erythrocyte surface [11],[27],[29] causing some extensive membrane- wrapping around the merozoite. After this attachment phase, the merozoite reorientates without apparent deformation of the RBC and invades the host cell within seconds (Videos S2 and S3 and Figure S3). After invasion, the infected erythrocyte goes through a reversible echinocytosis (Video S4 and Figure S4). In the presence of the R1 peptide, the invasion process by 3D7 or AMA-13D7-GFP parasites appears to proceed normally through primary attachment and reorientation, but then fails to progress further (Figure 2C and Videos S5 and S6 and Figure S5). Interestingly, although host cell entry is blocked at this stage, echinocytosis still occurs (Video S7 and Figure S6), indicating that some decisive interactions with the RBC plasma membrane are not prevented in the presence of the R1 inhibitory peptide. This is further illustrated by a sudden forward movement which appears to exert a substantial force on the erythrocyte surface following reorientation (Videos S5, S6, and S7).
In order to analyze the involvement of the AMA-1 cytoplasmic domain in microneme trafficking and subsequent surface translocation, W2mef-derived AMA-1-GFP–expressing 3D7 parasites lacking the cytoplasmic domain were generated (AMA-1Δtail-GFP, Figure 3A). Expression of mutant AMA-1-GFP was verified in immunoblots (Figure 3B). Fluorescence microscopy of unfixed (Figure 3C) or fixed (Figure 3D) transgenic parasites reveals that the deletion of the cytoplasmic domain does not affect AMA-1Δtail-GFP localization. A putative interaction (and therefore assisted trafficking) with the endogenous AMA-1 was excluded by using immunoprecipitation and subsequent Western blot analysis (Figure 3E). AMA-1-GFP and AMA-1Δtail-GFP (data not shown) were immunoprecipitated using anti-GFP beads. Although anti-AMA-1 antibodies showed successful co-immunoprecipitation of AMA-1-GFP and the AMA-1 binding partner RON4 [12], no endogenous AMA-1 could be detected. This argues against a piggy-back trafficking of the deletion mutant with the endogenous AMA-1.
The complementation assay provided the basis for analyzing the phenotypic effects of various deletions and mutations introduced into the AMA-1 protein and as such to functionally characterize different parts of the AMA-1 molecule. First, we analyzed the effect of the cytoplasmic domain deletion. In the presence of the R1 peptide, W2mef-derived AMA-1Δtail-GFP could not rescue the invasion capability, unlike the full-length W2mef AMA-1-GFP (Figure 4E). Thus, although trafficking of AMA-1 does not require the cytoplasmic domain, invasion inhibition by R1 clearly demonstrates that the cytoplasmic domain (AMA-1Δtail-GFP) plays an essential role in the invasion process. To further dissect the role of the cytoplasmic domain we introduced multiple alterations into this domain and analyzed their functional consequences.
In order to analyze the functional sequence requirements within the cytoplasmic domain of P. falciparum AMA-1, we swapped domains either with i) the homologous domain of P. vivax or P. berghei or ii) the non-homologous domains of two well characterized type-I transmembrane proteins, that are implicated in erythrocyte invasion and possess an equally short cytoplasmic domain (EBA-175 and Rh2b, Figure 4A–4C). All chimeric proteins were correctly expressed as GFP fusion proteins (Figure 4D), colocalized with endogenous AMA-1 (data not shown) and were proteolytically cleaved like the endogenous protein (Figure 4D). Although AMA-1vivax and AMA-1berghei could readily complement the endogenous protein, the cytoplasmic domains of EBA-175 and Rh2b failed to do so (AMA-1EBA-175 and AMA-1Rh2b, Figure 4E). This points towards distinctive features within the AMA-1 cytoplasmic domain that seem to be required for its function. This notion is further supported by a high degree of conservation of this domain not only within the genus Plasmodium but also with other apicomplexan parasites (Figure 4B). Therefore, we tested which region of the cytoplasmic domain is important for AMA-1 function. Deletions of the proximal (AMA-1Δ571–588 and AMA-1Δ589–611) and C-terminal (AMA-1Δ11 and AMA-1Δ5) portions of the cytoplasmic domain functionally inactivated AMA-1. Furthermore, mutations of the highly conserved residues DE594 (AMA-1DE-AA), DPE599 (AMA-1DPE-AA), FW603 (AMA-1FW-AA) all resulted in functional inactivation of AMA-1 with some residual function in the AMA-1DE-AA parasite line. The only mutation that had no functional effect was YD577 (AMA-1YD-AA), a residue lying within the N-terminal part of the domain.
To further validate these findings and to improve the invasion capability of parasite lines ectopically expressing AMA-1, the C-terminal GFP tag was exchanged with a small TY1 epitope of 10 amino acids [30] in wild type AMA-1 (AMA-1W2-TY1) and in those AMA-1 mutations that led to its inactivation (Figure 5A and 5B). The substitution of GFP with the TY1 tag increased the efficiency of the functional complementation by approximately two fold (Figure 5C) and led to an invasion capability of the AMA-1W2-TY1 parasite line that was comparable with that of W2mef wild type parasites (Figure 5C). The lack of invasion capability was not significantly changed in parasite lines substituted with non-homologous cytoplasmic domains (AMA-1EBA-175-TY1 and AMA-1Rh2b-TY1), or mutations within the extreme C-terminus (AMA-1Δ5-TY1, AMA-1FW-AA-TY1 and AMA-1PM-TY1). Evaluation of the TY1-tagged parasite lines AMA-1DE-AA and AMA-1DPE-AA revealed an increased invasion capacity, which however did not reach that of wild type AMA-1W2-TY1. The lower invasion efficiency with the GFP tag might be due to sterical hindrances compared to the much smaller TY1 tag.
During the invasion process AMA-1 is shed by a protease from the merozoite surface releasing the extracellular domain into the supernatant (Figure 6A). The remaining small C-terminus of AMA-1 is carried into the host cell [24] (Figure 6B). A bioinformatics screen predicted six amino acids within the cytoplasmic domain to be phosphorylated (www.cbs.dtu.dk/services/NetPhos) (Figure 4B). To validate phosphorylation, all conserved putative phosphorylation sites (and those that might be phosphorylated due to the substitutions) were mutated to generate the parasite line AMA-1PM-GFP (Figure 4A). Taking advantage of the increased length of the processed C-terminal fragment due to the GFP tag, the released C-terminal fragments were easily detectable as 45 and 49 kDa fragments (Figure 6A and 6C). The introduced mutations (calculated to alter the MW by 0.6 kDa) led to a clearly reduced molecular weight (significantly more than 0.6 kDa) of both the 45 and the 49 kDa fragments (Figure 6C). In order to test if the observed shift in mobility was due to phosphorylation, AMA-1-GFP was treated with lambda-phosphatase (Figure 6D). This reduced the apparent molecular weight of wild type AMA-1-GFP, resulting in a size identical to that of the phosphorylation mutant AMA-1PM-GFP, whereas AMA-1PM-GFP itself showed no shift after the phosphatase treatment (Figure 6E). Thus the observed size difference between the mutated and wild type cytoplasmic domains of AMA1 is due to phosphorylation. Interestingly, this phosphorylation mutant (AMA-1PM) led to a functional inactivation of AMA-1 as shown in the complementation assays (Figures 4E and 5C).
The invasion of erythrocytes by malaria parasites is a well orchestrated and fast process relying on precise signaling and multiple protein-protein interactions [31]. Invasion can be resolved into a series of distinct steps: i) an initial attachment of the parasite and the host cell surface, which leads to a close interaction between the membranes of the two cells and often causes extensive deformation of the host cell, ii) reorientation of the merozoite, bringing about intimate contact between the apical end of the parasite and its host cell and iii) active penetration [27],[32],[33]. While some proteins involved in these steps have been characterized [31], the function of the leading blood stage vaccine candidate AMA-1 in this process remains elusive. Here we report on the detailed analysis of AMA-1 during invasion.
The expression of GFP-tagged full-length AMA-1 in transgenic parasites allowed us for the first time to visualize and analyze the distribution and function of AMA-1 during erythrocyte invasion. In agreement with previous publications [24]–[26], differentially distributed AMA-1 pools could be visualized: a major apically restricted population and a minor peripherally located fraction after merozoite egress. Does this imply dual function? On the one hand these different properties could argue for the involvement of peripheral AMA-1 in the reorientation process by its interaction with erythrocyte receptors [10],[11]. This could be achieved, for instance, by an AMA-1 density gradient with highest concentrations at the apical end. Apical AMA-1 could play an essential role in the intimate contact between the apical pole and host cell that culminates in the formation of a moving junction, a membrane contact that migrates down the length of the invading parasite. On the other hand, peripherally distributed AMA-1 could simply be functionally redundant excessive AMA-1, while only apical AMA-1 is required for invasion. Such a scenario was proposed for T. gondii, where it was shown that TgAMA-1 is associated with two proteins, RON2 and RON4, that colocalize with the moving junction [13]. Furthermore, the conditional knock-out of Tgama-1 that resulted in its depletion impaired the ability of the parasite to attach intimately to its host cell [14]. These two observations argue against an essential role of AMA-1 in the steps preceding the formation of the moving junction, at least in T. gondii. Video microscopy in the presence of the invasion inhibitory peptide R1 was used to further examine AMA-1 function. To date only very few video recordings of the actual erythrocyte invasion process have been published [27]–[29] and none of these were made in the presence of invasion inhibiting molecules. The results obtained by comparing invasion in the presence and absence of R1 peptide suggest a role for the hydrophobic cleft just prior to invasion, after binding and reorientation (Figure 2 and Videos S3 and S5). Interestingly, the essential reorientation seems to be independent of erythrocyte deformation, strengthening the hypothesis of merozoite reorientation by either active reorientation with the use of motor proteins or a gradient of adhesive surface proteins with higher affinity or avidity towards the apical pole of the merozoite. The forward movement in the presence of the peptide indicates triggering of motor proteins even though invasion is inhibited. The apparent reorientation of the merozoite in the presence of the R1 peptide was surprising given that AMA-1 was shown to play an essential role in this process. This was previously shown by using inhibitory, monoclonal antibodies directed against P. knowlesi AMA-1 in combination with electron microscopy analysis [11]. One simple explanation for this differing observation could be that the P. knowlesi epitope recognized by inhibitory antibodies and the hydrophobic pocket in P. falciparum AMA-1 recognized by the R1 peptide are distinct functional domains of the AMA-1 ectodomain. This, once again, points to multiple functions of AMA-1 during the invasion process. Alternatively, the inhibition of the reorientation step by antibodies might be primarily mediated by a sterical hindrance due to the size of the antibodies and therefore might not reflect a functional property of the AMA-1 molecule.
The observed induction of echinocytosis in the presence of R1 peptide could argue for the initiation of a moving junction. Alternatively, transient echinocytes might be a consequence of transient lipid bilayer breaks caused by the force acting on the host cell during the invasion attempt, as reported for T. gondii [34]. Therefore, blocking of the hydrophobic pocket in the ectodomain of AMA-1 could either induce a blockage of the formation of the moving junction or the progression of the latter. One conceivable explanation for this observation is that R1 binding directly affects the interaction of AMA-1 with PfRON4 [12] or other unknown proteins involved in the moving junction complex. Alternatively, binding could interfere with conformational changes that might be necessary for AMA-1 function, potentially disrupting the essential role of the cytoplasmic domain. Functional knowledge about the cytoplasmic domains of invasion-related type I transmembrane proteins like AMA-1 is limited. Here we report that the cytoplasmic domain does not play a role in trafficking or translocation of the protein to the micronemes and subsequently to the surface, but is essential for function. This intriguing finding is reminiscent of previous work showing that although the cytoplasmic domain confers a function in the members of the EBL superfamily, these proteins are trafficked independently of their cytoplasmic domain [22],[35]. This is further supported by the fact that cytoplasmic domain-swaps with either EBA-175 or Rh2b do not interfere with trafficking but cannot complement AMA-1 function, pointing to a decisive role of this AMA-1 domain in the invasion process. This function can be mimicked by the homologous regions of the AMA-1 protein of P. berghei and P. vivax that show a high degree of conservation (above 70% identity within the cytoplasmic domains). In the cytoplasmic domain the degree of conservation increases towards the extreme C-terminus. Coincidentally, only mutations of residues positioned towards the transmembrane domain such as YD577 show no effect on AMA-1 function.
We show that the cytoplasmic domain of AMA-1 is phosphorylated and mutation of all putative phosphorylation residues inactivates AMA-1 function. Furthermore, the phosphorylation mutant, which possesses a fully functional AMA-1 ectodomain is blocked just prior to invasion, suggesting a coordinated function of the hydrophobic cleft and phosphorylation. In this context it is interesting to note that AMA-1 appears not to be phosphorylated prior to invasion but after schizont rupture: mass spectrometric peptide mass fingerprinting did not recover any phosphorylated AMA-1 in schizont material [5] (AMA-166 fragment). It will be highly interesting to pinpoint the precise timing of AMA-1 phophorylation. This might help to unravel downstream effects triggered through AMA-1. Eight kinases are co-transcriptionally up-regulated together with AMA-1 [36]. Recently, Ono and co-workers [37] showed that the secretion of apical organelles is induced by the increase of the cAMP concentration in sporozoites of P. falciparum and other Plasmodium species. The major downstream effector of cAMP is protein kinase A (PKA), a serine threonine kinase l. Further, four of the up-regulated kinases display features of Ca2+-dependent kinases in P. falciparum. The dependence of efficient secretory organelle release on a raised concentration of intracellular Ca2+ is well established in T. gondii [38],[39], Cryptosporidium parvum [40] and was also suggested for Plasmodium [31]. Given that the cAMP and Ca2+ signaling pathways are intertwined in the parasite [41], the cytoplasmic domain of AMA-1 might function as a sensing device triggering downstream events like rhoptry secretion. It is noteworthy that the conditional AMA-1 knockout in Toxoplasma leads to defective rhoptry secretion [14]. Phosphorylation of the cytoplasmic domain might induce a conformational change in the extracellular domain resulting in its functional activation. Although phosphorylation seems to be a prerequisite for AMA-1 function, mutagenesis of conserved amino acids that are not predicted to be phosphorylated also interfered with AMA-1 function. These residues might be crucial for the interaction with downstream proteins that are involved in the invasion process. Alternatively, phosphorylation might depend on the recognition of surrounding non-phosphorylated residues. To further understand the functional effect of phosphorylation, it will be important to pinpoint the phosphorylation sites, the phosphorylating kinase(s), the downstream effector proteins, the precise moment of phosphorylation and then correlate this with individual invasion steps.
In summary, our findings provide evidence that AMA-1 is not only an essential part in the moving junction but also that phosphorylation of the cytoplasmic domain might be a prerequisite for invasion. This might translate into important novel therapeutic strategies using AMA-1-phosphorylating kinases as drug targets to inactivate this crucial player of erythrocyte invasion circumventing any antigenic escape mechanisms of the parasite.
Plasmodium falciparum asexual stages were cultured in human 0+ erythrocytes according to standard procedures [42]. W2mef is derived from the Indochina III/CDC strain. 3D7 parasites were transfected as described previously [43] with 100 µg of purified plasmid DNA (Invitrogen). Positive selection for transfectants was achieved using 10 nM WR99210, an antifolate that selects for the presence of the human dhfr gene.
ama-1 was either amplified from 3D7 or W2mef P. falciparum cDNA (s. Tab. 1). Additionally, cDNA from P. vivax and P. berghei was used. In vitro mutagenesis of ama1 was achieved by using a two-step primer directed PCR mutagenesis method [44] (Table S1) with proof reading Vent polymerase (NEB). To ensure correct timing of transcription, expression of the AMA-1 transgenes was controlled by the AMA-1 promotor using the pARL-AMA1-GFP Vector [22]. Constructs were cloned into the KpnI and AvrII restriction sites of the transfection vector and sequences were confirmed by sequencing.
Antibodies used in immunodetection were rabbit polyclonal anti AMA-1 [26] anti AMA-1 monoclonal 1F9 [23], monoclonal anti TY1 (Diagenode) and monoclonal anti-GFP (Roche). Anti-AMA-1 was diluted 1∶500, anti TY1 was used 1∶1500, 1F9 and anti-GFP antibodies were diluted 1∶1000 in phosphate-buffered saline (PBS) with 3% w/v skim milk. Immunoblots were performed using standard procedures and developed by chemiluminescence using ECL (Amersham International). Secondary antibodies were sheep anti-rabbit IgG horseradish peroxidase (Sigma) and sheep anti mouse IgG horseradish peroxidase (Roche) used 1∶5000.
Immunofluorescence assays (IFAs) were performed on fixed parasites as previously described [35],[45]. Fixed parasites were incubated for 1 h with primary antibodies in the following dilutions: rabbit anti-AMA-1 (1∶1000), mAB 1F9 (1∶2000). Subsequently, cells were incubated 1∶2000 with Alexa-Fluor 594 goat anti-rabbit IgG or Alexa-Fluor 488 goat anti-mouse IgG antibodies (Molecular Probes) and with DAPI at 1 µg/ml (Roche). Images of GFP-expressing parasites and immunofluorescence assays were observed and captured using a Zeiss Axioskop 2plus microscope, a Hamamatsu Digital camera (Model C4742-95) and OpenLab software version 4.0.4 (Improvision Inc.).
Confocal pictures were generated using a Fluoview 1000 (Olympus) or a SP5 (Leica) confocal microscope. Images were analyzed and processed using either Photoshop or ImageJ software (rsb.info.nih.gov/ij/). Parasites were taken from blood-culture, kept at 37°C and imaged immediately after withdrawal.
Video microscopy was performed using an Axiovert 200 M “Cell observer” with a temperature and CO2 controlled incubator at 37°C and 5% CO2. Briefly, parasites from culture where diluted 1∶100 in pre-warmed complete Media (Gibco) and 5% Albumax, (Sigma) without phenol red. 100 µl of this suspension was imaged either with, or without 100 µg/ml R1 inhibiting peptide in 35 mm glass bottom culture dishes (MatTek, Ashland, MA, USA). Image acquisition was performed with an AxioCam HRm camera (Zeiss) and Axiovision (Zeiss).
AMA-1-GFP was immunoprecipitated using conjugated anti-GFP antibody agarose beads (MBL). Briefly, synchronized late parasite cultures were collected and saponin lysed. The parasite pellet was incubated with lysis-buffer (50 mM Tris-HCL pH 7,2, 250 mM NaCl, 0,1% NP-40, 2 mM EDTA, 10% Glycerol) containing complete protease inhibitor without EDTA (Roche) for 3 h at 4°C. After centrifugation for 30 min at 16.000 g the supernatant was incubated with anti-GFP agarose beads for 1 h at 4°C. Beads were washed three times with lysis buffer. Bound proteins were eluted with non-reducing SDS sample buffer and cooked for 5 min. Supernatant, flow through, wash and elution fractions were subjected to Western blot analysis. The Blot was probed using either polyclonal anti-AMA-1-PR or monoclonal anti-RON4 antibodies.
Parasite erythrocyte invasion assays were performed using 3D7 and transgenic parasites expressing various AMA-1 mutants in the 3D7 background. Parasitemia was measured using a Becton-Dickinson FACSaria fluorescence activated cell sorter (FACS). Sorbitol synchronized parasite culture [46] (4% hematocrit, 0,5- 1% parasitemia, late trophozoites) was incubated in a 96-well Plate (100 µl per well) under standard culturing conditions for 48 hours to allow reinfection in the presence or absence (control) of 100 µg/ml R1 [20]. After reinvasion occurred, parasites where stained with 1 mg/ml ethidium bromide for 30 minutes at 37°C, washed three times with media and then counted using the FACS. Assays were performed in triplicates on three independent occasions.
Sorbitol synchronized parasites were harvested after reinfection of erythrocytes. Parasites were released from host cells by saponin-lysis. The parasite pellet was resolved in 10 volumes of ice cold lysis buffer (50 mM Tris-HCL pH 7,2, 250 mM NaCl, 0,1% NP-40, 10% Glycerol) containing complete protease inhibitor (Roche) w/o EDTA and incubated for 180 minutes at 4°C. The lysate was centrifuged at 16.000 g and aliquots of the supernatant were subjected to lambda protein phosphatase (λPPase, NEB Biolabs) treatment as stated in the manufacturer's instructions. Briefly, 160 µl supernatant was incubated for 15 minutes with 400 U of λPPase at 30°C. Subsequently, the mixture was precipitated with 4 volumes of ice-cold acetone (Sigma). Precipitated protein was dissolved in non-reducing SDS sample buffer and analyzed on SDS-Page and Western blot analysis using anti-GFP antibodies.
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10.1371/journal.pcbi.1004442 | A Bayesian Attractor Model for Perceptual Decision Making | Even for simple perceptual decisions, the mechanisms that the brain employs are still under debate. Although current consensus states that the brain accumulates evidence extracted from noisy sensory information, open questions remain about how this simple model relates to other perceptual phenomena such as flexibility in decisions, decision-dependent modulation of sensory gain, or confidence about a decision. We propose a novel approach of how perceptual decisions are made by combining two influential formalisms into a new model. Specifically, we embed an attractor model of decision making into a probabilistic framework that models decision making as Bayesian inference. We show that the new model can explain decision making behaviour by fitting it to experimental data. In addition, the new model combines for the first time three important features: First, the model can update decisions in response to switches in the underlying stimulus. Second, the probabilistic formulation accounts for top-down effects that may explain recent experimental findings of decision-related gain modulation of sensory neurons. Finally, the model computes an explicit measure of confidence which we relate to recent experimental evidence for confidence computations in perceptual decision tasks.
| How do we decide whether a traffic light signals stop or go? Perceptual decision making research investigates how the brain can make these simple but fundamentally important decisions. Current consensus states that the brain solves this task simply by accumulating sensory information over time to make a decision once enough information has been collected. However, there are important, open questions on how exactly this accumulation mechanism operates. For example, recent experimental evidence suggests that the sensory processing receives feedback about the ongoing decision making while standard models typically do not assume such feedback. It is also an open question how people compute their confidence about their decisions. Furthermore, current decision making models usually consider only a single decision and stop modelling once this decision has been made. However, in our natural environment, people change their decisions, for example when a traffic light changes from green to red. Here, we show that one can explain these three aspects of decision making by combining two already existing modelling techniques. This resulting new model can be used to derive novel testable predictions of how the brain makes perceptual decisions.
| Research in perceptual decision making investigates how people categorise observed stimuli. By presenting stimuli embedded in large amounts of noise, experimenters prolong the time it takes a subject to make a decision about the stimulus. This makes the decision making process observable for hundreds of milliseconds and enables experiments about the underlying mechanisms [1]. For example, in the well-known random dot motion task subjects typically have to categorise a cloud of moving dots according to whether it moves in one of two opposing directions [2–4]. By decreasing the fraction of coherently moving dots the task is made more difficult such that subjects respond slower and make more errors.
Such increases in reaction time for more difficult categorisations motivated models that describe decision making as an accumulation of noisy evidence towards a bound [5, 6]. One of the key findings is that such bounded accumulation models fit accuracy and reaction time distributions of decision makers well [6–8]. Furthermore, electrophysiological research has found support for an accumulation mechanism: neurons in different areas of monkey brains exhibit steadily increasing firing rates dependent on stimulus reliability, e.g. [1, 9–12]. In humans, correlates of evidence accumulation have been found with functional magnetic resonance imaging [13, 14] and magneto-/electroencephalography [15–19].
The two best-known models of perceptual decision making are drift-diffusion and attractor models. Drift-diffusion models implement accumulation to a bound using diffusion processes [7, 20–22] and can be understood in terms of statistically optimal sequential decision making [20]. Bayesian models of perceptual decisions provide a direct link between the computation of evidence from the sensory input and the statistically optimal accumulation of this evidence [23–25]. In contrast, attractor models were developed as neurophysiologically inspired spiking-neuron models of perceptual decision making [26], but can also be described by simpler firing rate models [27, 28]. Attractor models use winner-take-all dynamics to implement accumulation which is nonlinear over time. This nonlinear accumulation is the key difference to drift-diffusion models, which are based on linear accumulation. Both types of models seem to make mostly the same predictions [29, 30], yet exhibit subtle differences in favour of attractor models when considering experimental evidence [31–33] but see [34].
Bayesian inference provides an optimal approach for combining noisy sensory evidence with internal dynamics and seems generally useful as a basic mechanistic principle for perceptual decision making. For example, drift-diffusion models are strongly connected to Bayesian models of perceptual decision making [23–25]. Therefore, the question arises what exactly a Bayesian inference approach would have to offer for attractor models. Here, we address this question by combining a variant of the nonlinear attractor model with Bayesian inference. The resulting new model, which we call the Bayesian attractor model (BAttM), combines the neurophysiological motivation of the attractor model with the explicit evidence computation formalism of the Bayesian machinery. As we will show, the BAttM is a quantitative model and fits well to behavioural data (reaction times and choice). Furthermore, we will highlight three key advantages of the BAttM that go beyond the standard features of both attractor and drift-diffusion models.
First, the BAttM naturally models changes in decisions that reflect changes in the underlying category. Such changes of an already made decision are an important part of our environment, e.g., a switching traffic-light, but have not been considered by previous models. Rather, drift-diffusion [35] and attractor models [26, 31] have been adapted to model ‘changes of mind’ which are different from ‘re-decisions’ considered here (for more details on the difference see Discussion).
Second, the BAttM provides a natural explanation for top-down modulation of the sensory gain that controls evidence extraction during the decision making process. Such gain modulation has been implicated in attentional phenomena such as found in feature-based attention [36–38]. In addition, early sensory neurons have been found to exhibit within-trial gain modulation that appears to depend on the final choice in a trial [39, 40]. The BattM explains these phenomena in terms of a state-dependent, top-down gain mechanism which is absent from both drift-diffusion and attractor models.
Third, the BAttM provides an explicit measure of confidence that reproduces the experimentally established dependence of confidence ratings on decision outcome and task difficulty [41–43]. In particular and in contrast to both drift-diffusion and attractor models, the probabilistic formulation of the BAttM yields a quantitative measure of confidence that reflects the decision maker’s internal expectations and provides a meaningful quantitative interpretation of the bound.
The BAttM consists of four major components: i) an abstract model of the experimental stimuli used as input to the decision process of a decision maker, ii) a generative model of the stimuli implementing expectations of the decision maker, iii) a Bayesian inference formalism and iv) a decision criterion, see also [23]. In the following, we define these components in turn and, particularly, clarify the role of attractor dynamics in the model and how this differs from previously suggested attractor models of perceptual decision making. We start by formalising a notion of attractor models.
Attractor models of perceptual decision making were originally proposed as neurophysiologically plausible implementation of noisy decision making [26]. In particular, [26] introduced a spiking neuron network which implements decisions through an attractor dynamics based on two mutually inhibiting pools of neurons. By using a mean-field approach this model has been reduced to a relatively small set of differential equations [28], see also [27, 32].
Apart from the neurobiological motivation, attractor models mainly differ from prevalent diffusion models of decision making by the nonlinear accumulation of evidence: The mutual inhibition between alternatives leads to faster accumulation for an alternative as more evidence for that alternative is accumulated, that is, decisions for an alternative are attracting. In the present work we capture this decisive property of attractor models with a simpler, more abstract Hopfield network [44]. The Hopfield dynamics describes how state variables zi (the activities of units in the Hopfield network) evolve through time. Each state variable corresponds to one decision alternative. Intuitively, large values of state variable zi indicate large amounts of evidence for decision alternative i. The Hopfield dynamics implements lateral inhibition between and self-excitation of state variables. As a result, it exhibits winner-takes-all dynamics which ensures stable and unambiguous decision making between alternatives. In particular, the Hopfield dynamics has stable fixed points ϕi, each identifying one decision alternative i. For further details see Methods.
By abstracting from details of the particular attractor dynamics used in different models, previous attractor models of decision making may be formalised (in discretised form) as
z t - z t - Δ t = Δ t f ( z t - Δ t ) + I t (1)
where f(z) is a function defining an attractor dynamics for the vector of state variables z, which we also call decision state (cf. Table 1). The external input It varies with stimulus strength, includes noise, directly drives the attractor dynamics and reflects momentary evidence in decision making (see Fig 1A). Typically, when one of the state variables zi reaches a certain threshold, the model indicates a decision for the corresponding alternative i.
We refer to models of this type as ‘pure attractor models’ which include the attractor models described above [26–28]. Note that pure attractor models are not informed about the stimulus itself or its features. Rather, they presume that their noisy input carries some information about a stimulus which is interpreted as evidence for or against the considered alternatives. Therefore, these models implicitly postulate that evidence for a decision is extracted by lower level sensory processes which are independent of the state of an ongoing decision. Under this assumption, pure attractor models cannot exhibit top-down gain control as a mechanism, because the decision state cannot provide feedback to the lower sensory level, see Fig 1A.
Bayesian models infer the state of an unobserved variable (here the identity of a stimulus) from realisations of an observed variable [24, 45–47]. Here, we define these ‘observations’ and motivate them as feature representations in the brain.
Even though the BAttM can model tasks with multiple alternatives, we here focus on two-alternative forced choice tasks, as most commonly employed when investigating perceptual decisions. For example, in typical random dot motion (RDM) tasks subjects have to judge into which of two opposing directions a randomly moving cloud of dots moves on average [2–4]. By varying the percentage of coherently moving dots the task difficulty can be controlled.
We assume that the brain translates low-level sensory information, such as moving patters of light and dark spots on the retina, into stimulus feature vectors that are relevant for the current decision. In the RDM task a suitable feature may be the dominant motion direction in the stimulus, or a distribution over it. As the motion in the stimulus becomes less coherent, the dominant motion direction becomes more noisy.
The precise feature representation that the brain uses when making decisions, including the particular distribution of feature vectors, is largely unknown. Consequently, we take a suitably parsimonious approach and model (abstract) feature vectors as samples from one of two Gaussian distributions which represent the two alternatives in the decision task. In particular, a feature vector at time t is xt ∼ 𝓝(μi, s2 I) where s is the standard deviation of the noise, or noise level (cf. Table 1) and μi is the feature vector that would result, if alternative i was presented without noise. We set μ1 = [0.71,0.71]T (alternative 1) and μ2 = [−0.71,−0.71]T (alternative 2), that is, the feature vectors of the two alternatives occupy opposite positions on the unit circle.
This (feature) representation of the noisy stimulus has itself an interpretation as a perceptual decision making task. We use this interpretation here to illustrate the task that the brain, as decision maker, presumably solves when given noisy feature vectors as observations in a decision task: The feature vector x can be interpreted as the location of a single dot on a plane which moves randomly around one of two target positions. The single dot positions are sampled from an isotropic two-dimensional Gaussian with mean equal to one of the two targets. The task of the decision maker is to infer around which of the two target locations the single dot moves. Similarly to the RDM task, the difficulty of the task can be continuously varied by manipulating the ratio between the noise level and the distance between the two targets. In the two extremes, there is either no noise so that the correct target can be inferred easily, or the random movements are so large that one cannot infer the true target (i.e., the mean of the underlying Gaussian) with sufficient certainty. In Fig 2 we illustrate the dot movements across an example trial in this task.
The generative model of the decision maker implements its expectations about the incoming observations. More precisely, the generative model is a probabilistic model that defines the likelihood of observations under all possible hypotheses that the decision maker considers. Compared to pure attractor models the flow of information is reversed in the generative model: The generative model predicts a probability distribution over observations based on the current decision state and its winner-take all attractor dynamics. In contrast, in pure attractor models evidence extracted from the stimulus perturbs the decision state without any feedback from the decision state to the sensory evidence (cf. Fig 1).
A previous Bayesian model of perceptual decision making [23] defined independent generative models for the different alternatives in the decision task. The Bayesian attractor model complements the generative model with a competition between alternatives as implemented by attractor dynamics. In particular, the generative model defines a change in decision state from one time step to the next as
z t - z t - Δ t = Δ t f ( z t - Δ t ) + Δ t w t (2)
where f(z) is the Hopfield dynamics (Methods, Eq 9). wt is a (Gaussian) noise variable with wt ∼ 𝓝(0,Q) where Q = (q2/Δt)I is the isotropic covariance of the noise process and we call q ‘dynamics uncertainty’. It represents the (expected) state noise at the attractor level which can be interpreted as the propensity to switch between decisions (the higher the dynamics uncertainty, the more likely the state switches between the decision alternatives).
Given a decision state z the generative model predicts a probability distribution over observations by interpolating prototypical observations that represent the different alternatives:
x = M σ ( z ) + v (3)
where M = [μ1,…, μN] contains the mean feature vectors defined in the input model above. This choice implements the reasonable assumption that the decision maker has learnt the average representations of the stimuli in feature space either through experience with the task, or from a suitable cue in the experiment. σ(z) is the sigmoid-transformed decision state, that is, all state variables zj are mapped to values between 0 and 1. Due to the winner-take-all mechanism of the Hopfield dynamics, its stable fixed points ϕi will map to vectors σ(ϕi) in which all entries are approximately 0 except for one entry which is approximately 1. Hence, the linear combination M σ(z) associates each stable fixed point ϕi with feature vectors (observations) from one of the decision alternatives. When the Hopfield network is not in one of its stable fixed points, M σ(z) interpolates between mean feature vectors μi dependent on the sizes of individual state variables zj. Finally, v is a (Gaussian) noise variable with vt ∼ 𝓝(0,R) where R = r2 I is the expected isotropic covariance of the noise on the observations and we call r ‘sensory uncertainty’. It represents the expected noise level of the dot movement in the equivalent single dot decision task explained above (the higher the sensory uncertainty, the more noise is expected by the decision maker).
By inverting the generative model using Bayesian inference we can model perceptual inference. Specifically, we use Bayesian online inference to infer the posterior distribution of the decision state zt, that is, the state of the attractor dynamics at time t, from sensory input, that is, all the sensory observations made up to that time point: XΔt:t = {xΔt,…, xt}, given the generative model (Eqs 2, 3). The generative model postulates that the observations are governed by the Hopfield dynamics. Hence, the inference must account for the assumption that observations of consecutive time points depend on each other. In this case, inference over the decision state zt is a so-called filtering problem which could be solved optimally using the well-known Kalman filter (see, e.g., [48]), if the generative model was linear. For nonlinear models, such as presented here, exact inference is not feasible. Therefore, we used the unscented Kalman filter (UKF) [49] to approximate the posterior distribution over the decision state zt using Gaussians. Other approximations such as the extended Kalman filter [48], or sequential Monte Carlo methods [50] could also be used. We chose the UKF, because it provides a suitable tradeoff between the faithfulness of the approximation and computational efficiency.
The UKF is based on a deterministic sampling technique called the unscented transform [51][52], which provides a minimal set of sample points (sigma points). These sigma points are propagated through the nonlinear function and the approximated Gaussian prediction is found by fitting the transformed sigma points. Following [49], we use for the unscented transform the parameter values α = 0.01, β = 2, κ = 3−D where D is the dimension of the state representation inside the UKF.
In the following, we provide an intuitive description of the UKF computations. For the mathematical details, we refer the reader to [49]. The unscented transform is performed twice. First, it is used to approximate the distribution over the decision state in the next time step, as predicted by the generative model from the current estimate based on previous observations, with a Gaussian: p ( z t ∣ X Δ t : t − Δ t ) ≈ 𝓝 ( z ^ t , P ^ t ). Second, the unscented transform is used to approximate the predicted distribution of the corresponding next sensory observation: p ( x t ∣ X Δ t : t − Δ t ) ≈ 𝓝 ( x ^ t , Σ ^ t ). The conceptual idea of Kalman filter algorithms is to compare the predicted distribution with the actual observation and update decision state estimate z ‾ t proportional to the observed discrepancy while taking the uncertainty over predictions into account. Practically, for the Gaussian approximation used in the UKF we compute a prediction error ϵ t = x t − x ^ t between predicted mean x ^ t and actual observation xt and then update the decision state prediction z ^ t via a Kalman gain Kt:
z ¯ t = z ^ t + K t ϵ t . (4)
The Kalman gain represents the relative importance of the prediction errors with respect to the predictions and is computed from the estimated covariance of the predicted observations and the cross-covariance between predicted observations and decision state:
K t = C ^ t Σ ^ t - 1 (5)
where C ^ t is the cross-covariance between predicted decision state z ^ t and predicted observation x ^ t which is strongly affected by dynamics uncertainty q (larger q, larger cross-covariance) and Σ ^ t is the covariance matrix of the predicted observations which is strongly affected by sensory uncertainty r (larger r, larger variance). These relations mean that an increase in q mostly leads to an increase in gain whereas an increase in r leads to a reduction in gain.
In addition to affecting the updates of the mean decision state, the Kalman gain is further used to estimate the posterior covariance P ‾ t of the state variables zi,t which completes the UKF approximation of the posterior distribution over the decision state p(zt∣XΔt:t). Fig 3 illustrates the described Kalman filtering scheme.
The final component of the Bayesian attractor model is its decision criterion. In decision models based on evidence accumulation the decision criterion implicitly sets a particular level of accumulated evidence that needs to be reached before the decision maker commits to a decision. In contrast, we here define the criterion directly on a measure of confidence in the decision. In particular, the model makes a decision for alternative i at time t, if
p ( z t = ϕ i | X Δ t : t ) ≥ λ (6)
where p(zt = ϕi∣XΔt:t) is the posterior density over the decision state evaluated at the stable fixed point ϕi corresponding to alternative i, that is, p(zt = ϕi∣XΔt:t) is the posterior belief of the decision maker that alternative i is the true alternative. Then the threshold λ can directly be interpreted as a confidence level. This decision criterion requires that all state variables are at their expected values as given by the stable fixed points ϕi. Note that this is different from pure attractor models which do not use a bound around the fixed point location, but rather threshold individual state variables zj, see below in results.
Uncertainty parameters and the confidence bound interact: Larger dynamics uncertainty leads to wider posterior distributions, faster evidence accumulation and smaller density values (Fig 4). For reporting results we therefore fixed the bound to λ = 0.02 in all reported experiments which was sufficiently small to be reached for all considered settings of uncertainties. Note that p(zt = ϕi∣XΔt:t) is not a probability, but a probability density value, that is, it can be larger than 1 and should not be expressed in %. Technically, a probability density value is the slope of the cumulative distribution function of a probability distribution evaluated at a given point in the continuous space over which it is defined.
In the standard, single decision experiments below we report the decision of the first time point for which the decision-criterion (Eq 6) was met. In the re-decision experiment we report the fraction of time in which the criterion was met for the correct alternatives.
Here we show that the BAttM has ‘inherited’ several key features from the pure attractor model and, in addition, provides for several novel and useful functionalities.
First, we show how the Bayesian attractor model implements the speed-accuracy tradeoff underlying most perceptual decision making experiments. In particular, we show how choice accuracy and mean reaction times can be explained by a combination of input noise level s and sensory uncertainty r of the decision maker. In other words, we use relative uncertainties to explain specific speed-accuracy tradeoffs. This explanation is a simple consequence of using a probabilistic attractor model in combination with Bayesian inference.
Second, we show that the model can easily explain switches in already made categorical decisions when the sensory input changes. Such re-decisions under uncertainty are often made in our natural dynamic environment but do not seem to be considered by standard experiments and computational models.
Third, we highlight that the BAttM uses a decision state-dependent, top-down modulation of sensory gain such that sensory input affects decisions most, when the decision maker internally predicts the sensory input to be most informative about the decision. Such gain modulation has been hinted at experimentally [39, 40, 53], but has not been considered in the drift-diffusion and attractor models.
Fourth, we show that this formalism enables the explicit computation of confidence in the model. This means that the model not only computes a decision state reflecting the accumulated evidence (as for example in the pure attractor model) but also another dynamic measure, the confidence about making a specific decision. Further, we show that the BAttM can model trial-by-trial variability in confidence judgements as, for example, reported in [41].
Finally, we demonstrate that the BAttM can be used for quantitative analysis of standard perceptual decision making tasks. As an example, we use behavioural data taken from a recent experiment [54] and show that the Bayesian attractor model can fit these data well.
In the BAttM, the speed and accuracy of decisions are primarily controlled by the noise level of the sensory input (s), the sensory uncertainty (r) and the dynamics uncertainty (q). Additionally, the initial state uncertainty p0 (see Methods) influences the rate of evidence accumulation at the beginning of a trial. First, we demonstrate the effect of the sensory uncertainty r, i.e., the decision maker’s internal expectation of how noisy the input is, on decisions.
Fig 5 shows the dynamics of the decision state over time for three different settings of the decision maker’s sensory uncertainty r. After an initial non-decision time of 200ms, the decision variables start accumulating evidence. If the sensory uncertainty is too low, i.e., the decision maker puts too much weight on the noisy input relative to the attractor dynamics (Fig 5A), the decision state overshoots and initially misses the associated fixed point representing a decision. Only after hundreds of milliseconds the decision state relaxes back to a fixed point. This uncertainty setting leads to inaccurate decisions with rather long reaction times. If the sensory uncertainty is too high (Fig 5C), decision making is accurate but relatively slow, because the decision maker expects a much higher noise level than the actual one. When using a suitable sensory uncertainty for the actual noise level of the input (Fig 5B), decision making is fast and accurate as typically observed in subjects.
To investigate the quantitative dependence of decision state trajectories on both the noise level s and the sensory uncertainty r we systematically varied these two parameters. We sampled single trial trajectories from each parameter combination while keeping the remaining parameters of the model fixed (q = 0.1, p0 = 5). For more reliable results, we computed the accuracy and mean reaction time over 1,000 single trials for each parameter combination (Fig 6). As expected, the accuracy (Fig 6A) decreases from perfect to chance level as the noise level s increases. In general, below s < 2, any setting of sensory uncertainty r leads to perfect accuracy whereas the mean reaction time (RT) increases with sensory uncertainty r (with r > 10 RTs can become slower than 1000ms; we excluded these parameter settings from further analysis, see the light blue areas in Fig 6). In contrast, when the noise is large (s > 20), the random movement of the dot is too large to recover the stimulus identity reliably, whatever the setting of the sensory uncertainty r. For intermediate values of s, 3 < s < 20, a relatively high accuracy level can be maintained by increasing the sensory uncertainty appropriately; this is reflected by the diagonal gradient between the white and dark grey area in Fig 6A. In Fig 6B there is a narrow valley of fast mean RTs stretching from the lower left to the upper right of the image. Note that the slower RTs below this valley result from trajectories as in Fig 5A. Slower RTs above this valley are due to slow accumulation as seen in Fig 5C. Most importantly, both fast and accurate decisions can be achieved by appropriately adapting the sensory uncertainty r to the noise level s of the stimulus. The practical use of the results shown in Fig 6 is to fit subject behaviour, i.e., to identify parameter settings which explain the observed accuracy and mean reaction time of a subject.
As our environment is dynamic, a specific stimulus may suddenly and unexpectedly change its category. For example, traffic lights turn red and other people may suddenly change their intentions and actions. In these cases one has to make a ‘re-decision’ about the category of the attended stimulus. This is different from the typical ‘single decision’ forced-choice experiments considered in the previous section. These investigate the special case in which the underlying category of a single trial does not change. The corresponding models, like the drift-diffusion model, were designed to model precisely this case and focus on the tradeoff between speed and accuracy of decisions. With re-decisions, another tradeoff, between flexibility and stability in decisions, presents itself. This tradeoff stresses the dilemma of the decision maker to either explain away evidence for an alternative as noise (stability), or rather switch to the alternative decision rapidly (flexibility).
Although one may consider extending the ‘single trial’ models so that re-decisions can be modelled (see Discussion), we found that the BAttM is already an appropriate model of re-decisions. In particular, the sensory uncertainty r and dynamics uncertainty q are two well-interpretable parameters which control the balance between flexibility and stability. Therefore, the BAttM lends itself naturally as a quantitative analysis method for reaction times and accuracy of re-decisions, as we will demonstrate next.
We investigated the re-decision behaviour for a range of parameter settings, see Fig 7. In contrast to the above findings for single decisions, the dynamics uncertainty q here plays an important role because it enables the Bayesian attractor dynamics to display different behaviours: When q is large, the decision maker will switch readily between fixed points, i.e. decisions. When q is small, switching will occur only when sensory input very clearly indicates the alternative. As a proof of principle, we varied the sensory uncertainty r and the dynamics uncertainty q in logarithmic steps (with fixed noise level s = 4), over many (1,000) trials. In each trial, after showing noisy exemplars from one target location (blue alternative) for about 800ms, we switched to the other target (orange alternative) for the same duration (cf. Fig 2).
As a measure for accuracy we report in Fig 7 the mean percentage of time spent in the correct decision state. There are three main regions in the plot: (i) uncertainty settings in the white region lead to extremely slow decisions, (ii) the grey region in which an initial decision (first 800ms) is made but not appropriately updated after a switch and (iii) the black region in which the decision dynamics is sufficiently flexible to make two appropriate decisions. As expected, and in congruence with Fig 6, we find that the sensory uncertainty r must be set appropriately (here approximately between 1.5 to 3.0) in relation to the sensory noise level (here s = 4.0) to make fast and accurate initial decisions. For further analysis we focus on one of these values (r = 2.4), which is consistent with the behavioural data fitting reported below (in our fitting results r = 2.4 roughly corresponds to noise level s = 4.0 and a coherence of about 25%). We selected three different settings of q (0.1, 0.5, 1) as a representative illustration of the results. We display samples of the corresponding trajectories of the decision state in Fig 7A–7C. To compare the impact of the dynamics uncertainty q, these samples are based on the same sensory input.
For high dynamics uncertainty q = 1.0 (Fig 7A) both the initial decision and the re-decision are made appropriately. However, the decision maker sometimes changes its decision due to sensory noise, i.e., without an underlying switch of stimulus (see Fig 7A at 350ms), exhibiting a high level of flexibility. On average, as re-decisions are made correctly, the performance is relatively large (73%). Although a performance of 73% does not sound very high, it is an open experimental question how human participants would perform in the re-decision experiment. Like the model, a participant will require switching time and may experience transient false beliefs as seen in Fig 7A. In the model, the 73% performance compares well against the two other dynamics uncertainty settings. For example, for a smaller uncertainty (q = 0.5, Fig 7B) spurious, noise-induced switches are greatly reduced, but re-decisions are slower. This leads to a reduction in time spent in the correct decision state (53%) in exchange for an increased stability of the decisions. In the grey region (point location and panel C in Fig 7) the dynamics uncertainty is too low (0.1) to make a re-decision based on the sensory input. Only 35% of the time was on average spent in the correct decision state with this setting of q, i.e., decisions were detrimentally stable.
In summary, the dynamics uncertainty q is a useful parameter for modelling the tradeoff between flexibility and stability of re-decisions. Importantly, similar to the fitting of the experimental data of [54], the mapping of parameters s, r, and q (i.e., noise level, sensory uncertainty and dynamics uncertainty) can be used to quantitatively analyse experimental data in re-decision tasks.
The BAttM suggests an intuitive mechanism of re-decisions: Once an initial decision has been made, the decision state is located in a stable fixed point of the attractor dynamics. As long as sensory observations are consistent with the decision maker’s expectations, the fixed point location is held. When the underlying stimulus changes, however, violated expectations, i.e., large prediction errors (see Fig 1B), force the decision state to move away from the currently occupied fixed point and towards the fixed point representing the identity of the new stimulus, eventually leading to a re-decision. Both sensory uncertainty and dynamics uncertainty control the gain with which prediction errors influence the decision state (cf. Eqs 4 and 5 in models): the sensory uncertainty primarily controls the overall amount of evidence extracted from sensory observations (high uncertainty means low evidence) while the dynamics uncertainty controls how sensory evidence is translated to the decision state (high dynamics uncertainty usually means large effects of sensory evidence on the decision state). Similarly, the gain of the sensory evidence on the decision state is influenced by the decision state itself, implementing state-dependent top-down gain modulation of sensory information. We describe this effect next.
There is growing evidence that higher level cognitive processes modulate neural responses already in early sensory areas [36–38, 55–58]. More specifically, recent findings [39, 40, 53] indicate that neural activity in early sensory areas is modulated by the final choice of subjects in simple perceptual decision tasks. These findings suggest that top-down feedback influences sensory processing already on the temporal scale of single decisions, i.e., within a trial of a perceptual decision making task. Pure attractor and drift-diffusion models, however, do not account for top-down feedback that modulates the extraction of evidence on the sensory level. In this section, we show that the BAttM offers such a top-down computational mechanism that leads to a stabilisation of the fixed points of the attractor dynamics and, consequently, allows the decision maker to make confidence-informed decisions.
This mechanism can be best understood by comparing the within-trial dynamics of the decision state for both pure attractor models (Eq 1) and the BAttM. Bayesian inference in the BAttM implements a predictive coding scheme (Eq 4) in which state predictions z ^ t are updated with information from prediction errors ϵt dependent on a Kalman gain matrix Kt (Eq 5) which embodies uncertainty and the relation between observations x and decision variables z. To compare the pure attractor model with the BAttM we first note that both models have the same form: After approximating the mean state prediction z ^ t with the (expected) attractor dynamics of the generative model,
z ^ t ≈ z ¯ t - Δ t + Δ t f ( z ¯ t - Δ t ) , (7)
we can plug this approximation into Eq (4). The resulting Bayesian inference formalism replicates the form of the attractor model in Eq (1):
z ¯ t - z ¯ t - Δ t ≈ Δ t f ( z ¯ t - Δ t ) + K t ϵ t . (8)
The critical difference of the BAttM formalism of Eq (8) to the pure attractor model in Eq (1) is that the BAttM prescribes an input consisting of a prediction error scaled by the gain. In particular, the input to the Bayesian attractor model depends on the last state z ‾ t − Δ t both through the gain matrix Kt and the mean prediction x ^ t (see Models). This means that sensory observations pass through two processing steps which are applied in each time step: (i) Computation of prediction error using the top-down prediction, and (ii) modulation of the prediction error by the gain which also translates the sensory information (prediction errors) into the decision space (through linear transformation by the gain matrix Kt).
In this model, the effect of the gain is driven by two opposing components: In general, when predictions are more certain, the gain is increased. This effect is primarily mediated by the uncertainty r at the sensory level. Importantly, the gain is also driven by the cross-covariance of the predicted decision state z ^ t and predicted sensory observations x ^ t (Eq 5). The cross-covariance describes the information about changes in the decision state that can explain variation in sensory observations. It defines how prediction errors in sensory observations induce necessary changes in the decision state. This effect is largest in the space between fixed points of the attractor dynamics, because here a change in the decision state almost linearly maps to a change in sensory predictions. In contrast, the effect is relatively small close to the fixed points (see Methods for details). As uncertainty in the decision state increases, it becomes more likely that the underlying distribution covers more of the space between fixed points, thereby increasing cross-covariance. Consequently and opposite to uncertainty at the sensory level, higher uncertainty at the decision level typically leads to larger top-down gain.
Fig 8 demonstrates this within-trial gain modulation mediated by cross-covariance, for the empirically inferred parameters of point B of Fig 7 (s = 4, r = 2.4, q = 0.5). Fig 8A shows the inferred decision state as a function of time. After the switch of the stimulus, between 800 and 1,500ms, the decision state moved between fixed points of the attractor dynamics. As can be seen in Fig 8B, the predicted cross-covariances between decision state and sensory observations were large during this time period and became small again once the dynamics settled into a fixed point after 1,500ms, i.e., when a decision had been made. Similar dynamics can be seen for the initial decision around 0 to 200ms. Fig 8C plots the elements of the gain matrix Kt over time. The trajectories follow those of the cross-covariance closely demonstrating that within-trial changes in gain were driven nearly exclusively by changes in the cross-covariance. Although the uncertainty over the decision state also varied within the trial (Fig 8A, shading), the effect on the uncertainty of predicted observations was small in comparison to the effect exerted by the sensory uncertainty r, which remained constant throughout the trial.
In summary, the within-trial, state-dependent modulation of gain is a useful mechanism when making decisions: It stabilises the representation of the stimulus category (low gain close to fixed points, see below), but also implements fast accumulation of evidence, when needed (high gain between fixed points).
A graded feeling of confidence appears to be a fundamental aspect of human decision making. Corresponding confidence judgements can inform about underlying decision processes [42, 43]. Through the probabilistic formulation, the BAttM directly provides a continuous measure of confidence that may be compared to experimentally measured confidence judgements. In the following we describe how confidence is computed in the BAttM, explain its use within the decision criterion and demonstrate that it conforms to experimental findings about confidence judgements [41, 42].
The substantial and sudden decrease of gain close to a fixed point (e.g., Fig 8C, at 1,400ms) contributes to an important feature of the BAttM: The location of fixed points is the same for different stimulus strengths. As we will show in this section, stable fixed point locations are the basis for defining a decision criterion directly on an explicit measure of confidence.
Pure attractor models do not have stable fixed points: Because noisy evidence directly feeds onto the decision variable (see Eq 1 and Fig 1A), the location of fixed points depends on the magnitude of the evidence, i.e., stimulus strength. We show this effect in Fig 9A, see also [59]. Therefore, in pure attractor models, as long as stimulus strength is assumed to be unknown, one cannot tell how close the current decision state is to a fixed point, that is, fixed points have no particular meaning in pure attractor models except that the dynamics will eventually converge to them.
In contrast, in the BAttM the speed of evidence accumulation, as caused by a particular, underlying stimulus strength, can vary without affecting fixed point locations (Fig 9B and 9C). This is because the BAttM implicitly represents stimulus strength in its uncertainty parameters r and q such that expected stimulus strength is automatically taken into account during evidence computation from the stimulus. As a consequence of stable fixed point locations, a deviation of the decision state from a fixed point can be readily interpreted as violation of the expectations about the stimulus associated with that fixed point, irrespective of stimulus strength. In general, the more such expectations are violated, the less confident the decision maker should be about choosing one of the alternatives. We implemented this mechanism in the BattM by deriving the confidence in a decision alternative directly from the probabilistic model and using a threshold on it as decision criterion (see Models, Eq 6).
In Fig 10 we illustrate how confidence values relate to the posterior density of the decision state (Fig 10A), and how confidence-based decisions are made (Fig 10B). Intuitively, the confidence for a specific alternative measures the distance of the current decision state (blue and orange lines in Fig 10A) from the stable fixed point of that alternative (at [0, 10] or [10, 0]) scaled by the posterior uncertainty of the decision state. Consequently, the confidence for all alternatives can be tracked across time (cf. blue and orange lines in Fig 10B). Strikingly, the confidence dynamics are different from the decision variable dynamics: While the decision state gradually moves towards a fixed point, thus reflecting the relatively slow gradual accumulation of evidence (e.g., time period 800 to ∼ 1100ms), the confidence rises abruptly as soon as the posterior density of the decision state starts concentrating around a fixed point (e.g., from ∼ 1100ms onwards).
How does the confidence-based decision making formalism compare with experimental findings? Early behavioural work with humans [42], indirect confidence judgements by rats [41] and general theoretical considerations [42, 43] suggest that confidence in correct choices increases with stimulus strength whereas confidence in erroneous choices decreases with stimulus strength. At first glance, this seems at odds with a confidence-based decision criterion, as used by the BAttM, where the decision is made exactly when the confidence is at a specific level, independent of stimulus strength (Fig 10B). This apparent contradiction can be resolved by noting that subjects, in the typical experimental setup, keep observing the stimulus for a short time after reaching the threshold, because of the delay between an internal decision and the production of the corresponding motor output, such as a button press. In standard models, this time period is usually considered to be part of the total non-decision time. Importantly, the same mechanism of continued accumulation of evidence in this time period is thought to contribute to ‘changes of mind’ observed in a reaching task [35] where subjects revise their internal categorization before being able to fully execute the reaching movement. We implemented this mechanism in the BAttM by continuing the accumulation of evidence after crossing the confidence threshold for about half of the estimated non-decision time of 200ms, i.e., for 100ms. Critically, during this continued accumulation period, the confidence values evolve further and replicate the reported experimental results that show a dependence of confidence on stimulus strength and correctness of decision (Fig 11).
To establish the validity of the proposed model and show that the model can be used to analyse data of decision making tasks, we fit behavioural macaque monkey data on the RDM two-alternative forced choice task presented in [54]. These authors used a drift-diffusion model to fit the average responses based on 15,937 trials. Stimuli were presented at eight different coherence levels ranging from 0% to 75%. We extracted the averages of the behavioural data from Figure 1 d,f in [54] and re-plotted the data in Fig 12B and 12C (black dots). We obtained the model fit by stochastically minimising an objective function which quantified the discrepancy between values sampled from the model and the behavioural data (cf. Methods). The sampled RTs contained a non-decision time which was reported in [54] (see Methods for details). We plot the fits of mean reaction time and accuracy in Fig 12B and 12C. In Fig 12A, we show the fitted model parameters, noise level s and sensory uncertainty r, see also Table 2.
These results demonstrate that the model can fit the mean RTs and accuracy for different coherence levels by varying the sensory noise and the internal uncertainty of the decision maker. As can be seen in Fig 12A and Table 2, we found, as expected, that both the sensory uncertainty and the noise level decrease as a function of coherence. The estimated posterior parameter variances indicate that parameters of the model can be estimated reliably for intermediate accuracies. When accuracy reaches its ceiling at 100% for coherences greater than 25% many different noise levels s can lead to equivalent predictions, simply because noise is not needed anymore to explain erroneous choices and can be set arbitrarily small.
It has previously been found that the drift in a drift diffusion model scales linearly with coherence (e.g., [54]). We found an equivalent relation between the sensory uncertainty r and coherence (Fig 12A, red line). In particular, it has been shown for a simple probabilistic model ([23], Eq 22) that sensory uncertainty r relates to drift v in the drift diffusion model as r2 = 2/(vΔt2). If v = Kc as in [54], r can be written as r2 = K′/c. We applied this relation to the BAttM and fitted K′ to the values of r reported in Table 2 (see Methods for details). The result captures the previously reported relation between coherence and sensory uncertainty well for most coherences (red line in Fig 12A) and only deviates from the fitted parameter values for coherences greater than 25%; see Discussion for a potential, interesting reason.
In all work presented here we fixed the confidence threshold λ to a constant value. This was necessary, because λ and sensory uncertainty r have very similar effects on mean RT and, thus, are interchangeable in many conditions (cf. [23]). To verify this relationship we repeated fitting of the data used here, but fixed r = s and allowed λ to vary. With this parameterisation, we could fit behaviour for high and intermediate coherences equally well, but observed a drop in quality of fit for low coherences (0% and 3.2%, results not shown).
We have embedded an attractor model into a Bayesian framework, resulting in a novel Bayesian attractor model (BAttM) for perceptual decision making. The model can be used as an analysis tool to fit choices and response times of subjects in standard perceptual decision making tasks (Table 2, Fig 12). It also extends to re-decision tasks where the participant has to detect stimulus changes and make another decision (Fig 7). In addition, the model predicts state-dependent, within-trial gain modulation of sensory processing by top-down feedback of the decision state (Eq 8, Fig 8). This top-down gain modulation enables an explicit measure of confidence in decisions (Fig 10) that reproduces recent experimental findings about confidence judgements in perceptual decision tasks (Fig 11).
In typical perceptual decision making experiments, e.g. [54], the response of the participant automatically ends a trial and the stimulus disappears. In natural conditions, however, an object typically does not disappear after the brain has made its categorisation and the object should be represented as long as it is behaviourally relevant. In addition, the brain has to be able to rapidly update a decision in response to a change in the environment, for example, when a green traffic light turns red. These decisions, which we called re-decisions, are currently rather not considered by perceptual decision making models. In particular, drift-diffusion and similar probabilistic models of perceptual decisions are not good models for behaviour in response to stimuli that switch occasionally. This is simply because the amount of accumulated evidence for a decision depends on the time the stimulus supporting the decision is observed: To switch to the alternative decision, this accumulated evidence must be overcome by an equal amount of evidence in favour of the alternative. This means that the reaction time in response to a switch would depend on how long the previous stimulus was shown. If the previous stimulus was present for several seconds, standard drift-diffusion and related models predict that the reaction time for a switch would be several seconds as well. This would clearly depart from the expected decision behaviour of participants with typical reaction times of several hundred milliseconds.
Pure attractor models, on the other hand, provide a basis for successful re-decisions: Once the decision state is in a fixed point no additional evidence is accumulated. Consequently, only a fixed amount of evidence for the alternative category is required to reverse an initial decision by moving the decision state into a different attractor [26]. The BAttM enhances this property through its embedding in a probabilistic framework: It provides a single, interpretable parameter, the dynamics uncertainty q (cf. Table 1), that controls the timing of re-decisions independently of the timing of initial decisions and, thus, implements a tradeoff between flexible and stable decisions (Figs 7, 9C).
Note that the drift diffusion model could be extended to allow for re-decisions that do not depend on the duration of the previous stimulus. In a neural model of a drift diffusion process this could be achieved by using neurons with a maximal firing rate. In mathematical formulations based on a stochastic differential equation [6, 20], such a maximal firing rate mechanism translates to a condition which would increasingly limit the size of state changes as the maximum state value is approached. To the best of our knowledge, such a mechanism has not been described yet and would reproduce a key feature of attractor models where state changes decrease as a fixed point is approached.
So-called changes of mind [31, 35] differ from re-decisions. In [35] a change of mind occurred very quickly to correct an initial decision, that is, without a change of stimulus subjects changed their decision, presumably, in response to stimulus information that was processed just after the initial decision had been made. In contrast, re-decisions can also occur long after a decision that was made with high confidence. Specifically, the model of changes of mind described in [35] extended a standard drift-diffusion model with an additional bound which only comes into effect after one of the initial bounds has been crossed, that is, after an initial decision has been made. This second bound is only defined for the initially unchosen alternative. Other than in the standard drift-diffusion model, accumulation of evidence continues after the decision. If the second bound is reached within a given deadline, a change of mind is executed. There are two properties of this model which prevent modelling re-decisions in response to a change in stimulus: 1) the deadline and 2) (as described more generally for drift diffusion models above) the dependence of re-decision times on the time of the underlying stimulus switch. The deadline in the change-of-mind model was designed to capture motor costs that prevent a change-of-mind too close to the end of the trial. The deadline, therefore, could simply be dropped in a re-decision experiment. However, the more general drawback of drift diffusion models, i.e., the dependency of re-decisions on the duration of the previous stimulus, would have to be fixed more elaborately (see previous paragraph).
To investigate re-decisions in experiments, standard perceptual decision making paradigms need to be adapted. Especially, single trials need to be prolonged in order to present changing stimuli to the participants and allow them to react to these changes.
As stated above, although there may be differences in detail, pure attractor models can, in principle, explain re-decisions as well. One question is what the BAttM can offer beyond what pure attractor models can do. An important advantage of a probabilistic formulation is that it allows to define confidence measures, as discussed further below. Another crucial advantage is that the BAttM explicitly models how evidence for a decision is extracted from the concrete features of a given stimulus. This means that the BAttM can in principle predict reaction times and choices of the subject given the stimulus features of the actual stimulus shown to the subject in each single trial. Although this may appear as a technical detail, we believe this input model (see Fig 3) is a vital model component. For example, pure attractor models require that the modeller provides the evidence input. This ‘manual’ specification of the evidence input is not necessarily an advantage because the exact shape of the input is a key to explain the data. This would make the manual input specification an important but rather ill-constrained component of the model as there is no measure of the degrees of freedom spent on the input specification. In contrast, the BAttM explicitly constrains evidence computation via the Bayesian update equations. As a result, stimulus features shown to the subject enter the behavioural analysis in a highly constrained fashion. This formally described evidence computation also defines the top-down modulation predicted by the BAttM, as discussed next.
In the BAttM, there are two different ways how top-down gain modulation of sensory processing emerges. The first depends on the sensory uncertainty r, which we implicitly assume here is a between-trial effect because most experiments keep the amplitude of the sensory noise constant over a trial, but see ‘Adapting stimulus expectations’ below for a discussion of this assumption. The second gain effect varies due to the dynamics of the internal decision state, which is a within-trial modulation.
The between-trial gain modulation offers a novel understanding of variations in reaction times caused by varying stimulus noise level. In explanations of perceptual decision making it is generally assumed that stronger stimuli, i.e., with higher signal-to-noise ratio, translate into larger pieces of evidence which lead to faster accumulation [1]. The BAttM makes this translation explicit and models higher stimulus strength by less observation noise s and correspondingly less sensory uncertainty r (Table 2, Fig 12). A key prediction of the BAttM is that different speeds of evidence accumulation, e.g., across task difficulty levels, are caused by different amounts of top-down gain modulation: the lower the sensory uncertainty, the higher the gain of sensory input (Eq 5). Such a top-down mechanism has been described in general by proponents of the Bayesian brain hypothesis [45, 46, 60], the free energy principle [61] and predictive coding [62]. In particular, it has been suggested that internal uncertainty is tightly linked to neuronal modulator mechanisms [63–65] that implement attentional, top-down modulation of sensory areas [36–38, 55–58].
In addition to these between-trial effects, experimental findings prompted the suggestion that sensory gain may be modulated within-trial by the state of an ongoing decision [39, 40, 53]. Drift-diffusion and pure attractor models do not account for such top-down modulation of gain, because there is no top-down connection from decision state to sensory input in these models. In the BAttM, however, this connection is provided by the state-dependent Kalman gain, see Eqs (8, 5). In particular, the BAttM predicts that sensory gain is large when transitioning between decision alternatives and small when the decision is imminent or has been made (Fig 8). This modulation is driven by the cross-covariance between predicted observations and decision states (Fig 8). Intuitively, this cross-covariance measures what changes can be expected on the observation level due to a change of the decision state, or, inversely, what changes in the decision state are likely to explain changes on the observation level. Therefore, the described formalism underlying within-trial gain modulation differs from the between-trial modulation which is purely based on changes in sensory uncertainty.
Previous experiments [39, 53] showed only coarse-grained evidence for decision-dependent modulation of activity in sensory areas, or are currently difficult to translate into our formalisation due to the type of measurement [40]. Therefore, further research is needed to test the hypothesis of specific temporal structure of gain modulation as predicted by the BAttM. Note that the BAttM was not designed by us to employ such a state-dependent top-down modulatory mechanism; rather, this property emerges from the Bayesian formulation in which decision states explicitly connect to particular sensory observations. Furthermore, the gain modulation in the BAttM has two functional benefits: First, it leads to a common, stable representation of the decision across task difficulties while still allowing decisions to be made with varying accuracy and timing. This is not the case for pure attractor models (Fig 9) but is useful for a neuronal implementation because the next higher level can more easily read out a stable representation. Second, within-trial gain modulation facilitates rapid updating of decisions in response to a changed stimulus, because it quickly destabilises a made decision when sufficient evidence to the contrary is available. Consequently, the increased gain speeds up the transition to an alternative decision. Note that the initial movement out of a fixed point that represents a previously made decision is mediated by prediction errors (Eq 8) which tend to be large when the decision deviates from the real stimulus and small otherwise.
Although there are some reports of potential within-trial top-down gain modulation [39, 40, 53], the formalism implemented by the BAttM is, at the current time point, a purely theoretical prediction which may be tested in future experimental work. Diffusion models often successfully explain decision behaviour without using top-down feedback mechanisms. Therefore, it may appear that the brain does not use top-down feedback when making simple perceptual decisions. However, a simple experiment testing the existence of top-down modulation may proceed as follows: Participants would be cued about the upcoming stimulus strength only in some trials but not in others. If the predictive cue had an effect on decisions, the BAttM would predict that this was partially due to between-trial top-down modulation through updated expectations of the participants. It is harder to test the existence of within-trial top-down modulation that discriminates the BAttM from pure attractor and diffusion models. Novel tasks may be required to elicit measurable effects of such within-trial top-down modulation. For example, the BAttM predicts that top-down modulation varies strongly in experiments with longer trials including re-decisions. In addition, the BAttM could be used to test this particular question by removing within-trial top-down gain modulation in the model and comparing choices predicted from this reduced model with those predicted from the full BAttM.
“It has been definitely shown that the recognition process is attended by varying degrees of confidence; that the correctness of recognition tends to vary directly with the degree of confidence, and that our belief-attitudes appear with varying degrees of strength, or varying degrees of confidence, assurance, or certainty.” [66] Since 1926 this account has been consolidated and given a theoretical basis [42]. More recently, behavioural paradigms were developed in which confidence could be measured from non-verbal responses [41, 67]. These developments have been accompanied by extensions of drift-diffusion and attractor models that explain measured confidence ratings: For drift-diffusion models explicit confidence values can be computed as function of the decision variable and time [67] under the assumption that subjects’ confidence equals their true probability of making an error, but see [68]. Alternatively, the decision variable itself can be related to subjective confidence in the drift-diffusion model [23]. In pure attractor models, the decision state has been related to confidence judgements only indirectly: The increasing magnitudes of the decision state at the fixed point locations for increasing stimulus strengths (cf. Fig 9A) have been interpreted as increasing confidence in the decision [59]. This account assumes that the decision state continues to evolve towards the fixed points of the dynamics after the decision threshold has been reached.
Other than both drift-diffusion and pure attractor models, the BAttM computes an explicit (i.e., in addition to the decision state) and ongoing measure of confidence based on subjective uncertainties of the decision maker (see Fig 10 and Fig 4). This enables us to model confidence-based decisions using a threshold on the ongoing confidence (Fig 10B) which, in the BAttM, is defined as the posterior density that the decision state is in a stable fixed point of the generative model (cf. Eq 6 in Methods). This posterior density can be interpreted as the decision maker’s internal belief that a category is the true category of the stimulus and can be easily computed from the estimated posterior over the decision state for an arbitrary number of alternatives. Note that the threshold on confidence may be implemented by a simple threshold on firing rates of neurons that represent the corresponding posterior density. As a density, however, it cannot be expressed in percent and, therefore, lacks an intuitive connection to typical measures of confidence in behavioural experiments. This connection may instead be provided by alternative measures of confidence that can also be derived from the posterior distribution over the decision state. For example, one can compute, as a measure of confidence, the probability that any one of the decision state variables exceeds all other state variables. This probability can be expressed in percent. It is possible that subjects compute such a measure when asked to explicitly report confidence after the decision, but it is an open experimental question how to identify forms of confidence judgements actually used by the brain.
As the BAttM uses a threshold on the confidence to make a decision, the confidence at decision time is always equal to the threshold. This fact appears to contradict key experimental findings showing a dependence of confidence judgements on decision outcome and stimulus strength [42, 43]. Yet, this apparent mismatch can be resolved (Fig 11) simply by continuing accumulation of evidence during part of the non-decision time period. This continued accumulation is motivated by a corresponding assumption in [59] and by recent experimental findings regarding changes-of-mind in decision making [35]. It has also been shown that a wide range of findings about confidence ratings can be replicated under the assumption that evidence accumulation continues until the confidence rating [69]. In further congruence, potential neural correlates of continued processing of the stimulus after reaching a threshold were reported in [70].
Furthermore, the BAttM predicts direct, intuitive relations between the internal uncertainties of a decision maker and the absolute level of confidence that can be reached: Larger uncertainties lead to smaller confidence (e.g., see Fig 4). As these uncertainties simultaneously control choices, response times and re-decision times, we propose to validate the consistency of these predicted relations in future experiments.
We fitted the BAttM to average behaviour reported in [54] and found that the BAttM explains decision making behaviour well (Fig 12B and 12C) even though we assumed a simplified representation of the stimulus (cf. section input). This was expected, because 1) a similar, abstract stimulus representation was sufficient to fit behavioural data (of humans) before [23] and 2) [54] originally used a similar computational representation to fit a drift-diffusion model to the data considered here.
For the BAttM, estimates of the reliability of parameter fits indicate that fitted parameter values are highly reliable for experimental conditions in which subjects exhibit intermediate accuracy in response to coherences from 3.2% to 12% (Fig 12A). In these conditions our fits suggest that the noise level s exceeded sensory uncertainty r in the subjects which would mean that the subjects’ generative model of the stimulus underestimated the amount of noise in the stimulus. In contrast, an optimal Bayesian decision maker should have a generative model in which, ideally, r would equal s. It has been proposed that variability in subjects’ responses may be due to suboptimal inference [71], that is, inference based on suboptimal, or wrong assumptions about the underlying statistical structure of the inference problem. Our observation that s exceeds r suggests that subjects indeed perform suboptimal inference in the corresponding choice task. This finding, however, only holds under the assumption that the confidence threshold is set to a constant, low value (λ = 0.02), because r and λ have very similar effects on accuracy and mean RT. Indeed, we also found that behaviour in most conditions could be fit equally well, when r was constrained to be equal to s, but λ was allowed to vary freely. Although the drop in quality of fit for coherences 0% and 3.2% (cf. results) indicates a disadvantage of the constraint s = r compared to the constraint λ = 0.02 we cannot draw definite conclusions about whether subjects perform suboptimal inference, or not, from the present data.
For coherences above about 25% parameter estimates became less reliable (Fig 12A), because accuracy reached its ceiling of 1 and became uninformative. We expect that parameter estimates become more reliable in these experimental conditions, if reaction time distributions are used for fitting instead of only mean reaction times [54].
In the original fits of behaviour in [54] the drift was constrained to be a linear function of coherence ([54], Supp. Fig. 6), where a single parameter, the slope of the linear function replaced coherence-specific drifts. In contrast, in our fits of the BAttM to the same data we allowed both, sensory uncertainty r and noise level s, to freely vary across coherences. Although this increased flexibility of the BAttM, in principle, could have led to overfitting, it is unlikely that this is the case for our results: The noise in the data is small compared to the effect of the coherence, because the data are averages based on 15,937 trials ([54], Fig 1). The low variance of parameter estimates for intermediate coherences (Fig 12A) also indicates that our fitting method identified unique parameter values for these coherences. Furthermore, by relating the sensory uncertainty parameter in our fits to drift in the drift diffusion model [23], we observed that the fitted values of sensory uncertainty r obey the linear constraint employed by [54] for coherences of up to 25% without explicitly using this constraint during fitting. It is currently unclear why the parameters for high coherences do not follow the previously assumed linear relation between drift and coherence. One possible explanation is that the urgency signal, which we did not model in the BAttM, has a larger effect for high coherences than for low ones. The estimated shape of the urgency signal ([54], Supp. Fig. 6b) supports this speculation, because it exhibits a steep rise early in a trial such that its effect should be relatively large for fast decisions. However, clearly further research is required to substantiate this potential mechanism.
The BAttM explains different behaviour in response to stimuli with different strength using particular combinations of input noise level s and sensory uncertainty r (Table 2, Fig 12). It, therefore, appears that decision makers adapt their expectations about the stimulus (r) to stimulus strength even before they experience the stimulus (we fixed r within trials). In experiments in which trials with the same stimulus strength are blocked, or in which stimulus strength is cued before onset of the stimulus, this is plausible. In experiments in which stimulus strength changes randomly across trials, this assumption seems flawed. This consideration has led others to discuss whether the brain implements Bayesian models [72]. Here, we speculate that decision makers rapidly adapt their expectations in parallel with decision making as they sample observations from the stimulus. Such adaptation is compatible with the timescale of short-term synaptic plasticity in the brain [73]. Also, it has previously been demonstrated that sensory reliability (akin to r) can be inferred together with stimulus identity in a Bayesian model [25].
Even though we believe that decision makers adapt their stimulus expectations within a trial, the BAttM currently does not employ such a mechanism. Nevertheless, assuming fixed r led to good fits of accuracy and mean RTs as recorded in [54] (cf. Fig 12). This is not very surprising: The behavioural data has originally been fit by a drift-diffusion model with constant drift throughout a trial [54]. Such constant drift implements the assumption that the average amount of evidence extracted from the stimulus at a given moment is constant throughout the trial. Critically, the ‘evidence’ is not a fundamental, sensory quantity, but needs to be computed by the brain specifically for the given decision problem. It can further be shown [23] that ‘evidence’ depends on sensory uncertainty in probabilistic models. Therefore, the assumption of a constant drift throughout a trial is, in the BAttM, equivalent to maintaining stable expectations about the stimulus throughout the trial. As a result, keeping r fixed in the BAttM is a simplification that follows previous approaches based on drift diffusion models and still allows to fit behaviour (accuracy and mean RTs) of subjects well (see Fig 12). Similar to within-trial effects of top-down gain modulation, however, future work may aim at elucidating potential effects of within-trial variations in expected sensory uncertainty r due to adaptation of stimulus expectations. In particular, experiments with longer re-decision trials and continuously changing stimulus reliability may induce strong adaptations of stimulus expectations that have measurable behavioural effects.
One of the strengths of the original pure attractor models is their link to possible neurobiological implementations in networks of spiking neurons (cf. Section: pattm). We have abstracted from this perspective and have embedded a pure attractor model in a dynamic Bayesian inference framework. Consequently, the question arises how this apparently more complicated construct may map to a neurobiological substrate. The BAttM is a probabilistic filter that recursively updates posterior beliefs by evaluating the likelihood of the state of a dynamic generative model given a stream of observations (cf. models). A wide range of proposals have been made for how probabilistic filters can be implemented by networks of neurons [47, 74–81]. For example, [80] discusses how computations defined by predictive coding approaches, which derive from probabilistic filters (cf. Section Bayesinf), can map onto canonical microcircuits in cortex. More abstractly, [47, 77, 79] show how networks of rate neurons may implement probabilistic filters and [74–76, 78, 81] provide implementations based on spiking neuron networks. Given these proposals, it seems reasonable to assume that the computations defined by the BAttM can be implemented by the brain.
We have presented a novel perceptual decision making model, the Bayesian attractor model, which combines attractor dynamics with a probabilistic formulation of decision making. The model captures important behavioural findings and makes novel predictions that can be tested in future experiments. In particular, we have highlighted a re-decision paradigm which can be used to investigate the tradeoff between flexibility and stability in perceptual decisions. Furthermore, the BAttM predicts particular, within-trial modulation of sensory gain which may explain recent experimental findings. Finally, the BAttM predicts experimentally testable links between choice, response times and confidence.
We used a Hopfield network as an example of a pure attractor model. Hopfield networks have originally been suggested as a neurobiologically plausible firing-rate models of recurrently connected neurons [44]. We define a general Hopfield network with N state variables as follows (here summarised in one equation using matrix notation, see Fig 13 for a graphical representation of the binary case N = 2):
z ˙ = k ( L σ ( z ) + b l i n ( g 1 - z ) ) (9)
where z ∈ ℝN is the decision state consisting of the state variables zi, k is a rate constant, σ(⋅) is a multidimensional logistic sigmoid function and blin is a parameter determining the strength of a goal state attractor g = g1. Lateral inhibition for winner-take-all dynamics is implemented using
σ i ( z ) = 1 1 + e - r ( z i - o ) and L = b l a t ( I - 1 ) (10)
where r and o determine the slope and centre of the sigmoid function, respectively, blat determines the strength of the lateral inhibition, 1 ∈ ℝN×N is a matrix of ones, and I is the identity matrix. One can see that the fixed points with one state variable zm ≈ g, while all others are zj ≠ m ≈ 0, are local minima of the underlying Lyapunov function and therefore stable [44] provided that o = g and blat/blin = 2g. We denote these stable fixed points as ϕm where m indicates the state variable that is equal to g. As parameter values we used k = 4, g = 10, r = 1, o = g, blat = 1.7, blin = blat/(2g) in all experiments, because these provided for numerically stable Hopfield dynamics which exhibited the desired fixed points and reasonably fast convergence to these. For interpolating observations in the generative model (Eq 3) we use the same form of sigmoid as defined in Eq (10), but with parameters r = 0.7, o = g/2. This choice increases the range of values for which the sigmoid is approximately linear and increases robustness of the inference with the generative model.
When modelling perceptual decisions, we follow [26, 28] and initialise the attractor dynamics in a neutral state. In particular, we set a prior distribution over the decision state as z0 ∼ 𝓝(μ0,P0) where μ0 is an unstable equilibrium point of the Hopfield dynamics for which
μ i = μ j and μ ˙ i = 0 ∀ i , j ∈ 1 , ⋯ , N . (11)
This starting point ensures that a relatively long time is spent close to the equilibrium, while once the dynamics has sufficiently differentiated, the decision state will rapidly move to its closest stable fixed point. We set the covariance of the initial decision state to P 0 = p 0 2 I and call p0 the initial state uncertainty which is a parameter of the model that controls the susceptibility of the decision state to incoming evidence at the beginning of a trial.
In Fig 6 we plotted contour lines. These were approximated from the noisy data points underlying the grey scale maps as follows. We defined four values for four contours for each map as reported in the caption of Fig 6. For each value, e.g., 500ms, we found all points in the parameter grid for which their own associated value lay within a limit to the chosen contour value (limit of 0.01 fraction correct and of 10ms). We then fitted the hyperparameters of a Gaussian process [82] to the found points in logr-logs space (one per contour line) using the GPML Matlab toolbox (http://mloss.org/software/view/263/). In particular, the Gaussian process mapped the logarithm of the noise level, logs, onto the logarithm of the sensory uncertainty, logr and used a standard squared exponential covariance function with a Gaussian likelihood [82]. The contour lines in Fig 6 represent the mean predictions of sensory uncertainty obtained from the fitted Gaussian processes for the corresponding noise level.
To fit the data from the experiment reported in [54] we defined a temporal scaling between our discrete model and the times recorded during the experiment. This scaling corresponds to Δt = 4ms in Eq (2). It was chosen as a tradeoff between sufficiently small discretisation steps and computational efficiency and means that about 200 time steps are sufficient to cover the full range of reaction times observed by [54]. Furthermore, we used a non-decision time of T0 = 200ms which is roughly the value that was estimated by [54] (cf. their Table 1). The non-decision time captures delays that are thought to be independent of the time that it takes to make a decision. These delays may be due to initial sensory processing, or due to the time that it takes to execute a motor action.
We used a form of stochastic optimisation based on a Markov Chain Monte Carlo (MCMC) method to find parameter values that best explained the observed behaviour in the experiment for each coherence level independently. This was necessary, because we could not analytically predict accuracy and mean reaction times from the model and had to simulate from the model to estimate these quantities. In particular, we simulated 1,000 trials per estimate of accuracy and mean RT, as done to produce Fig 6. We then defined an approximate Gaussian log-likelihood of the parameter set used for simulation by using the estimated values as means:
L ( s , r ) ∝ ( A - A ^ ) 2 σ A 2 + ( R T - R T ^ ) 2 σ R T 2 + P ( s , r ) (12)
where A and RT are the accuracy and mean RT, respectively, measured in the experiment for one of the coherences and A ^ and R T ^ are estimates from the model. σA = 0.05 and σRT = 10ms are ad-hoc estimates of the standard deviation of the estimates which we chose large enough to account for the variability we observed in the data of Fig 6. P(s,r) is a penalty function which returned values greater than 10,000, when more than half of the simulated trials were timed out (cf. light blue areas in Fig 6) and when the particular combination of s and r lead to too strong overshoots of a state variable (cf. Fig 5A). We identified overshoot parameters as those which lay below a straight line from r = 0.47, s = 1.45 to r = 3.66, s = 80 in Fig 6. We embedded the approximate likelihood of Eq (12) into the DRAM method of [83] (Matlab mcmcstat toolbox available at http://helios.fmi.fi/~lainema/mcmc/) which implements adaptive Metropolis-Hastings sampling with delayed rejection. We log-transformed the parameters to ensure that only positive samples are generated and defined wide Gaussian priors in this log-space (logs ∼ 𝓝(0,102), logr ∼ 𝓝(0,102)), but also constrained s > 0.1 to ensure a minimum level of noise. We then ran the MCMC method for 3,000 samples, discarded the first 499 samples and chose every 5th sample to reduce correlations within the Markov chain. The resulting set of 501 parameter samples is a rough approximation of the posterior distribution over parameters for the given data. It is not statistically exact, because of the approximate likelihood, but it still indicates when parameter estimates become unreliable, as demonstrated in Fig 12. The parameter values reported in Table 2 are those of the sample (of the 501) which fitted the behaviour for a given coherence best, as determined by Eq (12).
Note that, different from [54], we did not a priori assume a particular relationship between coherence and the parameters of the BAttM during fitting. In [54] coherence linearly scaled the drift in their drift-diffusion model using a scaling parameter K that was common across coherences ([54], Supp. Fig. 6), that is, the average amount of momentary evidence accumulated in the model was determined from the coherence used in a trial. In the BAttM the fitted parameters, sensory uncertainty r and noise level s, determine how stimulus features are translated into momentary evidence. Since we did not want to assume, a priori, a specific relationship between the level of coherence and parameters s and r, we chose to let the parameters vary independently of coherence during fitting. However, we investigated whether an equivalent relation between r and coherence holds for the fitted values of r. As stated in the main text, this relation can be written as r2 = K′/c where c is coherence and K′ is an arbitrary constant. Consequently, we used a least-squares approach to estimate K′ from given pairs of coherence (in %) and sensory uncertainty r (Table 2). The best fitting value was K′ = 381.9. As suggested by one reviewer, it may be useful to assume the above relation between r2 and c as a constraint when fitting noisy data. This can be easily done by fitting K′ to the data across coherences instead of directly fitting one r per coherence.
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10.1371/journal.pcbi.1003098 | Consistent Estimation of Gibbs Energy Using Component Contributions | Standard Gibbs energies of reactions are increasingly being used in metabolic modeling for applying thermodynamic constraints on reaction rates, metabolite concentrations and kinetic parameters. The increasing scope and diversity of metabolic models has led scientists to look for genome-scale solutions that can estimate the standard Gibbs energy of all the reactions in metabolism. Group contribution methods greatly increase coverage, albeit at the price of decreased precision. We present here a way to combine the estimations of group contribution with the more accurate reactant contributions by decomposing each reaction into two parts and applying one of the methods on each of them. This method gives priority to the reactant contributions over group contributions while guaranteeing that all estimations will be consistent, i.e. will not violate the first law of thermodynamics. We show that there is a significant increase in the accuracy of our estimations compared to standard group contribution. Specifically, our cross-validation results show an 80% reduction in the median absolute residual for reactions that can be derived by reactant contributions only. We provide the full framework and source code for deriving estimates of standard reaction Gibbs energy, as well as confidence intervals, and believe this will facilitate the wide use of thermodynamic data for a better understanding of metabolism.
| The metabolism of living organisms is a complex system with a large number of parameters and interactions. Nevertheless, it is governed by a strict set of rules that make it somewhat predictable and amenable to modeling. The laws of thermodynamics play a pivotal role by determining reaction feasibility and by governing the kinetics of enzymes. Here we introduce estimations for the standard Gibbs energy of reactions, with the best combination of accuracy and coverage to date. The estimations are derived using a new method which we denote component contribution. This method integrates multiple sources of information into a consistent framework that obeys the laws of thermodynamics, and provides a significant improvement in accuracy compared to previous genome-wide estimations of standard Gibbs energies. We apply and test our method on reconstructions of E. coli and human metabolism and, in addition, do our best to facilitate the use of these estimations in future models by providing open-source software that performs the integration in a streamlined process.
| A living system, like any other physical system, obeys the laws of thermodynamics. In the context of metabolism, the laws of thermodynamics have been successfully applied in several modeling schemes to improve accuracy in predictions and eliminate infeasible functional states. For instance, several methodologies that reflect the constraints imposed by the second law of thermodynamics have been developed [1]–[3] and were shown to remove thermodynamically infeasible loops and improve overall predictions. Alternatively, thermodynamic data have been integrated directly into genome-wide models and analysis methods [4]–[10]. Unfortunately, this integration has been hindered by the fact that thermodynamic parameters for most reactions are effectively missing (sometimes due to scattered accessibility or non-standard annotations).
The nearly ubiquitous method for experimentally obtaining thermodynamic parameters for biochemical reactions, specifically their standard transformed Gibbs energies , is directly measuring the apparent equilibrium constant and then applying the formula , where is the gas constant and is the temperature. Typically, the substrates of the reaction are added to a buffered medium together with an enzyme that specifically catalyzes the reaction. After the concentrations reach a steady-state, the reaction quotient is calculated by dividing the product concentrations by the substrate concentrations. It is recommended to do the same measurement in the opposite direction as well (starting with what were earlier the products). If the experiment is successful, and the steady-state reached is an equilibrium state then both values for (measured in both directions) will be equal to and thus to each other. Notably, due to the nature of this method, only reactions with close to the equilibrium value of zero can be directly measured since current technology allows measuring metabolite concentrations only within a range of a few orders of magnitude. Although this method involves purifying substantial amounts of the enzyme, it has been applied to many of the enzyme-catalyzed reactions studied throughout the last century and the results were published in hundreds or even thousands of papers. A comprehensive collection of measured (among other thermodynamic parameters), for more than 400 reactions, has been published by the National Institute of Standards and Technology (NIST) in the Thermodynamics of Enzyme-Catalyzed Reactions Database (TECRDB [11]). However, even this wide collection covers less than 10% of biochemical reactions in standard metabolic reconstructions, such as the E. coli model iAF1260 [5].
In 1957 [12], K. Burton recognized that these apparent equilibrium constants can be used (together with chemically derived standard Gibbs energies for some simple compounds) to calculate equilibrium constants of reactions with no known values. This method is based on the notion that by knowing the of two different reactions, one can calculate the of the combined reaction by summing the two known standard transformed Gibbs energies – as dictated by the first law of thermodynamics. For example, although the reaction of ATP hydrolysis () might be too far from equilibrium to be measured directly, one can more easily measure the of the reactions of glucose kinase (; kJ/mol) and of glucose-6P phosphatase (; kJ/mol), which are both closer to equilibrium. The standard transformed Gibbs energy for the total reaction (i.e. ATP hydrolysis) would thus be kJ/mol.
In order to facilitate these calculations, Burton published a table of about 100 inferred standard Gibbs energies of formation () which are defined as the standard Gibbs energy of the formation reaction, i.e. the reaction of forming a compound out of pure elements in their standard forms (e.g. ). Some of these values were taken from chemical thermodynamic tables, and the rest were derived by Burton using the arithmetic approach of combining reactions. For instance, if all species except one in an enzyme-catalyzed reaction have known , and the reaction's can be obtained experimentally, then the last remaining can be calculated and added to the table. After compiling such a table, the of any reaction involving species that appear in the table can be easily calculated by summing the formation energies of all the products and subtracting those of the substrates. Throughout this paper we will refer to this method of calculating as the Reactant Contribution (RC) method, since it is based on the contribution of each reactant to (i.e. its standard Gibbs energy of formation).
In the 50 years following Burton's work, several such tables of formation Gibbs energies have been published. Some of the most noteworthy are the table by R. Thauer [13] and the larger collection by R. Alberty [14], [15]. Using these values, one can determine Gibbs energies for more reactions at a wider range of physiological conditions (pH, ionic strength) than the set of reactions measured and stored in TECRDB. However, even this advanced method covers less than 10% of reactions in the E. coli model. This gap has prompted scientists to develop methods that can estimate the missing thermodynamic parameters for genome-wide models.
Quite coincidentally, a year after Burton published his thermodynamic tables, S. Benson and J. Buss [16] published their work on additivity rules for the estimation of molecular properties. Benson and Buss called the law of additivity of atomic properties a zero-order approximation, the additivity of bond properties a first-order approximation, and the additivity of group properties a second-order approximation. Groups were defined as pairs of atoms or structural elements with distances of 3–5 Å. The contribution of each group to the total was determined by linear regression. Using the second-order approximation, is calculated as the sum of the standard Gibbs energy contributions of groups that are produced in the reaction, minus the contributions of groups that are consumed. This method is commonly called the Group Contribution (GC) method. Burton's method of calculating the Gibbs energy of formation for compounds (which we denote RC) can be thought of as a -order approximation, where the entire molecule is taken as the basic additivity unit for estimating the (of course, this is not actually an approximation).
Group contribution methods were relatively successful in estimating the thermodynamic parameters of ideal gases [16]–[19], and later extended to liquid and solid phases [20]. Only in 1988 [21] was it brought to the world of biochemical reactions in aqueous solutions and has since become the most widely used technique for estimating the Gibbs energy of reactions [22]–[24]. GC methods can cover the majority of relevant biochemical reactions ( and of the reactions in E. coli and human cell metabolic models respectively) [5], [10], [24]. The downside of GC lies in its accuracy, since it relies on a simplifying assumption that the contributions of groups are additive. Evidently, the average estimation error attributed to GC is about 9–10 kJ/mol [23]. In a recent study, we showed that an improvement of can be achieved by considering different pseudoisomers that exist simultaneously for many of the compounds [24] (see Section S1 in Text S1 for details). Even with this improvement, error in GC estimates is still significantly higher than in RC estimates (Figure 1).
In this paper, we aim to unify GC and RC into a more general framework we call the Component Contribution method. We demonstrate that component contribution combines the accuracy of RC with the coverage of GC in a fully consistent manner. A plot comparing the component contribution method to other known methods is given in Figure 1.
The extensive use of formation Gibbs energies for calculating might create the impression that combining these two frameworks (RC and GC) is a trivial task. Traditionally, the formation energy of all pure elements in their standard forms is set to zero by definition. All other compounds' formation energies are calculated in relation to these reference points. This is a sound definition which creates a consistent framework for deriving the of any reaction which is chemically balanced. However, the difficulty of calculating the formation energy for some complex but useful co-factors has been side-stepped by creating a somewhat looser definition of formation Gibbs energy, where several non-elemental compounds are defined as reference points as well (with a standard formation energy of zero). For some rare reactions, this definition can create a conflict that will result in a very large mistake in the .
For instance, Alberty's formation energy table [15] lists 16 compounds as having . Among these, only 5 are elements (, , , , and ) and 11 are co-factors (CoA−1, NAD(ox)−1, FAD(ox)−2, FMN(ox)−2 and seven other redox carriers). In most reactions which use these co-factors as substrates, the “zeros” will cancel out since one of the products will match it with a formation energy which is defined according to the same reference point (e.g. FAD(ox)−2 will be matched with FAD(red)−2 whose formation energy is kJ/mol in Albery's table). Nevertheless, there are a handful of reactions where this matching doesn't occur. In the reaction (catalyzed by FAD nucleotidohydrolase, EC 3.6.1.18), there is a violation of conservation laws for FAD and FMN (both have in Alberty's table). Therefore, using the table naïvely for this reaction would yield a wrong value, namely kJ/mol. Combining formation energies derived using GC with ones from RC greatly increases the number of reactions where different reference-points are mixed together, and mistakes such as the one described above become much more common.
One way to deal with the problem of reference-point conflicts, is to use either RC or GC exclusively for every reaction being estimated. Specifically, RC will by applied to all reactions that can be completely covered by it. Only if one or more reactants are missing from the formation energy table, we would use the less precise GC method for the entire reaction. Unfortunately, combining the frameworks in such a way can easily lead to violations of the first law of thermodynamics. This stems from the fact that inconsistent use of formation energies across several reactions, coming from inaccuracies in the estimation method that do not cancel out, can create situations where futile cycles will have a non-zero change in Gibbs energy. An example for such a futile cycle is given in Figure 2. Applying this method for large-scale metabolic reconstructions will most likely lead to non-physical solutions.
Reference-point conflicts and first-law violations can both be avoided, by adjusting baseline formation energies of compounds with non-elemental reference points to match group contribution estimates. This approach was taken in [8] and [10]. The formation energies of FAD(ox)−2 and all other reference points in Alberty's table were set equal to their group contribution estimates. All formation energies that were determined relative to each reference point were then adjusted according to Alberty's table to maintain the same relative formation energies. The main disadvantage of this approach is that the set of reference points is fixed and limited to a few common cofactors. The coverage of reactant contributions could be increased by also defining less common metabolites as reference points, but listing them all in a static table would be impractical and inefficient.
The component contribution method, which is described in detail in the following sections of this paper, manages to combine the estimates of RC and GC while avoiding any reference-point conflicts or first-law violations. In the component contribution framework, the maximal set of reference points given a set of measured reactions is automatically determined. We maintain the notion of prioritizing RC over GC, but rather than applying only one method exclusively per reaction, we split every reaction into two independent reactions. One of these sub-reactions can be evaluated using RC, while the other cannot – and thus its is calculated using GC. We use linear orthogonal projections in order to split each of the reactions, ensuring that all estimated values are self-consistent. The choice of orthogonal projections is somewhat arbitrary, and is based on the assumption that it is beneficial to minimize the euclidean distance to the projected point that can be estimated using RC. This framework also enables us to calculate confidence intervals for standard reaction Gibbs energies in a mathematically sound way.
The component contribution method integrates reactant contributions and group contributions in a single, unified framework using a layered linear regression technique. This technique enables maximum usage of the more accurate reactant contributions, and fills in missing information using group contributions in a fully consistent manner. The inputs to the component contribution method are the stoichiometric matrix of measured reactions, denoted , and a list of measurements of their standard Gibbs energies (see Table S2 in Text S1 for a list of mathematical symbols). In our case, all data is taken from TECRDB [11] and tables of compound formation energies [13], [15]. As a pre-processing step which is used to linearize the problem, we apply an inverse Legendre transform to the observed equilibrium constants in TECRDB and the formation energies, if necessary (same as in [24], see Section S1 in Text S1). To provide context for the mathematical formulation of the component contribution method, we precede it with general formulations of the reactant and group contribution methods, and discuss the limitations of each. The reactant and group contribution methods are both based on linear regression. The difference between the two methods lies in the regression models used in each.
In order to evaluate the improvement in estimations derived using component contribution compared to an implementation of group contribution [24], we ran a cross-validation analysis (see section Leave-one-out cross-validation for details). The results of this analysis are shown in Figure 4, where we compare the distributions of the absolute residuals (the difference between each method's estimated and the observed according to TECRDB). For each estimation, the value of for that reaction (or any other measurement of the same reaction) was not used for training the group contribution and component contribution methods.
Our results show a significant improvement for component contribution compared to group contribution when focusing on reactions in the range of . The median of all residuals (absolute value) was reduced from 4.6 to 1.0 kJ/mol (p-value) for this set of reactions. For reactions that were not in , there was no significant difference (p-value = 0.45) in the median absolute residual between the two methods. The error in group contribution estimates that is due to the assumption of group additivity does not depend on the extent to which group contribution is used (see Section S4.2 in Text S1). Because component contribution uses group contribution to some extent for all reactions that are not in , the error in component contribution estimates for those reactions is not significantly lower than the error in group contribution estimates. Note that it is still very important to use component contribution for these reactions (and not GC) for the sake of having consistent estimations across whole metabolic models (see section Unifying reactant and group contribution methods in the Introduction).
In each iteration of the cross-validation, one reaction was excluded from the training set. To further validate the component contribution method, we used the results of each iteration to predict independent observations of the reaction that was excluded. All available observations of that reaction were then compared against the prediction intervals for its standard Gibbs energy (see section Calculation of prediction intervals in the Methods). Overall, we found that 73% of observations fell within their respective 68% prediction intervals, 89% fell within their 90% prediction intervals, 92% fell within their 95% prediction intervals, and 97% within their 99% intervals. Prediction intervals obtained with the component contribution method were on average 36% smaller than those obtained with group contribution. Taken together, these results show that the component contribution method yields estimates with reliable confidence intervals, as well as increased accuracy and reduced uncertainty compared to group contribution.
A major application of the component contribution method is estimation of standard Gibbs energies for reactions in genome-scale reconstructions. Such large reaction networks require consistent and reliable estimates with high coverage. If estimates are not consistent, the risk of reference point violations increases with network size. As discussed in section Adjustment to in vivo conditions, metabolic models generally require estimates of standard transformed Gibbs energies, , at in vivo conditions. To meet this requirement, we have integrated the component contribution method into a new version (2.0) of von Bertalanffy [28] (see section Implementation and availability of code).
Here, we apply von Bertalanffy 2.0 to two reconstruction; the E. coli reconstruction iAF1260 [5] and the human reconstruction Recon 1 [29]. Standard transformed reaction Gibbs energies had previously been estimated for both reconstructions, with older versions of von Bertalanffy [8], . Those older versions relied on a combination of experimentally derived standard formation energies from [15], and estimated standard formation energies obtained with the group contribution method presented in [23]. We compare estimates obtained with the new version of von Bertalanffy, to both experimental data in TECRDB, and estimates obtained with the older versions.
were obtained for 90% (1878/2078) of internal reactions in iAF1260, and 72% (2416/3362) of internal reactions in Recon 1. External reactions i.e., exchange, demand and sink reactions are not mass or charge balanced and therefore have no defined Gibbs energies. To validate our estimates we compared them to available experimental data. Measurements of apparent equilibrium constants () were available in TECRDB for 163 of the evaluated iAF1260 reactions, and 186 Recon 1 reactions. Multiple measurements, made at different experimental conditions, were often available for a single reaction. To enable comparison, the data in TECRDB was first normalized to standard conditions by applying an inverse Legendre transform as described in Section S1 in Text S1. The resulting standard reaction Gibbs energies () were then adjusted to the conditions in Tables 1 and 2 with von Bertalanffy, to obtain standard transformed reaction Gibbs energies, . Comparison of to gave a root mean square error (RMSE) of 2.7 kJ/mol for iAF1260, and 3.1 kJ/mol for Recon 1.
von Bertalanffy 2.0 relies on component contribution estimated standard reaction Gibbs energies, whereas older versions relied on a combination of experimental data and group contribution estimates. Table 3 compares standard transformed Gibbs energy estimates, for iAF1260 and Recon 1, between versions. Use of component contribution resulted in both higher coverage and lower RMSE than was achieved with the previously available data. The greater coverage was due to reactions where groups or compounds that were not covered by component contributions canceled out, because they appeared unchanged on both sides of the reactions. Such reactions are easily identified and evaluated within the component contribution framework.
Another improvement achieved with the component contribution method was the lower standard error, , of standard reaction Gibbs energy estimates compared with previously available methods (Table 3). This is an important improvement as standard error has previously been shown to affect predictions made based on reaction Gibbs energy estimates [6], [8], [10]. The reduction in was obtained by accounting for covariances in parameter estimates (see section Calculation of confidence intervals). As we showed in section Validation results, the lower standard errors of component contribution estimates yielded reliable prediction intervals for observed standard reaction Gibbs energies. They can therefore be expected to also yield reliable confidence intervals for true standard reaction Gibbs energies.
The lower RMSE achieved with component contribution stems primarily from two factors. The first is the normalization of the training data by the inverse Legendre transform, which in [24] was shown to lead to significant improvements in group contribution estimates of Gibbs energies. The second factor is the greater number of reactions that are fully evaluated with reactant contribution (Eq. 6). Close to 10% of all evaluated reactions in both iAF1260 and Recon 1, were fully evaluated using only reactant contribution (Figure 5). Although this category represents a minority of all reactions, it includes the majority of reactions in central carbon metabolism. The greater accuracy in Gibbs energy estimates for reactions in central carbon metabolism is expected to have a disproportionally large effect, as these reactions are involved in most metabolic activities. To support this claim, we predicted 312 flux distributions for iAF1260 and 97 flux distributions for Recon 1 (see Section S6 in Text S1 for details). We found that the tenth of reactions that were fully evaluated with reactant contributions carried approximately half of the total flux in iAF1260 and a third of the total flux in Recon 1 (Figure 5).
The component contribution method presented in this paper merges two established methods for calculating standard Gibbs energies of reactions while maintaining each of their advantages; accuracy in the case of reactant contribution (RC) and the wide coverage of group contribution (GC). By representing every reaction as a sum of two complementary component reactions, one in the subspace that is completely covered by RC and the other in the complementary space, we maximize the usage of information that can be obtained with the more accurate RC method. Overall, we find that there is a 50% reduction in the median absolute residual compared to standard GC methods, while providing the same wide coverage and ensuring that there are no reference-point inconsistencies that otherwise lead to large errors. Furthermore, since our method is based on least-squares linear regression, we use standard practices for calculating confidence intervals for standard Gibbs energies (see section Calculation of confidence intervals), and for weighing the measured standard Gibbs energies used as training data (see Section S1.2 in Text S1).
Since the empirical data used in our method is measured in various conditions (temperature, pH, ionic strength, metal ion concentrations, etc.) – it is important to “standardize” the input data before applying any linear regression model [24]. In this work, we used an inverse Legendre transform to normalize the pH and ionic strength, but ignore the temperature effect and the metal ion concentrations (see Section S1.1 in Text S1). In addition, the proton dissociation constants were obtained from a third party software estimator (by Marvin, see Methods) and have a mean absolute error of about 0.9 pH units [30]. Notably, a commendable effort for creating a database of thermodynamic quantities [31] has been published recently, where the data was standardized using more reliable parameters and considering more effects. This database currently only covers reactions from glycolysis, the tricarboxylic acid cycle, and the pentose phosphate pathway. Therefore, we chose to use the more extensive TECRDB database and perform the inverse Legendre transform ourselves, effectively increasing the coverage while compromising on the accuracy of the data. Since the changes brought forward in the component contribution method are independent of the source of input data, we believe that it will benefit from any future improvements in these databases.
The precision of the component contribution method is limited by the accuracy of the measured reaction equilibrium constants used in the regression model. In cases of isolated reactions, where the empirical data cannot be corroborated by overlapping measurements, large errors will be directly propagated to our estimate of those reactions' standard Gibbs energies. As the number of measurements underlying an estimate is reflected in its standard error, however, confidence intervals for such reactions will be large. It is therefore recommended to use confidence intervals, and not point estimates, for simulations and predictions based on standard Gibbs energy estimates. In the future, it might be worthwhile to integrate several promising computational prediction approaches [32] which are not based on RC and GC, such as molecular mechanics methods [33], density functional methods [34], and post Hartree-Fock approaches [35], [36]. Although the computational cost of these methods can be substantial depending on the theoretical method and the solvation models [37] used, they have the advantage of being based on computable chemical and physical principles, implying that a 100% coverage of all biochemical reactions is achievable (though not yet practical). Currently, the accuracy of these methods for reactions in solution is limited. Nevertheless, they might already be useful for estimating of reactions that are not covered by component contributions, or for validating the sparse measurements. Alternatively, a method that infers from reaction similarities named IGERS [38] manages to be much more accurate than GC when predicting the standard Gibbs energy of reactions which are very similar to a reaction with a measured . Adding IGERS as another layer between RC and GC using the ideas presented in this paper might contribute to the overall accuracy of our estimations. Finally, the laws of additivity suggested by [16] include single atom (zero-order) and single bond (first-order) contributions, which would be too crude to use for approximating Gibbs energies directly, but might be useful as two extra layers in a method like component contribution and help cover a wider fraction of the reaction space.
The use of thermodynamic parameters in modeling living systems has been hindered by the fact that it is mostly inaccessible or requires a high level of expertise to use correctly, especially in genome-scale models. In order to alleviate this limitation, we created a framework that facilitates the integration of standard reaction Gibbs energies into existing models and also embedded our code into the openCOBRA toolbox. The entire framework (including the source code and training data) is freely available. We envisage a collaborative community effort that will result in a simple and streamlined process where these important thermodynamic data are widely used and where future improvements in estimation methods will seamlessly propagate to modelers.
The component contribution estimated standard Gibbs energy in Eq. 10, is a point estimate of the true standard Gibbs energy for reaction vector . To quantify the uncertainty in this estimate, we need to calculate confidence intervals for . An important advantage of integrating the reactant and group contribution methods in a single, unified framework is that it greatly simplifies calculation of confidence intervals. We present the key equations in this section. A summary of the statistical theory underlying these equations [39] is given in Section S7 in Text S1.
The covariance matrix for the reactant contribution estimates ( in Eq. 3) is calculated as(11)where the matrix is scaled by the estimated variance of the error term in Eq. 2. Our estimate of the variance was (kJ/mol)2. The covariance matrix for the group contribution estimates () is likewise obtained as(12)where the estimated variance of from Eq. 7 was (kJ/mol)2.
For a reaction , the standard error of is given by(13)The confidence interval for , at a specified confidence level , is given by(14)where is the value of the standard normal distribution at a cumulative probability of . The 95% confidence interval for is therefore .
In calculating , we employ the covariance matrices for estimated parameters and . In contrast, Jankowski et al. used only the diagonal of the covariance matrix for in their implementation of the group contribution method [23]. The main advantage of using covariance matrices is that it leads to more appropriate confidence intervals for , that can be much smaller. Knowledge about the relative Gibbs energy of two groups or compounds, increases with the number of measurements for reactions where those groups or compounds occur together. This knowledge should be reflected in smaller confidence intervals for reactions where the groups or compounds co-occur. Covariance matrices provide a means for propagating this knowledge. If only the diagonal of the covariance matrix is used, this knowledge is lost and confidence intervals will often be unnecessarily large.
The covariance matrices can likewise be used to propagate lack of knowledge to . If is not in then the reaction is not covered by the group contribution method or by the component contribution method. Then obtained with Eq. 10 will not be a valid estimate of , and should have a large (infinite) standard error. This can be achieved by adding a term to Eq. 13;(15)where , and is a projection matrix onto the null-space of . Eq. 15 will give for all reactions that cannot be evaluated with component contributions because has a nonzero component in the null-space of . In practice, we use a very large value instead of (e.g. kJ/mol) which will dominate any reasonable Gibbs energy in case is not orthogonal to this null-space.
Both group contribution and component contribution are parametric methods that use a set of training data in order to evaluate a long list of parameters. In order to validate these models, we need to use more empirical data which has not been used in the training phase. Since data regarding reaction Gibbs energies is scarce, we apply the leave-one-out method in order to maximize the amount of data left for training in each cross-validation iteration. As a measure for the quality of the standard Gibbs energy estimations from each method we use the median absolute residual of the cross-validation results compared to the observations.
Our entire training set consists of 4146 distinct reaction measurements. However, since many of them are experimental replicates – measurements of the same chemical reaction in different conditions or by different researchers – we can only use each distinct reaction once. We thus take the median over all replicates (after applying the inverse Legendre transform) as the value to be used for training or cross-validation. We choose the median rather than the mean to avoid sensitivity to outliers. After this process of unifying observations, we are left with 694 unique reaction observations. Note that the repetitions do play a role in determining the standard error in standard Gibbs energy estimates (see section Calculation of confidence intervals). Finally, the vector of values for the unique reactions is projected onto the range of since we assume that the actual values comply with the first law of thermodynamics (see section Reactant contribution method) and that any deviation is caused by experimental error.
The prediction interval for a reaction , with estimated standard Gibbs energy , is calculated as(16)where was defined in Eq. 14, and , the standard error of , was defined in Eq. 15. is calculated as(17)i.e., it is a weighted mean of the estimated variances for reactant and group contribution, where the weights are the fractions of that are in and , respectively. A summary of the statistical theory underlying calculation of prediction intervals [39] is given in Section S7 in Text S1.
For an input reaction , the component contribution method outputs an estimate of the reaction's standard chemical Gibbs energy . In a chemical reaction each compound is represented in a specific protonation state. This is in contrast to biochemical reactions, where each compound is represented as a pseudoisomer group of one or more species in different protonation states. To thermodynamically constrain models of living organisms we require Gibbs energies of biochemical reactions at in vivo conditions, known as standard transformed reaction Gibbs energies .
We estimated with version 2.0 of von Bertalanffy [8], [10], [28]; a Matlab implementation of biochemical thermodynamics theory as presented in [14]. A comprehensive summary of the relevant theory is given in [10]. In addition to component contribution estimates of standard Gibbs energies, required inputs to von Bertalanffy are a stoichiometric matrix for a metabolic reconstruction of an organism, values for compounds in , and literature data on temperature, pH, ionic strength () and electrical potential () in each cell compartment in the reconstruction.
We estimated for reactions in two multi-compartmental, genome scale metabolic reconstructions; an E. coli reconstruction iAF1260 [5], and a human reconstruction Recon 1 [29]. The environmental parameters pH, and were taken from [8] for E. coli (Table 1), and from [10] for human (Table 2). values were estimated with Calculator Plugins, Marvin 5.10.1, 2012, ChemAxon (http://www.chemaxon.com).
The component contribution method has been implemented in both Matlab and Python. The Matlab implementation is tailored towards application to genome-scale metabolic reconstructions. It is fully compatible with the COBRA toolbox [40] and is freely available as part of the openCOBRA project on Sourceforge (http://sourceforge.net/projects/opencobra/). The component contribution method has been integrated into version 2.0 of von Bertalanffy to provide an easy-to-use tool to estimate transformed Gibbs energies at in vivo conditions. The Python implementation is a stand-alone package that can be used by researchers with suitable programming skills. The Python package includes a simple front-end called eQuilibrator (http://equilibrator.weizmann.ac.il/), which is a freely available online service. The Python code for component contribution is licensed under the open source MIT License and available on GitHub (https://github.com/eladnoor/component-contribution). Our code depends on the open source chemistry toolbox called Open Babel [41].
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10.1371/journal.pgen.1003188 | Suv4-20h Histone Methyltransferases Promote Neuroectodermal Differentiation by Silencing the Pluripotency-Associated Oct-25 Gene | Post-translational modifications (PTMs) of histones exert fundamental roles in regulating gene expression. During development, groups of PTMs are constrained by unknown mechanisms into combinatorial patterns, which facilitate transitions from uncommitted embryonic cells into differentiated somatic cell lineages. Repressive histone modifications such as H3K9me3 or H3K27me3 have been investigated in detail, but the role of H4K20me3 in development is currently unknown. Here we show that Xenopus laevis Suv4-20h1 and h2 histone methyltransferases (HMTases) are essential for induction and differentiation of the neuroectoderm. Morpholino-mediated knockdown of the two HMTases leads to a selective and specific downregulation of genes controlling neural induction, thereby effectively blocking differentiation of the neuroectoderm. Global transcriptome analysis supports the notion that these effects arise from the transcriptional deregulation of specific genes rather than widespread, pleiotropic effects. Interestingly, morphant embryos fail to repress the Oct4-related Xenopus gene Oct-25. We validate Oct-25 as a direct target of xSu4-20h enzyme mediated gene repression, showing by chromatin immunoprecipitaton that it is decorated with the H4K20me3 mark downstream of the promoter in normal, but not in double-morphant, embryos. Since knockdown of Oct-25 protein significantly rescues the neural differentiation defect in xSuv4-20h double-morphant embryos, we conclude that the epistatic relationship between Suv4-20h enzymes and Oct-25 controls the transit from pluripotent to differentiation-competent neural cells. Consistent with these results in Xenopus, murine Suv4-20h1/h2 double-knockout embryonic stem (DKO ES) cells exhibit increased Oct4 protein levels before and during EB formation, and reveal a compromised and biased capacity for in vitro differentiation, when compared to normal ES cells. Together, these results suggest a regulatory mechanism, conserved between amphibians and mammals, in which H4K20me3-dependent restriction of specific POU-V genes directs cell fate decisions, when embryonic cells exit the pluripotent state.
| The quest of modern developmental biology is a detailed molecular description of the process that leads from the fertilized egg to the complex and highly differentiated adult organism. This process is controlled largely on the level of gene expression. While early embryonic cells are pluripotent and capable of transcribing most of their genome, older cells have become committed to the germ layer and differentiation programs during gastrulation. They express then a subset of genes compatible with their future physiological function. Young, pluripotent cells and post-gastrula, committed cells express different networks of transcription factors and contain chromatin of different structure and composition. How these two regulatory layers are interconnected during development is incompletely understood. We describe a novel and unexpected link between the pluripotency-associated POU-V gene Oct-25 and xSuv4-20h histone methyltransferases. XSuv4-20h enzymes are required to repress the Oct-25 gene, a homolog of mammalian Oct4, in the neuroectoderm of frog embryos as a prerequisite for neural differentiation. Consistently, murine Suv4-20h double-null ES cells show increased Oct4 protein levels before and during in vitro differentiation and display compromised differentiation in comparison to wild-type ES cells. Thus, Suv4-20h enzymes control specific POU-V genes and are involved in germ-layer specific differentiation.
| Embryonic development is controlled by fine-tuned differential gene expression. A succession of regulatory protein networks unfolds the zygotic gene expression program along a hierarchy of decisions, leading from the embryonic ground state to the epiblast and then to germ layers, which become patterned into cell type and organ precursor territories. The pluripotent trait, key feature of embryonic stem (ES) cells [1], is progressively restricted and finally lost as soon as embryonic cells become specified to germ layer fates. Recent studies have revealed that alterations in chromatin structure, dynamics and composition represent fundamental processes, which define the epigenetic landscape that directs cell type specification along this hierarchy [2], [3].
Besides important contributions from ATP dependent chromatin remodelling factors [4], [5] and histone variants [6] in modulating nucleosome dynamics, histone post-translational modifications (PTMs) have been linked to gene expression [3], [7]. The transition from pluripotent to differentiated cells is characterized by a progressive increase in heterochromatin formation, in a process that changes the hyperdynamic open chromatin structure into a less accessible architecture [1], [8]. At the same time transcriptional silencing of non-lineage specific genes is achieved via acquisition of repressive histone marks. In vivo studies have shown that dynamic alterations in the levels of histone modifications characterize early stages of development both in mammals [9]–[11] and other vertebrates [12]–[14].
Lysine methylation of histones is catalyzed by SET domain-containing histone methyltransferases (HMTases), and can be linked both to transcriptional activation and repression [15], [16]. In particular, repressive histone methyl marks are found on lysine residues at position 9 and 27 on histone H3 and in position 20 on histone H4. H3K27 trimethylation is catalyzed by polycomb repression complex (PRC) 2, which predominantly represses developmental regulatory genes [17]–[19]. Trimethylation of H3K9 and H4K20 relies on Suv39h and Suv4-20h enzyme activities, respectively [20], [21], and predominantly marks repetitive genomic DNA at pericentromeric and telomeric heterochromatin [16], [21]. While H3K9-specific HMTases have been characterized in significant depth [20], [22], [23], we know little about the functions of Suv4-20h1 and Suv4-20h2 enzymes with regard to gene regulation. In vivo analysis of H4K20 methylation states in mouse embryos reveals specific patterns both in cellular or subnuclear abundance [9], [24]. Suv4-20h DKO pups die perinatally, indicating an essential function of the two enzymes during embryogenesis [24]. Moreover, quantitative analysis of histone PTMs in X. laevis revealed a progressive and significant accumulation of H4K20me3 levels during embryogenesis, suggesting developmental functions for these enzymes [13].
To characterize the functional role of H4K20me2/3 during vertebrate development we have investigated the consequences of both morpholino-mediated protein knockdown and mRNA-born overexpression of the Xenopus Suv4-20h1 and h2 homologs in frog embryos. Our data reveal a specific and selective requirement for Suv4-20h enzyme acitivities in neuroectodermal differentiation, in a process which involves transcriptional repression of pluripotency associated POU-V genes, both in Xenopus embryos and in murine ES cells.
We initially identified X. laevis Suv4-20h1 and h2 ESTs via database mining. Mouse and frog Suv4-20h1 and h2 protein sequences are well conserved, particularly within the SET domains (≥88% identity), even though the xSuv4-20h2 open reading frame is longer than its mouse homolog due to C-terminal insertions (supplementary data, Figure S1). XSuv4-20h1/h2 genes are both maternally and zygotically expressed in a ubiquitous manner, as shown by RNA in situ hybridisation and RT-PCR analysis (Figure S2A–S2D). XSuv4-20h1 mRNA abundance decreases during the initial stages of development and subsequently rises from mid-gastrula on, reflecting the switch from maternal-to-zygotic transcription at midblastula. In contrast, the initially high xSuv4-20h2 mRNA level falls and stays low at late stages (Figure S2D).
To test the acivities of these Xenopus HMTases, we first analyzed their ability to rescue H4K20me3 levels in Suv4-20h1/h2 DKO mouse embryonic fibroblasts (MEF Suv4-20h DKO; [24]), which lack this modification. Both frog cDNAs re-established a proper H4K20me3 pattern, which was strongly enriched at heterochromatic regions that were identified as DAPI-dense chromocenters within nuclei (Figure 1A). Thus, Xenopus laevis Suv4-20h homologs are biologically active and can direct H4K20 trimethylation. To test, whether they generate this histone mark in frog embryos, we designed antisense Morpholino oligonucleotides (MO) to reduce synthesis of xSuv4-20h1 and h2 proteins from endogenous mRNAs (Figure S3A, S3B). These MOs specifically inhibited translation of their cognate templates in vitro (Figure S3C). To avoid possible functional complementation between the xSuv4-20h enzymes in vivo, we decided to inject the two MOs simultaneously into both blastomeres of 2-cell stage embryos and performed western blots with nuclear protein extracts from these double-morphant embryos at the tadpole stage (NF30-33). Compared to uninjected controls or embryos injected with an unrelated control MO (control-morphants), the double morphants contained significantly less H4K20me2 (p = 0.0011) and H4K20me3 (p = 0.0164), which was coupled to an increase in H4K20me1 (p = 0.0034) (Figure 1B and 1C).
This result was confirmed by MALDI-TOF mass spectrometry (Figure S4A). As described in Schneider et al. [13], the relative abundance of histone modifications was calculated by quantifying the amount of a specific modification relative to the amount of all modification states determined for the same histone peptide. As reported before [13], the H4K20me3 mark could not be quantitated reproducibly for technical reasons. Compared to control embryos, however, xSuv4-20h double morphants contained approximately 2.5-fold less of H4K20me2 (p = 0.0153) and three-fold more H4K20me1 (p = 0.0185), while the abundance of the unmodified peptide state remained unaffected. Importantly, the levels of histone H3 methylation on two tryptic peptides, covering the K9, K27 and K36 positions, were indistinguishable between control and double-morphant embryos (Figure S4B and S4C). Western blot analysis with antibodies against trimethylated H3K9 or H3K27 also showed no difference in the abundance of these two marks between control embryos and xSuv4-20h double morphants (Figure S4D).
To further characterize the effects of xSuv4-20h enzyme depletion on the cellular level, we performed immunohistochemistry on sections from tailbud stage embryos (NF30), which were injected with the xSuv4-20h MO-mix into one of two blastomeres at 2-cell stage together with fluorescently labelled dextranes as lineage tracer. While H3 staining was comparable between injected and uninjected sides under all conditions (Figure S5A), staining for H4K20me2 and –me3 was clearly reduced on the double-morphant side of the neural tube (Figure 1D). In agreement with our western blot and mass spec results, the reduction in the di- and tri-methyl mark was coupled to an increase in H4K20me1 staining. Altogether these results indicate that xSuv4-20h1 and h2 downregulation leads to a quantitative reduction of H4K20 di- and trimethyl marks in the frog embryo, without affecting the bulk abundance of other repressive histone marks such as H3 K9/K27 methylation.
RNA-based overexpression of Suv4-20h HMTases had the opposite effect. When injected singly, xSuv4-20h1 or h2 mRNAs increased both di- and trimethylated H4K20 in a dose-dependent manner (Figure S8A). A comparable result was achieved by injection of either mouse Suv4-20h1 or h2 mRNAs (Figure S9A). Together, these results identify the frog cDNAs as orthologs of mammalian Suv4-20h enzymes. Loss and gain of function experiments also indicate that the bulk abundance of di- and trimethylated H4K20 can be manipulated over a wide range without compromising embryonic viability.
We next tested, whether depletion of xSuv4-20h HMTases affects embryonic development. We injected xSuv4-20h1/h2 MO-mix into one blastomere of two-cell stage embryos and scored phenotypic alterations by comparing injected with uninjected sides. No obvious differences were observed during early development, including gastrulation, axial extension and dorsoventral patterning. From tailbud stages on, two main phenotypes became manifest. First, in the injected side of xSuv4-20h double morphants the eye formation was strongly compromised. The eye rudiments contained no or barely visible retinal pigment and typically had no lens (Figure 2A). Secondly, melanophores that are found on the dorsal part of the head and the lateral portion of the trunk, were severely reduced in numbers or completely lost from the double-morphant side (Figure 2A). Both phenotypes had a penetrance between 80–90% in xSuv4–20h double morphants (p<0.0001, Fisher's exact test) in several independent experiments (Figure 2B). Control-morphant embryos had normal eyes and melanocytes (Figure 2A) and were indistinguishable from uninjected siblings in most cases (Figure 2B).
The distinct eye phenotype prompted us to investigate the underlying molecular changes. RNA in situ hybridization experiments revealed a clearly reduced expression of the homeobox transcription factor Rx-1 (Figure 2C) and the paired box transcription factor Pax-6 (Figure S5D) in xSuv4-20h double morphants. The reduction of these two master regulators of eye differentiation explains the morphological eye phenotype, but we noticed that embryonic transcription was already misregulated upstream of these factors. The pan-neural markers Nrp1 (Figure 2C) and N-CAM (Figure S5E), which are induced during gastrula stages, were also strongly reduced in double morphants. However, several key markers of embryonic patterning were not perturbed, such as the organizer genes Chordin, Goosecoid and Xnr-3 at gastrula stages (Figure S5B). The anteroposterior patterning of the central nervous system (CNS) appeared also to be normal given the wild-type-like expression patterns of Otx2 and Krox20 in fore- and hindbrain territories, respectively (Figure S5C). These results provide first evidence that H4K20 di- and trimethylation serves to regulate distinct developmental genes in a selective manner.
The specificity of the developmental phenotypes arising from xSuv4-20h enzyme depletion was validated by rescue experiments, in which we coinjected increasing doses of murine Suv4-20h1/h2 mRNAs together with the xSuv4-20h MO-mix. Due to sequence divergence, transcripts of the murine orthologs escape inhibition by the MOs targeting the frog mRNAs. Already 2 ng of murine Suv4-20h transcripts were sufficient to rescue the eye defect in two thirds of the double morphant embryos (p<0.0001, Fisher's exact test). In most cases, the retinal neuroepithelium regained its circular structure and near normal size, as well as a central lens (Figure 2A). The rescue efficiency did not increase with higher concentrations of mouse transcripts (Figure 2B, columns 4–6). The number of melanophores was also increased at their proper sites under rescue conditions (Figure 2A). Furthermore, the expression domains of Rx-1 and Nrp1 (Figure 2C), as well as Pax-6 and N-CAM (Figure S5D and S5E) were efficiently restored.
To test, whether this phenotypic rescue requires Suv4-20h proteins or their enzymatic activity, we generated catalytically inactive murine Suv4-20h protein variants (Figure S6A), based on structural predictions [25], [26]. Unlike the wild-type proteins, neither variant restored the H4K20me3 mark at heterochromatic foci in Suv4-20h DKO MEFs (Figure S6B). When tested side by side with the wild-type enzymes, the mutants did neither increase the abundance of the H4K20me2 and -me3 marks in wild-type frog embryos (Figure S6C, compare lanes 1, 3 and 5), nor rescue H4K20 methylation levels in xSuv4-20h double morphants (Figure S6C compare lanes 1, 4 and 6), although being expressed at similar levels (Figure S6D). Consequently, the inactive variants also failed to rescue the eye and melanophore phenotype (Figure S7A–S7C and S7D, compare columns 2–4).
In the course of these experiments we noticed that overexpression of either frog or mouse Suv4-20h1 and h2 proteins never caused any obvious morphological or molecular changes in the embryos (Figures S8B, S8C and S9B, S9C), despite strongly enhanced H4K20me3 levels in bulk chromatin (Figures S8A and S9A). In particular, morphological landmarks such as eyes and melanophores formed normal in size, number and location under overexpression conditions. Expression domains of marker genes such as Rx-1 and Pax-6 were unaffected (Figures S8D and S9D). Thus, H4K20 di- and trimethylation is required for normal development, but excess deposition of these marks has no apparent phenotypic consequences.
The apparent functional selectivity of the ubiquitously expressed enzymes encouraged us to test, whether xSuv4-20h HMTases control additional aspects of germ layer formation and patterning. Therefore, we compared the expression of key developmental regulatory genes in uni-laterally injected control-morphants versus xSuv4-20h double morphants by RNA in situ hybridisation (listed in Figure 3A).
Based on our previous results, we continued with genes involved in neurogenesis (Figure 3B). At the open plate stage, primary neurons are specified in three stripes next to the dorsal midline on each side. At this time, each stripe expresses the neural specific regulatory genes Neurogenin-related 1a (Ngnr-1a) and Delta-like 1, as well as the differentiation marker N-tubulin. The expression of these three genes was extinguished in almost all of the xSuv4-20h MO-injected sides, while being unaffected by control-MO (Figure 3B). In addition to these stripes, Delta-like 1 mRNA delineates the anterior border of the neural plate, and this domain was also extinguished (Figure 3B). In contrast, mesodermal expression of Delta-like 1 around the blastoporus remained unaffected in morphant condition (Figure 3B, arrow). Delta-like 1 and N-tubulin stripes were effectively rescued by coinjection of wild-type mSuv4-20h1/h2 mRNAs, while Ngnr-1a was restored in a broad, diffuse manner (Figure 3B, right column). Notably, inactive mouse Suv4-20h HMTases could not rescue N-tubulin expression (Figure S7E, middle column). At the same time, mesodermal control genes like MyoD were unaffected (Figure S7E, right column) Together, these results implicate xSuv4-20h enzymes in neuronal fate selection.
Next, we extented our analysis to marker genes expressed in other germlayers and territories (Figure 3C and Figure S5). The epidermal keratin gene XK81 demarcates non-neural ectoderm and was expressed normally on the surface of morphant epidermis; however, due to a slight retardation in neural tube closure on the injected side, its expression appears asymmetric in anterior views. This may indicate an involvement of xSuv4-20h enzymes in morphogenetic processes during neurulation and/or neural crest specification. This phenotype led to a mild broadening of the neural plate markers Sox2 (Figure 3C), Sox3 and Sox11 (Figure S5F) at apparently normal mRNA levels. Prior to these neural plate markers, a group of genes including FoxD5, Geminin, Zic1, Zic2, Zic3 and members of the Iroquois family are induced in the prospective neuroectoderm and stabilize the neural fate by their regulatory interactions (reviewed in ref [27]). At midgastrula (NF11), FoxD5 and Geminin did not respond to xSuv4-20h enzyme depletion (Figure S5F), but Xiro1, Zic1 (Figure 3C), Zic2 and Zic3 (Figure S5F) mRNAs were strongly reduced. In contrast, key mesodermal factors such as Xbra, MyoD (Figure 3C) and VegT (Figure S5F), as well as regulators of endodermal differentiation like Sox17 α and Endodermin (Figure 3C) were expressed normally in both morphants and in embryos overexpressing frog xSuv4-20h proteins (Figure S8E). Taken together these results demonstrate that xSuv4-20h HMTases are critical for neural development, but apparently dispensable for mesoderm and endoderm formation in X. laevis.
To further verify the specific role of Xenopus Suv4-20h enzymes in neural development, we considered two different approaches; in a first series of experiments we performed injections at 8-cell stage in the animal or vegetal pole blastomeres, selectively labelling cells predominantly belonging either to mesendoderm (vegetal injections, Figure 4A) or ectoderm (animal injections, Figure 4D). Vegetal pole blastomere injections led to no evident morphological and molecular phenotypes (Figure 4B and 4C). Conversely, animal injections reproduced the eye and melanophore phenotypes from half-injected embryos, while mesodermal and endodermal structures developed normally (Figure 4E). Consistent with the morphological defects, Delta-like 1 expression in the neural plate was suppressed, while MyoD and Sox17 α genes were unaffected (Figure 4F). These results provide strong evidence that the neural and melanocyte phenotypes originate in the ectoderm.
As second approach we took advantage of animal cap (AC) explants, which form epidermis in isolation but can be neuralized by the BMP-inhibitor Noggin. Specifically, we tested whether the downregulation of xSuv4-20h HMTases prevented neural induction by Noggin. Without Noggin, wt and double morphant explants were positive for XK81 and negative for Nrp1 (Figure 5A). They were also negative for Xbra, indicating absence of contaminating mesoderm. Noggin-mediated Nrp1 expression was clearly visible in wt caps, but strongly reduced upon co-injection of xSuv4-20h morpholinos, while XK81 expression was downregulated in both the samples (Figure 5A). Thus, double morphant caps are both refractory to neural induction and restrained in epidermal differentiation. However, they differentiate into mesoderm upon stimulation with Activin A just like control explants, as shown by immunostaining for muscle myosin heavy chain (Figure 5B). These results confirm the crucial role of xSuv4-20h enzymes in coordinating the formation of ectodermal tissues, and show that in the absence of the two enzymes neural induction is impaired.
Loss of H4K20 di- and trimethylation is known to compromise DNA damage repair in mice and to partially block G1/S transition [24]. This prompted us to test, whether xSuv4-20h depletion affects apoptosis and cell proliferation in frog embryos. Immunostaining for activated Caspase3 revealed an increase in apoptotic cells on the injected side of double morphant embryos (Figure S10A). Coinjection of xBcl-2 mRNA, an anti-apoptotic factor, reduced the Caspase3 positive cells to levels of the uninjected control side, however, without re-establishing a proper Delta-like 1 and N-tubulin pattern in the double-morphant side. Overexpression of xBcl-2 mRNA alone had no effect on the expression of the tested markers (Figure S10A). Thus, although embryonic frog cells depleted for the H4K20me2/me3 marks become apoptotic at higher rate than wt cells, the absence of neurons in the double-morphant neural plate cannot be explained by selective cell death.
Double morphant embryos stained for the mitotic marker H3S10P, showed a two-fold reduction (p = 0.0058) in the number of proliferating cells at midneurula stage, compared to control morphant embryos (Figure S10B). This mild phenotype might be correlated with the observed increase in apoptosis. Since neural induction continues in frogs, even when cell proliferation is blocked from gastrulation onwards [28], it is unlikely that the nearly complete loss of N-tubulin positive neurons is brought about by this mild reduction in cell proliferation. Taken together, the main xSuv4-20h morphant phenotype represents not a selective loss of neuroblasts, but a block in neural differentiation.
So far, our analysis in xSuv4-20h morphant embryos has indicated a specific and selective loss of gene expression in ectodermally derived tissues. The earliest affected markers - Zic and Xiro genes - become induced at early gastrula stage and help establish the neural plate state [27]. At this time in frog development, embryonic cells in the animal hemisphere are still plastic and express members of the POU-V gene family – i.e. Oct-25, Oct-60 and Oct-91 - that encode paralogs of the mammalian pluripotency regulator Oct4 [29], [30]. Because Oct-25 and Oct-91 regulate germ layer differentiation in Xenopus [31]–[34], we investigated their expression (Figure 6A). Oct-25 is initially expressed throughout the animal hemisphere at early gastrula, but gets restricted to the presumptive floor plate (notoplate) by midneurula [31]. On the injected side of the vast majority of double morphants, however, Oct-25 expression was expanded from the notoplate down to the ventral midline. Interestingly, ectopic Oct-25 expression was restricted to the sensorial cell layer of the ectoderm, which contains neural and epidermal precursor cells, respectively (Figure 6A, sections). The Oct-60 gene, which is expressed during oogenesis, was not activated under these conditions. Oct-91 staining appeared normal in the majority of the embryos, although some showed a mild upregulation in double morphants as well (data not shown). The ectopic expression of Oct-25 is a specific consequence of xSuv4-20h enzyme depletion, because its normal pattern was re-established in morphants upon coinjection of mRNAs encoding wild-type, but not inactive, mouse Suv4-20h proteins (Figure S7E, left column). Notably, the selective derepression of the Oct-25 gene was also observed in double-morphant AC explants (Figure 6B), excluding indirect effects from non-ectodermal tissues.
We then performed qRT-PCR analysis to quantitate the relative changes in gene expression. It is frequently observed that embryo cohorts develop in slight asynchrony as a non-specific consequence of Morpholino injection, possibly obscuring transcriptional responses. To minimize this potential artifact, we analysed the RNAs of matching pairs of wt and xSuv4-20h depleted samples by dissecting embryos at early neurula stage (NF14) into uninjected and injected halves, based on the coinjected fluorescent lineage tracer (Figure S11A). As shown in Figure 6C, the Oct-25 mRNA is about three-fold higher in xSuv4-20h double-morphant halves (p = 0.0123), while being similar between control-morphant and uninjected halves. In the same sample, Oct-91 expression was unaffected (Figure 6C). We used this assay also to confirm the diminished expression of neural plate marker genes detected earlier by RNA in situ hybridisation. With the exception of Ngnr 1a, Nrp1 and N-tubulin, mRNA levels were clearly reduced in the morphant halves (p = 0.0122 and 0.0163, respectively; Figure S11B).
To gain further information about the complexity of the underlying transcriptional misregulation, we performed transcriptome analysis in wild-type and double-morphant embryos, again dissecting embryos in corresponding pairs of injected and uninjected halves (Figure S12A). Six percent of the 11639 annotated probe sets present on the microarray were significantly altered in their expression as a consequence of xSuv4-20h enzyme depletion, about equally split into upregulated (n = 319) and downregulated (n = 404) probes (Figure S12B and S12C; for a complete list of the responding probesets see NCBI's GEO Series accession number GSE41256). This result suggests that the observed phenotypes in the double morphants originate from transcriptional misregulation of distinct genes, rather than from global, pleiotropic effects. Indeed, Oct-25 mRNA is also specifically upregulated in the microarray data set, where it is among the ten most upregulated mRNAs in the double-morphant condition (Figure S12D).
The sustained expression of Oct-25 in xSuv4-20h morphant embryos fits the prediction of Oct-25 being a direct target of H4K20me3 mediated transcriptional silencing. To test this assumption directly, we carried out chromatin-immunoprecipitation (ChIP) experiments with H4K20me3-specific antibodies at the neurula stage (NF15-16). For ChIP experiments we used X. tropicalis embryos, since the available genome sequence of this closely related frog species [35] allowed us to design primer amplicons for non-exon derived DNA sequences. RNA in situ hybridization performed on neurula stage X. tropicalis embryos, confirmed that the expression patterns of Oct-25 and N-tubulin were up- and down-regulated, respectively, to the same extend as observed for X. laevis (Figure S13). We retrived the pericentromeric major satellite repeat sequence (MSAT3) as positive control amplicon for the experiment. Genic regions, which are H4K20me3-free and, thus, could be used as negative controls, are difficult to predict, since genome-wide analysis in mammalian cells reported only enrichment of this modification on pericentromeric and subtelomeric heterochromatin [36], [37]. As negative controls we considered: GAPDH, a constitutively expressed housekeeping gene; thyroid hormone receptor α (thra), a gene whose expression can be detected at neurula; and thra-induced bzip protein (thibz) that is expressed from metamorphosis on (Figure S14A). Statistical analysis of qRT/PCR data indicates that expression of GAPDH and thra was not significantly altered under the double-morphant condition (). Therefore, the relative H4K20me3 levels at these genes were defined as background, and compared to the levels on other loci (Figure S14A). The modification strongly decorated the pericentromeric MSAT3 repeat region (Figure 6D), as expected from the analysis in murine cells [21]. At the 5′UTR amplicon of the Oct-25 gene, H4K20me3 was significantly enriched compared to the control genes GAPDH (p = 0.0155), thra (p = 0.0103) and thibz (p = 0.0128) (Figure S14A and Figure 6D). In a second set of experiments, we compared the abundance of H4K20me3 between wild-type and xSuv4-20h double-morphant embryos (Figure 6E). In morphants, the modification was selectively reduced at the 5′UTR amplicon of Oct-25 (p = 0.004). Together, these ChIP experiments validate the 5′ end of the Oct-25 gene as direct target of xSuv4-20h mediated transcriptional silencing.
Xenopus Oct-25 has been implicated in germ layer formation [32], [34]. We wanted to know, whether the sustained expression of Oct-25 in xSuv4-20h morphants could cause the observed downregulation of early neural plate and neural differentiation markers. This question is difficult to address, since the role of Oct-25 in neural induction is ambiguous - both overexpression and morpholino knockdown inhibit neural differentiation [32], [34]. Thus, Oct-25 acts in pleiotropic fashion, perhaps switching target genes or protein interaction partners. In a previous report [38], human Oct4 protein was shown by ChIP analysis to bind to promoters of early neural markers, including Zic and Sox genes. In order to link Xenopus Oct-25 mechanistically to these genes, we have misexpressed constitutively activating and repressing Oct-25 fusion proteins in animal caps (Figure S15A). Zic1, Zic3 and Sox2 responded to the Oct-25 variants in a manner consistent with direct regulator/target gene interaction, i.e. they were hyperactivated by Oct-25-VP16 (p = 0.0143; 0.0456; 0.01622, respectively) and suppressed by Oct-25-EnR (p = 0.0236; 0.0167; 0.0231, respectively) compared to the uninjected sample. In line with this assumption, inspection of the X. tropicalis gene sequences detailed the presence of multiple Oct-25 DNA binding motifs within 2.0 Kb distance from the transcriptional start site for each of these genes (Figure S16). For the two Zic genes, which are misregulated in the forming neural plate of morphant embryos (Figure 3C and Figure S5F), we confirmed the misregulation by Oct-25 variants via RNA in situ hybridisation (Figure S15B).
Interestingly, Sox2 expression was affected only in AC explants, but not in the double morphant embryos. This can be explained by considering two points: First, in animal caps levels and activities of the injected Oct-25 protein variants most likely exceed endogenous Oct-25 protein activity and regulate Sox2 expression in a dominant fashion. Secondly, formation of neural tissue in the embryo requires inductive influences including FGF signalling [39],and Sox2 transcription is stimulated by FGF8 [27], which is normally expressed in the mesoderm. Thus, the stimulating influence of FGF signalling on Sox2 transcription in the embryo may neutralize the repressive influence from deregulated Oct-25 expression, while the repressive activity of the deregulated Oct-25 levels prevails in animal caps in the absence of FGF signalling.
The remaining genes either failed to respond to one of the two Oct-25 protein variants (Zic2, Xiro1), or did not respond (Ngnr 1a, N-tubulin). These observations suggest an indirect effect. While it is possible that additional factors that are misregulated in xSuv4-20h morphants contribute to the neural phenotype, the combined results from ChIP experiments and Oct-25 variants define a pathway, in which xSuv4-20h enzyme dependent repression of Oct-25 is needed during gastrulation for proper neuroectoderm differentiation.
To further analyse the mechanistic interaction between xSuv4-20h enzymes and Oct-25, we performed rescue experiments with triple-morphant embryos, in which synthesis of Oct-25 and xSuv4-20h proteins was simultanously blocked (Figure 7). The Oct-25 morpholino that we used has been shown before to inhibit efficiently Oct-25 translation from both non-allelic gene copies [40]. Because global Oct-25 depletion inhibits the formation of anterior neural structures [40], we employed two different strategies for the triple-knockdown to circumvent this problem. In a first series of analysis we injected a single A1 blastomere of 32-cell stage embryos to target cells that predominantly contribute to the retina and brain. Also in this experimental series, the morphology of double morphant eyes was strongly affected (Figure 7A). 71% of the injected embryos showed a clear reduction of retinal pigment, the remainders often restricted to the dorsal-most portion of the eyecup. The majority of the eyes contained no lens (Figure 7C). When the downregulation of xSuv4-20h enzymes was coupled to a concomitant knockdown of Oct-25 (triple morphants), the percentage of embryos showing this defect was reduced to 49% (p = 0.0188, Fisher's exact test). The retinal pigment was rescued in the triple morphants, whose eyes also regained a properly structured lens (Figure 7C). To confirm the morphological phenotypes, we investigated the basal neural gene expression in AC explants. The expression of a subset of genes involved in the establishment of the neural plate state (Zic1, Zic2, Xiro1, Sox2 and Sox3) was strongly reduced upon downregulation of xSuv4-20h enzymes at early neurula (NF14-15), compared to uninjected animal caps (p = 0.0068; p = 0.0127; p = 0.0113; p = 0.0321; p = 0.0037, respectively). With the exception of Sox2, the simultaneous downregulation of xSuv4-20h enzymes and Oct-25, rescued neural gene expression. In fact, under the triple morphant condition most of these genes were expressed at higher levels than normal, suggesting that they are partly repressed by Oct-25 in unmanipulated explants (Figure 7D). Most importantly, the combined results of the two triple-knockdown experiments indicate that both morphological and molecular features of the xSuv4-20h double morphant phenotype can be rescued to a significant extent by reducing Oct-25 protein levels. This result firmly establishes that the sustained and elevated expression of Oct-25 protein is responsible for the neural differentiation defect of xSuv4-20h double-morphant embryos.
Oct-25 plays multiple roles during early frog development, including interference with Activin/BMP-dependent mesendoderm formation before gastrulation, and with neural induction during gastrulation [32], [34]. A similar role is considered for its mammalian paralog Oct4, which is required for the pluripotent state of ES cells, but antagonizes ectodermal differentiation as soon as these cells exit pluripotency [30], [41], [42]. Although previous genome-wide studies of histone modifications in mammalian cells have not detected H4K20me3 on the Oct4 gene [36], [37], this apparent similarity made us investigate Oct4 protein expression in wild-type and Suv4-20h1/h2 DKO murine ES cells. We tested two independently derived DKO cell lines (B4-2 and B7-1), and compared them with two wild-type controls, i.e. wt26, an isogenic ES cell line, and the well-characterized GSES-1 cell line [43]. All four cell lines formed comparable ES cell colonies in LIF-containing medium (Figure 8A and Figure S17B), although the two DKO lines grew slightly slower. Upon aggregation they formed embryoid bodies, which were clearly smaller than those of the wild-type lines, both at day 2 and day 6 of differentiation (Figure 8A and Figure S17). After replating the differentiated cells for one day, the two DKO lines frequently formed again colonies resembling undifferentiated ES-cells (day 7 in Figure 8A and Figure S17B). To obtain a quantitative measure of Oct4 gene expression, we fixed and stained the four cell lines before (day 0) and during (day 6) differentiation for Oct4 protein and subjected equal cell numbers to FACS-analysis. The Oct4 signals were quite similar between wt26 and GSES-1 cells, as they were between the two DKO lines. In contrast to the wild-type cell lines, however, the signals of the DKO lines were reproducibly shifted to the right (Figure 8B and Figure S17C). Based on normalized median fluorescence intensity, the two DKO lines contained approximately three-fold higher Oct4 protein amounts than the wild-type lines at day 0 (p = 0.00604), and still two-fold more at day 6 (p = 0.01266) (n = 3; Figure 8C and Figure S17B). We conclude that Oct4 expression is being reduced during differentiation in Suv4-20h1/h2 DKO cells. However, these cells have higher Oct4 levels in the undifferentiated state, and maintain higher levels during differentiation in comparison to wild-type cells.
Oct4 protein levels are known to be tightly regulated [1] and to influence lineage decisions during ES cell differentiation [41], [42]. We therefore investigated the biological significance of the elevated Oct4 protein levels in Suv4-20h DKO ES cell lines. Unfortunately, the applied EB differentiation protocol promotes predominantly mesendodermal differentiation, which prevented the analysis of neural markers. Nevertheless, we performed FACS analysis on wt and Suv4-20h DKO cell lines stained for the chemokine receptor 4 (CXCR4) protein, whose expression indicates mesendoderm induction in embryoid bodies. At day 6 of differentiation, wt cell lines showed a robust increase in CXCR4 positive cells compared to day 0 (Figure 8D and data not shown). In contrast, both Suv4-20h DKO cell lines contained a significantly lower percentage of CXCR4 positive cells at day 6 when compared to the wild type cell lines (p = 0.03255; Figure 8). We also noted that replated wt cells frequently formed autonomously beating areas at differentiation day 14 (see Video S1), indicating functional cardiomyocyte formation, while contracting areas were never observed in the Suv4-20h DKO cells (Video S2; n = 4 experiments). Finally, qRT-PCR analysis indicated a reproducible and statistically significant shift in mesendoderm gene expression in the DKO ES cells, which show enhanced induction of FoxA2 (p = 0.00706) and reduced levels of Gata4 (p = 0.00037), compared to the wt ES cell lines (Figure S17D). Together, these results reveal a compromised and biased differentiation capacity for Suv4-20h DKO ES cell lines, and provide an entrypoint for further experimentation in the murine system.
In this study, we have investigated the developmental functions of the histone- methyltransferases Suv4-20h1 and h2 during frog embryogenesis, which are responsible for the establishment of the H4K20 di- and trimethylated states. These modifications have been implicated in heterochromatin formation, DNA damage repair and G1/S-transition [21], [24] and are also involved in transcriptional regulation [44], [45]. Our experiments identify a specific and selective role of xSuv4-20h HMTases in the formation of the ectodermal germlayer through control of mRNA expression of key regulators of the neural plate state and neuronal differentiation circuits. Indeed, our results indicate for the first time that H4K20me3 controls transcription in a rather gene-specific manner. The mRNA profile of double morphant embryos shows appr. 6% of the annotated probesets to be misregulated, when H4K20me3 levels have been reduced to appr. 25%. About half of the responding mRNAs are transcriptionally upregulated and, thus, their genes may qualify as being directly controlled by H4K20me3 deposition. Surprisingly, our molecular analysis revealed that xSuv4-20h enzymes are required to restrict the expression of the pluripotency-associated Oct-25 gene during gastrula and neurula stages. In the absence of proper H4K20me3 deposition, the Oct-25 gene becomes transcriptionally derepressed and interferes with neural differentiation. The successful rescue of key morphological and molecular aspects of the neural defect in double-morphant embryos by the simultanous inhibition of Oct-25 translation establishes this pathway formally. At least in Xenopus, the regulatory interaction between xSuv4-20h enzymes and Oct-25 is needed for embryonic cells to exit the pluripotent state and differentiate as neuroectoderm.
The genetic interaction between Suv4-20h enzymes and POU-V genes appears also to be conserved in mouse ES cells, although the H4K20me3 mark has not yet been detected on the Oct4 gene locus. To this point, we have shown that Suv4-20h DKO ES cells contain significantly elevated Oct4 protein levels, compared to wt ES cells. During ES cell differentiation the mammalian Oct4 gene is known to become repressed by a battery of epigenetic mechanisms including DNA methylation, incorporation of somatic linker histones and repressive histone modifications (H3K9me3/H3K27me3), which cooperate to achieve chromatin compaction of the Oct4 gene locus [46]. Our finding that Oct4 protein levels are increased in the DKO ES cells both before and during differentiation actually suggests that Suv4-20h enzymes regulate mammalian Oct4 transcription in a way that is at least partly independent from the other repressive mechanisms targetting this locus.
Our results in Xenopus rest predominantly on loss of function analysis, achieved by morpholino-mediated knockdown of endogenous xSuv4-20h protein translation. Specifically, we have shown that our antisense oligonucleotides block translation of xSuv4-20h1 and h2 isoforms in vitro, and significantly decrease H4K20me2 and –me3 levels in vivo, without altering the bulk abundance of other repressive histone marks such as H3K9me3 and H3K27me3. The morpholinos produced specific phenotypes, which were rescued on the morphological and molecular level by RNA-born co-expression of heterologous xSuv4-20h enzymes and, thus, originate from deficient H4K20me2/me3 states.
While xSuv4-20h double morphant embryos showed consistent phenotypes at high penetrance, we were surprised to see that H4K20me2 and –me3 states could be quantitatively increased in frog embryos without any obvious morphological or molecular consequences (Figures S8 and S9). This result can be explained considering first of all the higher stability of the knockdown by non-degradable morpholinos compared to the transient protein upregulation by RNA injection; secondly, demethylation of higher-methylated states may occur rather rapidly through H4K20me2 and me3 demethylases at specific sites, where H4K20me1 is required, e.g. Wnt/β-Catenin inducible genes [47]. However, we did not observe evidence for compromised transcription of Wnt target genes under overexpression (Figures S8 and S9) or morphant condition (Figure S5). Since mono- and dimethylated H4K20 states are quite abundant modifications in Xenopus embryos (30–40% each; see ref. [13]), it is most likely the loss of H4K20 trimethylation, which interferes with normal development.
XSuv4-20h double-morphant embryos were frequently defective for eye and melanocyte differentiation, indicating a prominent impairment of neuroectodermal differentiation. This selectivity is surprising, given that the two HMTases are expressed throughout the entire embryo (Figure S2). As a matter of fact, the phenotypes originate in the neuroectoderm, as shown by targeted injection into animal or vegetal blastomeres of 8-cell stage embryos (Figure 4). A large panel of marker genes that were investigated by RNA in situ hybridisation indicates that mesodermal and endodermal gene expression patterns are not perturbed by xSuv4-20h enzyme depletion (Figure 3A). This includes markers, which are required for specification of embryonic axes and formation and patterning of the mesendodermal germlayers (Figure S5). We also note that morphant animal cap explants were refractory to Noggin-dependent neural induction, but could be induced to differentiated skeletal muscle by a mesoderm inducing signal (Figure 5). We therefore assume that a major function of xSuv4-20h enzymes lies in the transcriptional control of genes that coordinate and execute neuroectodermal differentiation. Consistent with this hypothesis, many of the genes that we found downregulated in xSuv4-20h morphants, are key regulators of eye development (Rx-1, Pax-6), neuronal differentiation (Ngnr 1a, Delta-like 1) or regulators of neural competence and neural plate state (Zic-1, -2, -3, Xiro-1, Nrp1, N-CAM; [27]).
While these molecular results explain the overt morphological phenotypes in a consistent manner, it should be noted that these HMTases are clearly involved in additional cellular aspects. The mild reduction in mitotic cells and the increased apoptotic rate of morphant embryos (Figure S10) is reminiscent of findings in Suv4-20h1/h2 DKO MEFs, which are less resistant to DNA damage and compromised at the G1/S checkpoint [24]. The data reported here indicates a need for deeper analysis of the regulatory impact of Suv4-20h enzymes on transcription in both mammals and non-mammalian vertebrates.
According to current models, xSuv4-20h enzymes mediate transcriptional repression, based on the enrichment of the H4K20me3 mark on heterochromatic foci. Genes that are regulated by these enzymes should therefore become derepressed under loss of function condition. Following this logic, many of the genes, which are misregulated in morphant frog embryos, would be classified as indirect targets, since they were downregulated. One very notable exception, which we have validated as direct target, is Oct-25 (Figure 6). Oct-25 is induced broadly in the animal hemisphere at the blastula/gastrula transition, before it becomes restricted to the notoplate at neurula stages [31]. Oct-25 plays multiple roles during early frog development, including interference with Activin/BMP-dependent mesendoderm formation before gastrulation, and with neural induction during gastrulation [31], [32], [34]. Our study reveals now a new function for Oct-25, namely to control the transit from a pluripotent cell to a neural cell that differentiates, when Oct-25 expression has faded. As depicted in our model (Figure 7E), this function depends on the precise dose and duration of Oct-25 transcription, which is controlled by the level of H4K20me3 deposition on the first exon of the Oct-25 gene through xSuv4-20h enzymes. As we have shown here, deregulated transcription of Oct-25 in double-morphant embryos elicits massive consequences on the differentiation of neuroectodermal organs and cell types. We have traced back the origin of the malformations to the gastrula stage, when a gene network, defining the neural state, become perturbed by Oct-25. Some members of this network are good candidates for direct regulation through Oct-25 (e.g. Zic and Sox genes). However, since Oct-25 transcription persists ectopically at least until the mid-neural fold stage in the ectoderm, subsequent gene cascades involved in regional differentiation of the neuroectoderm could also be directly misregulated by Oct-25.
The specific and selective deregulation of Oct-25 transcription in a precise spatial domain, i.e. the sensorial cell layer of the ectoderm, implies a very intriguing role for xSuv4-20h enzymes. This domain contains not only the uncommitted precursors of neuronal and epidermal cell types, but – with regard to the involuting marginal zone – includes also mesodermal and endodermal precursor cells. The observed derepression of Oct-25 in this domain may thus reflect a conserved mechanism, by which Suv4-20h enzymes control pluripotency in the embryo. As discussed above, we have found Oct4 protein to be increased in two independent Suv4-20h double knockout ES cell lines under LIF-maintained self-renewal conditions, when compared to wt ES cells (Figure 8 and Figure S17). The DKO cell lines also maintain higher Oct4 levels during differentiation than wt ES cells, although their Oct4 levels get diminished in the course of 6 days. Recent data from several labs suggest that the pluripotency regulators Sox2 and Oct4 guide ES cells towards specific germ layer differentiation programs, when they exit the pluripotent state [41], [42]. Indeed, our findings are in agreement with Thomson and colleagues, who describe Oct4 to antagonize ectodermal specification and to direct mesendodermal cell fate decisions. The conserved Suv4-20h-dependent restriction of Oct4 expression may thus contribute to the germ-layer specification of embryonic cells, when they exit the pluripotent state.
Animal work has been conducted in accordance with Deutsches Tierschutzgesetz; experimental use of Xenopus embryos has been licensed by the Government of Oberbayern (Projekt/AK ROB: 55.2.1.54-2532.6-3-11).
Full length X. laevis Suv4-20h1a (NM_001092308) and Suv4-20h2a (NM_001097050) cDNAs in pCMV-SPORT6 were provided by ImaGenes. Capped mRNAs were synthesized in vitro with SP6 RNA-Polymerase after HpaI linearization. Both cDNAs were subcloned via XhoI/EcoRI sites into pBluescript II KS to generate digoxygenin-labelled antisense probes with T3 RNA-Polymerase. Xenopus Bcl-2, Oct-25-VP16 and –EnR constructs were transcribed with SP6 RNA-Polymerase from NotI- (Bcl-2 and Oct-25-VP16) and SacII- (Oct-25-EnR) linearized pCS2+ plasmids, respectively. Mouse Suv4-20h1 and h2 enzymes were transcribed with SP6 from PvuI-linearized pCMVmyc-constructs [24]. Enzymatically inactive mouse Suv4-20h HMTases were generated via PCR-mutagenesis (see Text S1, Table S1 for primers). Synthetic mRNAs were injected in the animal pole of two-cell stage embryos at 2, 3 or 4ng per embryo. Rescue experiments with wt and mutated mRNAs were performed with 3ng of a 1∶1 mix of wt or mutated Suv4-20h1 and h2 mRNAs, injected into the animal pole of a single blastomere at two-cell stage. Xenopus Bcl-2 mRNA was injected unilaterally in the animal pole of two-cell stage embryos at 800 pg per embryo. Xenopus Oct-25-VP16, -EnR mRNAs were injected in the animal pole of two-cell stage embryos at 100 pg per embryo.
Mouse embryonic fibroblasts (MEF) wild type and Suv4-20h DKO cells [24] were cultivated in High Glucose DMEM with L-Glutamine and sodium pyruvate, complemented with 10% FCS, β-mercaptoethanol, non essential amino acids and penicillin/streptomycin in a 37°C incubator at 5% CO2. Lipofectamine 2000 (Invitrogen) was used for the transfection of plasmid DNAs. Immunofluorescence analysis was performed as described in the Text S1.
Mouse ES cells were cultivated on gelatinized plates in High Glucose DMEM with L-Glutamine and sodium pyruvate, complemented with 15% FCS, 0.1 mM ß-mercaptoethanol, non essential amino acids, penicillin/streptomycin and LIF. Cells were maintained at 37°C in a humidified atmosphere of 5% CO2. ES in vitro differentiation and FACS analysis were carried out as described [43] The incubation steps with the primary Oct4 (1∶250, Abcam) or CxCR4 (1∶50, BD Pharmingen) antibody and subsequently a FITC-conjugated secondary antibody (1∶250, Invitrogen) were performed at RT for 45 min with two washing steps after each antibody incubation. For the isotype controls purified, IgG was used instead of the Oct4-antibody. All FACS analyses were performed with an Epics XL (Beckman-Coulter) using the analysis software FlowJo.
Translation-blocking Morpholino oligonucleotides targeting Xenopus Suv4-20h1 (X.laevis and X.tropicalis: 5′-GGATTCGCCCAACCACTTCATGCCA-3′), Xenopus Suv4-20h2 (X.laevis: 5′-TTGCCGTCAACCGATTTGAACCCAT-3′: X.tropicalis: 5′-CCGTCAAGCGATTTGAACCCATAGT-3′) and Xenopus Oct-25 (X.laevis: 5′-TTGGGAAGGGCTGTTGGCTGTACAT-3′) mRNAs were supplied by Gene Tools LLC. Each Morpholinos recognizes the two non-allelic isoforms of each gene in X.laevis (see Figure S3A, S3B). GeneTools' standard control Morpholino was used to monitor non-specific effects. Morpholino activity was tested by in vitro translation (SP6-TNT Kit, Promega), adding 2 pg of control Morpholino or 1 pg of Suv4-20h1 and/or h2 Morpholinos per TNT reaction. Unless stated otherwise, embryos were injected at a dose of 60–80 ng per embryo (30–40 ng each of Suv4-20h1 and h2 Morpholinos, or 60–80 ng control Morpholino per embryo). For 8-cell stage experiments, morpholinos were injected in two neighbouring, animal or vegetal blastomeres on one side of the embryos, at half the dose (i.e. 40 ng total). For morphogical epistasis experiments, Xenopus Suv4-20h1 and h2 Morpholinos (5 ng each per embryo) and Oct-25 Morpholino (1 ng per embryo) were injected into A1 blastomere at 32-cell stage.
Xenopus laevis eggs were collected, fertilized in vitro, and handled following standard procedures; embryos were staged according to Nieuwkoop and Faber (1967). The embryos were injected with maximally 10 nl volume. When required, they were sorted into left side or right side injected cohorts before fixation, based on the coinjected lineage tracer Alexa Fluor-488 Dextran (Invitrogen). Alkaline-phosphatase stained and refixed embryos were either sectioned after embedding in paraffin (10 µm), or in gelatine/albumin mixture supplemented with 25% glutaraldehyde before sectioning (30–50 µm) with a Vibratome 1000 (Technical Products International, INC.) as described [48]. Animal caps were manually dissected at NF9 and transferred singly into wells of a 96-well plate, coated with 1% agarose and filled with 1X Steinberg's solution, 0.1% BSA with or without Activin A (1∶10 diluted conditioned cell culture supernatant). For neural induction, embryos were injected into the animal pole with Noggin mRNA (60 pg per embryo) alone or together with xSuv4-20h1 and h2 morpholinos (40 ng each per embryo) at two- to four-cell stage. For mesoderm induction, embryos were injected animally 4 times with 2.5 nl of control morpholino (80 ng per embryo) or a mix of xSuv4-20h1 and h2 morpholinos (40 ng each) at two or four cell stage. For Oct-25-VP16 and –EnR overexpression experiments, embryos were injected animally 4 times with 2.5 nl of each mRNAs (100 pg per embryo). For epistasis experiments on animal caps, embryos were injected 4 times with 2.5 nl of xSuv4-20h1 and h2 Morpholinos (40 ng each per embryo) and Oct-25 Morpholino (30 ng per embryo) at two or four cell stage.
Nuclei extraction from Xenopus embryos and mass spectrometry analysis of histone modifications were performed as described [13]. Histone marks were quantitated as relative abundances of a specific modification state as a fraction of the amount of all modifications found for this peptide (for details see ref 13).
Whole-mount RNA in situ hybridizations were performed as described (Sive et al. 2000). Embryos were photographed under bright light with a Leica M205FA stereomicroscope. The following antibodies were used for immunocytochemistry: H3S10P antibody (1∶300, Upstate Biotechnology), active Caspase3 antibody (1∶500, Promega), and myosin heavy chain antibody MF20 (1∶100 hybridoma cell culture supernatant), anti-mouse or anti-rabbit alkaline phosphatase-conjugated secondary antibodies (1∶1000, Chemicon).
Embryonic histones were purified via acidic extraction of nuclei as described [13], size-separated by SDS-PAGE and blotted onto PVDF membranes (Roth). Membranes were blocked with 3% BSA (Roth) in PBS and subsequently incubated o/n at 4°C with polyclonal rabbit antibodies against H4K20me1 (1∶6000), H4K20me2 (1∶1000), H4K20me3 (1∶500) [21], [24] and pan H3 (1∶25000, Abcam). Infrared (IR) 680 or 800 conjugated Goat anti Rabbit IgG (1∶5000, Li-Cor) were used as secondary antibodies (incubation o/n at 4°C). Signals were detected with an ODYSSEY Infrared Imaging System. To extract exogenous myc-tagged fusion proteins embryos were treated as described in the Text S1. Proteins were separated by SDS-PAGE, BSA-blocked PVDF membranes were incubated o/n at 4°C with anti-myc 9E10 antibody (1∶50), followed by anti-mouse HRP- conjugated antibody (1∶3000, Jackson Immunoresearch). Proteins were detected with ECL plus western blotting detection reagents (GE Healthcare). Histological sections were stained with pan H3 (1∶2000, Abcam), H4K20me1 (1∶5000), H4K20me2 (1∶2000), H4K20me3 (1∶5000) antibodies [24].
Total cellular RNA was isolated with TRizol (Qiagen) and phenol/chloroform extraction. On-column RNA clean-up, including a DNAse digestion step, was performed using RNeasy-Mini-Kit (Qiagen). Samples for qRT-PCR and microarray profiling were collected as described in the Text S1.
Microarray data were processed using R/Bioconductor (www.bioconductor.org). If not indicated otherwise, we used standard parameters in all functions calls. Expression values were calculated using ‘gcrma’. Probe sets were kept for differential expression analysis if there were more ‘present’ calls (calculated using ‘mas5calls’) in one of the treatment groups than non-‘present’ calls, if their expression level variance was higher than 0 across all arrays and if the probe set had an Entrez identifier annotation according to the Entrez database with a date stamp of 2011- Mar16. One gene to many probe set relationships were resolved by retaining only the probe set with the highest variance across all arrays. Differential expression statistics were obtained using a linear model (library ‘limma’). A significant response was defined if the local false discovery (‘locfdr’ package) rate calculated on the moderated t statistic was smaller than 0.2. The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus and are accessible through GEO Series accession number GSE41256 (http://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE41256).
ChIP experiments were performed using Xenopus tropicalis as described [49], with minor changes (see Text S1 for details).
A published weight matrix (PMID:17567999) was used to scan 2 kb upstream regions of selected X. tropicalis genes (Xenbase version 7.1) for binding site occurrence. Scanning was performed using RSA matrix-scan (PMID:18802439) with default parameters.
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10.1371/journal.ppat.1006615 | Cell cycle stage-specific transcriptional activation of cyclins mediated by HAT2-dependent H4K10 acetylation of promoters in Leishmania donovani | Chromatin modifications affect several processes. In investigating the Leishmania donovani histone acetyltransferase HAT2, using in vitro biochemical assays and HAT2-heterozygous genomic knockout we found the constitutively nuclear HAT2 acetylated histone H4K10 in vitro and in vivo. HAT2 was essential. HAT2-depleted cells displayed growth and cell cycle defects, and poor survival in host cells. Real time PCR and DNA microarray analyses, as well as rescue experiments, revealed that downregulation of cyclins CYC4 and CYC9 were responsible for S phase and G2/M defects of HAT2-depleted cells respectively. Leishmania genes are arranged in unidirectional clusters, and clustered genes are coordinately transcribed as long polycistronic units, typically from divergent strand switch regions (dSSRs) which initiate transcription bidirectionally on opposite strands. In investigating the mechanism by which CYC4 and CYC9 expression levels are reduced in HAT2-depleted cells without other genes in their polycistronic transcription units being coordinately downregulated, we found using reporter assays that CYC4 and CYC9 have their own specific promoters. Chromatin immunoprecipitation assays with H4acetylK10 antibodies and real time PCR analyses of RNA suggested these gene-specific promoters were activated in cell cycle-dependent manner. Nuclear run-on analyses confirmed that CYC4 and CYC9 were transcriptionally activated from their own promoters at specific cell cycle stages. Thus, there are two tiers of gene regulation. Transcription of polycistronic units primarily initiates at dSSRs, and this most likely occurs constitutively. A subset of genes have their own promoters, at least some of which are activated in a cell-cycle dependent manner. This second tier of regulation is more sensitive to H4K10 acetylation levels, resulting in downregulation of expression in HAT2-depleted cells. This report presents the first data pointing to cell cycle-specific activation of promoters in trypanosomatids, thus uncovering new facets of gene regulation in this parasite family.
| Leishmania donovani causes Visceral Leishmaniasis (VL), a disease that plagues the world’s poor, particularly in Brazil, Sudan and the Indian sub-continent. If not treated in timely manner VL can be fatal, and due to emerging drug resistance the search for new drug targets continues. Thus, the parasite’s cellular processes remain an area of continued research interest. Eukaryotic DNA is packaged into nucleosomes to compact it, and the histone proteins that constitute a major part of the nucleosomes carry several modifications that affect biological processes. Our laboratory has been studying histone modifications with special attention to the impact of histone acetylation events in the parasite. The present report addresses the significance of one particular histone acetylation event, H4K10, and the protein that mediates this acetylation, HAT2. We find that HAT2 is essential for cell survival, and decrease in HAT2 levels leads to growth and cell cycle defects. From the results of further experiments performed by us we attribute these defects to lowered expression of certain cyclins. We find that the expression of these cyclins are activated in a cell cycle-dependent manner, and this activation is regulated by H4K10 acetylation.
| Histone post-translational modifications (PTMs) impact nuclear processes either by generally altering chromatin structure to make it more permissive/repressive to incoming transcription/replication/repair machinery, or by serving as flags recognized by specific proteins that modulate the various DNA-related processes. Due to the wide range of cellular processes they regulate the functional roles of histone PTMs have been extensively investigated. Though these are highly conserved among most eukaryotes, trypanosomatid histones are divergent in sequence [1–3]. Trypanosomatids include several protozoan pathogens that cause communicable diseases such as sleeping sickness, Chagas disease and various forms of Leishmaniases. Visceral Leishmaniasis (VL), caused by Leishmania donovani in the Indian subcontinent, can be fatal if not treated at the appropriate time. Due to the emergence of drug resistant strains coupled to risks of dual HIV-Leishmania infection the study of the organism’s cellular biology remains an area of continued interest. While histone PTMs have not been globally identified in any Leishmania species so far, they are likely to carry the same repertoire of modifications as Trypanosoma species since the histone sequences are highly conserved between these family members [4].
Histone acetylation and deacetylation events are dynamic in nature and their timing is regulated by the cellular milieu. Targeting lysine residues (and arginine residues to a lesser degree) in either the N-terminal tails of histones or in the histone core, histone acetylations modulate a myriad of processes such as histone deposition, transcription, DNA replication, and DNA repair, primarily by loosening the interaction between the histone and DNA in the nucleosome, thus decreasing nucleosomal stability (reviewed in [5]). In Trypanosoma species, histone acetylations have been identified at H4K2, H4K4, H4K10, H4K14, H3K76, H3K23 and H3K19, and most unusually, at multiple residues on the H2B C-terminal tail [1–3,6]. The enzymes that mediate histone acetylation (histone acetyltransferases or HATs) fall into two categories–those that acetylate histones prior to nucleosomal deposition (Type B HATs), and those that typically acetylate nucleosomal histones (Type A HATs). Four MYST-family (Type A) HATs were annotated in the Leishmania whole genome sequences [7,8]. Leishmania donovani HAT3 has been found to acetylate H4K4 in vitro as well as in vivo, and deletion of LdHAT3 leads to deficiencies in histone deposition. LdHAT3 has been identified as an essential player in UV-induced DNA damage repair pathway, via its mediation of PCNA acetylation that must precede PCNA monoubiquitination in order for translesion DNA synthesis-based repair to occur efficiently [9]. Leishmania donovani HAT4 acetylates H4K14 in vitro, and its deletion leads to prolonged G2/M phase due to down regulation of Cdc20 [10]. Trypanosoma brucei HAT1 and HAT2 have been found to be essential, with TbHAT1 knockdown compromising DNA replication as well as telomeric silencing [11]. TbHAT3, though non-essential [11], has been found to facilitate DNA repair at internal sites within chromosomes by promoting Rad51-dependent homologous recombination [12]. In investigating the role of the Leishmania donovani MYST-family histone acetyltransferase HAT2, the present study uncovers new facets of gene regulation in this parasite.
The cloned Leishmania donovani 1S HAT2 (S1 Methods; GenBank Accession No. KY445835) was found to carry the core catalytic acetyltransferase domain that is typical of the members of the MYST family (Fig 1A, MOZ/SAS box). The MOZ/SAS box harboured the conserved cysteine and glutamate residues (Cys273 and Glu332) that have been identified as critical to catalysis—catalysis occurs via a ping pong mechanism wherein the glutamate residue arbitrates the deprotonation of the cysteine residue, which then catalyzes the transfer of the acetyl group from acetyl-CoA to the target lysine residue on the histone substrate via an acetyl-Cys catalytic intermediate [13]. By tagging one of the genomic alleles with eGFP (S1 Methods; S1A Fig), followed by immunofluorescence analysis of the tagged protein at different cell cycle stages using kinetoplast morphology and segregation pattern as marker [14], the protein was found to be constitutively nuclear like LdHAT3 [9] (Fig 1B, S1B Fig). In vitro biochemical assays were performed using peptide substrates whose sequences were derived from the termini of Leishmania histones. For this purpose the HAT2 proteins (wild type and mutant E332A) were episomally expressed in fusion with FLAG tag in Leishmania promastigotes (S1 Methods; S2A Fig), and the FLAG-tagged HAT2 proteins were pulled down and HAT assay performed as described in Materials and Methods. LdHAT2-FLAG had autoacetylation activity, in addition to targeting the N-terminus of histone H4 (Fig 1C). The LdHAT2-E332A-FLAG was catalytically inactive as predicted. Using H4 peptide substrates that were pre-acetylated at K4/K10/K14 as well as various H4-derived peptides where specific lysine residues had been mutated to alanine residues, it was revealed that H4K10 was the primary target of LdHAT2-mediated histone acetylation in vitro (Fig 1D and 1E). These findings are in keeping with results from TbHAT2 [11]. To determine if H4K10 is the target of LdHAT2-mediated acetylation in vivo also, we raised H4acetylK10-specific antibodies (S1 Methods) and confirmed their specificity using peptide competition assays [9](S2B Fig). H4K10 acetylation was maintained at more or less equivalent levels at all stages of Leishmania (S2C Fig), and was detected only in the nucleus at all stages of cell cycle (S2D Fig). Coupled to previous data from the lab indicating rapid nuclear import of histones post-synthesis [9], it appears that acetylation most likely takes place in the nucleus, in keeping with the fact that LdHAT2 is constitutively nuclear.
As Leishmania donovani lacks a canonical RNAi pathway, RNAi-mediated knockdown of genes cannot be carried out. Hence we attempted to create HAT2 genomic knockout. Heterozygous knockouts LdHAT2-hKO:neo and LdHAT2-hKO:hyg were created (S1 Methods) and their authenticity verified (S3A and S3B Fig). However, repeated efforts to obtain HAT2-nulls failed, suggesting that LdHAT2 is essential to cell survival. To confirm this, LdHAT2 was ectopically expressed in LdHAT2-hKO:hyg cells first, and cells of the resultant LdHAT2-hKO:hyg/HAT2-eGFP-blecherry line (S1 Methods) were then transfected to replace the second genomic allele of HAT2. In this case, a successful replacement could be obtained (S3C Fig). The importance of LdHAT2 in mediating cell survival was in contrast to LdHAT3 and LdHAT4, both of which are dispensable [9,10]. Expression of HAT2 in LdHAT2-hKO:hyg cells was ~0.6 of that seen in wild type cells (Fig 2A), and western blot analysis revealed that H4K10 acetylation was reduced by ~ 50% in LdHAT2-hKO cells while H4K4 acetylation levels remained unaltered (Fig 2B). These data confirmed that LdHAT2 mediates H4 acetylation at K10 residue in vivo also.
All further analyses were carried out using the HAT2 heterozygous knockout LdHAT2-hko:hyg. When the effect of HAT2 depletion on cell growth was monitored over a period of seven days it was observed that LdHAT2-hKO:hyg cell cultures grew considerably slower than control Ld1S-hyg cells (Ld1S carrying hygromycin resistance cassette;[9]), never reaching the same cell density as the control (Fig 2C). On scoring the percent of survivors every 24 hours over the seven-day period we found that the difference was not significant enough to account for the disparity in growth rate (S3D Fig). The slow growth of HAT2-depleted cells was subsequently determined to be due to increase in generation time to ~18.6 hours (compared to ~ 9.4 hours in case of control cells; S1 Methods and S3E Fig). Work from our laboratory has found H4K4 acetylation to play a role in histone deposition in Leishmania [9]. However, analysis of the DNA-associated protein fraction of LdHAT2-hKO cells in comparison with Ld1S-hyg cells revealed that H4K10 acetylation does not impact nucleosomal deposition (S1 Methods and S3F Fig).
Possible links between increase in generation time and perturbation of one or more cell cycle stage in LdHAT2-hKO cells were probed by synchronizing cells at the G1/S transition using hydroxyurea and then releasing them into S phase. This enabled the monitoring of cell cycle progression from G1 through S and G2/M, and back into G1 by flow cytometry. Heterozygous knockout cells displayed delay in navigating S and G2/M phases in comparison with control cells, returning to G1 hours later (Fig 2D and 2E). Probing of DNA replication pattern by pulse-labelling cells with EdU at different time-points after release from hydroxyurea block indicated that DNA replication was protracted in heterozygous knockout cells in comparison with wild type (Fig 2F). This is in keeping with findings in Trypanosoma cruzi where overexpression of histone H4 that is non-acetylatable at the 10th and 14th residues leads to reduced uptake of EU and EdU, suggesting that H4K10 acetylation is important for replication and transcription [15]. The survival of LdHAT2-hKO:hyg parasites within host cells was examined by infecting macrophages with metacyclic promastigotes [10]. Depletion of HAT2 did not impair their ability to infect macrophages, with the number of parasites per 100 macrophages at the end of the five-hour infection period being the same as in control promastigotes (Fig 2G, 0h time-point). However, the internalized parasites were unable to multiply efficiently within the host cells (Fig 2G, 24h and 48h time-points), underlining the importance of HAT2 for parasite propagation within the mammalian host also. To confirm the defective phenotypes of HAT2-hKO cells were a consequence of HAT2 depletion we expressed LdHAT2 ectopically in the heterozygous knockout cells (Fig 3A shows western blot analysis of lysates isolated from transfectant cells) and analyzed the resulting growth and cell cycle phenotypes. Ectopic HAT2-eGFP expression largely rescued growth and cell cycle defects of HAT2-depleted cells (Fig 3B and 3C), but only partially rescued the heterozygous knockout phenotype when survival within host macrophages was examined (Fig 3D). The reasons for the latter are not understood at the present time.
In considering possible reasons for LdHAT2-hKO:hyg cells moving through S phase and G2/M and then back into G1 later than usual (Fig 2D), we analyzed the expression of the cyclins that govern the transition from G1 through S phase in LdHAT2-hKO cells. RNAi studies have identified four cyclins playing a role in steering cells from G1 through S phase in T. brucei, with depletion of any of these four cyclins being accompanied by defects in DNA replication [16,17]. Their Leishmania donovani orthologs (based on genome sequence annotation; www.tritrypdb.org; [18]) are: CYC2 (LdBPK_320870.1), CYC4 (LdBPK_050710.1), CYC5 (LdBPK_330830.1) and CYC7 (LdBPK_303690.1). On analyzing the relative expression of these four cyclins in logarithmically growing LdHAT2-hKO:hyg cells in comparison with wild type cells, it was observed that CYC4 expression was downregulated to ~0.6 of wild type levels (Fig 4A). To address the possibility of this being the cause of defects in cell cycle progression the CYC4 gene was expressed episomally in HAT2-depleted cells (GenBank Accession No. KY445836 and S1 Methods; Fig 4B shows western blot analysis of lysates from transfectant cells), and the resultant phenotypes analyzed. Episomal expression of CYC4 only partially rescued the growth defects of LdHAT2-hKO cells, and ectopic expression of CYC4 in Ld1S-hyg cells had no impact on growth (Fig 4C). CYC4 (ectopic) expression had a limited impact on cell cycle progression of LdHAT2-hKO cells—by flow cytometry analyses of hydroxyurea-synchronized promastigotes, it was observed that upon release from the HU block LdHAT2-hKO/CYC4-eGFP promastigotes (HAT2-depleted cells expressing CYC4 ectopically) traversed S phase and reached G2/M in timely manner comparable to control cells (Ld1S-hyg/eGFP; Fig 4D). However, they showed prolonged G2/M and delayed re-entry into G1 as did the heterozygous knockout cells. This finding was confirmed by synchronizing cells at G2/M using flavopiridol and then releasing them into G1. The data obtained (Fig 4E) reaffirmed our conclusion that while ectopic expression of CYC4 permitted HAT2-depleted cells to move through S phase smoothly, the cells still displayed delayed navigation through G2/M back into G1. Thus, HAT2 depletion was impacting an additional branch of cell cycle regulation.
Mitotic cyclins are characterized by the presence of a nine amino acid motif that plays a role in flagging the cyclin for destruction once mitosis is complete. Three mitotic cyclins have been identified in T. brucei–CYC3, CYC6 and CYC8. Depletion of either CYC6 or CYC8 (by RNAi) in T. brucei is coupled to mitotic defects, with the appearance of zoids, and in case of CYC6 knockdown, cells with one nucleus but multiple kinetoplasts as well [19–21]. Additionally, CYC9 has been identified as an essential cyclin of T. brucei, with RNAi-mediated knockdown of CYC9 resulting in cytokinesis defects in the bloodstream form of the parasite though it has no effect on the procyclic form [22]. The possibility of altered expression of one or more of these four cyclins being responsible for perturbation of G2/M and/or cytokinesis in LdHAT2-hKO:hyg cells was examined by analyzing the expression of the Leishmania donovani orthologs of these four cyclins—as annotated in the Leishmania donovani strain BPK282A1 genome sequence: LdBPK_300080.1 (CYC3), LdBPK_323520.1 (CYC6), LdBPK_260320.1 (CYC8), and LdBPK_320800.1 (CYC9). Analysis of RNA isolated from logarithmically growing cells revealed that only one of the four was significantly downregulated in HAT2-depleted cells—CYC9 (Fig 5A). We attempted to rescue G2/M and/or cytokinesis defects associated with HAT2 depletion by expressing the Ld1S CYC9 gene (GenBank Accession No. KY445837) in fusion with FLAG tag in these cells (S1 Methods). After confirming expression of CYC9-FLAG in LdHAT2-hKO:hyg/CYC9-FLAG cells (Fig 5B shows western blot analysis of lysates of transfectant cells), when their growth pattern was analyzed it was found that growth defects were partially alleviated (Fig 5C; LdHAT2-hKO:hyg/CYC9-FLAG cells). Flow cytometry analysis of flavopiridol-synchronized cells revealed that G2/M (and/or cytokinesis) defects were alleviated by CYC9-FLAG expression to the same extent as by ectopic expression of HAT2-eGFP (Fig 5D). To determine if CYC9 down regulation in HAT2-depleted cells was accompanied by post-mitotic defects that were delaying re-entry into G1, we analyzed HAT2-depleted cells 8 hours after release from HU-induced block, in comparison to similarly treated Ld1S-hyg cells. Microscopic observation of DAPI-stained cells revealed that almost twice as many cells with two nuclei and two kinetoplasts (2N2K) accumulated in heterozygous knockout cells in comparison to control cells (16% versus 8.8% of each cell type scored; Fig 5E). This suggests that CYC9 down regulation is accompanied by post-mitotic defects linked to delay in cytokinesis, and thus delayed re-entry into G1. When the effect of ectopic expression of CYC4 or CYC9 in LdHAT2-hKO cells on parasite survival/propagation within macrophages was examined, we observed that CYC4 expression did not improve parasite survival within the host cells (Fig 5F; left panel). On the other hand, CYC9 expression alleviated survival defects to the same extent as did ectopic expression of HAT2-eGFP (Fig 5F; right panel). This suggests that CYC9 is relevant to the parasite in both, the promastigote as well as intracellular stage.
Towards investigating the mechanism by which HAT2 depletion was down regulating CYC4 and CYC9 expression, we first examined the H4K10 acetylation status of putative promoters in LdHAT2-hKO versus control cells. In trypanosomatid genomes functionally unrelated genes are clustered together unidirectionally, and their transcription occurs polycistronically, followed by posttranscriptional processing of these long pre-mRNA units into mature transcripts prior to translation, by trans-splicing of a 39-nucleotide leader sequence at the 5’ end and subsequent polyadenylation at the 3’ end of individual gene units on the pre-mRNA. The initiation of transcription of the polycistronic transcription units (PTUs) occurs at divergent strand switch regions (dSSRs) on the chromosomes in a majority of cases, with transcription initiating bidirectionally on opposite strands. In a few cases transcription initiates in head-tail (HT) regions, with transcription of a group of genes initiating adjacent to where transcription of the neighbouring group of genes terminates, on the same strand [23–27] (Fig 6A). Transcription initiation site of a PTU may also lie at one end of the chromosome.
H3 acetylation peaks have been detected in dSSRs in Leishmania major, and H3K4 methylation and H4K10 acetylation peaks have been detected in dSSRs in Trypanosoma species [28–30]. The assessment of the role of H4K10 acetylation in modulating Leishmania donovani gene transcriptional events was initiated by probing two dSSRs, one on each of the chromosomes carrying CYC4 and CYC9 genes (chromosomes 5 and 32 respectively; locations indicated in genome map in S4 Fig), in logarithmically growing Ld1S-hyg and LdHAT2-hKO:hyg cells. When chromatin immunoprecipitations were performed with the H4acetylK10 antibody (Materials and Methods), H4K10 acetylation was found to be enriched at both the dSSRs in Ld1S-hyg as well as LdHAT2-hKO cells, with the levels of acetylation being lower in heterozygous knockout cells (Fig 6B, first panel). On analyzing non-dSSR intergenic regions in chromosomes 5 and 32 they were found to be devoid of H4K10 acetylation in both cell types (Fig 6B, second panel). Analysis of the HT region on chromosome 35 that was previously identified as an HT region in Leishmania major [31] (location indicated on chromosome 35 genome map in S4 Fig) revealed that here too there was an enrichment of H4K10 acetylation, almost to the same extent as at the dSSRs, with decreased levels of acetylation in HAT2-depleted cells (Fig 6B, third panel). If decreased H4K10 acetylation at dSSRs was indeed the cause of down regulation of CYC4 and CYC9 genes, all the genes lying in the same clusters (PTUs) would be expected to be down regulated. To check this as well as to analyze the global effects of decreased H4K10 acetylation on transcription, microarray analysis of mRNA was carried out (Materials and Methods). To our surprise, we found that genes in the same PTUs were not coordinately up/down regulated in LdHAT2-hKO cells (data submitted to GEO: Accession number GSE76574). Only ~220 genes were downregulated while ~ 440 genes were upregulated, 1.4-fold or more in heterozygous knockout compared to control cells (S4 and S5 Tables). The differentially regulated genes were scattered throughout the genome. In no case was an entire PTU up/down-regulated. In multiple cases there were both, up-regulated and down-regulated genes lying within the same PTU. The coordinately-regulated (either down- or up-regulated) genes were clustered together in only some cases, with ~ 35 dicistronic clusters, ~ 10 tricistronic clusters, and ~ 4 tetracistronic clusters overall from among the ~660 genes that were up/down-regulated (locations of downregulated genes indicated in green in genome map of S4 Fig). It is possible that some of these clusters, particularly the tetracistronics, are coordinately transcribed from head-tail (HT) sites lying internally. Interestingly, none of the five genes that were downregulated in the PTU harbouring CYC9 lay adjacent to each other. On the other hand, among the four genes that were downregulated in the PTU carrying CYC4, one was located immediately downstream of the CYC4 gene—suggesting the possibility of the two genes being coordinately regulated. To address the possibility of the downregulation of the various genes in HAT2-depleted cells being secondary effects of CYC4 and/or CYC9 depletion we co-expressed CYC4-eGFP and CYC9-FLAG ectopically in LdHAT2-hKO cells (as described in S1 Methods; western blot analysis showing co-expression of the two proteins is depicted in S5A Fig) and analyzed the expression pattern of five unrelated genes that were down regulated in HAT2-depleted cells. We found that while HU-synchronized cells co-expressing CYC4-eGFP and CYC9-FLAG ectopically (LdHAT2-hKO/CYC4-eGFP:CYC9-FLAG cells) displayed a similar pattern of cell cycle progression as LdHAT2-hKO:hyg/HAT2-eGFP cells, moving through S phase and G2/M back into G1 in a comparable manner (S5B Fig), these five genes remained downregulated (S5C Fig), indicating that their regulation was independent of CYC4 and CYC9 levels in the cell. Based on these data it is difficult to definitively assert that H4K10 acetylation impacts transcription globally–as these were only heterozygous knockout cells where H4K10 acetylation was not completely abolished, the extent of H4K10 acetylation occurring at the dSSRs and HT sites in these cells (as seen in two dSSRs and the chromosome 35 HT site in LdHAT2-hKO:hyg cells in Fig 6B) was possibly sufficient to permit transcriptional activation from dSSRs and HT sites. The complete absence of H4K10 acetylation might, on the other hand, lead to a more global effect on transcription.
The microarray data confirmed that CYC4 and CYC9 expression levels were down regulated ~1.6-fold and ~1.45-fold (respectively) in HAT2-depleted cells while expression of the other cyclins remained unaffected (S4 and S5 Tables). However, in light of the fact that expression levels of all the genes clustered with CYC4 and CYC9 on the same PTUs were not altered we considered the possibility of H4K10 acetylation in the vicinity of the genes themselves playing a role in the regulation of their expression. ChIPs were performed with H4acetylK10 antibodies as well as antibodies to unmodified H4 using logarithmically growing promastigotes (HAT2-depleted and control cells), and the regions immediately upstream of as well as within the eight cyclin genes, tubulin gene, and HAT4 gene, were analyzed by PCRs followed by gel electrophoresis. H4K10 acetylation was detectable only upstream of CYC4 and CYC9 in both cell types (Fig 6C; “upstream” lanes). H4 with unmodified N-terminus was detected only upstream of CYC3, CYC5, CYC6, CYC8, and HAT4: suggesting the presence of modifications other than H4acetylK10 in the upstream regions of the other genes. These modifications could include H4K4 acetylation, H4K2 acetylation, or H4K14 acetylation, and would preclude the interaction of the anti-H4unmod antibodies (which are directed to the unmodified H4 N-terminus peptide and do not cross-react with the H4 N-terminus acetylated peptides) with H4. No H4K10 acetylation or H4 with unmodified N-terminus was detected within any of the genes (Fig 6C; “gene specific” lanes). The extent of H4K10 acetylation upstream of the CYC4 and CYC9 genes in LdHAT2-hKO versus wild type cells was estimated by ChIP analyses of these two regions using the percent input method. In agreement with the data in Fig 6C, the extent of acetylation at the upstream regions was several hundred-fold higher than within the genes (Fig 6D: first panel). However, while H4K10 acetylation at the upstream regions of both genes was lower in HAT2-depleted cells than in control cells, the extent of this acetylation per se was two hundred-fold lower than that seen at the dSSRs and HT region (Fig 6D: second panel compared with Fig 6B: first and third panels), though hundred-fold higher than the non-dSSR intergenic regions (Fig 6D: second panel compared with Fig 6B: second panel).
Although the degrees of H4K10 acetylation at the regions upstream of CYC4 and CYC9 were much lower than at the dSSRs in a logarithmically growing asynchronous population, the fact remained that no H4K10 acetylation was detectable upstream of the other cyclin genes (Fig 6C). In light of this data along with the fact that genes that were clustered together were generally not coordinately down/up-regulated in LdHAT2-hKO cells (S4 and S5 Tables), we considered the possibility of the regions upstream of CYC4 and CYC9 themselves serving as promoters for the genes. Accordingly, the 1kb regions immediately upstream of the start codons of CYC4, CYC5, CYC8 and CYC9 (S6A Fig) were cloned upstream of the eGFP gene in plasmid pLEXSY_I-egfp-neo3 (as described in S1 Methods), such that they replaced the eGFP promoter region (the deleted region comprised the T7 promoter and utr1 region upstream of the eGFP gene in the pLEXSY plasmid; S6A Fig). The resultant plasmids were transfected into Ld1S promastigotes, and after maintaining selection pressure on polyclonal transfection mixes for two weeks, whole cell lysates were isolated for western blot analysis using anti-eGFP antibodies. We found that eGFP was expressed only from the CYC4 and CYC9 upstream regions (Fig 7A). In comparing this expression in HAT2-depleted cells relative to Ld1S-hyg cells, it was confirmed that eGFP was expressed only from upstream regions of CYC4 and CYC9, with expression being lower in case of HAT2-depleted cells (Fig 7B). Direct fluorescence microscopic analyses also confirmed that expression was downregulated in HAT2-depleted cells, and only detected with CYC4 and CYC9 upstream regions (S6B Fig). Taken together these data suggest that while the CYC4 and CYC9 upstream regions that were analyzed harboured promoter regions, the regions upstream of CYC5 and CYC8 did not. However, the possibility of non-expression of eGFP when the gene is coupled to CYC5 and CYC8 upstream regions being due to post-transcriptional eGFP mRNA processing artifacts cannot be ruled out.
The CYC9 gene on chromosome 32 is located 27 genes downstream of the dSSR, and the nearest down-regulated gene in the CYC9 PTU was ~15 genes downstream of CYC9. Similarly, the CYC4 gene on chromosome 5 is several genes downstream of the putative transcription initiation site of the PTU, and only the gene immediately downstream of it was also downregulated in HAT2-hKO cells. In the light of these facts, the data from the eGFP reporter assays suggested that the CYC4 and CYC9 genes had their own promoters, and these promoters were in addition to the dSSRs from which they were being transcribed as part of PTUs. Thus, we considered the possibility that a second tier of transcriptional activation exists in case of some genes: CYC4 and CYC9 genes among the cyclin genes we examined. Bearing in mind that H4K10 acetylation at the CYC4 and CYC9 promoter regions was ~ 200 fold lower than at the dSSRs on the same chromosomes in logarithmically growing cells (comparing Fig 6B and 6D), we investigated if this possible second level of transcriptional control is cell cycle stage-specific. Accordingly, Ld1S/hyg and LdHAT2-hKO:hyg cells were synchronized with hydroxyurea and cells harvested for ChIP analyses just after the 8 h-long HU-induced block (at G1/S), half an hour after release, 3 hours after release, 4.5 hours after release, and 6.5 hours after release. The two dSSRs on chromosome 5 and 32 (that we had examined in Fig 6) showed equivalent levels of H4K10 acetylation at all times in both cell types, with acetylation levels being overall lower in LdHAT2-hKO cells (Fig 7C, left panel). This was in keeping with previous studies in trypanosomatids reporting H4K10 acetylation peaks at dSSRs and the prevailing understanding that transcription initiation from dSSRs is globally constitutive [24,32,33]. The same pattern of H4K10 acetylation was observed in case of the chromosome 35 HT region as well (Fig 7D, left panel). Contrastingly, H4K10 acetylation levels at the CYC4 and CYC9 promoters varied across the different time-points. At the CYC4 promoter H4K10 acetylation was maximally detected starting at 0.5h after release from block, increasing ~100-fold over levels detected in an asynchronous population (Fig 7E, left panel). These levels of acetylation persisted at 3 hours after release. The acetylation levels dropped subsequently (4.5h R, 6.5h R, Fig 7E, left panel). In case of the CYC9 promoter on the other hand, H4K10 acetylation levels remained low in HU-blocked cells and at 0.5h and 3h after release from block, with slight increase in acetylation at 4.5 hours after release and maximal levels of acetylation (~100-fold greater than that detected in an asynchronous population) being detected 6.5 hours after release (4.5h R, 6.5h R, Fig 7E, left panel). The levels of acetylation in HAT2-depleted cells were about 50% that detected in control cells. These results suggested that the CYC4 and CYC9 promoters, unlike the dSSRs, were activated in a cell cycle -distinctive manner that allowed the upregulation of expression of these two genes around the times at which their products were relevant to the cell. To evaluate this hypothesis we isolated RNA from synchronized Ld1S-hyg and LdHAT2-hKO promastigotes at each of these time-points, and analyzed expression of CYC4 and CYC9 at these stages in comparison with asynchronously growing Ld1S/hyg cells. Flow cytometry profiles revealed that both cell types (Ld1S-hyg and LdHAT2-hKO) had entered S phase in similar pattern by 0.5h after release. However, subsequent to entry into S phase the LdHAT2-hKO cells slowed down, taking much longer to move through S phase (S6E Fig). RNA analyses (Fig 7E, right panel) showed that CYC4 expression in Ld1S/hyg cells was ~50-fold higher 30 min after release from HU-induced block in comparison with expression in asynchronous cells. High levels of expression were maintained 3 hours after release also, following which expression lowered. CYC9 expression, on the other hand, was ~50-fold higher at 6.5 hours after release in comparison with expression in asynchronous cells. Expression of both genes in HAT2-depleted cells was ~50–60% the levels detected in control cells. Considering that HAT2-depleted cells reach G2/M much later than wild-type cells, it is possible that CYC9 expression actually begins to peak earlier than 6.5 hours after release in wild type cells (though after 4.5h post-release). The impact the lower expression of these cyclins has on cell cycle progression in HAT2-depleted cells, emphasizes the importance of stringent modulation of CYC4 and CYC9 expression in Leishmania. When the expression of the genes coupled to the two dSSRs and the HT region were analyzed in wild type as well as HAT2-depleted logarithmically growing cells, no difference in expression levels was observed between the two cell types (Fig 7C, right panel and Fig 7D, right panel respectively), affirming that decreased acetylation at dSSRs and HT region in HAT2-depleted cells did not impact expression of the neighbouring genes. Taken together, these findings supported the inference that the expression of CYC4 and CYC9 genes are tightly regulated by H4K10 acetylation at their individual promoters, with transcriptional activation of the two genes from their promoters being linked to those phases of the cell cycle where they are functionally pertinent.
However, these experiments measured steady state RNA levels and therefore there was a possibility that the differential expressions of CYC4 and CYC9 that we were detecting at different cell cycle stages were the outcome of RNA processing and RNA stability effects. To confirm that transcription of these genes was being activated at specific cell cycle stages we carried out nuclear run-on assays using HU-synchronized promastigotes, as detailed in Materials and Methods. Genes at five different regions of the Leishmania genome were analyzed (Fig 8A). In analyzing CYC4 and CYC9 transcriptional activation we also examined the three genes immediately upstream and six genes immediately downstream of them, on chromosomes 5 and 32 respectively (Fig 8A, S4 Fig). A section of a chromosome 14 PTU harbouring a dicistronic as well as tetracistronic cluster that were both downregulated in HAT2-depleted cells, and a section of a chromosome 36 PTU harbouring a dicistronic cluster that was downregulated in HAT2-depleted cells (Fig 8A, S4 Table, S4 Fig) were also examined. Additionally, four genes coupled to a chromosome 18 dSSR were analyzed (Fig 8A, S4 Fig). Nuclear run-ons were performed with HU-synchronized cells 0.5 h and 6.5 h after release, as well as with logarithmically growing cells, and the data obtained are presented in Fig 8B.
As evident from the results presented in Fig 8B and S7 Fig, the CYC4 gene as well as the gene immediately downstream of it were both transcriptionally activated 0.5h after release from HU-block, but not 6.5h after release (Fig 8B, S7 Fig). No other genes in the cluster were activated to the same extent at either of the two time-points. In the chromosome 32 cluster that was analyzed CYC9 was transcriptionally activated only at 6.5h after release, and none of the other genes in the cluster were activated to the same extent at either time-point. The genes on chromosome 14 PTU displayed a dichotomous behavior—the tetracistronic cluster that is down regulated upon HAT2 depletion was transcriptionally activated 6.5h after release but not 0.5h after release, while of the two genes in the dicistronic cluster that is downregulated upon HAT2 depletion, only the second was transcriptionally activated, at 0.5h after release. Interestingly, the gene downstream of it was also transcribed actively, though to a lesser extent. The dicistronic cluster on chromosome 36 that is downregulated upon HAT2 depletion was transcriptionally activated only at 0.5h after release. Though CYC4 and its downstream partner both continued to be transcribed even 3 hours after release, the two dicistronic clusters on chromosomes 14 and 36 were no longer transcribed (S7 Fig). Other than the CYC4 dicistronic cluster and the genes coupled to the chromosome 18 dSSR none of the genes were activated at 3 hours after release (S7 Fig).
The remaining genes in all the PTUs analyzed were being transcribed at both time-points (and at 3 hours after release), but to a much lesser extent, as is evident from the longer exposures of the blots (S7 Fig). In looking at logarithmically growing cells none of these genes appeared to be upregulated, probably because in an asynchronous population most cells would be in G1. The genes coupled to the chromosome 18 dSSR were transcriptionally active in all cases, including in a logarithmically growing population (Fig 8B, S7 Fig). Interestingly, these genes were highly transcribed at all times, in contrast to the other analyzed genes (lying within chromosome 5, chromosome 32, chromosome 14, and chromosome 36) that were not activated in stage-specific manner but which were nevertheless transcribing at all times to a lesser extent (S7 Fig). This suggests that transcription may not be activated equally at all dSSRs. In fact, considering the fact that no chromosome 18 genes were downregulated in HAT2-depleted cells, suggesting the absence of any cell cycle stage-specific transcriptional activation of genes on this chromosome, it is possible that PTUs which are transcribed entirely and solely from dSSRs may be more actively transcribed. However, a separate detailed investigation would be necessary to determine if this is indeed so.
These data allowed us to definitively conclude that transcription of CYC4 and CYC9 was being activated at specific times during cell cycle progression. Moreover, transcription of the activated CYC9 gene appears to be terminating after the gene itself, in keeping with the microarray data which showed only CYC9 expression to be downregulated in LdHAT2-hKO cells (Fig 8B, 6.5hR). Likewise, transcription of the activated CYC4 and its adjacent gene appears to terminate after the adjacent gene, again in agreement with the coordinated downregulation seen in microarray data of LdHAT2-hKO cells. It is difficult to conclude if the coordinately activated genes that are clustered together are transcribed from the promoter region upstream of the first gene in the cluster, or each have independently activated promoter regions. Taken together these data demonstrate that transcriptional processes in trypanosomatids are much more complex than was believed to be thus far.
MYST-family HATs are ubiquitously found across eukaryotes, and regulate multiple DNA-related processes. The present study was undertaken with the aim of determining the functional role of Leishmania donovani HAT2. The constitutively nuclear LdHAT2 targeted H4K10 for acetylation both in vitro and in vivo. HAT2-depleted promastigotes displayed slower growth and longer generation time, and exhibited cell cycle and DNA replication defects, also being unable to propagate efficiently within macrophages. Knockdown of HAT2 expression by RNAi in T. brucei too results in severe growth defects and accumulation of cells pre-cytokinesis but the mechanism by which this is occurring has not been demonstrated [11]. The results of our study indicate that CYC4 and CYC9 downregulation were responsible for cell cycle defects in HAT2-depleted promastigotes.
The unique genome arrangement of kinetoplastids is linked to the coordinated transcription of functionally unrelated genes. In Leishmania major nuclear run-on analyses have demonstrated that transcription of the PTUs on chromosome 1 and chromosome 3 initiate bidirectionally on opposite strands in the divergent strand switch regions (dSSRs) [25,26,34], and subsequent results from a genome-wide study in asynchronously growing Trypanosoma brucei by Kolev et al [24] showed this to be the primary mode of transcription initiation in trypanosomatids. A few head-tail (HT) transcriptional start sites have also been identified, with the initiation of transcription of a cluster of genes occurring adjacent to the termination of transcription of the neighbouring cluster on the same strand [24,27]. Transcription from dSSRs is viewed as occurring constitutively and uniformly across the PTUs, and steady state levels of mRNA are believed to be the outcome of post-transcriptional processes that govern mRNA stability: for example, heterogeneity in pre-mRNA processing in terms of the sites at which the 5’ leader sequence is spliced and sites at which polyadenylation occurs [24,27,32,33]. The transcription start sites (TSSs) in dSSRs are enriched in H4K10 acetylation and H3K4 trimethylation in T. brucei and T. cruzi, and in acetylated H3 in L. major [27–30,35]. In accordance with these reports, we found dSSRs on the two chromosomes carrying the CYC4 and CYC9 genes to be enriched in H4K10 acetylation in Leishmania donovani cells, with levels of this acetylation being lower in HAT2-depleted cells as expected. However, contrary to our expectations, the genes lying in the CYC4 and CYC9 clusters were not all coordinately downregulated in HAT2-depleted cells. Additional peaks of H4K10 acetylation and H3K4 methylation have also been detected at a few non-SSR sites in T. brucei (asynchronous cultures), and some of these were later shown to be at head-tail (HT) transcriptional start sites [24,27,35]. In Leishmania major HT sites have been identified by Lombrana et al based on the H3 acetylation peaks and J base peaks identified by Thomas et al and van Luenan et al respectively [29,31,36] and although 51 sites have been identified, the regions immediately upstream of CYC4 and CYC9 are not among them, nor have any HT sites been identified around the one tetracistronic and two dicistronic clusters that we analyzed in the run-ons.
When we analyzed asynchronous cells, although we detected H4K10 acetylation immediately upstream of CYC4 and CYC9 genes, the extent of acetylation was two hundred-fold lower than at dSSRs, making it difficult to envisage a role in gene regulation although eGFP-based reporter assays suggested that these regions were active promoters. In the light of this data we examined the possibility of the existence of a second tier of activation of gene expression that may be cell cycle stage-specific. The results presented in this study revealed that unlike the dSSRs, and HT region on chromosome 35, which showed equivalent H4K10 acetylation levels at all times, CYC4 and CYC9 promoters displayed stage-specific enrichment of H4K10 acetylation, and this was coupled to upregulated CYC4 and CYC9 mRNA levels due to transcriptional activation. Though H4K10 acetylation was enhanced at the CYC4 and CYC9 promoters at specific times, the maximal levels of acetylation we detected (in wild type control cells) were still only about half the acetylation levels at the dSSRs in these cells, leading us to conclude that the CYC4 and CYC9 promoters are much more sensitive to H4K10 acetylation levels than dSSRs are. In the absence of specific antibodies we have been unable to check the status of H3K4 methylation at the two gene promoters. Promoter elements have remained largely undefined in trypanosomatid species in spite of continuing efforts, with no conserved consensus sequences being identified across dSSRs or HT sites thus far. A recent report proposes that GT-rich promoters help activate transcription in trypanosomatids in a context-dependent manner by modulating the local chromatin environment [37]. We examined the CYC4 and CYC9 upstream regions and compared them with the dSSRs of chromosome 5, chromosome 18 and chromosome 32 as well as the HT site on chromosome 35. While no common sequence motif was identifiable, it was observed that all analyzed sequences harboured homopolymeric tracts (S8 Fig). Previous analysis of the chromosome 1 dSSR of Leishmania revealed the presence of a poly(C) tract that was conserved across fifteen Leishmania species [38]. It is believed that homopolymeric tracts have a negative impact on nucleosome assembly due to their relative rigidity [39], thus promoting transcription.
The findings of the present study reinforce the fact that transcriptional regulation in trypanosomatids is a complex process. It appears that there are two levels of control (Fig 9). The dSSRs are the primary sites of transcription initiation, and their activation is believed to be linked to H3 acetylation, H4K10 acetylation and H3K4 methylation as these modifications are enriched at the transcription start sites. From our data it is difficult to attribute a role to H4K10 acetylation in transcriptional activation from dSSRs in Leishmania donovani as an ~ 50% decrease in H4K10 acetylation in HAT2-depleted cells at the dSSRs (and HT region) that we examined, did not have an impact on gene expression (Fig 7C and 7D). However, the equivalently high levels of H4K10 acetylation that we detect at dSSRs throughout the cell cycle are in accordance with the prevailing thought that the dSSRs are constitutively active. A small subset of genes are controlled at a second level by gene-specific promoters. This second level of regulation appears to be cell cycle stage-dependent for some genes at least, with promoter activation occurring specifically at or near the time when the proteins encoded by those specific genes are functionally critical to the cell. While decreased levels of H4K10 acetylation at the dSSRs (an ~ 50% decrease) appear to be sufficient to support global gene expression at the same levels as wild type cells, the second tier of regulation at gene-specific promoters is perturbed by HAT2-depletion. This level of control that is more sensitive to H4K10 acetylation levels results in downregulation of expression and consequently the defective phenotypes associated with HAT2-depletion. The results of the nuclear run-on assays indicate that the genes in these PTUs which are not transcriptionally activated at specific cell cycle stages are nevertheless being transcribed at lower levels. As stage-specific transcription is terminating directly downstream of the activated genes it appears that the other genes in the cluster must be transcribing from the dSSRs as part of the PTUs, perhaps constitutively. It is possible that hitherto unidentified accessory proteins are involved in RNA polymerase II-mediated transcription initiation (and elongation) in trypanosomatids. The factors regulating initiation events at internal promoters may not be involved in transcription initiation events at dSSRs, and the transcription complexes transcribing PTUs from dSSRs may differ in composition from the complexes transcribing genes from internal promoters. Previous studies have identified base J enrichment peaks at the sites of transcription termination of PTUs. However, no base J peaks were identified downstream of any of the genes activated from internal promoters that we have analyzed as part of this study [36]. The accessory protein factors that are part of the complexes transcribing from internal promoters may mediate transcription termination immediately downstream of the genes. Much more detailed investigations are necessary to unravel the mechanism(s) by which internal transcriptional activation and termination events are occurring. Future efforts would be directed towards understanding how cell cycle-dependent transcriptional activation within long PTUs results in the upregulation of only one to four genes immediately adjacent to the internal promoter, but not genes beyond them.
A large number of genes are upregulated in HAT2-depleted cells. These increased levels of mRNA may be due to perturbations in posttranscriptional processing in response to the slower growth rate of HAT2-depleted cells. Differential regulation of specific mRNAs at specific cell cycle stages has been seen in only a handful of cases in trypanosomatids so far, and has been put down to being the consequence of post-transcriptional events governing mRNA stability. This report presents the first data suggesting that there are gene-specific promoters lying within the PTUs that are activated in a cell cycle-distinctive manner. Our results underscore the complexities of transcriptional processes in trypanosomatids and add a new dimension to our knowledge of the mechanisms regulating gene expression in these unicellular eukaryotes.
Leishmania donovani 1S cells were maintained at 26°C in M199 medium (Lonza) supplemented with 10% fetal bovine serum (Invitrogen), adenine, glutamine and hemin (all from Sigma Aldrich, USA), as described earlier [40]. Details of isolation and fractionation of cell extracts, growth and survival analyses, determination of generation time, synchronization regimes, flow cytometry regimes are given in S1 Methods.
Histone acetyltransferase assays were performed with HAT2-FLAG proteins (wild type and E332A mutant) pulled down from whole cell lysates isolated from transfectant clonal lines, using the HAT Assay Kit (Active Motif, USA) as detailed earlier [9]. The assay is based on the fluorescence-based detection of the free thiol groups produced on CoA when the acetyl group from acetyl-CoA is transferred to the peptide/protein substrate. In a typical experiment, the FLAG-tagged proteins were pulled down from lysates isolated from ~2x1010 promastigotes, using FLAG M2 agarose beads (Sigma Aldrich). The bead-bound protein fraction was then equally divided into five parts, and each part (equivalent to pull down from 4x109 promastigotes) used in a single histone acetyltranferase reaction. In every experiment one part was used to determine autoacetylation levels (reaction performed in absence of any peptide substrate; a measure of the extent of HAT2-mediated self-acetylation) and the other parts were used to determine the sum of autoacetylation and peptide acetylation. The sequences of the peptides (synthesized by Peptron Inc, South Korea or Abgent, USA) used as substrates in these reactions (depicted in the boxes above the bar charts in Fig 1C–1E) corresponded to the sequences of the N-terminal tails of the Leishmania core histones H2A, H2B, H3 and H4 as well as the C-terminal tail of H2A.
Reactions were performed as per the manufacturer’s instructions and the free thiol groups produced at the end of the reaction were detected by using the developer (provided in the kit), resulting in the production of fluorescence. The extent of fluorescence obtained (excitation: 360–390 nm, emission: 450–470 nm) was used as a measure of the extent of acetylation that had occurred. Values presented in the bar charts are arbitrary fluorescence units, and have been plotted after subtracting background values (values obtained in reactions carried out in the absence of HAT2 protein and peptide substrate). The data presented in the bar charts are average values of three independent experiments. Error bars indicate standard deviation. Student t-test (two-tailed) was applied to analyze the results, and P values are presented in figure legends.
EdU labeling analysis was done as detailed earlier [10]. Briefly, Ld1S-hyg and LdHAT2-hKO:hyg promastigotes were synchronized using hydroxyurea, released into drug-free medium, and aliquots of cells pulsed with 5-ethynyl-2-deoxyuridine (EdU) at different time-points after release. EdU-pulsed cells were analyzed microscopically using the Click-iT EdU Imaging Kit (Invitrogen) followed by confocal imaging and analysis as above.
Isolation of RNA, synthesis of cDNA, and analyses of gene expression by real time PCR were carried out using tubulin as internal control as detailed earlier [10], by the 2-ΔΔCT method [41]. Primers used for expression analyses are listed in S2 Table. All primers were designed using the Leishmania donovani BPK282A1 published sequence.
RNA was isolated from Ld1S and LdHAT2-hKO logarithmically growing promastigotes using the PureLink RNA mini kit (Invitrogen), and analyzed using Bioanalyzer to confirm purity and integrity prior to microarray analysis. Microarray analysis of mRNA was performed as detailed earlier [10] using Gene Expression Leishmania 8x15K, AMADID:035638 (Genotypic Technology), and data analysis was done using GeneSpring GX Version 12.1. The analysis was carried out using biological replicates (RNA isolated from two separate experiments).
Macrophages were infected with metacyclics as detailed previously [10]. For each experiment, three biological replicates were set up in parallel. Data presented here represents the mean values of the three experiments with error bars depicting standard deviation. Student t-test (two-tailed) was applied to analyze the results, and P values are presented in figure legends.
Chromatin immunoprecipitation procedure was adapted from the protocol of Lowell and Cross [42]. Leishmania promastigotes (1x109 cells) were resuspended in fresh M199 complete medium (50 ml), and fixed with 1% formaldehyde for 30 min at room temperature. The reaction was stopped by incubation with 125 mM glycine at room temperature for 5 min. Cells were collected by centrifugation, washed twice with 1X PBS, harvested, and resuspended in lysis buffer (50 mM Tris.Cl (pH 8.0), 10 mM EDTA, 1% SDS) containing protease inhibitors and sodium butyrate (50 mM), followed by incubation on ice for 15 min. The cell suspension mix was sonicated on ice (30 sec on/60 sec off), cell debris removed by high speed centrifugation, and supernatant stored in 200 μl aliquots at -20°C. DNA fragment size was confirmed to be between 300–600 bp by reversing cross-links at 65°C overnight, followed by purification of the DNA (using Qiaquick PCR purification kit) and analysis using agarose gel electrophoresis.
For chromatin immunoprecipitations, the input lysates (200–300 μl) were diluted with immunoprecipitation buffer (16.7 mM Tris.Cl (pH 8), 1.2 mM EDTA, 150 mM NaCl, 1.1% Triton-X, 0.01% SDS, 50 mM sodium butyrate, and protease inhibitors) and pre-cleared by incubation with 30 μl protein A-Sepharose beads (Invitrogen) for 90 min at 4°C with rotation. The supernatant was collected following low speed centrifugation, 5 μl antibodies (anti-H4acetylK10 or anti-H4 unmodified) added to it, and incubated overnight at 4°C with rotation. Further addition of 25 μl protein A-sepharose beads was followed by incubation for 90 min at 4°C with rotation. After removal of unbound fraction by low speed centrifugation, the beads were successively washed in four different buffers: Wash Buffer A (20 mM Tris.Cl (pH 8.0), 2 mM EDTA, 150 mM NaCl, 0.1% SDS, 1% Triton-X, protease inhibitors and sodium butyrate), Wash Buffer B (same as Wash Buffer A except 500 mM NaCl), Wash Buffer C (10 mM Tris.Cl (pH 8.0), 1 mM EDTA, 250 mM LiCl, 1% NP-40, 1% sodium deoxycholate, protease inhibitors and sodium butyrate) and Wash Buffer D (TE buffer containing protease inhibitors and sodium butyrate). Each wash included incubating the beads in the wash buffer for 15 min at 4°C with rotation and collecting the wash following low speed centrifugation. The bound fraction was eluted in 0.1 M sodium bicarbonate, 1% SDS (250 μl) by rotation for 10 min at room temperature. Cross-links were reversed after addition of sodium chloride (200 mM) at 65°C overnight. Following RNase treatment for 30 min the DNA in the fraction was purified using the Qiaquick PCR purification kit, and eluted in 10 mM Tris.Cl (pH 8.5). 1/50th of the eluted fraction was used per PCR. ChIP analyses were carried out using real time PCR coupled to the percent input method as described [43], using the appropriate primer pairs designed against the regions under investigation (S3 Table). All ChIP analyses experiments were done three times, and in each experiment samples were analyzed in triplicates. Values presented are the average of three experiments, with error bars depicting standard deviation. No antibody was added in mock reactions which were otherwise carried out exactly as actual reactions.
Nuclear run-on assays were carried out as described earlier [25,44], with modifications. For each run-on reaction nuclei were isolated from 1x109 promastigotes (either logarithmically growing or synchronized). Harvested cells were washed once with 1X PBS, and resuspended in 2.5 ml ice-cold hypotonic buffer (0.25M sucrose, 5 mM HEPES pH 7.5, 1 mM spermidine, 0.1 mM PMSF, 1 mM EDTA, 1 mM EGTA, 1 mM DTT). Cells were lysed by adding Nonidet P-40 and Triton X-100 (final concentration 0.5% each) and vortexing vigorously for 35 seconds. This was followed by the immediate addition of 5 ml of ice-cold wash buffer (40 mM Tris.Cl pH 7.5, 0.64 M sucrose, 1 mM spermidine, 0.1 mM PMSF, 1 mM EDTA, 1 mM EGTA, 1 mM DTT and 60 mM KCl), quick vortexing to mix, and centrifugation at 3000g for 10 minutes at 4°C. After decanting the supernatant the wash was repeated once and this time the nuclei were collected by centrifugation at 1000g for 10 minutes at 4°C. The supernatant was removed carefully and 0.1 ml transcription buffer reaction mix (100 mM HEPES pH 7.5, 2 mM MgCl2, 4 mM MnCl2, 0.15 mM spermine, 0.5 mM spermidine, 50 mM NaCl, 50 mM KCl, 25% glycerol, 2 mM ATP, 2 mM GTP, 2 mM CTP, 100 μCi UTP (α-32P UTP, 3000 Ci/mmole, 10 mCi/ml from BRIT, India), 2 mM DTT, 40 U RNasin) added to the pelleted nuclei before tapping to suspend evenly. Reaction was incubated at 26°C for 8 minutes, followed by the addition of 2 mM UTP and further incubation at 26°C for 2 minutes. 25 U of DNase I were added and reaction moved to 37°C for 5 minutes, before the addition of 0.1 ml Stop buffer (10 mM Tris.Cl pH 7.5, 10 mM EDTA, 1% SDS, 200 μg/ml proteinase K solution) and further incubation at 37°C for 5 minutes. Reactions were subjected to phenol: chloroform extraction and the labeled nascent RNA collected by precipitation using ethanol.
Slot blots were prepared, on which the linearized and denatured DNA probes were immobilized (5 μg of each probe clone). The probes comprised of ~1 kb fragments from the 5’ ends of the genes being analyzed, that had been amplified off genomic DNA and cloned into plasmid vector (sequences of primers used to amplify the probe fragments available on request). In case of genes smaller than 1 kb entire genes were used as probes. The blots were pre-hybridized for 6 hours at 42°C in 50% formamide, 4X Denhardt’s solution, 5X SSC and 0.2% SDS, to which 100 μg/ml salmon sperm DNA was added. The labeled nascent RNA was added (2.5x106 cpm/5ml) and hybridization allowed for 72 hours at 42°C. Blots were washed with 2XSSC/0.1% SDS at room temperature for 20 min and again at 42°C for 20 min before exposing the blots for phosphorimaging for 3–10 days.
The authors confirm that all data underlying the findings are fully available without restriction. All DNA microarray files are available from the Gene Expression Omnibus database (accession number GSE76574).
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10.1371/journal.pntd.0001370 | Proteomic Analysis of Excretory-Secretory Products of Heligmosomoides polygyrus Assessed with Next-Generation Sequencing Transcriptomic Information | The murine parasite Heligmosomoides polygyrus is a convenient experimental model to study immune responses and pathology associated with gastrointestinal nematode infections. The excretory-secretory products (ESP) produced by this parasite have potent immunomodulatory activity, but the protein(s) responsible has not been defined. Identification of the protein composition of ESP derived from H. polygyrus and other relevant nematode species has been hampered by the lack of genomic sequence information required for proteomic analysis based on database searches. To overcome this, a transcriptome next generation sequencing (RNA-seq) de novo assembly containing 33,641 transcripts was generated, annotated, and used to interrogate mass spectrometry (MS) data derived from 1D-SDS PAGE and LC-MS/MS analysis of ESP. Using the database generated from the 6 open reading frames deduced from the RNA-seq assembly and conventional identification programs, 209 proteins were identified in ESP including homologues of vitellogenins, retinol- and fatty acid-binding proteins, globins, and the allergen V5/Tpx-1-related family of proteins. Several potential immunomodulators, such as macrophage migration inhibitory factor, cysteine protease inhibitors, galectins, C-type lectins, peroxiredoxin, and glutathione S-transferase, were also identified. Comparative analysis of protein annotations based on the RNA-seq assembly and proteomics revealed processes and proteins that may contribute to the functional specialization of ESP, including proteins involved in signalling pathways and in nutrient transport and/or uptake. Together, these findings provide important information that will help to illuminate molecular, biochemical, and in particular immunomodulatory aspects of host-H. polygyrus biology. In addition, the methods and analyses presented here are applicable to study biochemical and molecular aspects of the host-parasite relationship in species for which sequence information is not available.
| Gastrointestinal (GI) nematode infections are major causes of human and animal disease. Much of their morbidity is associated with establishment of chronic infections in the host, reflecting the deployment of mechanisms to evade and modulate the immune response. The molecules responsible for these activities are poorly known. The proteins released from nematode species as excretory-secretory products (ESP) have potent immunomodulatory effects. The murine parasite Heligmosomoides bakeri (polygyrus) has served as a model to understand several aspects related to GI nematode infections. Here, we aimed to identify the protein components of H. polygyrus ESP through a proteomic approach, but the lack of genomic sequence information for this organism limited our ability to identify proteins by relying on comparisons between experimental and database-predicted mass spectra. To overcome these difficulties, we used transcriptome next-generation sequencing and several bioinformatic tools to generate and annotate a sequence assembly for this parasite. We used this information to support the protein identification process. Among the 209 proteins identified, we delineated particular processes and proteins that define the functional specialization of ESP. This work provides valuable data to establish a path to identify and understand particular parasite proteins involved in the orchestration of immune evasion events.
| Gastrointestinal (GI) nematode infections are major causes of disease in both humans and animals. Infections with Ascaris lumbricoides, hookworms (Necator americanus and Ancylostoma duodenalis), Trichuris trichiura, and Strongyloides stercoralis are highly prevalent in developing countries, affecting ∼1 billion people and posing a burden estimated at ∼2 M DALYs (Disability-adjusted life years) (http://apps.who.int/ghodata) [1]. GI nematodes usually establish chronic infections, surviving in the host for considerable periods of time. This characteristic reflects the ability of these parasites to evade and modulate the host immune response from the early stages of infection while optimizing both feeding and reproduction [2], [3]. As a result, in addition to their commonly associated effects on host physiology including malnutrition, growth stunting, and anaemia, infection with GI nematodes influences the development and/or severity of co-occurring infections and immune-mediated diseases such as malaria or type 1 diabetes, respectively [4], 5.
Infection with the nematode Heligmosomoides polygyrus, a natural GI pathogen of mice, has provided a convenient experimental model to understand the biology of GI nematodes and the pathology associated with chronic infections with this class of helminth parasites [6]. Primary infection with H. polygyrus induces a highly polarized Th2 immune response in mice; despite induction of this response, the parasite survives and establishes a chronic infection with the differentiation and activation of host cell types that mediate potent immunoregulatory mechanisms, such as regulatory T cells and alternatively activated macrophages (AAMΦs) [7], [8]. Recent studies indicate that these regulatory responses, especially regulatory T cells, can be stimulated by treatment with H. polygyrus excretory-secretory products (ESP) [9]–[12]. These observations suggest that this fraction of the proteome contains many of the immunomodulatory factors responsible for evasion of the host immune response, but the proteins in ESP that mediate these effects remain largely unknown.
The use of mass-spectrometry based proteomics has overcome many limitations in the analysis and identification of helminth-derived proteins in ESP [13]. In general, these analyses achieve a remarkable sensitivity in protein identification if either genome, transcriptome, or proteome sequence information is available to support the interrogation of experimentally obtained mass spectra with peptide matching algorithms in database search programs [14]. However, most of this sensitivity is lost when assignation is based on homology with proteins identified in other species, as is the case for H. polygyrus and almost all other relevant parasitic nematode species for which sequence information is not available [15]–[17].
To better understand the molecular mechanisms that lead to the activation and modulation of the host immune response by GI nematodes, we used transcriptome next generation sequencing (RNA-seq) technologies and several bioinformatic tools to overcome the limitations in the proteomic analysis of ESP from H. polygyrus. Illumina sequencing (www. illumina.com) was employed to generate transcriptomic sequence data in a rapid and cost-efficient way [18]. The transcriptome assembly was used to identify proteins in the ESP using an experimental proteomic approach.
Animal procedures were conducted in accordance with the guidelines and policies of the Canadian Council on Animal Care and the principles set forth in the Guide for the Care and Use of Laboratory Animals, Animal Resources Centre, McGill University. The protocol was approved by the McGill University Animal Care Committee (Permit Number: 4543). All efforts were made to minimize discomfort and suffering to the animals during handling and manipulation.
H. polygyrus was maintained and propagated in male BALB/c mice (Charles River Laboratories, St. Constant, Canada) by oral gavage inoculation of 400–450 third-stage larvae (L3) as described [19]. Adult parasites were collected from the small intestine on day 21 post infection under a dissection microscope. Worms were washed extensively with sterile endotoxin-free PBS (Invitrogen, Burlington, ON, Canada) containing 80 µg/ml gentamicin (Schering, Montreal, QC, Canada), 100 U/ml penicillin G, 100 µg/ml streptomycin (Invitrogen), and 20 µg/ml polymyxin B (Sigma, St. Louis, MO). Mice were housed in the Animal Care Facility at the Research Institute of the McGill University Health Centre.
For RNA extraction, viable worms were harvested from 6 infected mice on day 21 post-infection, and ∼1000 adult female and male worms free of host tissue were selected and extensively washed. After resuspension in 0.5 ml PBS, 3.0 ml Trizol (Invitrogen) were added to the worm suspension. Worms were disrupted with a Polytron homogenizer at maximum speed for 3 min with the tube positioned on ice. Following centrifugation at 12,000× g for 10 min at 4°C, the clear upper phase was collected and extracted with chloroform. After centrifugation at 12,000× g for 10 min at 4°C, the upper aqueous phase was collected, and RNA was precipitated with isopropanol. RNA was centrifuged at 12,000× g for 10 min at 4°C. The RNA pellet was washed with 75% ethanol, followed by centrifugation at 7,500× g for 5 min, and the RNA pellet was dissolved in water. The 260/280 ratio of the sample was >1.6. The RNA samples were stored at −70°C and until sequencing at the McGill University and Génome Québec Innovation Centre.
Total RNA quality was verified on an RNA chip using an Agilent 2100 Bioanalyzer and quantified using a NanoDrop ND-1000 UV-VIS spectrophotometer (Thermo Fisher). A cDNA library was prepared from 5 µg total RNA using the mRNA-Seq Sample Preparation Kit (Illumina), according to the manufacturer's recommendations. Quality of the library was verified on a DNA 1000 chip using the Agilent 2100 Bioanalyzer and quantified by PicoGreen fluorimetry. The library was subjected to 108 single-read cycles of sequencing on an Illumina Genome Analyzer IIx as per the manufacturer's protocol. Cluster generation was performed on a c-Bot (Illumina) with a single read cluster generation kit. Sequencing was performed once using a 36 cycle sequencing kit v4.
ESP were prepared using a modification of previously described methods [9]. Briefly, adult worms were collected as described above, and viable worms were selected, washed, and cultured at a density of ∼1000 worms per ml of serum-free RPMI 1640 medium (Invitrogen) supplemented with 2% glucose (Sigma) and antibiotics for 36 h at 37°C. The supernatant was harvested, centrifuged at 8,000× g for 10 min to remove eggs and debris, and concentrated using an Amicon centrifugal filter device with a 3 kDa cut-off (Millipore, Billerica, MA). The protein concentration in ESP preparations was determined with a Bradford Reagent kit (Bio-Rad, Hercules, CA) according to the manufacturer's instructions. For proteomic analysis, a pooled sample of ESP prepared from 4 harvests of adult worms from a total of 40 mice was used. The 4 ESP preparations were pooled after their migration patterns on 4–20% acrylamide SDS-PAGE were confirmed to be similar. Pooled ESP was stored at −80°C until analysis at the McGill University and Génome Québec Innovation Centre.
ESP were resuspended in loading buffer containing 2-mercaptoethanol, and ∼100 µg protein were separated by SDS-PAGE through a 3 cm gradient gel (7–15% acrylamide) as described [20]. Following gel staining with Coomassie Brilliant Blue G, the entire lane was subjected to automated band excision using the Picking Workstation ProXCISION (Perkin Elmer) to generate 15 bands per lane (5–7 pieces/line).
Proteins from gel bands were subjected to reduction, cysteine-alkylation, and in-gel tryptic digestion in a MassPrep Workstation (Micromass, Manchester, UK) as previously described [20]. Twenty µl of the tryptic digest solution were injected on a Zorbax 300SB-C18 pre-column (5×0.3 mm, 5 µm) previously equilibrated with water containing acetonitrile (5%) and formic acid (0.1%) using the Micro Well-plate sampler and the IsoPump modules of an Agilent 1100 Series Nanoflow HPLC. Following washing for 5 min at 15 µl/min, the pre-column was back-flushed to a 75 µm i.d. PicoFrit column (New Objective, Woburn, MA) filled with 10 cm of BioBasic C18 packing (5 µm, 300 Å) by the acetonitrile gradient supplied by the Agilent series 1100 Nanopump to allow elution of the peptides towards the mass spectrometer at a flow rate of 200 ηl/min as described [20]. Eluted peptides were analyzed in a Q-TOF micro (Waters Micromass, Manchester, UK) equipped with a Nanosource modified with a nanospray adapter (New Objective, Woburn, MA). The MS survey scan was set to 1 s (0.1 s interscan) and recorded from 350 to 1,600 m/z. MS/MS scans were acquired from 50 to 1,990 m/z, scan time was 1.35 s, and the interscan interval was 0.15 s. Doubly and triply charged ions were selected for fragmentation with collision energies calculated using a linear curve from reference collision energies.
MS raw data from a single run were acquired on the Data Directed Analysis feature in the MassLynx (Micromass) software with a 1, 2, 4 duty cycle (1 sec in MS mode 2 peptides selected for fragmentation, maximum of 4 sec in MS/MS acquisition mode). MS/MS raw data were transferred from the Q-TOF Micro computer to a 50 terabyte server and automatically manipulated for generation of peaklists by employing Distiller version 2.3.2.0 (http://www.matrixscience.com/distiller.htmls) with peak picking parameters set at 5 for Signal Noise Ration (SNR) and at 0.4 for Correlation Threshold (CT). The peaklisted data were then searched by employing Mascot version 2.3.01 (http://www.matrixscience.com) and X! Tandem version 2007.01.01.1 (http://www.thegpm.org) against the 6 open reading frames (ORF) translation of the transcriptomic assembly (see below). Searches were restricted to up to 1 missed (trypsin) cleavage, fixed carbamidomethyl alkylation of cysteines, variable oxidation of methionine, 0.5 mass unit tolerance on parent and fragment ions, and monoisotopic. Scaffold (version Scaffold_2_05_02, Proteome Software Inc., Portland, OR) was used to validate MS/MS-based peptide and protein identifications. Peptide identifications were accepted if they could be established at greater than 95.0% probability as specified by the Peptide Prophet algorithm [21]. Protein identifications were accepted if they could be established at greater than 95.0% probability and contained at least 2 identified peptides. Protein probabilities were assigned by the Protein Prophet algorithm [22]. Proteins that contained similar peptides and could not be differentiated based on MS/MS analysis alone were grouped to satisfy the principles of parsimony.
Reads from Illumina sequencing were trimmed in a process that consisted of search and clipping for adapter sequences, elimination of the first 16 bases of the reads to remove random hexamers, and quality trimming using a Q20 threshold on the 3′ end. The assembly was done with Velvet 1.0.13 with a kmer value set at 43 [23]. Oases 0.1.6 (http://www.ebi.ac.uk/~zerbino/oases/) was then used for final transcriptome assembly.
Loci generated from the Oases assembler were subjected to analysis by BLASTx and BLASTn to identify putative homologues in C. elegans, other parasitic nematodes, and organisms other than nematodes (e-value of ≤1e-05). Full assembly will be available at Nembase4 (http://www.nematodes.org/nembase4/) (Submission date: May 6th, 2011) [24].
Gene Ontology (GO) annotations were performed using BLAST2GO [25]. Mapping of GO terms was performed on the hits retrieved from the initial search with BLASTx for protein homologues against the NCBI non-redundant database with a minimum expected value of 1×10−3 and a high scoring segment pair cut-off of 33. The annotation algorithm was set with default parameters; pre-eValue-Hit-Filter of 1×10−6, annotation cut-off of 55, and GO weight of 5. Identification of enriched GO terms in the secretome dataset compared to the transcriptome was done by assessing P values from Fisher's exact tests applying robust false discovery rate (FDR) using the integrated framework Gossip [26].
InterProScan [27], [28] searches were performed using the built-in feature of BLAST2GO using the conceptual translation from the longest ORF of each locus. Enrichment analysis of exported InterPro terms in the ESP vs. transcriptome datasets was also performed by assessing adjusted P values to control for FDR from Fisher's exact tests run using FatiGO [29] on the integrative online platform Babelomics (http://babelomics.bioinfo.cipf.es) [30].
To identify proteins in H. polygyrus ESP, ∼100 µg ESP were separated by SDS-PAGE. The entire lane was excised in 15 pieces, digested with trypsin, and analyzed by LC-MS/MS. A preliminary protein identification attempt was performed on the complete MS data set (10,227 spectra) using the protein sequences from nematodes in the UniProt database (taxonomy ID:6231, September 17, 2010) as a search source for the peptide matching algorithms. After validation with Scaffold (v. 2_05_02), 20 proteins were assigned (95% probability) with a total number of assigned spectra ranging between 2 to 12 and between 2 to 7 unique peptides assigned per sequence. Nineteen of 20 identified proteins were homologues of proteins from nematodes other than H. polygyrus (Table S1).
To provide a more suitable information source for the peptide assignation software and to increase the number of proteins identified in the H. polygyrus ESP, an RNA-seq analysis of this organism was carried out. Using the GAIIx platform from Illumina, ∼24.7 million reads of raw data, amounting to >2.7 Gbp, were obtained from H. polygyrus poly-A selected mRNA. Initial assembly was performed after the removal of adapters, random hexamer primer sequences, and quality control trimming using Velvet 1.0.13 [23], generating 76,616 contigs. Final assembly with Oases 0.1.6 resulted in 33,641 total transcripts (isoforms) in 29,918 loci (3723 alternative splice events) (Table 1). These values do not include sequences <100 bp, which were removed for downstream analysis.
Searching for protein homologues in the H. polygyrus assembly with BLASTx identified 18,816 (55.9%) transcripts sharing homology with proteins from C. elegans (E cut-off 1×10−5) and 15,338 (45.6%) with proteins from Brugia malayi (Table 1). Only 4 sequences were found to return mouse proteins as the first BLASTx output (E cut-off 1×10−15), indicating a low degree of host RNA contamination in the preparations.
Functional annotation using BLAST2GO allowed us to assign GO terms to 14,094 (41.9%) sequences; 764 different cellular component terms were assigned to 8,694 sequences, 1,805 molecular function terms to 11,671 sequences, and 4,078 biological process terms to 11,296 sequences (Table 1). The most frequently annotated GO terms within these three categories were “integral to membrane” (GO:0016021, 1,513 sequences), “protein binding” (GO:0005515; 3,782 sequences), and “embryonic development ending in birth or egg hatching” (GO:0009792, 2,239 sequences) (Tables 2 and S2).
Distribution of GO terms at level two indicated that “binding” (GO:0005488, 49% of annotated sequences) and “catalytic activity” (GO:0003824, 32%) were the two major molecular function categories (Figure 1, left). In the case of biological process, the most represented categories at level two were “cellular process” (GO:0009987, 17%), “metabolic process” (GO:0008152, 13%), “multicellular organismal process” (GO:0032501, 10%), “developmental process” (GO:0032502, 10%), and “biological regulation” (GO:0065007, 10%).
Functional domains and protein families were assigned to H. polygyrus transcripts using InterProScan [27], [28]; 17,342 (51.5%) sequences retrieved at least one protein signature, 2,204 different functional domains were predicted in 15,435 (45.9%) sequences, and 1,896 protein families in 5,760 sequences (17.1%) (Table 1). A protein kinase-like domain (IPR011009) was found in 360 sequences (1.1%), which represents the most frequently found predicted domain in the annotated dataset. In the case of protein families, the P-type, K/Mg/Cd/Cu/Zn/Na/Ca/Na/H-transporter family (IPR001757) was the most abundant, found on 80 sequences (0.2%) (Table 3).
The translation of the 6 ORFs of each transcript from the RNA-seq assembly was used as input for the matching algorithms in the protein identification software. Using this strategy, 209 proteins were identified with a total number of assigned spectra between 132 and 2 with 2 to 19 unique peptides assigned per sequence. It should be noted that one sequence appears twice as it was assigned to 2 different ORFs (Locus_541_Transcript_1/4_Confidence_0.692, frames 4 and 5) (Tables 4 and S3). Manual verification of peptide assignments showed that all the identified peptides group in a single ORF.
Annotations from the non-redundant list of ESP hits (208 proteins) were extracted from the full transcriptome data set for further analysis. 642 GO terms could be annotated to sequences from the ESP subset (54.8% of the identified sequences), identifying 52 different cellular component terms in 47 (22.6%) sequences, 87 molecular functions in 107 (51.4%) sequences, and 167 biological processes in 89 (42.8%) sequences (Table 1).
At Level 2, within the molecular function category, 8 of the 14 terms initially found in the full transcriptome dataset were also found in the ESP subset. The GO terms “binding” (GO:0005488, 49% of annotated sequences) and “catalytic activity” (GO:0003824, 36%) were the most abundant terms in this category (Figure 1, right). In the biological process category, 19 of 25 terms were found in the ESP subset. Although the proportion of annotated terms in the ESP subset was slightly different than in the whole transcriptome dataset, the terms “multicellular organismal process” (GO:0032501, 14%), “developmental process” (GO:0032502, 13%), “biological regulation” (GO:0065007, 12%), “cellular process” (GO:0009987, 12%), and “metabolic process”( GO:0008152, 10%) were the most represented terms in both (Figure 1, left panels).
InterProScan hits assigned to the ESP subset predicted at least one protein signature for 158 (76.0%) sequences, identifying 70 functional domains in 104 (50.0%) sequences and 41 protein families for 70 (33.7%) sequences. The cysteine-rich secretory protein, antigen 5, and pathogenesis-related 1 protein (CAP) domain (IPR014044) with 25 (12.0%) sequences identified, was the most abundant domain in the ESP subset. The allergen V5/Tpx-1-related family (IPR001283), associated with the CAP domain, was the most prevalent found in the ESP subset (Table 3).
The proteins were organized according to the number of assigned spectra, indicative of protein abundance (Table 4) [31]. The most abundant hits organized in this manner were categorized into 3 main groups according to their annotated features. The first group is the proteins predicted to contain the CAP domain belonging to the allergen V5/Tpx-1-related family. This group of proteins is described in the annotation tables as homologues of venom allergen-like proteins (VAL), A. caninum secreted proteins, or activation-associated secreted proteins (ASP). The second group is composed of globin homologues. Proteins found within this group were annotated with the biological process GO term “oxygen transport” (GO:0015671) and the molecular function terms “heme binding” (GO:0020037), “oxygen transporter activity” (GO:0005344), and “oxygen binding” (GO:0019825). Although not predicted from the InterproScan in all these sequences, the globin-like domain (IPR009050) and globin family (IPR000971) were also annotated to some of these hits. The third group of most abundant proteins contains vitellogenin (Vtg) homologues. Most of these proteins are predicted to contain the characteristic Vtg open β-sheet (IPR15255, IPR 15817) domain as well as domains associated with lipid transport (IPR015819, IPR001747, and IPR015816) and GO terms associated with the molecular function of “protein binding” (GO:0005515) and the biological processes “embryonic development ending in birth or egg hatching” (GO:0009792), “determination of adult lifespan” (GO:0008340), and “positive regulation of growth rate” (GO:0040010) (Figure 1, right panels).
GO terms enrichment analysis using GOSSIP [26] identified terms that were over-represented in the ESP subset compared to the total transcriptome dataset (Table S4). Using adjusted P-values to control FDR (significance set at p<0.05) as criterion for statistical significance, 14 terms within the biological process category and 8 within the molecular function category were enriched in the ESP subset.
In accordance with the number of globin homologues found in the ESP subset, the biological process term “oxygen transport” (GO:0015671) and its parent “gas transport” (GO:0015669) were enriched in the ESP subset. Consistent with the globins and their putative role in oxygen transport via heme prosthetic groups, the molecular function terms “oxygen binding” (GO:0019825), “oxygen transporter activity” (GO:0005344), and “heme binding” (GO:00200037), together with their parent terms, “iron ion binding” (GO:0005506) and “tetrapyrrole binding” (GO:0046906), were also enriched in this subset.
Two other groups of hierarchically-related enriched biological process terms were delineated for their association with Vtg homologues in the ESP subset. The first group comprises the term “determination of adult lifespan” (GO:0008340) and its parent “multicellular organismal process” (GO:0032501). The second group consists of “positive regulation of growth rate” (GO:0040010) and parent terms “regulation of growth rate” (GO:0040009), “positive regulation of growth” (GO:0045927), and “regulation of growth” (GO:0040008). The identification of 3 homologues of glutathione-S-transferase also accounts for the enrichment of these terms.
In the molecular function category, two groups of enriched terms were associated with proteins of lower relative abundance. One group includes homologues of retinol and/or fatty acid binding protein as well as repetitive ladder antigens and Vtg homologues, which have the putative ability to bind and transport vitamin A and/or lipids. These proteins were annotated under the terms “retinol binding” (GO:0019841) and their parents, “retinoid binding” (GO:0005501), “isoprenoid binding” (GO:0019840), and “lipid binding” (GO:0008289). The other group is composed of certain proteases in the ESP subset, particularly several zinc metallopeptidase homologues. GO annotations in this group included the term “metallopeptidase activity” (GO:0008237) and the parent terms “peptidase activity acting on L-aminoacid peptides” (GO:0070011) and “peptidase activity” (GO:0008233).
In addition, the molecular function terms “intramolecular oxidoreductase activity” (GO:0016860) and “nucleoside diphosphate metabolic process” (GO:0009132) were also enriched in the ESP dataset. The first was associated with homologues of protein disulfide isomerase, triosephosphate isomerase (TPI), and macrophage migration inhibitory factor (MIF). The latter included homologues of nucleoside diphosphate kinases (NDPK), calcium activated nucleosidases, and ribonucleotide reductases (RNR).
Furthermore, InterPro domain enrichment analysis was performed using FatiGO [29] (Table S5). Likewise, adjusted P-values to control FDR were used as criteria for statistical significance (p<0.05); 23 domains and families were enriched in the ESP subset compared to the transcriptome dataset. Consistent with what was found in the enrichment analysis of GO terms, there was an enrichment of predicted families and domains associated with homologues of peptidases, globins, nucleosidases, glutathione-S-transferases, Vtg and retinol and/or fatty acid binding proteins. In addition, CAP domain (IPR014044) and its related allergen V5/Tpx-1-related family (IPR001283) and Ves allergen (IPR002413), along with the transthyretin-like family (IPR0001534), were enriched in the ESP dataset.
The ESP fraction of the proteome from parasitic nematodes is thought to contain many of the effector molecules that contribute in a direct or indirect way to establishment and survival within the host [32]. The H. polygyrus-mouse model is a convenient system for the study of human chronic gastrointestinal parasitism; potent immunomodulatory effects of ESP preparations from this parasite have been documented [9]–[12]. Specification of the protein composition of ESP is an important step toward compiling a comprehensive list of the proteins responsible for these effects. In addition, the transcriptomic analysis-based protein identification presented here highlights other aspects related to the biology of GI nematode infections that may illuminate new therapeutic strategies.
Proteomic approaches involving mass spectrometry have been applied for the characterization of ESP in several helminth species [33]. Protein identification in this manner has typically been empowered by the availability of information resulting from genome sequencing projects. Our preliminary results exemplify how the lack of this type of information and the reliance on sequences of protein homologues from different nematode species severely limit protein identification of H. polygyrus ESP; these factors would similarly limit such analyses from other unsequenced species.
To overcome this limitation, we sequenced the transcriptome of H. polygyrus using Illumina technology to provide the peptide matching software with the resulting RNA-seq de novo assembly. Next-generation sequencing technologies applied to the study of parasitic nematode transcriptomes offer an efficient way to understand how these organisms orchestrate their biochemical and molecular processes within the host [34]–[36]. However, we show here that its potential includes the use of this information to study specific aspects of the proteome. In particular, the H. polygyrus RNA-seq assembly was used as a reference for the identification of proteins present in the ESP.
Mass spectrometry-based proteomics has started to be exploited for the validation and/or correction of sequence datasets and associated annotations [37]. To a certain extent, this is the case for the present analysis. On the other hand, the overall output of the protein identification process is dependent on the searching space explored, in this case the 6 ORFs of the RNA-seq assembly. In addition to the sequence coverage, factors that may affect the quality of the de novo RNA-seq assembly include the performance of the assembly program as well as errors in individual reads during sequencing and genetic variation in the transcribed sequences, which complicates the recognition of sequence overlap during assembly [38]. How these factors and others (e.g., instrumental aspects of mass spectra acquisition) alter the final output has not been studied extensively. In practical terms, this imposes the need for further validation when using such a dataset for downstream analysis.
Comparison of frequencies and distribution of annotations provides a way to describe the degree of functional specialization of proteins in the ESP relative to the total transcriptome. GO terms enrichment analysis revealed how some of the components of the H. polygyrus ESP may be involved in processes associated with the transport and/or uptake of nutrients from the host as well as possible involvement in signalling pathways.
Globin homologues in the ESP were enriched in functional annotation categories related to oxygen and heme binding. Nematode globins are distantly related to those in vertebrates and are known or predicted to play a role in several processes, given their expression in different anatomical patterns and diversity in gene structure and amino acid sequence [39], [40]. Although a more precise understanding of the multiple functions of nematode globins is needed, it can be expected that their role in oxygen transport and supply must be critical in the low oxygen conditions of the host microenvironment, where the adult H. polygyrus attaches to and coils around the duodenal villi [41]. In this context, globin functions can vary from transport and delivery to oxygen sink depending on the affinity of oxygen binding. For example, the high oxygen affinity globin from A. suum has been proposed to prevent toxic effects of oxygen for this parasite [40], [42]. In addition, parasitic as well as free living nematodes are heme auxotrophs [43], and thus secreted globins may also participate as heme carriers for the supply of this prosthetic group required for many other biological processes.
Another group of enriched functions found in the ESP are related to binding of lipids and retinoids. Proteins associated with these functions are involved in the transport of these hydrophobic molecules as substrates for energy metabolism, membrane biosynthesis, and signalling [44]. Identified proteins in this group include homologues of nematode polyprotein allergens/antigens (NAR), fatty acid and retinol binding (FAR) proteins, and Vtg proteins. NAR and FAR proteins comprise classes of small (∼14 kDa and ∼20 kDa, respectively) lipid binding proteins from nematodes. NAR proteins bind both retinol and fatty acids; they are synthesized as repetitive polypeptides in tandem and are subsequently cleaved into multiple functionally similar proteins [45], [46]. FAR proteins exhibit higher affinity for retinol than for fatty acids [47]. In addition to a role in the acquisition of small lipids from the host or the microbiota, their role as parasite secreted proteins has been proposed to be the sequestration or delivery of signalling lipids to host cells [44]. Their possible role in sequestering vitamin A from the host has been associated with the pathology of parasitic nematode infections. Among these are visual impairment caused by infections with Onchocerca volvulus [47] and vitamin A deficiency in patients infected with A. lumbricoides, possibly due to malabsorption [48]. Sequestration of vitamin A may also contribute to immunomodulation as it is required for host adaptive immunity and is involved in the differentiation of CD4+ T helper (Th) cells and B cells. In particular, vitamin A deficiency leads to impaired intestinal immune responses, including antibody-mediated responses directed by Th2 cells [49], [50].
Vtg proteins form a highly diverse family in the large lipid transfer protein (LLTP) superfamily. In addition to the ESP from H. polygyrus, these proteins have also been identified in ESP from other parasitic GI nematodes [51], [52]. In C. elegans, Vtgs are implicated in the delivery of nutrients to support embryonic development, hence the enrichment of biological process terms associated with growth regulation. They are secreted from the intestine to the pseudocoelomic space where they transit through the gonadal basal lamina and then through the sheath pores for receptor-mediated oocyte endocytosis [53], [54]. Therefore, it is likely that their presence in ESP from parasitic GI nematodes is the result of egg release. However, the involvement of Vtg-like proteins in modulation of insect host immune responses [55]–[57] suggests a possible additional role in negotiation of the host-parasite interface.
Peptidase activity was another GO function enriched in the ESP protein set. Helminth proteases participate in the establishment, development, and maintenance of infection [58]. In H. polygyrus, developmental regulation of ESP-proteases suggest possible roles in exsheathment, invasion of the mucosa, and immune regulation during the larval stages, and feeding and migration during the adult stage [59]. Nothing is known about the substrate specificities of the H. polygyrus ESP-proteases. However, by analogy to the proteolytic cascade required for haemoglobin degradation by hookworms [60], several components of which were also identified in A. caninum ESP [52], the identified aspartyl, cysteine, and metalloproteinases from H. polygyrus are predicted to participate in degradation of host proteins acquired during tissue feeding.
The identification of enzymes involved in nucleotide metabolism suggests a possible role of ESP in modulation of host signalling pathways. Regulation of local levels of extracellular nucleotides could affect the activity of host purinergic receptors, which mediate a variety of cellular responses, including elements of the immune system [61]. Enzymes involved in nucleotide metabolism have previously been identified in ESP from parasitic nematodes [20], [62]–[64]. These include nucleoside diphosphate kinases, nucleosidases, and adenosine deaminases that participate in the formation of activators of purinergic receptors from ATP or UTP, such as AMP, UMP, adenosine, or inosine [61]. In addition, the homologue of ribonucleotide reductases in H. polygyrus ESP may contribute precursors for this pathway through the generation of deoxynucleotides from ribonucleotides.
In addition to proteins of interest based on comparison of GO annotation between datasets, homologues of ASP or VAL proteins were also highlighted for their abundance and number of isoforms identified. These proteins are characterized by the presence of the CAP domain (also known as SCP-like domain) and belong to the allergen V5/Tpx-1-related family of proteins, a group of evolutionarily related eukaryotic extracellular proteins whose function remains largely unknown [32], [65], [66]. InterPro terms associated with this domain and families were found to be enriched in the ESP dataset. Members of this family include cysteine-rich sperm proteins (CRISPs), insect venom allergens, and plant pathogenesis family-1 (PR-1) proteins. Reasons to suspect a role for these proteins at the nematode-host interface (including pathogenesis) include the rapid and specific release of N. americanus ASP-2 during the transition from larval to parasitic stages as well to their neutrophil chemoattractant activity [67], [68], and the angiogenic effects of several O. volvulus ASPs [69].
In addition to proteins highlighted on the basis of enrichment of functional annotation, other relevant proteins in H. polygyrus ESP include homologues of glycolytic and metabolic enzymes. Of particular interest are triosephosphate isomerase (TPI), fructose bisphosphate aldolase A (FBPA), and enolase (ENO), which have consistently been reported in nematode ESP, a pattern suggesting that their release cannot be simply due to worm death or damage during culture [32]. While the function of these proteins remains obscure in the context of host-nematode relationships, there is evidence of the association of these enzymes with host cell surface components and their involvement in functions unrelated to glycolysis, including microbial pathogenesis and autoimmune disorders [70]–[73].
Possible immunomodulators also include a homologue of MIF, a parasite protein that mimics a mammalian cytokine, which has been reported in many nematode ESPs. MIFs are usually associated with pro-inflammatory responses. However, in contrast to the mammalian cytokine, nematode MIF acts in a Th2 environment to induce AAMΦs [32], [74], [75]. In addition, the cysteine protease inhibitor (CPI) homologue identified in H. polygyrus ESP may modulate immune responses to unrelated antigens by inhibition of antigen processing and presentation by antigen presenting cells or by inhibition of T-cell proliferation, which may contribute to the state of cellular hypo-responsiveness characteristic of chronic parasitic nematode infections [76]–[78]. Also of interest are the previously characterized C-type lectins (CTL) from H. polygyrus and galectin homologues identified in the ESP in the present study [79]. Their role as immunomodulators is suggested by the involvement of these carbohydrate-binding proteins in a variety of immune functions [80]–[83] as well as the eosinophil attracting activity that has been reported for a galectin from Haemonchus contortus [84].
Finally, the presence of homologues of peroxiredoxin (PRX) and glutathione S-transferase (GST) in H. polygyrus ESP suggests a role for enzymes involved in detoxification of reactive oxygen species (ROS) released from the host [85], [86]. Other roles for these enzymes may include the induction of AAMΦs, as shown for a helminth PRX, promotion of Th2 immune responses, and the involvement of GST in heme transport and detoxification [87]–[89].
In conclusion, we employed the next-generation sequencing and proteomic approaches to gain insights into the transcriptome of adult H. polygyrus and used the dataset to identify protein components of the ESP. Comparison of functional annotation categories of the total transcriptome, which provides a picture of the total proteome, with those of the ESP subset allowed us to identify functions and associated proteins that may play a role at the host-parasite interface, where many events critical for success of the infection occur. The data presented here contribute to the identification of individual components that may be responsible for the immunomodulatory activity that has been reported for H. polygyrus ESP. Moreover, methods and analyses presented here are useful for the study of biochemical and molecular aspects of nematode biology in other species for which sequence information is not available.
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10.1371/journal.pbio.1002132 | Targeting the Cell Stress Response of Plasmodium falciparum to Overcome Artemisinin Resistance | Successful control of falciparum malaria depends greatly on treatment with artemisinin combination therapies. Thus, reports that resistance to artemisinins (ARTs) has emerged, and that the prevalence of this resistance is increasing, are alarming. ART resistance has recently been linked to mutations in the K13 propeller protein. We undertook a detailed kinetic analysis of the drug responses of K13 wild-type and mutant isolates of Plasmodium falciparum sourced from a region in Cambodia (Pailin). We demonstrate that ART treatment induces growth retardation and an accumulation of ubiquitinated proteins, indicative of a cellular stress response that engages the ubiquitin/proteasome system. We show that resistant parasites exhibit lower levels of ubiquitinated proteins and delayed onset of cell death, indicating an enhanced cell stress response. We found that the stress response can be targeted by inhibiting the proteasome. Accordingly, clinically used proteasome inhibitors strongly synergize ART activity against both sensitive and resistant parasites, including isogenic lines expressing mutant or wild-type K13. Synergy is also observed against Plasmodium berghei in vivo. We developed a detailed model of parasite responses that enables us to infer, for the first time, in vivo parasite clearance profiles from in vitro assessments of ART sensitivity. We provide evidence that the clinical marker of resistance (delayed parasite clearance) is an indirect measure of drug efficacy because of the persistence of unviable parasites with unchanged morphology in the circulation, and we suggest alternative approaches for the direct measurement of viability. Our model predicts that extending current three-day ART treatment courses to four days, or splitting the doses, will efficiently clear resistant parasite infections. This work provides a rationale for improving the detection of ART resistance in the field and for treatment strategies that can be employed in areas with ART resistance.
| Resistance to artemisinin antimalarials, some of the most effective antimalarial drugs, has emerged in Southeast Asia, jeopardizing malaria control. We have undertaken a detailed study of artemisinin-sensitive and-resistant strains of Plasmodium falciparum, the parasite responsible for malaria, taken directly from the field in a region where resistance is developing. We compared these strains to lab strains engineered with either mutant or wild-type resistance alleles. We demonstrate that in sensitive P. falciparum, artemisinin induces growth retardation and accumulation of ubiquitinated proteins, indicating that the drugs activate the cellular stress response. Resistant parasites, on the other hand, exhibit reduced protein ubiquitination and delayed onset of cell death following drug exposure. We show that proteasome inhibitors strongly synergize artemisinin activity, offering a means of overcoming artemisinin resistance. We have developed a detailed model of parasite responses and have modelled in vivo clearance profiles. Our data indicate that extending artemisinin treatment from the standard three-day treatment to a four-day treatment will clear resistant parasites, thus preserving the use of this critical therapy in areas experiencing artemisinin resistance.
| Malaria remains a scourge of humanity, affecting hundreds of millions of people and causing ~600,000 deaths each year [1]. Infection with Plasmodium falciparum is responsible for the majority of severe malaria cases. During the asexual blood phase of its lifecycle, this protozoan parasite invades, grows, and multiplies within red blood cells (RBCs). The initial stage of intraerythrocytic growth (0–~24 h), during which the parasite exhibits an unfilled cytoplasm in Giemsa-stained smears (referred to as “rings”), is characterized by a relatively slow metabolism [2]. Ring-stage—infected RBCs are freely circulating and are thus the predominant stage detected in samples taken from the peripheral blood of infected patients. From ~24 h to ~40 h post-invasion (p.i.), in the “trophozoite” (or growing) stage, the parasite increases the rate of uptake and digestion of hemoglobin from the host cytoplasm and shows a large increase in metabolic rate. These mature parasites are characterized by the presence of hemozoin, the classic “malaria pigment” that results from hemoglobin digestion. Trophozoites are rarely observed in the circulation of infected patients because of their adherence to endothelial cells and consequent sequestration away from the circulation. Complications associated with cerebral sequestration are responsible for much of the malaria-related mortality and morbidity [3]. From ~40 h p.i., the parasite undergoes cytokinesis, forming a schizont that can contain up to 32 daughter parasites (merozoites). At ~48 h p.i., the schizont bursts, releasing the merozoites and heralding a new round of infection.
Artemisinin and its derivatives (collectively referred to as ARTs) have contributed enormously to decreasing rates of malaria deaths over the last decade. ARTs are among the few antimalarials that are active against ring-stage parasites, thus reducing the parasite burden in P. falciparum infections quickly and providing prompt therapy for severe infections [3]. The ARTs contain an endoperoxide group that is critical for their activity. The mechanism of ART action remains poorly understood, but ARTs are thought to be pro-drugs that need to be activated by opening of the endoperoxide ring, i.e., splitting the bonded oxygen atoms [4]. This process requires the presence of heme or non-heme iron sources (and possibly other activators) [5,6]. The activated ART intermediates are thought to react with susceptible (nucleophilic) groups within parasite proteins and other cellular components, leading to parasite killing; however, the details remain unclear [7].
A disadvantage of ARTs is their short half-lives in vivo (~1–2 h). Accordingly, they are co-administered with longer half-life partner drugs in ART combination therapies (ACTs) to prevent recrudescence and to slow the emergence of resistance [8]. Current antimalarial control is highly dependent on ACTs, which makes the emergence of ART resistance extremely concerning [9–11]. Decreased sensitivity to ARTs, which manifests as delayed parasite clearance, is now a problem in six Southeast Asian countries and is translating into decreased clinical efficacy in areas with concomitant partner-drug resistance [12,13]. Enormous efforts are underway to contain and eliminate ART resistance.
Initially, monitoring ART resistance was hampered by the lack of a suitable in vitro correlate [10]. Recently, assays employing short pulses mimicking clinical drug exposure [14–16], such as the ring-stage survival assay (RSA), have provided a good correlation between reduced in vitro sensitivity to dihydroartemisinin (DHA), the clinically relevant ART derivative, and delayed parasite clearance [15]. Combined with whole genome sequencing, this allowed the identification of mutations in a protein with a Kelch domain (i.e., β-propeller tertiary structure, referred to as K13; PF3D7_1343700) [17] that are strongly associated with the slow-clearance phenotype. A large Genome-Wide Association Study (GWAS) added further support to the suggestion that K13 is the major locus controlling P. falciparum resistance to ARTs [18]. Recent studies using genetic modification of the K13 locus have confirmed a central role for K13 mutations in conferring ART resistance [19,20].
Here we present a detailed in vitro investigation of the drug responses of K13 wild-type and mutant isolates of P. falciparum sourced from a region in Cambodia (Pailin) with a marked penetrance of ART resistance [10,21]. Our results provide insights into the molecular basis of ART action and resistance and point to a class of compounds that could be used to synergize the activity of ARTs against both sensitive and resistant parasites. Modelling of parasite responses suggests alternate therapeutic regimens that should be vigorously pursued and provides tools that will immediately impact the way resistance is measured in the field.
We determined the whole genome sequences of four laboratory-adapted isolates of P. falciparum collected from adult patients enrolled in clinical trials conducted between 2009 and 2010 in the Pailin Referral Hospital in Cambodia [21]. High sequencing coverage with good mapping quality was achieved across all four genomes (mean sequence coverage of 77x and mean mapping quality >30; see S1 Table). A Pairwise Distance matrix (5x5) built on 26,438 SNPs in coding genes was used to generate a Neighbor Joining tree to examine isolate relatedness. The isolates are quite divergent, with ~16,000 non-synonymous SNPs between any two isolates (excluding var, rif, and stevors), and they exhibit multiple mutations in many of the drug-resistance—related genes.
One strain (PL2) encodes the wild-type K13 genotype, while the others (PL1, Y493H; PL5, C580Y and PL7, R539T) represent three K13 mutants that are commonly observed in Cambodia (S2 Table). A recent fine-structure analysis of parasite samples collected as part of the large-scale Tracking Resistance to Artemisinin Collaboration (TRAC) study revealed that particular non-synonymous polymorphisms in apicoplast ribosomal protein S10 (arps10), multidrug resistance protein 2 (mdr2), ferredoxin (fd), and chloroquine resistance transporter (pfcrt) are markers of a genetic background on which K13 mutations are likely to arise, but individually they have little contribution to ART resistance [18,22]. Other GWAS have reported SNPs in other genes that show association with the ART resistance phenotype [23–27]. The K13 mutant and wild-type Pailin strains exhibited the expected SNPs at the arps10 (V127M) and mdr2 (T484I) loci and at one of the pfcrt (I356T) loci but showed variable sequences at the other loci (see S2 Table for a summary of some relevant loci). Both K13 mutant and wild-type strains exhibited the fd (D193Y) and pfcrt (N326S) SNPs that are strongly associated with ART resistance founder populations [18]. The entire genomes for the four Pailin strains have been made available through the European Nucleotide Archive (ENA) database under the accession number PRJEB8074.
We previously demonstrated that the sensitivity of laboratory strains of P. falciparum to ARTs exhibits a complex dependence on drug exposure time and concentration [14]. For this work, we used assays comprising very short drug pulses in an effort to mimic in vivo exposure and to maximize the discrimination of subtle differences in parasite responses. We defined parasite viability as the fraction of the parasite population that survives drug exposure and is able to enter the next parasite cycle. We found that the 50% lethal dose (LD50) and the viability at saturating drug concentrations (the minimum viability, Vmin) are the most useful measures of cellular cytotoxicity, in agreement with other studies of ARTs and other drugs [15,28,29].
In the current work, tightly synchronized cultures of the four strains were subjected to 3-h pulses of DHA at different stages of the asexual lifecycle (Fig 1A and 1B). Notably, the PL2 strain exhibits a low Vmin(3h) (<5%) across all stages of development (Fig 1A), confirming an ART sensitive phenotype. In contrast, the PL1, 5, and 7 strains displayed 5%–60% survival over the first 10–12 h p.i. (Fig 1A and 1B). The youngest ring stages (1.2 h p.i.) of the mutant strains exhibited the greatest viability following drug exposure (Vmin(3h) >40%), as previously reported [15], confirming the resistant phenotype of these strains. Of note, the PL2 and PL7 strains exhibit similar LD50(3h) values at 6 h p.i., but PL7 shows a higher capacity to survive drug exposure (Vmin(3h) = 20%) (Fig 1A). Thus Vmin(3h) appears to be the more sensitive indicator of the resistance genotype, as suggested previously [14].
We also monitored the drug response of mature parasites during their transition into the next cycle (Fig 1A and 1B, b panels). Continuous in vitro culturing of parasite cultures results in broadening of the age distribution over time. We measured the degree of broadening in this study (e.g., the schizont-to-ring transition shown in the b panels in Fig 1). In agreement with a previous study [14], ~80% of parasites that are synchronized to a 1-h window undergo the schizont-to-ring transition over a period of ~4 h in the next cycle (i.e., ~48 h later). Late trophozoites and early schizonts (average ages earlier than 6 h pre-invasion) of all strains exhibit similar LD50(3h) values with no detectable survival when exposed to 0.7 μM DHA. By contrast late-stage PL1, 5, and 7 schizonts (average ages later than 4 h pre-invasion), which form rings during the course of the assay, exhibit increased LD50(3h) and Vmin(3h) values. This indicates that the mutant strains have an increased ability to survive a clinically relevant drug pulse (0.7 μM DHA; 3 h) from -4 to +12 h p.i., encompassing almost one-third of the intraerythrocytic cycle. The ability of late-stage schizonts to survive exposure is particularly important, considering each surviving parasite forms approximately ten daughter cells.
There are subtle differences between the responses of the different K13 mutants. These may be due to additional genetic factors as well as to direct effects of the different mutations, as indicated by a detailed reverse genetics analysis of the contribution of K13 to resistance in recent Cambodian isolates and reference lines [20]. The time points for transition from ring to trophozoites (i.e., the 50:50 point; indicated with "T" in Fig 1A and 1B) and the lifecycle durations for the K13 mutant and wild-type parasites were: PL1 (30 h/49 h), PL5 (30 h/59 h), PL7 (27 h/57 h) and PL2 (27 h/52 h), and 3D7 (22 h/41 h). Thus, among this small sample size, there does not appear to be a simple correlation between the K13 mutation and the length of the ring stage or of the intraerythrocytic cycle.
Our previous work with laboratory strains showed that ART-mediated killing requires that parasites are exposed to a sufficient concentration of activated ART for a sufficient period of time [14]. We defined a semi-empirical cumulative effective dose (CED) model that accounts for the complex in vitro dependence of parasite viability on drug concentration and exposure time and permits the interpretation of stage and strain-dependent differences in drug action in terms of easy to understand underlying parameters. Here we analyzed the dependence of DHA action on time of exposure in K13 wild-type (PL2) and K13 mutant (PL7, R539T) field strains (Figs 1C and S1).
A 3-h exposure of PL2 (2 h p.i.) to 1.3 μM DHA is sufficient to reduce parasite viability to almost zero (Fig 1C, red). Remarkably, a significant fraction (20%) of PL7 early rings (Fig 1C, blue) remain refractory to a 9-h exposure to 1.3 μM DHA, even though the LD50(9h) value (20 nM) suggests potent drug action at longer exposures; this further illustrates that Vmin is more informative than LD50 in revealing the resistance-associated phenotype. Late rings (18 h p.i) and trophozoites (34 h p.i.) from both strains show similar sensitivity to >3 h exposure to DHA, but small differences in LD50 are evident at very short exposure times (Fig 1C).
We found that the CED model is able to adequately describe the response of the Cambodian strains to DHA at different stages of development, as well as the dependence of that response on concentration and exposure time. That is, the CED model can be used to generate the fitted curves in Fig 1C and S1 Fig at the very early ring (2 h p.i.), the late ring (18 h p.i.) and the mid trophozoite (34 h p.i.) stages. In this model, the effective dose (ED) is a saturable function of the drug concentration and is defined by Km, the drug concentration resulting in half the effect of the maximally effective dose, EDmax. Parasite viability is then a sigmoidal function of the cumulative ED (EDcum) with slope γ and midpoint t50e,sat. The t50e,sat value is the time taken to kill half of the parasites at saturated (and fixed) drug concentration. (See [14] for further explanation of the model).
The drug response of late rings and mid trophozoites was similar for both wild-type and mutant strains and the analysis indicated similar CED model parameters (Table 1; Fig 1C). Interestingly, early rings from the K13 wild-type and mutant strains, which produce quite a different drug response, exhibit similar Km values (14–19 nM; Table 1). This suggests that DHA initiates an effect in both drug-sensitive and drug-resistant strains at a similar concentration, even in the early ring stages. Since the ARTs are pro-drugs that are converted to the active form by reaction with iron or heme [4,6], this suggests that all strains activate the drug with similar efficiency. By contrast, there is a 3.5-fold increase in the t50e,sat values for DHA for early ring stage PL7 parasites (Table 1), and this underlies most of the difference in the response of this strain. As previously demonstrated for laboratory strains [14], such an increase in t50e,sat manifests as an increase in the lag time for drug action. In other words, there is a longer delay following drug treatment before the onset of the killing of the K13 mutants. Since t50e,sat=ED50cum/EDmax (where ED50cum is the cumulative ED required to kill 50% of the parasites [14]), this indicates that PL7 requires longer exposure to an effective dose to induce killing (or the maximal effective dose produced is less). This difference is expected to be very important in vivo given the very short in vivo half-lives of ARTs.
A number of studies have suggested that ARTs can exert cytostatic effects at sub-lethal concentrations [30,31]. Here we have used the RNA-binding dye SYTO-61 (which can readily distinguish parasites of different ages [30]) to determine whether the parasites that survive DHA exposure exhibit growth retardation. (See Materials and Methods for labelling strategy).
We initially examined growth effects in the laboratory strain, 3D7, at the ring stage of development, where it exhibits 10-fold lower sensitivity to a 3-h DHA pulse compared to the trophozoite stage [14]. We found that mid-ring (6 h p.i.) and later ring (18 h p.i.) stage parasites that survive exposure to a 4-h drug pulse exhibit a dose-dependent decrease in the intensity of the SYTO-61 signal following treatment (Fig 2A, green curves). The IC50(4h) for the growth retardation effect was similar across the ring stage (~10 nM) and was 5- to 10-fold lower than the corresponding LD50(4h) value (i.e., cytotoxic effect). Examination of the population profile of SYTO-61 staining shows an absolute increase in the number of viable parasites with decreased SYTO-61 signals in the drug-treated samples compared to the untreated controls (Fig 2B, asterisks). This indicates that the decrease in the SYTO-61 signal following drug exposure reflects drug-induced growth retardation rather than selective killing of the oldest parasites.
Similarly for both the K13 wild-type (PL2) and R539T mutant (PL7) strains, early ring-stage parasites (2 h p.i.) that survive drug exposure exhibit dose-dependent growth retardation (Fig 2C, green curves). Interestingly, the two strains exhibit similar IC50(3h) values for growth retardation (10 nM) despite their very different sensitivities to killing (LD50(3h) = 10 and >1000 nM for PL2 and PL7, respectively; Fig 2C, compare green and grey curves). These IC50(3h) values remain relatively constant throughout the ring-stage, with the maximum effect evident in late rings (Fig 2C, right panels).
We compared the effect of the time of exposure to drug on growth retardation and viability. The response of the PL2 and PL7 strains at late-ring stage to exposure to DHA for a very short period (1.5 h) is similar, and mainly comprises growth effects without loss of viability (Fig 2D). Differences in viability manifest at longer exposures (Fig 2D, 3 and 6h). While PL2 parasites succumb to longer drug exposure, the growth-retarded PL7 parasites are able to withstand drug pressure for longer. This demonstrates that it is the ability of the growth-retarded PL7 parasites to withstand subsequent drug pressure that is responsible for their resistance phenotype.
Drug-induced growth retardation effects are important as they will influence the stage at which surviving parasites are exposed to recommended ART regimens, which often include daily doses administered 24 h apart. We quantitated the magnitude of this growth effect by subjecting tightly synchronized PL7 parasites (1.5 h p.i.) to a 3.5-h DHA pulse (1 μM) and periodically examining parasite morphology by Giemsa staining over a period of 40 h, following the drug pulse. At times >30 h, two distinct parasite populations were identified corresponding to those containing hemozoin (predominantly trophozoite morphologies) and those with pyknotic or early ring morphologies (Fig 3A). The fraction of parasites exhibiting a trophozoite-like appearance (30%; n >100 parasites) matched the viability as measured by flow cytometric analysis in the cycle following the drug pulse (28%), indicating that these trophozoites represent the viable population and confirming this as a simple method for measuring loss of viability (i.e., parasites rendered incapable of reproduction) in the same cycle as the drug treatment. A comparison of the sizes (areas of Giemsa-stained parasites) of the surviving trophozoites with those from an untreated culture shows that the surviving parasites were, on average, delayed 6 h in their progression through the cycle and exhibited a broader age distribution (Fig 3A and 3B).
The above analysis indicated that unviable parasites (i.e., incapable of reproduction) can exhibit normal ring morphologies for some time after exposure to ART. We examined the timing of the appearance of pyknotic forms in Giemsa smears, following a DHA pulse, to determine whether the differential drug response of sensitive and resistant strains also influenced the rate of generation of pyknotic forms. Early ring-stage parasites (1.5 h p.i.) were pulsed with 1 μM DHA for 3.5 h to generate unviable parasites (100% and 72% of PL2 and PL7 parasites, respectively, based on flow cytometric analysis in the cycle following the drug pulse), and parasite morphology was quantitated (from Giemsa smears) every 2–4 h following the drug pulse. Surprisingly unviable PL2 and PL7 parasites retained a ring-like morphology 13 h after the drug pulse, with pyknotic forms comprising <10% of the unviable population (Fig 3C and 3D). A significant fraction of unviable parasites from both strains (12% and 28% for PL2 and PL7, respectively) exhibited ring-like morphologies 35 h following the drug pulse. Interestingly, the half-time for adoption of pyknotic morphology was significantly longer for PL7 (32 h) than for PL2 (23 h). This indicates that unviable resistant parasites retain a ring-like morphology for longer following DHA treatment. In vivo, these unviable K13 mutant parasites may remain in the blood stream for longer.
The growth retardation caused by exposure to DHA is reminiscent of the cell stress response observed in other organisms. In other systems, cellular insults can result in protein unfolding, culminating in polyubiquitination of proteins and their destruction via the proteasome. Interestingly, a very recent analysis of the transcriptomes of parasite samples collected as part of the TRAC study provided evidence for up-regulation of protein homeostasis genes (such as the ubiquitin-proteasome pathways) that correlates with delayed clinical clearance of parasites [32]. To determine the level of protein damage following ART treatment, we sought to determine the level of ubiquitinated proteins in parasite extracts.
We found that we were not able to reliably measure the parasite-associated signal above the host RBC background at the very early ring stage (mean fold change = 0.9 ± 0.2; n = 5). Therefore we examined effects in trophozoites (which have much higher levels of protein ubiquitination) where K13 wild-type and mutant trophozoite stage parasites show less dramatic, but still measureable differences in response to very short pulses of DHA (see Fig 1C, 34 h p.i). We found that this differential response was more pronounced when parasites were treated with the less potent parent drug, artemisinin (qinghaosu; QHS). We found that a very short pulse (90 min) of QHS killed PL7 much less efficiently than PL2 and 3D7 (LD50(1.5h) values of 857, 208, and 153 nM, respectively.
To determine the effect of QHS treatment on cell stress levels, infected RBCs were saponin-lysed to release the soluble RBC cell contents and parasite extracts were subjected to SDS-PAGE and probed with an antibody that recognizes ubiquitin (Fig 4A). A profile of ubiquitination was observed similar to that reported previously [33], with protein ubiquitination in trophozoites notably higher than in uninfected RBC ghosts. The level of protein ubiquitination increased significantly upon ART treatment (90 min pulse of 1 μM QHS), consistent with engagement of the ubiquitin-proteasome system. The level of protein ubiquitination was higher in ART-treated 3D7 and PL2 parasites than in PL7 parasites (Fig 4A), consistent with the resistant parasites experiencing a lower level of cellular stress. By contrast, a 90 min pulse treatment with 20 nM WR99210 (a concentration sufficient to cause 100% killing) had no effect on the level of ubiquitination (Fig 4A, right panel). A quantitative analysis of several experiments is presented in Fig 4B.
Given the evidence for accumulation of ubiquitinated proteins following ART treatment, we examined the effects of inhibitors of the proteasome; a proteinase complex that plays a critical role in degrading unfolded proteins. Proteasome complexes are present in both the host and parasite cytoplasm, though selective inhibition of the host proteasome does not affect parasite growth or replication [34,35]. Epoxomicin is a well-characterized and highly specific proteasome inhibitor [36] and has activity against the P. falciparum proteasome [37]. Epoxomicin showed activity against all stages of the field strains, when used alone, with maximal potency against early ring-stages (see y-axis intercepts in right panels in Figs 5 and S2). This activity was independent of K13 genotype.
We examined the interaction between DHA and epoxomicin by examining the effect of epoxomicin on the dose response profile of DHA, and by measuring the isobologram for the pair of drugs [38] at the 50% lethal dose level. We initially examined 3D7 parasites. Unlike the Pailin strains, early ring-stage 3D7 parasites (2 h p.i.) exhibit ART hypersensitivity [14] and we observed no interaction between DHA and epoxomicin at this stage (Fig 5A, top) panel. In contrast, a sub-lethal concentration of epoxomicin (18 nM) enhances the potency of DHA ~10-fold against the ring-stage of 3D7 (Fig 5A, middle panel); this is illustrated by the concave shape of the LD50 isobologram. This suggests that the proteasome inhibitor overcomes the cell defense systems that protect the mid-ring stage of 3D7.
We next examined the ability of epoxomicin to synergize the action of DHA against the K13 mutant isolate, PL1. A pronounced synergistic interaction is evident at early ring and ring stages (Fig 5B). Notably, some synergism is also evident in the trophozoite stage (Fig 5B, bottom panel). This is in stark contrast to the effect of hemoglobinase inhibitors, which produce strong antagonistic interactions in the trophozoite stage [30]. We observed a similar synergistic interaction with two other K13 mutant strains (PL5 and 7), with pronounced synergism at early-ring and ring stages (S2B and S2C Fig). The synergistic effect was less pronounced but still evident in the Pailin K13 wild-type isolate (PL2) (S2A Fig).
Very recently, modification of the P. falciparum K13 locus in defined genetic backgrounds was used to demonstrate a central role for K13 mutations in conferring ART resistance [20]. These studies included a laboratory-adapted K13 mutant isolate from Cambodia (Cam3.II_ R539T) and a reverted transfectant in the same line, in which the K13 wild-type genotype has been restored. We examined the sensitivity of these parasites to DHA at different stages of intraerythrocytic development (S3 Fig). Cam3.II exhibited marked resistance to DHA in the very early ring-stage with a Vmin(3h, 1 μM) value of 69% and an LD50 value of >>1 μM (S3 Fig), while the Cam3.II_rev line showed markedly enhanced sensitivity with a Vmin(3h, 1 μM) value of 8% and an LD50 value of 47 nM, in good agreement with the recent report [20]. We examined the interaction of epoxomicin with DHA against Cam3.II_R539T and Cam3.II_rev. Epoxomicin exhibited very strong synergism with DHA in the very early ring-stage of the Cam3.II_R539T isolate (S3 Fig), consistent with the proteasome inhibitor overcoming the K13-mediated resistance mechanism. However, synergism was also observed in the ring and trophozoite stages of Cam3.II and in all stages of the revertant line (S3 Fig). This confirms that proteasome inhibitors can enhance the activity of ARTs against both sensitive and resistant parasites.
Proteasome inhibitors are used clinically in humans to treat myeloma [26]. We examined the effect of two of these compounds, Carfilzomib, an epoxyketone, and Bortezomib, a peptide boronate [39]. Like epoxomicin, Carfilzomib was potent against all strains and stages examined and exhibited strong synergism with DHA, particularly in the less sensitive, very early ring stage (S4 Fig). Similarly, the clinically used proteasome inhibitor Bortezomib exhibited strong synergism with DHA (S5 Fig). These results confirm that the proteasome is involved in the parasite's response to DHA and that inhibiting its activity enhances the level of killing of the parasite.
Proteasome inhibitors such as Carfilzomib have previously been tested for in vivo activity against a murine model [40]. While the activity of Carfilzomib alone against P. berghei is low, we were interested to determine whether sub-lethal concentrations of Carfilzomib might synergize the activity of DHA in the P. berghei mouse model. In agreement with a previous report [40], we found that treatment with up to 1 mg/kg of Carfilzomib monotherapy had no toxic effects, but also had no beneficial effects in reducing parasite burden (Fig 6A). We found that DHA treatment alone (initiated at ~1% parasitaemia) at a dose of 5 or 10 mg/kg/day gave a moderate decrease in parasite burden (Fig 6B and 6C), while 15 or 20 mg/kg was sufficient to abrogate the parasite burden (S6A and S6B Fig). By contrast, a combination of Carfilzomib (0.5 or 1 mg/kg/day) and DHA (5 mg/kg/day) was associated with a significant reduction in the parasite growth (Fig 6B, red and blue curves) while a combination of Carfilzomib (0.5 or 1 mg/kg) and DHA (10 mg/kg) almost completely abrogated the parasite burden (Fig 6C, red and blue curves). Of particular interest is the observation that a combination of DHA (5 mg/kg) + Carfilzomib (1 mg/kg) (S6C Fig, green curve) or DHA (10 mg/kg) + Carfilzomib (0.5 and 1 mg/kg) (S6C Fig, blue and purple curves) largely abrogated the parasites in the circulating reticulocytes, the blood cell preferentially parasitized by P. berghei. This further confirms the role of the proteasome in protecting malaria parasites against the toxic effects of DHA and points to a possible means of synergizing the activity of ARTs in vivo, using repositioned proteasome inhibitors.
The availability of the data from our extensive kinetic analysis of the K13 wild-type and mutant Pailin strains offers the possibility of modelling the drug response of resistant and sensitive parasites at different exposure times and concentrations in vitro and also of extending this analysis to infer behavior in vivo [14]. To do this, we extended the CED mathematical model to take into account the age- and exposure-time—dependence of the drug responses of the sensitive and resistant strains, as well as drug-induced growth retardation and population broadening. We provide an Excel spreadsheet that presents the Mathematical Model in a user-friendly format (S1 Spreadsheet). We have provided a presentation in Prezi that explain the steps involved in using the spreadsheet (https://prezi.com/9xo9b8igvjzl/).
We applied the model to predict parasite clearance rates in vivo during a three-day course of ART monotherapy. We assumed DHA is applied at a rate of 2 mg/kg at 0, 24, and 48 h, i.e., in a typical regimen [41]. We took into account the age distribution of parasites at the time of patient presentation (which varies depending on disease severity [32,42,43]), the age-dependence of drug action (CED parameters, from Table 1), the broadening of the age distribution with time (S1 Appendix), and drug-induced growth effects (from Figs 2 and 3). This simulation evaluates the in vivo consequences of the different in vitro drug responses of ring-stage K13 wild-type and mutant parasites (see S1 Appendix). Strikingly, the first and third ART doses given to a hypothetical patient with a PL7-like (resistant) infection result in <10-fold reduction of viable parasites (Fig 7). In contrast, the PL2 (sensitive) strain shows a 50-fold reduction in parasite burden at the corresponding times (Fig 7A, orange curves). As a result, the parasite burden in a PL7-like infection is ~50-fold higher after a three-day treatment. As a consequence of drug-induced synchronization and growth retardation, the parasite age distribution at 72 h consists predominantly of late rings (S7 Fig). Importantly, administration of an additional dose at 72 h is predicted to decrease the parasite load of a PL7-like infection at 96 h to the level observed with a three-day treatment of a PL2-like sensitive infection (Fig 7A, asterisks). In consequence, we predict that a four-day course of ACT will significantly reduce the incidence of treatment failure in areas with ART resistance.
In field studies, Giemsa smears are used to monitor the effectiveness of drug treatment. This approach, however, only detects circulating rings and not mature, sequestered parasites. Our simulation incorporates sequestration and shows that the density of circulating viable parasites will exhibit time-dependent fluctuations over many orders of magnitude, particularly in the period 24–48 h after treatment (Fig 7A, grey curves). These fluctuations are not evident in real patient-averaged data [41], as shown in Fig 7B (symbols), nor in data from individual patients [42]. We posit that this discrepancy arises because our initial simulation assumes that unviable parasites are immediately removed from the circulation. Splenic clearance represents the major in vivo route for removing killed rings [44,45], with unviable parasites persisting for >60 d after ART chemotherapy in splenectomized patients [46]. Our analysis in Fig 3C and 3D shows that unviable rings retain their ring-like morphologies for many hours, indicating splenic clearance would similarly occur over a period of hours. Estimates of the half-life for splenic clearance of dead parasites range from 3 to 6 h [47,48]. Incorporating a clearance half-life of 5 h for removal of unviable parasites (Fig 7B, dashed lines) produces clearance profiles that resemble those measured in patients exhibiting delayed clearance from Pailin (blue symbols), but predicts that both the mutant and wild-type strains would be removed from the circulation at similar rates, despite clear differences in killing (Fig 7A, orange curves). This strongly suggests the existence of additional strain-dependent factors involved in the clearance of unviable parasites.
A possible explanation for a strain-dependent effect is that unviable parasites persist in the circulation (with a ring-like morphology) before changing their physico-mechanical properties sufficiently to initiate splenic clearance. This is consistent with the delayed appearance of pyknotic forms following treatment of cultures with DHA (Fig 3C and 3D), which indicates that unviable parasites retain ring-like morphologies for an extended period. It is also of interest that the half-time to pyknosis is particularly extended in PL7 (K13 mutant) parasites. Indeed, incorporation of an additional strain-dependent term, as well as maintaining a strain-independent splenic clearance rate, permits improved predictions of the observed parasite clearance curves (Fig 7B, solid curves).
This analysis has implications for monitoring parasite clearance times in the field. We anticipate that killed rings that retain a ring-like morphology would persist in circulation and be counted in Giemsa smears. Moreover, unviable resistant parasites would persist for longer in the circulation. Our simulations indicate that in clinical practice, the ring-stage parasites that are detected in Giemsa smears, after ART treatment, likely comprise mainly unviable parasites (Fig 7C, dashed curves). A significant fraction of these will have been rendered unviable after the first dose (dotted lines), especially in fast clearing, sensitive strains. Consequently, the relationship between the in vitro marker of resistance (decreased parasite killing) and the clinical marker of resistance (delayed parasite clearance) is more complex than previously appreciated.
To examine the potential effect on the interpretation of field studies, we simulated a scenario to examine the effect of a split-dose ART treatment on parasite clearance. A recent clinical trial reported that a split-dose ART treatment regimen did not improve parasite clearance times for malaria infections with either ART-sensitive or-resistant P. falciparum [49]. Indeed, the simulations (see S1 Appendix, S4A Fig) show that splitting the dose will have only a very small effect on the parasite clearance curves. By contrast, there is a very large effect on the number of viable parasites. The simulation predicts that a split-dose regimen should reduce the load of a resistant (PL7-like) infection to a level well below that observed in a sensitive (PL2-like) infection subjected to a standard treatment.
Direct quantitation of circulating viable parasites during drug treatment would complement the current parasite clearance approach for classifying ART resistance in the field. Our simulations indicate that the fraction of circulating parasites that are viable 3 h after commencement of treatment is independent of the typical variation of serum DHA concentrations (Cmax = 0.5 to 20 μM [9,49]) and represents a useful parameter for characterizing the in vivo response of a particular strain (S8 Fig). Within this short period of time, there is sufficient difference in loss of parasite viability to distinguish between sensitive and resistant infection, without the complicating effects of significant loss of viable parasites due to sequestration, or from splenic clearance of unviable parasites, which complicates the interpretation at longer times (S8 Fig).
It is generally accepted that ARTs are pro-drugs. That is, they are administered in an inactive form and are activated by reductive cleavage of the endoperoxide ring (see reviews [4–6,50]). The resulting free radicals are thought to react with susceptible groups within a range of parasite proteins and other components, leading to cellular damage and killing. When applied in vitro as clinically relevant short pulses, ARTs are significantly more active against trophozoite-stage parasites than against the mid-ring stage [14]. This likely reflects, in part, the higher availability of iron-containing activators (as a result of hemoglobin degradation) at the trophozoite stage. However, the rate and efficiency of parasite killing will also depend on the ability of the parasite to defend itself against cellular damage. In this work, we have undertaken a careful analysis of the response of K13 wild-type and mutant parasites to ART at different stages and at different exposure times with a view to understanding and overcoming the resistance mechanism.
We found that at low concentrations of DHA, we were able to distinguish cytostatic effects (growth inhibition) from cytotoxic activity (which renders the parasites incapable of reproducing). Interestingly, while different stages and strains of P. falciparum exhibit very different levels of sensitivity to killing by DHA, the IC50 values for induction of the growth effects are similar. This is consistent with the suggestion that cytostatic effects are initiated as soon as the toxic insult is detected, with downstream killing, if and when the cell defense systems are overwhelmed.
The observed growth retardation is reminiscent of the cellular stress responses reported in other organisms. For example, oxidative and non-oxidative stress events activate an unfolded protein response, leading to shut-down of protein translation and other metabolic pathways [51,52]. While P. falciparum appears to lack the genes for a classical unfolded protein response [53], it possesses a functional ubiquitin-proteasome system [33,54] and has been shown to undergo eIF2-α–mediated arrest of protein translation, leading to stalling of growth [55].
Consistent with this, we found that the level of ubiquitinated proteins is increased upon exposure to an ART insult, indicating that activated ART damages proteins and initiates a stress response that engages the ubiquitin-proteasome system. By contrast, exposure to a lethal pulse of an anti-folate inhibitor (WR99210) had no effect on the level of ubiquitination. Because ARTs are very short-lived in vivo, the growth stasis that is induced by ART exposure would buy time for the proteasome to degrade ubiquitinated proteins, enabling survival until the ART concentration has declined.
Resistance could arise as a result of decreased ART activation or via the mitigation of downstream damage. ART-sensitive and-resistant Pailin strains show similar Km values for ART-induced killing and similar IC50 values for growth inhibition, indicating similar rates of ART activation. By contrast the extended lag phase before onset of killing and the delayed conversion to the pyknotic state exhibited by K13 mutants, as well as the lower levels of ubiquitinated proteins and the synergism with proteasome inhibitors, are consistent with the suggestion that an enhanced cellular stress response underlies resistance. We present a possible model for ART action and the cell stress response in Fig 8, in which death occurs when the level of damage overwhelms the parasite's proteasome system.
As predicted by this model, we found that proteasome inhibitors, such as epoxomicin, Carlifzomib, and Bortezomib, markedly synergize the action of DHA. In the laboratory strain 3D7 this effect is particularly marked in the mid-ring stage of development, when the parasite shows low sensitivity to DHA, but is not evident in the very early ring stage, when the parasite exhibits ART hypersensitivity. In the K13 mutant strains, PL1, 5, and 7, the synergism is particularly marked at the very early ring stage when these parasites are especially resistant to DHA. In an effort to distinguish the role of the K13 gene product from other contributing genetic differences, we examined the level of synergism in a Cambodian K13 mutant (Cam 3.II) and a genetically matched K13 wild-type (reverted) transfectant. The revertant shows very early ring stage sensitivity that is similar to that that observed for the Pailin wild-type strain (PL2). Epoxomicin markedly synergized the activity of DHA against the very early ring stage of the K13 mutant, but also increased its activity against the revertant. This suggests that a proteasome-engaging cell stress response is involved in protecting both sensitive and resistant parasites from the action of ARTs, but that this response is more effective in the K13 mutant parasites. Our data suggest that proteasome inhibitors could provide a synergistic combination with ARTs that would enhance their effectiveness against both K13-mutant and wild-type parasites.
K13 shares some sequence similarity with KEAP1 and KLHL8, which are involved in E3 ubiquitin ligase complexes that regulate the cytoprotective and developmental responses in mammalian systems [56]. A recent transcriptomic analysis showed up-regulation of the unfolded protein response in K13 mutant parasites, including genes involved in protein folding, unfolded protein binding, protein export, post-translational translocation, signal recognition particle, endoplasmic reticulum retention sequences, the proteasome, and the phagosome [32]. Taken together with our data, this strongly indicates a role for enhanced proteostasis mechanisms as the basis for ART resistance in P. falciparum.
The transcriptomic analysis of Mok et al. also suggested that, at a population level, resistant parasites exhibit decelerated development at the ring stage. We found that parasites (e.g. PL1 and PL2) that exhibit similar lifecycle profiles (as judged by Giemsa analysis) can have very different responses to ARTs. This indicates that while decelerated development may be a contributor, other factors are also important. Further work is required to fully elucidate the role of K13 in enhancing the cell stress response.
Importantly, our work suggests that a proteasome inhibitor could be used to synergize the activity of ARTs in vivo, and potentially to overcome resistance. Carfilzomib and Bortezomib are FDA-approved for the treatment of multiple myeloma [57] and inhibitors that specifically target the plasmodial proteasome have been identified [39,58,59]. These compounds show low toxicity in human cell lines [59] and limited toxicity in mice [40,58]. In agreement with previous reports, we find that Carfilzomib shows only weak antimalarial activity, when used alone, against the ring-stage of a mouse model of malaria [40]. In contrast we observed marked synergism of the action of DHA by Carfilzomib, against P. berghei in vivo, particularly in reducing the parasite burden in reticulocytes, the preferred host blood cell. While further work is needed to determine the efficacy of proteasome inhibitors as ART-synergizing-agents in patients, this work offers a potential avenue to overcome ART resistance.
We analyzed the concentration and exposure-time—dependence of the response of K13 mutant and wild-type parasites in terms of the CED model, enabling for the first time prediction of in vivo parasite clearance profiles from in vitro assessments of ART sensitivity. Our modelling indicates that slower in vivo clearance of resistant strains may reflect both decreased killing of parasites and a slower rate of clearance of parasites that have been rendered unviable. Prolonged circulation of unviable (but morphologically unchanged) ring-stage parasites will complicate the analysis of Giemsa-stained peripheral blood smears. This should be considered in the analysis of parasite clearance curves in surveillance studies. While current methods for monitoring clearance curves are adequate for detecting reduced sensitivity of infecting parasites, they largely reflect parasite killing during the first treatment dose and may not be suitable for evaluating the effectiveness of new antimalarials or alternative treatment regimens. For example, our simulations predicted that splitting the ART dose would substantively decrease the level of viable parasites but would have no effect on parasite clearance curves. This suggests that a recent clinical study of a split-dose regimen [49] may have underestimated its effectiveness.
Methods enabling direct monitoring of parasite viability after ART treatment are needed. Our simulations suggest that a suitable parameter to measure is the fraction of circulating viable parasites at a time point 3 h after the commencement of treatment. Our data suggest that culturing drug-exposed ring-stage parasites in a drug-free environment for 35 h will enable the ready distinction of the viable (trophozoites) from non-viable (rings and pyknotic) parasites and readily distinguish K13 wild-type from mutant parasites. We anticipate that this approach can be made field-adaptable by culturing washed blood collected from patients 3 h after treatment. Assessment of parasites with a trophozoite-like morphology after 30–40 h in culture would provide a robust and direct measure of the likelihood of treatment failure.
Another important implication of our findings is that extended ACT treatment courses could reduce the viable parasite load to curative levels in ART-resistant falciparum malaria, since further parasite maturation on the fourth day of treatment will render them much more sensitive to ART. This is consistent with recent trial data [13] showing 97.7% efficacy of a six-day treatment course, and adds further support to calls for urgent testing of extended treatments in affected areas. The implementation of new treatment regimens could help battle ART resistance. This is critical, given that a recent modelling study suggest that even a 30% ACT failure rate worldwide would result in more than 116,000 additional deaths per year, and US$385 million of annual productivity losses [60].
P. falciparum isolates were collected from adult patients enrolled in clinical trials conducted between 2009 and 2010 in Pailin Referral Hospital in western Cambodia as described previously [21]. Strains were adapted to cultivation in vitro in RPMI supplemented with glutamine and 10% human serum. K13 propeller genotyping of strains were performed as previously described [21]. Parasite lines were expanded and aliquots frozen. All analyses were performed within 4 wk of thawing. All strains are independent isolates based on typing of MSP1, MSP2, and GLURP, and all exhibit a PfCRT mutant genotype (CVMNT), as typical for this region [21,61]. A slow-clearance isolate from Pursat province in Cambodia (RF967/ Cam3.II), harboring the R539T mutation, and a cloned reverted line (Cam3.IIrev), carrying the wild-type allele, were generated as described elsewhere [20]. In vitro culturing, including generation of very tightly synchronized cultures, was carried out as previously described [14,28]. All data presented pertains to cultures synchronized to a 1-h window. The parasite ages defined correspond to the average post-invasion (p.i.) age at the beginning of each drug pulse. Parasite age distributions during the schizont-to-ring transition were quantitated from Giemsa smears by periodically examining the number of schizonts and rings of untreated cultures during the transition, as previously described [14].
Genomic DNA was extracted using the ISOLATE II Genomic DNA kit (Bioline, United Kingdom). Genomic DNA (200 ng) was sheared using an ultrasonicator, and Illumina TruSeq Nano DNA library preparation was carried out according to the Manufacturer’s instructions. The libraries were pooled and run with paired-end 300 bp reads on a MiSeq platform over 600 cycles of sequencing using the MiSeq Reagent kit v3 (Illumina, United States). All raw reads have been submitted to the European Nucleotide Archive (ENA) under the accession number PRJEB8074. Following adapter trimming, the raw reads were mapped to the reference 3D7 genome (version 3.0) using BWA mem and filtered for mapping quality score of at least 30. Duplicate reads were marked and removed using SAMtools and Picard, and the reads were realigned around INDELS using Genome Analysis Tool Kit (GATK) RealignerTargetCreator (Broad Institute, US). Following recalibration of base quality scores, SNPs were called using GATK UnifiedGenotyper, and hard-filtering of SNPs was performed to obtain high-quality variants. SNPs that failed any of the following cut-off filters were removed from the analysis: depth of read <5, variant quality as function of depth QD <2.0, strand bias (P) <10-6, mapping quality <40, MappingQualityRankSum <−12.5, ReadPosRankSum <−8.0. Genetic variants were annotated using snpEFF.
Drug pulse assays used to estimate LD50 and Vmin have been described previously [14,28]. Parasite viability following a drug pulse is defined as the fraction of the parasite population that survives drug exposure and is able to enter the next parasite cycle. Viability was determined by measuring the parasitemia in the parasite cycle following the drug pulse. For this, parasites were fluorescently labelled with the RNA-binding dye SYTO-61 and the parasitemia quantitated by flow cytometry [62]. Viability was calculated in relation to parasitemia in the "untreated parasite" control (parasites not exposed to drug) and "kill" control cultures. The latter refer to parasites maintained under constant drug pressure (>100 times the LD50(48 h)) for 48–96 h to ensure quantitative killing of parasites. The sensitivity limit for viability using this assay is 5%. LD50 is the drug concentration producing 50% viability. Vmin is defined as the viability at saturating drug concentration and was established by examining viability at the highest drug concentration employed in a particular assay.
SYTO-61 labelling of parasites was also used to measure growth effects related to drug treatment. Such measurements require labelling to be performed when the untreated parasite control culture has progressed to mid-–late trophozoite stage in the cycle following the drug pulse. This ensures that the SYTO-61 signal from viable parasites exhibiting growth effects show a measurable decrease compared to the no drug control. The SYTO-61 frequency histogram at a particular drug concentration was corrected for the presence of unviable/dead parasites by subtracting the appropriate amount of the "kill" control histogram based on the measured viability at that drug concentration. Corrected SYTO-61 signals refer to the median value of the viable parasite population and were calculated from the corrected histograms, and are only reported for sample measurements exhibiting the following criteria: parasitemia >0.3%, number of parasites measured >500, and fraction of total parasites measured that are viable >0.7.
Trophozoite-infected RBCs (25–35 h) or uninfected RBC (3% hematocrit) were incubated with 6 μM QHS or 20 nM WR99210 for 90 min at 37°C. Cells were pelleted, washed three times in PBS supplemented with anti-protease mixture (APM), 20 mM N-ethylmaleimide (NEM), 2 mM PMSF, 0.5 mM EDTA, and complete mini EDTA-free protease inhibitor mixture (Roche). Cell pellets were resuspended in 10 volumes of 0.15% (w/v) saponin in PBS for 10 min on ice. Cells were pelleted and washed twice with PBS + APM. Parasite pellets were subjected to SDS-PAGE (4%–12% acrylamide; Life Technologies), transferred to nitrocellulose (iBlot, Life Technologies), and probed with polyclonal rabbit anti-ubiquitin IgG (Z 0458, Dako, 1:100 dilution in PBS), followed by goat anti-rabbit IgG coupled to horseradish peroxidase. Chemiluminescence was detected using a LAS-3000 Imaging System. Membranes were stripped with 0.2 M glycine, 0.1% (w/v) SDS, 0.01% (v/v) Tween-20, pH 2.2, and re-probed with rabbit anti-PfGAPDH [63]. Initial control experiments compared the signal in uninfected RBCs and very early ring-stage—infected (1–2 h p.i.) RBCs (5% parasitemia). Densitometric analysis of the region of the gel between 80 and 200 kDa was performed using ImageJ software. The data were corrected for background and for loading based on the PfGAPDH signal.
Mice were housed with strict temperature control (21°C) and under a 12:12 light:dark cycle. All mouse experiments were approved by the Animal Ethics Committee at Macquarie University (ARA 2012/018) and conformed to the National Health and Medical Research Council guidelines. For rodent malaria infection, 250 μl of thawed P. berghei ANKA parasitized blood were intraperitoneally injected onto SJL donor mice. Once the donor mice reached 5%–15% parasitemia, the mice were exsanguinated and the blood was diluted in Kreb’s buffered saline [64] at a dose 1x106 parasitized RBCs. The parasitized RBCs were injected into the peritoneal cavity of BALB/c female mice. After inoculation, the mice were monitored daily or twice daily using tail bleed smearing or flow cytometry using JC-1 dye gated on TER119, cd71, and cd45 [65]. For the drug administration, infected BALB/c mice (~21 g) were treated with intravenous doses of Carfilzomib (0.5, 1, and 1.5 mg/kg), intra-peritoneal doses of DHA (5, 10, 15, and 20 mg/kg), a combination of Carfilzomib and DHA (5 or 10 mg/kg of Carfilzomib, with respectively 5 and 10 mg/kg of DHA), or vehicle (10 mM citrate buffer, 20% Kleptose for Carfilzomib and 60% dimethyl sulfoxide, 40% Polysorbate 80 for DHA). The mice were drug-injected for three consecutive days, starting day 3 post-inoculation. A Student’s one tailed t test was performed and corrected with a Bonferroni procedure for multiple testing to investigate the statistical differences between drug treatments.
Parasite viability (V) as a function of drug concentration (C) and drug exposure time (te) were analyzed according to the CED model [14]:
V(C,te)=[1+(Ctet50e,sat(Km+C))γ]−1
where Km is the drug concentration resulting in half the maximum effective dose, γ is the slope of the sigmoidal function, and t50e,sat is the minimum time required to kill 50% of the parasites (at saturating drug concentrations). The model was fitted to the data, using Microsoft Excel with the Solver add-in. The method employed for simulating parasite load during a three-day course of ART monotherapy is presented in S1 Appendix. An Excel spreadsheet for the simulation of parasite clearance is also supplied (S1 Spreadsheet).
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10.1371/journal.pbio.1001218 | Lhx2 and Lhx9 Determine Neuronal Differentiation and Compartition in the Caudal Forebrain by Regulating Wnt Signaling | Initial axial patterning of the neural tube into forebrain, midbrain, and hindbrain primordia occurs during gastrulation. After this patterning phase, further diversification within the brain is thought to proceed largely independently in the different primordia. However, mechanisms that maintain the demarcation of brain subdivisions at later stages are poorly understood. In the alar plate of the caudal forebrain there are two principal units, the thalamus and the pretectum, each of which is a developmental compartment. Here we show that proper neuronal differentiation of the thalamus requires Lhx2 and Lhx9 function. In Lhx2/Lhx9-deficient zebrafish embryos the differentiation process is blocked and the dorsally adjacent Wnt positive epithalamus expands into the thalamus. This leads to an upregulation of Wnt signaling in the caudal forebrain. Lack of Lhx2/Lhx9 function as well as increased Wnt signaling alter the expression of the thalamus specific cell adhesion factor pcdh10b and lead subsequently to a striking anterior-posterior disorganization of the caudal forebrain. We therefore suggest that after initial neural tube patterning, neurogenesis within a brain compartment influences the integrity of the neuronal progenitor pool and border formation of a neuromeric compartment.
| The thalamus is the interface between the body and the brain. It connects sensory organs with higher brain areas and modulates processes such as sleep, alertness, and consciousness. Our knowledge about the embryonic development of this central relay station is still fragmented. Here, we show that the transcription factors Lhx2 and Lhx9 are essential for the development of the relay thalamus. Zebrafish embryos lacking Lhx2/Lhx9 have stalled neurogenesis - neuronal progenitor cells accumulate but do not complete their differentiation into thalamic neurons. In addition, we find that the neighboring Wnt-expressing epithalamus expands into the space containing mis-specified thalamus in these embryos. We identified a thalamus-specific cell adhesion modulator, Pcdh10b, which is controlled by canonical Wnt signaling. Altered Wnt-dependent Pcdh10b function in Lhx2/Lhx9-deficient embryos leads to intermingling of the thalamus and adjacent brain compartments and consequently regionalization within the caudal forebrain is lost. Organization of the developing CNS into molecularly distinct but transient segments and the implications for regional differentiation are well established for the developing hindbrain. We conclude that this applies to caudal forebrain too: Lhx2 and Lhx9 emerge as crucial factors driving neurogenesis and maintaining the regional integrity of the caudal forebrain. These are two prerequisites for the formation of this important relay station in the brain.
| Segmentation is a fundamental step during vertebrate brain development. It involves patterning of the cranial neural tube into distinct and segregated transverse units aligned serially along the longitudinal axis [1]. The most important prerequisite for segmentation are borders between the successive neuromeres to allow individual regionalization, growth, and acquisition of distinct functional identity. This process may be hindered in an embryonic brain by the fact that it rapidly increases in size and complexity. Molecular mechanisms underlying segmentation have been studied during development of the relatively simple hindbrain region [2],[3]. Expression patterns of many regulatory genes also suggest a neuromeric organization of the embryonic forebrain [4],[5]. Recent studies support a segmental forebrain bauplan with three prosomeres (P1–P3) (reviewed in [1]). Based on morphology and gene expression the alar plate of the diencephalon is divided into the prethalamus (P3), thalamus (P2), and pretectum (P1). The epithalamus including epiphysis and habenular nuclei are part of P2. The border between prethalamus and thalamus is defined by compartment borders with the interposed narrow region known as the zona limitans intrathalamica (ZLI). Extracellular cell adhesion proteins such as Tenascin within the ZLI have been suggested to mediate lineage restriction between the ZLI and the anteriorly adjacent prethalamus and posteriorly adjacent thalamus [6]–[8]. Similarly, the diencephalic-mesencephalic border (DMB), at the posterior limit of the pretectum, has been identified as a compartment boundary where, in addition to Tenascin, an Eph-ephrin dependent mechanism has been suggested to maintain cell segregation [6],[9],[10]. Recent fate mapping studies suggest that the border between the thalamus and the pretectum may also be lineage restricted [11]. However, little is known about a possible mechanism leading to cell lineage restriction between these compartments. The embryonic thalamus (P2) becomes subdivided into two molecularly distinct domains: the rostral thalamus (rTh) marked by expression of the proneural gene Ascl1 and the caudal thalamus (cTh), which expresses Neurog1 [12]–[14]. In tetrapods, the rTh contributes to the majority of the GABAergic neurons in the thalamus including ventral lateral geniculate (vLGN) and intergeniculate leaflet (IGL), whereas the caudal thalamus gives rise to predominately glutamatergic nuclei projecting to the pallium [15]–[17].
LIM homeobox (Lhx) genes regulate developmental processes at multiple levels including tissue patterning, cell fate specification, and growth [18]. These selector genes act as highly similar and highly conserved paralogs. They show a restricted expression pattern in the developing caudal forebrain in frog and mouse; Lhx1/Lhx5 mark the rTh and the pretectum, whereas expression of the Apterous group of Lhx2/Lhx9 is confined to the cTh [19]–[22]. In the mouse, Lhx2 function is required for the acquisition of neuronal identity in different regions such as the telencephalon and nasal placode [23],[24]. In the cortex, Lhx2 is required to limit the adjacent cortical hem, which expresses BMP as well as canonical Wnts. Both signaling pathways orchestrate hippocampal development [25],[26]. This suggests that Lhx2-mediated neurogenesis is involved in maintaining the integrity of cortex. In the diencephalon, the Lhx2/Lhx9 positive cTh is also enriched in Wnt signaling pathway components in monkeys [27]. Correspondingly, this region is located next to sources of canonical Wnt ligands at the mid-diencephalic organizer (MDO), the signal-generating population in the ZLI, and at the diencephalic roof plate [8],[28]. Although the arrangement of these two Wnt positive organizers and the Lhx2/Lhx9 expression pattern in the adjacent Wnt receiving tissue is similar to that in the cortex, our knowledge on their function during diencephalon development is still lacking. During early patterning, Wnt signaling was suggested to have an influence on induction of the thalamus [29]–[31], but the function of Wnts during regionalization remains unclear.
After initial anterior-posterior patterning of the neural tube during gastrulation, it is believed that brain segments develop largely independently. Here we show that Lhx2 and Lhx9 are redundantly required to drive neurogenesis in the zebrafish thalamus. Furthermore, we show that neuronal differentiation mediated by Lhx2/Lhx9 has an impact on maintenance of the thalamus boundaries. Lhx2/Lhx9 restrict the expression of the cell adhesion factor Pcdh10b to the thalamus and therefore sustain the thalamus as a true developmental compartment. Thus, Lhx2/Lhx9 is required for proper development of the thalamus, the core relay station in the brain, and for the integrity of the entire caudal forebrain.
In zebrafish, the Apterous group of LIM genes contains three members: lhx2a, lhx2b, and lhx9 [32]. Lhx2a is expressed only in the early-born olfactory relay neurons [33], whereas Lhx2b resembles the expression pattern of Lhx2 as described in other model organisms. To facilitate species comparison, Lhx2b is named as Lhx2 throughout the article.
To explore neuronal differentiation in the thalamus, we examined the expression dynamics of lhx2 and lhx9 at early stages of caudal forebrain development (Figures 1 and S1). We detect expression of lhx9 in the diencephalon first at 30 hpf (primordial stage 15; Figure 1a, asterisk), while at 42 hpf (high-pec stage), the lhx9 expression domain broadens and an overlapping domain of lhx2 expression becomes apparent (Figure 1b). At 48 hpf (long-pec stage), lhx2 and lhx9 are co-expressed in the thalamus (Figure 1c, asterisk). This expression is maintained at later stages (Figure S1). A cross-section validates the overlap of Lhx2 and Lhx9 positive cells, predominantly laterally in thalamic neuroepithilium (Figure 1c′).
At 48 hpf, lhx9 expression is in proximity to, but with a distinct separation from, the Shh-positive MDO and basal plate (Figure 1d,d′). In order to determine the fate of cells in this shh and lhx9 negative domain, we cloned the zebrafish homolog of the hey-like transcription factor (helt). Helt has been described as a specific marker of the prospective GABA interneurons of the rostral thalamus (rTh), pretectum, and midbrain [34],[35] and is required for the formation of these interneurons in the mouse mesencephalon [36]. The expression domain of helt abuts the rostral, ventral, and caudal extent of the lhx9 expression domain (Figure 1e,e′). Complementary to the helt expression, we find an overlap with glutamatergic neurons marked by vglut2.2 at 3 dpf (Figure S1). This suggests that lhx9 marks the caudal thalamus (cTh) and is absent in the GABAergic rTh and pretectum in zebrafish. The ßHLH factor neurogenin1 is strongly expressed in an intermediate layer of the neuroepithelium of the cTh, most likely the subventricular zone (Figure 1f,f′). Expression of neurog1 abuts the expression of lhx9 in the cTh. The medial part of the lhx9 expression domain overlaps with the expression of the differentiation marker id2a (Figure 1g,g′). The expression domain of the thalamus-specific post-mitotic neuronal marker lef1 [16],[37] overlaps entirely with lhx9 (Figure 1h,h′). The dorsal limit of the Lhx9 domain is adjacent to that of Wnt3a, a marker of the central epithalamus (Figure 1i,i′). Nevertheless, the lhx9 expression domain overlaps with the expression of the Wnt target axin2 in the diencephalic alar plate (Figure S1), suggesting that Wnt expression at the epithalamus/MDO might be required to activate the Wnt signaling cascade in the thalamic territory.
Thus, we can define Lhx2/Lhx9 as a marker for post-mitotic neurons of the thalamic mantle zone in zebrafish at 48 hpf.
At 48 hpf key markers for neurogenesis in the zebrafish brain are expressed in a pattern representing best comparability with amniote brains [38]. Therefore, we chose this stage for the following analyses. To address the function of Lhx2 and Lhx9 in the developing caudal thalamus, we used an antisense Morpholino-based knock-down strategy (Figure S2). Neither lhx2−/− zebrafish mutant embryos (beltv24) [39] (n = 13) nor single morphant embryos for either lhx2 or lhx9 (n = 29) are visibly distinguishable from uninjected wild type embryos (Figure S2) similar to the situation in the Lhx2 knock-out mouse. However, lhx2/lhx9 double morphant embryos showed significant disruption of thalamic structure (Figure 2). This is consistent with their overlapping expression domains in the diencephalon (Figure 1) and suggests a functional redundancy within the Apterous group during caudal thalamus development. Therefore, we focused on an approach to reduce both Lhx2 and Lhx9 messages simultaneously by generating double morphant embryos. In addition, we analyzed the lhx9 knock-down morphant in the zebrafish lhx2 mutant background. To define the step in thalamic neuronal differentiation that is dependent on Lhx2/Lhx9 function, we analyzed the expression of the following set of thalamus-specific markers: the neurogenic marker deltaA [40], the ßHLH factor neurog1, marking early thalamic progenitors [41], a regulator of neuronal differentiation id2a, and a marker for mature thalamic neurons lef1 [16],[42], the caudal thalamus-specific homeobox gene gbx2 [43],[44], and the pan-neuronal marker elav-like 3 (formerly Hu antigen C) [45]. These markers can be allocated to three layers in a neuroepithelium in zebrafish: the ventricular proliferation zone (VZ) is positive for deltaA, the intermediate or subventricular zone (SVZ) zone is marked by neurog1, and the post-mitotic mantle zone (MZ) by elavl3 [38].
At 48 hpf, we observe a lateral expansion of the deltaA positive ventricular zone in lhx2/lhx9 morphant embryos (36/54; Figure 2a–b′). Likewise, the expression of the proneural factor neurog1 (n = 18) in the subventricular zone expands laterally (Figure 2c–d′). Consequently, the expression of the post-mitotic thalamic neuronal markers id2a (19/31) and lef1 (13/20) is significantly reduced (Figure 2e–h). Interestingly, the Shh-dependent homeobox transcription factor gbx2 (n = 25) as well as the Wnt mediator tcf7l2 show no alteration in compound morphant embryos (Figures 2i,j′, S3). The pan-neuronal marker elavl3 is decreased in the mantle zone (3/5; Figure 2k,l). This suggests that DeltaA and Neurog1 positive thalamic progenitors need Lhx2/Lhx9 function to proceed with neuronal differentiation (Figure 2m,n).
To validate our knock-down strategy and to restrict our analysis temporally and spatially to the thalamus after 24 hpf, we adapted the electroporation technique to the zebrafish system. We were thereby able to deliver DNA unilaterally into the neural tube by pulsed electric stimulation at 24 hpf (Figure 3a) and analyze the thalamus at 48 hpf (Figure 3b). Electroporation of EGFP DNA leads to neither molecular nor morphological alteration of the forebrain/midbrain area (Figure 3c,d; n = 15). Based on previous experiments, we asked if Lhx2 function is sufficient for the induction of post-mitotic thalamic neurons in the Lhx2/Lhx9-double-deficient embryos. Therefore, we re-introduced Lhx2 function unilaterally in the thalamus of Lhx2/lhx9 morphant embryos at 24 hpf corresponding to the endogenous onset of Lhx2 expression (Figure 1). At 48 hpf, the loss of id2a (7/19), lef1 (3/15), and Elavl3:GFP (8/15) expression within the thalamus of lhx2/lhx9 morphant embryos was restored in the electroporated hemisphere at 48 hpf (Figure 3f,h,j). It seems that the laterally expanded epithalamus of morphant embryos can be restored in the electroporated hemisphere (arrowheads).
Therefore, we conclude that Lhx2/Lhx9 function is crucial for neurogenesis in the caudal thalamus. Furthermore, Lhx2 alone can compensate for the loss of Lhx2 and Lhx9, suggesting a redundant function between these paralogs during thalamic neurogenesis. Finally, local electroporation is a valid tool to validate the specificity of a knock-down approach in zebrafish.
In the next set of experiments we analyzed the consequence of Lhx2/Lhx9 deficiency on adjacent tissues: the mid-diencephalic organizer (MDO) and the embryonic epithalamus (ETh). We find that in morphant embryos the expression domain of lmx1b.1, a marker for the MDO and the Eth, expands ventro-posteriorly into the thalamus at 36 hpf (31/36; Figure 4a–b′). Similarly, the expression domains of wnt3a (89/141) and wnt1 (8/11) also expand (Figures 4c–d′, S3). A cross-section reveals that the wnt3a expression is induced ectopically lateral to the habenula, presumably in the thalamic territory (Figure 4d′, arrow) although the forming habenula remains wnt3a negative [46]. To test whether the expanded Wnt expression affects thalamic development, we first monitored Wnt activity in the diencephalon. Here, we analyzed the expression pattern of the pan-canonical Wnt target gene axin2 at 24 hpf, 48 hpf, and at 72 hpf. As expected, we were not able to detect expansion of axin2 expression prior to onset of Lhx2/Lhx9 expression in the thalamus (Figure S3). From 48hpf, axin2 expression is progressively increased in the thalamus of Lhx2/Lhx9-deficient embryos (35/53; Figures 4e–f′, S3). We confirmed these results using a Wnt reporter zebrafish line 7×TCFsiam:GFP, which expresses GFP under the control of seven repetitive TCF-responsive elements driving a minimal promoter. The GFP expression is detectable around known canonical Wnt sources in the diencephalon—that is, the MDO/ETh area (Figure 4g,h). Lhx2/Lhx9 morphant embryos show expanded GFP expression in the thalamus (23/35; Figure 4g′,h′). In summary, we find that the knock-down of Lhx2/Lhx9 in zebrafish embryos results in an expansion of the epithalamic expression domain of Wnt ligands. This leads to an enhancement of Wnt signaling in the diencephalon, predominantly in the subjacent thalamus.
To address the consequences of the loss of Lhx2/Lhx9 and the subsequent upregulation of Wnt signaling on the integrity of the caudal diencephalon, we analyzed the expression pattern of regionally expressed cell adhesion factors in the caudal forebrain.
We find that the expression of the cell adhesion molecule, protcadherin10b (pcdh10b), starts in the cTh during late somitogenesis (Figure S4). At 48 hpf, pcdh10b is predominantly expressed in the progenitor layer, non-overlapping with the post-mitotic lhx2/lhx9 positive neurons (Figure 5a,a′). The expression domain of pcdh10b abuts dorsally the expression domain of the epithalamus including the wnt3a expression domain (Figure 5b,b′) and posteriorly with the domain of the pretectal marker gsx1 (Figure 5c,c′). Thus, pcdh10b marks specifically caudal thalamic progenitors at 48 hpf.
To investigate the functional interaction between Lhx2/Lhx9 and Pcdh10b, we electroporated lhx2 DNA unilaterally into the caudal diencephalon. Overexpression of Lhx2 proved to be sufficient to inhibit pcdh10b expression in the ventricular zone of the thalamus (16/36; Figure 5c,c′). Furthermore, the thalamic expression domain of pcdh10b in lhx2/lhx9-deficient embryos expands into the mantle zone of the cTh (17/23, Figures 5d′,e′, S4). This suggests a repressor function of Lhx2 on pcdh10b expression. Interestingly, and beyond a direct repressor effect in situ, pcdh10b also expanded posteriorly into the normally Lhx2/Lhx9 negative pretectum (Figure 5d,e).
How do we explain this non-autonomous expansion of pcdh10b following knock-down of Lhx2/Lhx9? We wondered whether this could be linked to increased Wnt signaling in the diencephalon of Lhx2/Lhx9-depleted embryos. Therefore, we altered canonical Wnt signaling by treating embryos with small molecule effectors of the Wnt signaling pathway such as the activator, BIO (a GSK3ß inhibitor) [47]. To mimic the situation in lhx2/lhx9 morphant embryos, and to avoid gross malformation due to altered patterning during gastrulation, we started ectopic activation of Wnt signaling at 16 hpf and treated the embryos up to 48 hpf. In treated embryos we see ectopic induction of axin2 expression at 48 hpf (Figure S4), an expansion of pcdh10b expression into the pretectum (30/36; Figure 5f,f′) similar to the outcome from Lhx2/Lhx9 depletion. In BIO treated embryos, the expression pattern of the principal signal of the MDO, shh, and the patterning marker pax6a are unaltered excluding pleomorphic effects of the treatment (Figure S4).
Following these results, we analyzed the expression of pcdh10b in embryos carrying a mutation in the Wnt pathway inhibitor Axin1 [48]. Although axin1 mutants lack most of the telencephalon and the eyes (Figure S4), we find an enlarged expression domain of pcdh10b in the cTh at 48 hpf (Figure 5g,g′). Accordingly, we treated embryos with the Wnt signaling antagonist IWR-1 (a tankyrase inhibitor, Figure S4) [49] from 16 hpf to 48 hpf. Inhibition of Wnt signaling exhibits a decrease of pcdh10b expression (55/58; Figure 5h,h′). To validate these results, we used a heatshock inducible transgenic fish line to overexpress the canonical Wnt antagonist Dickkopf1, Dkk1 (Figure S4) [50],[51] at 10 hpf. Indeed, we find a similar decrease of pcdh10b expression (Figure 5i,i′). This effect is seen before, but not after, endogenous pcdh10b induction, suggesting that Wnt signaling is required for induction of pcdh10b but not for its maintenance (Figure S4).
To dissect the regulatory contribution of Lhx2/Lhx9 and Wnt signaling to pcdh10b expression, we reduced Wnt3a function in Lhx2/Lhx9-deficient embryos (Figure 5j,j′). Interestingly, here we do not find the posterior expansion of the pcdh10b expression domain into the pretectum (40/84; Figure 5i). However, we still observe the expansion of pcdh10b into the neuronal layer (40/84; Figure 5i′).
In summary, these data suggest that Wnt signaling, most likely by Wnt3a, induces expression of pcdh10b in the caudal thalamus and Lhx2/Lhx9 are able to limit pcdh10b expression to the progenitor zone (Figure 5k). Furthermore, ectopic upregulation of Wnt signaling is able to induce pcdh10b expression also in the ventricular zone of the pretectum.
To study the consequences of altered Pcdh10b levels in the developing caudal forebrain, we analyzed the maintenance of the border zone between thalamus and pretectum in Lhx2/Lhx9 morphant embryos and Pcdh10b-deficient embryos (Figure 6 and Figure S5). We used five different sequential approaches from the onset of neuronal differentiation at 42 hpf to the formation of a mature thalamus at 4 dpf.
Firstly, we analyzed thalamus-specific GFP expression in the Gbx2:GFP transgenic zebrafish line (Figure 6a–c′) [52]. In embryos deficient for Lhx2/Lhx9, we observe that GFP-positive cells in the ventricular zone of the pretectum become detached from the Gbx2:GFP positive thalamus (8/14; Figure 6b,b′, white arrow), suggesting the loss of lineage restriction at the thalamus/prectectum boundary and the spread of thalamic cells into the pretectum. Assuming this to be the case, we next asked if different levels of pcdh10b are required to maintain lineage restriction at this border. Therefore, we interfered with Pcdh10b function by using a Morpholino antisense approach for Pcdh10b [53]. In pcdh10b morphant embryos we find Gbx2:GFP positive cells ectopically in the pretectal progenitor layer (18/25; Figure 6c,c′, white arrows).
Secondly, we examined the separation of thalamic and pretectal domains by the regional expression of the transcription factors lhx9 and gsx1 (Figure 6d–f′). Knock-down of Lhx2/Lhx9 (11/16) or Pcdh10b (46/73) leads to significant intermingling of lhx9 positive thalamic cells and gsx1 positive pretectal cells (Figure 6e–f′, white arrows).
Thirdly, considering the relay thalamus being mainly glutamatergic whereas the central pretectum remains mainly GABAergic, we looked at the localization of the ßHLH factors Tal1 and Neurog1. Tal1 marks the inhibitory neurons of the rTh and pretectum, whereas glutamatergic progenitors express Neurog1 [13]. To achieve single-cell resolution, we analyzed the offspring of a Tal1-GFP transgenic line crossed to a Neurog1-RFP transgenic line. We find the specification of ectopic Tal1 positive neurons in the territory of the caudal thalamus in Lhx2/Lhx9 double morphant embryos as well as in Pcdh10b-deficient embryos (Figure 6h–i′).
Fourthly, we analyzed the expression of Gad1, a marker of inhibitory GABAergic neurons by fluorescent ISH at 3 dpf (Figure 6j). In both Lhx2/Lhx9-deficient embryos (4/8) and pcdh10b morphant embryos (6/10) gad1 positive cells are mis-located within the glutamatergic caudal thalamic domain (Figure 6k–l′; white arrows, Figure S5).
Fifthly, we studied the anatomy of the caudal forebrain by analyzing areas of clustered cell nuclei at 4 dpf. In wild type embryos, we observe demarcations between prethalamus and thalamus (the ZLI), between the thalamus and the pretectum, and between the pretectum and the midbrain (the diencephlic-mesencephlic border; DMB) (Figure 6m). The observed anatomical compartition correlates with the described genetic profile of these territories (Figure S5). In lhx2/lhx9 morphant embryos, the demarcation between the thalamus and pretectum is not detectable, although the ZLI and the DMB are unaltered (Figure 6n). In pcdh10b morphant embryos, we are not able to identify the boundary between pretectum and thalamus (Figure 6o), while the ZLI and DMB are still visible. We hypothesize that similar adhesive properties in the thalamus and in the pretectum lead to a loss of separation of these brain parts. Thus, we conclude that a Pcdh10b positive thalamus and a Pcdh10b negative pretectum are required to establish a border between these compartments.
The molecular mechanisms that control the orderly series of developmental steps leading to mature thalamic neurons are poorly understood. Although numerous transcription factors are specifically expressed in the thalamus [14], only a few have been functionally characterized such as Gbx2, Neurog2, and Her6. Gbx2 knock-out mice show disrupted differentiation of the thalamus by the absence of thalamus-specific post-mitotic neuronal markers Id4 and Lef1, and subsequently lack cortical innervation by thalamic axons [44]. Although Neurog2-knock-out mice show a similarly severe failure in neuronal connectivity to the cortex, the expression of Lhx2, Id2, and Gbx2 is unchanged in these mice, suggesting that in the absence of Neurog2 thalamic neurons are not re-specified at the molecular level [54]. In contrast, Her6 regulates the thalamic neurotransmitter phenotype by repressing neurog1 function and subsequently the glutamatergic lineage. By contrast, Her6 function is a prerequisite for Ascl1a-positive interneuron development in the GABAergic rostral thalamus [13].
Here, we investigate the function of conserved Lhx2 and Lhx9 expression during thalamic development. Lim-HD genes form paralogs such as Lhx1 and Lhx5, and Lhx2 and Lhx9 [18]. These pairs have been implicated in various aspects of forebrain development. Lhx1/Lhx5 influence Wnt activity by promoting the expression of the Wnt inhibitors sFRPs. This local Lhx-mediated Wnt inhibition is required in the extra embryonic tissue for proper head formation [55] and establishment of the prethalamus [31]. The Apterous group, Lhx2 and Lhx9, is required for multiple steps during neuronal development. Lhx2 is required in mouse for maintenance of cortical identity and to confine the cortical hem, allowing proper hippocampus formation in the adjacent pallium [26],[56]. However, Lhx2 function during diencephalic development is still under debate. Although the Apterous genes are already present in the nervous system of the cephalochordate Amphioxus—that is, AmphiLhx2/9 [57]—and co-expression of Lhx2 and Lhx9 has been documented in the diencephalon of vertebrates, such as zebrafish (here), Xenopus [20],[22], and mouse [21], their function in the thalamus has remained unclear. Recent studies of Lhx2 mutant mice showed no alteration during thalamic neuronal regionalization [58]. Furthermore, the function of Lhx9 has not been described, but the expression pattern suggests a role during forebrain development and possibly in parcellation of the thalamus [21].
Here, we show that single knock-down of Lhx2 or Lhx9 has no diencephalic phenotype with the markers analyzed (Figure S2), comparable to the Lhx2 knock-out mouse, but that simultaneous knock-down of both Lhx2 and Lhx9 leads to stalling of thalamic neurogenesis at the late progenitor stage (Figure 2). Furthermore, the activation of Lhx2 alone is sufficient to compensate for the loss of both Lhx2 and Lhx9 (Figure 3). Our results suggest that Lhx2 is functionally redundant to Lhx9 to ensure proper thalamic development. In contrast to other vertebrates, zebrafish embryos show co-expression of Lhx2 and Lhx9 in the telencephalon until 48 hpf (Figure 1), which could again suggest redundancy [32]. Indeed the pallium is less affected in the lhx2−/− mutant fish compared to loss of the neocortex in Lhx2−/− mutant mice [39],[59]. Furthermore, in the Lhx9 negative nasal placode, the knock-out of Lhx2 has been shown to lead to a similar neuronal arrest [24],[60].
In the thalamus, Lhx2/Lhx9 may regulate genes that are essential to complete neuronal development, such that neurons do not reach the terminal neuronal stage. In Lhx2/Lhx9 morphant embryos, we find that the expression of deltaA, neurog1, as well as pcdh10b is increased. During neuronal development in fish, Neurog1 has been shown to activate delta genes directly by binding several E-box motives in the delta promoter region [40]. This suggests that in Lhx2/Lhx9 morphant embryos, neuronal progenitor development is arrested at the level of deltaA/neurog1 expression. Consistently, terminal thalamic neuronal markers such as Id2a and Lef1 are absent in Lhx2/Lhx9 morphant embryos. Interestingly, both of these markers have been shown to be activated by Wnt signaling [61],[62]. Although local Wnt activity is upregulated locally in the lhx2/lhx9 morphant embryos, these target genes are not transcribed, suggesting that Lhx2/Lhx9 thalamic neuronal differentiation is coupled to a second competence phase for Wnt signaling. Also, the late and restricted onset of Lhx2/Lhx9 expression in the thalamus and their requirement for Id2a and Lef1 expression may explain the thalamic neuronal specificity of the Wnt target lef1. Thus, we propose that Lhx2/Lhx9 are essential determinants for cells to reach the late stage of thalamic neuronal development.
In the spinal cord, Lim HD factors together with ßHLH factors have been shown to be required for cell cycle exit [63]. The Lim containing factor Isl-1 and Lhx3 together with the ßHLH factors Neurog2 and NeuroM act in a combinatorial manner to directly trigger motor neuron differentiation. In the thalamus, we find a similar process: Lhx2/Lhx9 inhibit the expression of progenitor markers such as pcdh10b and activate the expression of postmitotic differentiation markers such as id2a, lef1, and elavl3. Interestingly, proper differentiation of thalamic neurons is required to restrict the MDO and dorsal roof plate (Figure 7), a finding that reflects the conversion of neocortex in Lhx2 knock-out mice. Here, the Gdf7 positive cortical hem expands at the expense of the neocortex [23]. This supports the hypothesis that proper neuronal differentiation is required to maintain brain compartments and their borders.
In the mid-diencephalon, the central source of patterning cues is the MDO. Here, three different signaling pathways merge: Shh, Fgf, and Wnt [64]. Shh signaling has been shown to induce proneural genes such as Ascl1 in the rostral thalamus and Neurog1 in the caudal thalamus (cTh) [12],[13],[65] and a set of transcription factors assigning specific properties to the developing thalamic cells [14],[21],[66]–[68]. Furthermore, Fgf signaling influences the development of the rTh [69] and parts of cTh, the motor learning area [70]. Interestingly, although the mid-diencephalon expresses a set of canonical and non-canonical Wnt ligands and receptors [27],[28], the function of Wnt signaling is not clear. Wnt signaling seems to be required for mediating thalamic identity in chick embryonic explants [29] and mutation of the Wnt co-receptor Lrp6 leads to a severe reduction of thalamic tissue in mice [30].
Here, we show that Wnt signaling from the MDO and the roof plate influence compartition of the caudal diencephalon. The canonical Wnt signaling pathway plays a pivotal role in mediating adhesiveness and the key effector of the Wnt pathway, β-catenin, was initially discovered for its role in cell adhesion [71],[72]: it promotes adhesiveness by binding to the transmembrane, Ca2+-dependent homotypic adhesion molecule cadherin, and links cadherin to the intracellular actin cytoskeleton. Although several classes of molecules are involved in morphogenetic events, cadherins appear to be the major group of adhesion molecules mediating formation of boundaries in the developing CNS [73]. After a phase of ubiquitous expression, cadherins display a very distinct expression pattern in the neural tube [74]. In the developing diencephalon, classical cadherins, such as Chd2, Chd6b, and Chd7, mark presumptive nuclear gray matter structures within developmental compartments [75]. Still, these studies so far are not able to explain the different compartition in the caudal forebrain.
Here, we describe the expression pattern of the non-clustered protocadherin, pcdh10b, in the developing diencephalon and show that it marks the ventricular zone of the thalamus at mid-somitogenesis (Figure S5). During somitogenesis, pcdh10b modulates cell adhesion and regulates movement of the paraxial mesoderm and somite segmentation [53]. We find that the border of pcdh10b expression co-localizes with the border between thalamus and pretectum during diencephalic regionalization (Figure 5). Furthermore, we could link Pcdh10b expression to canonical Wnt signaling. In chick, some hallmarks of lineage restriction for the border between thalamus and pretectum have been observed previously; for example, vimentin and chondroitin sulfate proteglycans are strongly enriched at this border. Similar to the anatomical observation in fish (Figure 6j–l), the chick neural tube shows a morphological ridge where interkinetic movement is disrupted [6]. However, there are conflicting data from direct analyses of cell lineages in the caudal chick forebrain regarding cell compartment borders between thalamus and pretectum [6],[76]. This may be explained by the different stages of analysis. In other vertebrate models, Pcdh10 expression has been reported only at later stages in development, in chicken HH28, and in mouse E15 [77],[78], arguing against a comparable role in these model organisms. However, Pcdh10 together with Pcdh8, 12, 17, 18, and 19 belong to a structurally related subfamily, the non-clustered δ2 protocadherins, and several members indeed show an expression pattern during somitogenesis in mouse [79]. Although we have not carried out direct lineage restriction experiments by tracing small cell clones at the border, we suggest that the thalamic area intermingles with the pretectum when both areas express similar levels of this adhesion molecule (Figure 7). Our data are supported by the fact that pcdh10b knock-down or overexpression also lead to a similar phenotype in somite development [53]. Similarly in Gbx2 knock-out mice, thalamus cells start to intermingle with pretectum cells [11]. Interestingly, these authors observe a non-cell autonomous function for this transcription factor and claim a restriction mechanism mediated by an unknown cell adhesion factor. We suggest that, as for Lhx2/Lhx9, Gbx2 is required for the acquisition of proper neuronal identity and the lack of Gbx2 may lead to a similar sequence of events—that is, expansion of the Wnt-positive roof plate and alteration in pcdh10b expression. This hypothesis should be tested in the Gbx2 knock-out mouse. Notably, as pcdh10b is also expressed in hindbrain rhombomeres [80] its function should be determined during differentiation in this well-studied segmented part of the neural tube; should compartment formation in the caudal forebrain and hindbrain turn out to involve similar molecular effectors, we may reach a unifying mechanism for compartition of the neuraxis—whether it be in the generation of single units (thalamus, pretectum) or iterated modules (rhombomeres).
Thus, we suggest that Lhx2/Lhx9 is required for neurogenesis within the thalamus and is important to maintain longitudinal axis patterning of the CNS also at later stages. Alteration of neurogenesis in a brain part affects the development of the neighboring parts and thus leads to loss of the integrity over compartment boundaries.
Breeding zebrafish (Danio rerio) were maintained at 28°C on a 14 h light/10 h dark cycle [81]. To prevent pigment formation, embryos were raised in 0.2 mM 1-phenyl-2-thiourea (PTU, Sigma) after 24 hpf. The data we present in this study were acquired from analysis of wild-type zebrafish of KCL (KWT) and of the ITG (AB2O2) as well as the transgenic zebrafish lines; tal1:GFP [82], hs-dkk1:GFP [51], elavl3:GFP [83], GA079:RFP [84], shh:RFP, neurog1:RFP [41], gbx2:GFP [52], and the belladonna zebrafish mutant line with a loss of lhx2 [39] and masterblind mutant line carrying a mutation in axin1 [48]. In bel/lhx2 mutants, a 22 bp deletion in the third exon causes a frame-shift and therefore a stop codon after the second LIM domain. Embryos were staged [85] and ages are listed as hours post fertilization (hpf).
Transient knock-down of gene expression was performed as described in [13]. We used the following Morpholino-antisense oligomeres (MO, Gene Tools) at a concentration of 0.5 mM: lhx2 MO (5′-GCT TTT CTC CTA CCG TCT CTG TTT C-3′), lhx9 MO (5′-AGG TGT TCT GAC CTG CTG GAG CCG T-3′), wnt3a MO [86], and pcdh10b MO [53]. The injection of MO oligomers was performed into the yolk cell close to blastomeres at one-cell or two-cell stage. For electroporation, embryos were manually dechorionated and mounted laterally in 1.5% low melting-point agarose at 24 hpf. We locally injected 0.5 µg/µl GAP43-GFP DNA solution or 1 µg/µl pCS2+lhx2 DNA [32] solution in the III brain ventricle. The positive charged anode was positioned on top of the diencephalon, whereas the negative cathode was positioned underneath the diencephalon (Figure 3). For electroporation, we used a platinum/iridium wire with a 0.102 mm diameter (WPI Inc.). During the electroporation procedure the embryo was kept in 1× Ringer as conductive fluid. We used the stimulator CUY21 (Nepa Gene Ltd.) with the following stimulation parameters: 24 V voltage square wave pulse, 4 ms pulse length, 2 ms pulse interval, delivered three times. Settings are based on the published electroporation approaches in [87].
To manipulate Wnt signaling in vivo, we used BIO [47] ((2′Z,3′E)-6-Bromo-indirubin-3′-oxime, TOCRIS Bioscience or IWR-1 [49]; SIGMA) as pharmacological agonist and antagonist of the Wnt signaling pathway. For Wnt signaling analyses, embryos were dechorionated at 16 hpf (15–17-somite stage) and incubated with 4 µM of BIO in 1% DMSO, 40 µM IWR-1 in 0.2% DMSO, or with 1% DMSO only.
Prior to staining, embryos were fixed in 4% paraformaldehyde/PBS at 4°C overnight for further analysis.
Whole-mount mRNA in situ hybridizations (ISH) were performed as described in [88]. Antisense probes were generated from RT-PCR products for the following probes with primer pairs (forward/reverse): lhx2b, 5′-AGT GCG TCT CAC GGA AAT CT-3′/5′-GCA TCC ATG ATC GGT CTT CT-3′; lhx9, 5′-CGT TGG AGA AAG TGG ACT GG-3′/5′-TGG TGA AGA ATT CCG ATC AA-3′; sema3d, 5′-GCT GCA GAA ATC TCC TCG TC-3′/5′-ATT TTG CAC AAG TGG GCA TT-3′; helt, 5′-CCA AAA AGC TCG CCT TTA ATC-3′/5′-AAC ATA TTA AGA CGT ATT TAC AGA GCA-3′; lmx1b.1, 5′-GAC AAC AGC CGG GAT AAA AA-3′/5′-CCA TCC GAT TGG ACA TTA CC-3′.
The expression pattern and/or antisene RNA probes have been described for shha (formerly known as shh; [89]), gsx1 [90], pax6a [91], gbx2 [92], axin2 [46], lef1 [93], wnt3a [94], dla [95], id2a [96], lmx1b.1 [97], pcdh10b [53], gad1 (gad67) [17], and vglut2.2 [98].
Post-ISH, embryos were re-fixed in 4% paraformaldehyde/PBS at 4°C overnight and transferred to 15% sucrose/PBS and kept for 8 h at 4°C. For embedding, embryos were transferred to a mould filled with 15% sucrose/7.5% gelatine/PBS at 42°C for 10 min. The moulds were kept overnight at 4°C, frozen in liquid nitrogen on the following day, and stored at −80°C until required. Frozen blocks were sectioned coronal with 16 µm thickness on the cryostat.
To reveal neurons that have initiated axogenesis, we used a monoclonal antibody against acetylated tubulin (Sigma, T-6793) in a concentration of 1∶20 as described in [88].
For visualizing cell nuclei, embryos were fixed in 4% paraformaldehyde/PBS at room temperature for 2 h and transferred in 1× PBS. Fixed brains were hemisected and incubated in 25 µM SYTOX nucleic acid stain (Invitrogen) overnight. After washing in 1× PBS brains were mounted laterally for confocal imaging analysis.
Prior to imaging, embryos were deyolked, dissected, and mounted in 70% (v/v) glycerol/PBS on slides with cover slips. Images were taken on Olympus SZX16 microscope equipped with a DP71 digital camera by using the imaging software Cell A. For confocal analysis, embryos were embedded for live imaging in 1.5% low-melting-point agarose (Sigma-Aldrich) dissolved in 1× Ringer's solution containing 0.016% tricaine at 48 hpf. Confocal image stacks were obtained using the Leica TCS SP5 X confocal laser-scanning microscope. We collected a series of optical planes (z-stacks) to reconstruct the imaged area. Rendering the volume in three dimensions provided a view of the image stack at different angles. The step size for the z-stack was usually 1–2 µm and was chosen upon calculation of the theoretical z-resolution of the 40× objective. Images were further processed using Imaris 4.1.3 (Bitplane AG).
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10.1371/journal.pntd.0006036 | Complex antigen presentation pathway for an HLA-A*0201-restricted epitope from Chikungunya 6K protein | The adaptive cytotoxic T lymphocyte (CTL)-mediated immune response is critical for clearance of many viral infections. These CTL recognize naturally processed short viral antigenic peptides bound to human leukocyte antigen (HLA) class I molecules on the surface of infected cells. This specific recognition allows the killing of virus-infected cells. The T cell immune T cell response to Chikungunya virus (CHIKV), a mosquito-borne Alphavirus of the Togaviridae family responsible for severe musculoskeletal disorders, has not been fully defined; nonetheless, the importance of HLA class I-restricted immune response in this virus has been hypothesized.
By infection of HLA-A*0201-transgenic mice with a recombinant vaccinia virus that encodes the CHIKV structural polyprotein (rVACV-CHIKV), we identified the first human T cell epitopes from CHIKV. These three novel 6K transmembrane protein-derived epitopes are presented by the common HLA class I molecule, HLA-A*0201. One of these epitopes is processed and presented via a complex pathway that involves proteases from different subcellular locations. Specific chemical inhibitors blocked these events in rVACV-CHIKV-infected cells.
Our data have implications not only for the identification of novel Alphavirus and Togaviridae antiviral CTL responses, but also for analyzing presentation of antigen from viruses of different families and orders that use host proteinases to generate their mature envelope proteins.
| The arboviral pathogen Chikungunya virus (CHIKV) is a serious threat to global health, and is considered a priority re-emerging virus. This pathogen causes acute febrile infection in patients, leading to debilitating arthralgia and arthritis. In recent years, CHIKV has spread quickly in tropical and subtropical countries, causing outbreaks of more severe forms of the disease than previously reported. The nature and function of the T cell immune response, critical for clearance of viral infections, is largely unknown during acute and chronic CHIKV disease and their association with rheumatic disorders. In this study, we identified the three first CHIKV epitopes recognized by human T cells. We studied how one of these epitopes is generated in virus-infected cells, a process that involves the sequential proteolytic activity of several proteases at distinct subcellular locations. We postulate that this process could have broad implications when applied to other viral proteins.
| The mosquito-borne Chikungunya virus (CHIKV), a member of the Alphavirus genus of the Togaviridae family, causes an acute febrile infection in patients that leads to debilitating arthralgia and arthritis. Identified in the former Tanganyika territory in 1952 [1–3], this arboviral pathogen caused numerous epidemics in Africa and Asia from the 1960s–1980s [4, 5]. Following several decades of relative inactivity, CHIKV re-emerged in 2005 to cause an explosive epidemic in the Indian Ocean area, mainly on Reunion Island. In this French overseas department, the outbreak affected about half of its 700,000 inhabitants, with more than 250 deaths [5]. In 2006, several million people were infected by this virus in another large outbreak in India [6]. In recent years, this infectious disease has spread quickly from Africa and Asia to the Americas [7], causing outbreaks in tropical and subtropical countries of more severe forms than previously reported [8,9]. Morbidity due to CHIKV infection is a serious threat to global health and this virus is considered a priority emerging pathogen [10].
CHIKV is an enveloped virus with a positive-sense, single-stranded RNA genome that encodes two large polyproteins [11]. The nonstructural P1234 precursor is autocatalytically processed by the C-terminal domain of the nonstructural protein 2 (nsP2) and releases the four multifunctional nsP proteins. In contrast, in maturation of the structural polyprotein, viral and host proteases are both involved in producing capsid, E1, E2, and E3 envelope and 6K transmembrane proteins [11].
Although the immune mechanisms involved in CHIKV disease are not fully understood, CHIKV-infected humans show CD8+ T lymphocyte responses in early disease stages [12]; a large percentage of these activated CD8+ T cells can be detected more than 7 weeks postinfection in patient blood samples [13]. The nature and function of CD8+ T cells during acute and chronic CHIKV infection is largely unknown, as is their association with rheumatic disorders. Although the importance of the HLA class I-restricted immune response has been hypothesized [14], to date, no human T cell epitope has been described in CHIKV infection.
In cellular immunity, CD8+ T lymphocytes recognize short viral peptides exposed at the membrane of infected cells [15]. Most of these epitopes are generated by proteolytic degradation of the fraction of newly synthesized viral proteins whose sequence or folding are in some way defective (defective ribosomal products; DRiP) and are thus degraded immediately by the combined action of proteasomes and other cytosol degradative peptidases [16]. The antigen processing products are translocated to the endoplasmic reticulum (ER) lumen by transporters associated with antigen processing (TAP), where N-terminal trimming by the ER aminopeptidase (ERAP) is frequently necessary [17,18]. Some of these final peptides might bind the human histocompatibility complex (human leukocyte antigen; HLA) class I heavy chain and β2-microglobulin. The stable trimolecular peptide-HLA-β2-microglobulin complexes are then exported to the cell surface for cytotoxic T lymphocyte (CTL) recognition [15]. In addition to this classical antigen processing pathway, several alternative routes have been described that contribute to endogenous HLA class I-restricted antigen processing (reviewed in [19]). During maturation of the viral structural polyprotein, the short CHIKV 6K transmembrane protein is efficiently cleaved by the host ER signal peptidase, rendering it a possible source of viral epitopes via alternative pathways. To search for CHIKV 6K protein T cell epitopes, we infected HLA-A*0201-transgenic mice with a recombinant vaccinia virus that encodes the CHIKV structural polyprotein; we identified three epitopes presented by the HLA class I molecule, one of which is processed and presented in a pathway that involves proteases from distinct subcellular locations.
H-2 class I knockout HLA-A*0201-transgenic mice [20], a versatile animal model for the study of viral and cancer antigen processing and presentation by the human major histocompatibility complex, were bred in the animal facilities at Centro Nacional de Microbiología, Instituto de Salud Carlos III, in strict accordance with the recommendations in the Guide for the Care and Use of Laboratory Animals of the Spanish Comisión Nacional de Bioseguridad of the Ministerio de Medio Ambiente y Medio Rural y Marino (accreditation n° 28079-34A). The protocol was approved by the Research Ethics and Animal Welfare Committee of the Carlos III Health Institute (permit n°: PI-283). All surgery was performed under isoflurane anesthesia, and all efforts were made to minimize suffering.
The murine cell line RMA-S (TAP negative) transfected with HLA-A*0201 α1α2 domains, and the mouse H-2Db α3 transmembrane and cytoplasmic domains have been described [21]. The cell line was cultured in RPMI 1640 medium supplemented with 10% fetal bovine serum (FBS) and 5 μM β-mercaptoethanol (β-ME).
A vaccinia virus (VACV) Western Reserve (WR) strain expressing the CHIKV structural genes (rVACV-CHIKV) was constructed by inserting the capsid (CP), E3, E2, 6K and E1 structural genes of CHIKV clone LR2006-OPY1 into the TK locus of the WR genome [22]. The rVACV-CHIKV virus expresses the same CHIKV structural genes as those in the reported MVA-CHIKV vaccine candidate [22]. The WR strain used as the parental vector to generate rVACV-CHIKV is an optimized attenuated WR with deletions in the vaccinia immunomodulatory genes A48R, B19R and C11R (manuscript in preparation). CHIKV structural gene expression is under the transcriptional control of the viral synthetic early/late promoter. The rVACV-CHIKV virus was generated, grown in primary chicken embryo fibroblast cells and purified through two 36% (w/v) sucrose cushions. Correct CHIKV gene insertion was confirmed by PCR and sequencing, and correct CHIKV protein expression was analyzed by western blot. rVACV-CHIKV was free of contamination with mycoplasma, bacteria or fungi.
Peptides were purchased from Biomatik (Cambridge, Ontario, Canada). The correct molecular mass and composition of the peptides at >90% purity was established by quadrupole ion trap micro-high performance liquid chromatography (HPLC).
Brefeldin A (BFA) and all protease inhibitors were purchased from Sigma-Aldrich (Saint Louis, MO, USA), with the exception of lactacystin (from Dr. E.J. Corey, Harvard University, Cambridge, MA, USA), leupeptin (Amersham, Little Chalfont, Bucks., UK), pepstatin (Boehringer Mannheim, Mannheim, Germany), and Z-VAD-FMK (Enzyme System Products, Livermore, CA, USA). The specificity of inhibitors used is summarized in Table 1.
SYFPEITHI software (http://www.syfpeithi.de/Scripts/MHCServer.dll/EpitopePrediction.htm) was used to predict HLA-A*0201-specific ligands of the 61-residue CHIKV 6K protein.
Two synthetic peptides were used as positive and negative controls in complex stability assays, VACV A10L688-696 (ILDRIITNA, HLA-A*0201-restricted) [23] and CMV pp657-15 (RCPEMISVL, HLA-C*01-restricted) [24], respectively. HLA-A*0201 RMA-S transfectants were incubated in RPMI 1640 medium with 10% heat-inactivated FBS (16 h, 26°C). Cells were washed and incubated in the same medium (2 h, 26°C) with different peptide concentrations, further incubated (2 h, 37°C), and collected for flow cytometry. HLA levels were measured using the PA2.1 monoclonal antibody (anti-HLA-A*02; Abnova, Taipei, Taiwan), as described [25]. Samples were acquired on a FACSCanto flow cytometer (BD Biosciences, San Jose, CA, USA) and analyzed with FlowJo software (TreeStar Inc, Ashland, OR, USA). The fluorescence index (FI) was calculated as the ratio of the mean channel fluorescence of the sample to that of control cells incubated without peptides. Peptide binding was also expressed as the EC50, which is the molar concentration of peptides that produces 50% maximum fluorescence in a concentration range between 0.001 and 100 μM.
Polyclonal CHIKV 6K peptide-monospecific CD8+ T cell lines were generated by immunizing transgenic mice with 107 plaque-forming units (PFU) of rVACV-CHIKV [26]. Splenocytes from immunized mice were restimulated in vitro with mitomycin C-treated spleen cells pulsed with 10−6 M peptide and cultured Minimum Essential Medium (Alpha modification; α-MEM) with 10% FBS, 10−7 M peptide and 5 μM β-ME. Recombinant human interleukin-2 used for long-term propagation of peptide-specific CD8+ T cell lines was generously provided by Hoffmann-LaRoche (Basel, Switzerland).
Freshly prepared bone marrow cells were cultured in 200 U/ml GM-CSF (granulocyte-macrophage colony-stimulating factor; PeproTech, London, UK), which was renewed on days 3 and 6. After 7 days, nonadherent cells with a typical dendritic cell (DC) morphology and a myeloid DC phenotype (MHC class II+, CD11c+, CD8−) were collected as described [27].
ICS assays to detect recognition of peptide-pulsed or infected DC from HLA-A*0201-transgenic mice by polyclonal CTL cell lines were performed as reported [28]. Briefly, CD8+ T cell lines were stimulated (4 h) in the presence of 5 μg/ml BFA and of target DC previously infected with VACV-WR strain or rVACV-CHIKV (16 h). Cells were then incubated with FITC-conjugated anti-CD8 monoclonal antibody (mAb; ProImmune, Oxford, UK; 30 min, 4°C), fixed with Intrastain kit reagent A (DakoCytomation, Glostrup, Denmark), and incubated with phycoerythrin (PE)-conjugated anti-interferon (IFN)γ mAb (BD PharMingen, San Diego, CA, USA) in Intrastain kit permeabilizing reagent B (30 min, 4°C). Events were acquired and analyzed as for MHC/peptide stability assays.
When protease inhibitors were used, all drugs were added 15 min before the virus and maintained at a 2-fold higher concentration during the 1-h adsorption period than during infection. After washing the virus inoculum, inhibitors were maintained at indicated concentrations for individual experiments. The inhibitors were not toxic at these concentrations, as they did not affect antigen presentation by the VACV D12I251-259-specific CD8+ T cell line.
To analyze statistical significance, an unpaired Student t test was used. P values <0.05 were considered significant.
The epitope prediction tool SYFPEITHI, a reverse immunology algorithm for MHC ligand motifs [29], was used to identify possible candidate HLA-A*0201-binding peptides from CHIKV 6K protein. The five nonamers and three decamers ranked as potential HLA-A*0201 ligands (score >20) are depicted in Fig 1.
To study the binding ability of the eight predicted peptides to the HLA-A*0201 molecule, we performed MHC-peptide complex stability assays using HLA-A*0201-transfected, TAP-deficient RMA-S cells. Four peptides (6K31-39, 6K37-46, 6K45-54, 6K51-59) were bound to the HLA-A*02:01 class I molecules (Fig 2), with EC50 values in the range commonly found among natural high-affinity ligands such as the VACV A10L HLA-A*0201 epitope. In contrast, HLA affinity was substantially lower for 6K22-30 and 6K22-31 peptides, and both were considered medium-affinity ligands (Fig 2). 6K21-29 peptide binding to HLA-A*02:01 was residual, with a EC50 value >200 μM (Fig 2). Stable numbers of HLA-peptide surface complexes were not detected with the 6K28-36 peptide (Fig 2). These data suggest that most of these peptides could be presented by the HLA-A*02:01 molecule in CHIKV-infected cells.
In contrast to HLA-B*0702 transgenic mice, in which strong ex vivo VACV-specific T cell responses were detected [28], peptide-specific IFNγ-secreting cells from VACV-immunized HLA-A*0201 transgenic mice were usually detected only after in vitro stimulation. The cause of these differences is unclear, especially as both transgenic mouse types were generated in the same laboratory [20,30].
From rVACV-CHIKV-immunized HLA-A*0201 transgenic mice, we produced polyclonal CTL lines monospecific for each of seven CHIKV 6K peptides with stable numbers of HLA-peptide surface complexes detected in MHC-peptide complex stability assays (Table 2). The CTL lines stimulated with three of the four HLA-A*0201 high-affinity peptides (6K31-39, 6K45-54, 6K51-59) specifically recognized peptide-pulsed DC (Fig 3). There was no specific recognition of peptide-pulsed cells by the other four CHIKV 6K peptides (6K21-29, 6K22-30, 6K22-31, and 6K37-46; Table 2); this lack of response was confirmed using several immunization and in vitro stimulation protocols (not shown).
These data indicated that CHIKV 6K31-39, 6K45-54, and 6K51-59 peptides are HLA-A*0201-restricted CTL epitopes, and were recognized simultaneously as part of the memory response to rVACV-CHIKV. The small (61-residue) CHIKV 6K protein thus contains at least three distinct HLA-A*0201-restricted epitopes, two of which overlap partially.
As the three CHIKV 6K viral epitopes derive from the same 6K protein, we studied the CD8+ CTL line specific for the CHIKV 6K51-59 epitope as a representative of antigen processing of this viral protein. The CHIKV 6K51-59 epitope-specific CD8+ CTL line specifically recognized rVACV-CHIKV- but not wild type VACV-infected cells, while another T cell line specific for VACV D12I peptide 251–259 recognized both infected cells (Fig 4).
CHIKV 6K is a structural protein necessary both for virus budding and entry, which is incorporated in small amounts into the virion [11]. As rVACV-CHIKV expresses the five structural proteins of the pathogen, we cannot rule out the presence of CHIKV virus-like particles and possible exogenous antigen presentation. To test whether the CHIKV 6K51-59 HLA-A*0201-restricted epitope requires endogenous processing, we analyzed its presentation in the presence of BFA. Brefeldin A blocks class I export beyond the cis-Golgi compartment [31,32], preventing surface expression of newly assembled HLA class I-peptide complexes of endogenous origin (Table 1 summarizes the specificity of all inhibitors used). BFA addition during infection completely inhibited specific IFNγ secretion by the CHIKV 6K51-59 epitope-specific CD8+ T cell line (Fig 5), which demonstrated that this epitope was generated from CHIKV 6K protein endogenously processed in rVACV-CHIKV-infected cells. We also observed complete inhibition of specific IFNγ secretion by the VACV D12I251-259 epitope-specific CD8+ T cell line (Fig 5).
To study the antigen processing pathways involved in endogenous generation of the CHIKV 6K51-59 epitope, we performed ICS assays with several specific protease inhibitors on rVACV-CHIKV-infected cells. We tested E64 [33], leupeptin (LEU) [34], pepstatin (PEPST) [34,35], 1,10-phenanthroline (PHE), and phenylmethylsulfonyl fluoride (PMSF) [36] inhibitors, as they are specific for different protease families and cover a wide range of protease classes (Table 1). None of these inhibitors affected specific recognition of rVACV-CHIKV-infected target cells by the CHIKV 6K51-59-specific CD8+ T cell line (Fig 6). The enzymes inhibited by these drugs are thus not involved in generation of this epitope.
We also tested specific inhibitors of several cellular proteases, most of which were not relevant for antigen processing of the CHIKV 6K51-59 viral epitope (Fig 7). In contrast, dec-RVKR, an inhibitor of furin and other proprotein convertases (Table 1), partially inhibited CHIKV 6K51-59-specific CD8+ T cell recognition of infected cells (43 ± 20%; Fig 7). To exclude the possibility that this inhibition was due to toxic effects on target cells or on VACV replication rather than to a specific protease block, we performed parallel experiments using the rVACV-CHIKV-infected target cells with another T cell line. These infected cells were recognized efficiently by the VACV D12I251-259-specific CD8+ T cell line, and no inhibition was detected (4 ± 6%; Fig 7). These data indicate that the dec-RVKR-induced inhibition of specific recognition by CHIKV 6K51-59-restricted CD8+ T cells was due to protease blockade and not to nonspecific effects. These data indicate that proprotein convertases are involved in the generation of the CHIKV 6K epitope.
The inhibitor puromycin (PURO) [37] (Table 1) partially blocked specific recognition of rVACV-CHIKV-infected target cells by CHIKV 6K51-59-specific CD8+ T cells (47 ± 21%), but had no effect on VACV D12I251-259 epitope presentation (4 ± 6%) (Fig 8). PURO is a reversible inhibitor of the cytosol alanyl aminopeptidase and of lysosomal DPPII. To identify the specific peptidase involved in CHIKV 6K51-59 peptide processing, we treated rVACV-CHIKV-infected target cells with additional inhibitors. CHIKV 6K51-59-specific CD8+ T cell recognition was unaffected by two distinct inhibitory compounds that block cytosol alanyl aminopeptidase activity, bestatin (BEST) and EDTA (ethylenediaminetetraacetic acid) (Table 1 and Fig 8), which excludes this cytosolic enzyme from antigen processing of the CHIKV 6K51-59 epitope. The antimalarial drug chloroquine (CQ), a lysosomotropic agent that affects DPPII and other lysosomal enzymes (Table 1), nonetheless blocked recognition of infected cells by CHIKV 6K51-59-specific CD8+ T cells (69 ± 19%; Fig 8). These data indicate that DPPII is involved in CHIKV 6K epitope generation.
The inhibition of antigen recognition by dec-RVKR (Fig 7) or PURO (Fig 8) indicated that furin-like proteases and DPPII peptidase are both involved in antigen presentation of the CHIKV 6K51-59 epitope. The similar partial inhibition of rVACV-CHIKV-infected cell recognition by both drugs (dec-RVKR, 43 ± 20%; PURO, 47 ± 21%) is compatible with two explanations. The CHIKV 6K51-59 epitope might be processed sequentially by the two proteases. Alternatively, this epitope could be processed in parallel by proprotein convertases or by DPPII independently; in this case, both antigen processing pathways would have to be inhibited simultaneously to fully abrogate CHIKV 6K51-59 epitope presentation. To discriminate between these possibilities, we analyzed the effect on antigen presentation of the combined inhibitors on rVACV-CHIKV-infected cells. We observed a moderately increased blockage of presentation in target cells treated simultaneously with PURO and dec-RVKR (66 ± 6%; Fig 9), comparable and not statistically different to that observed when CQ and dec-RVKR were combined (71 ± 18%; Fig 9) or with CQ alone (69 ± 19%; Fig 8). The inhibitory effect of PURO and dec-RVKR was CHIKV 6K epitope-specific, as recognition of the VACV D12I251-259 epitope was not reduced in their presence (Fig 9). These results show that furin-like proteases and DPPII are found in the same CHIKV 6K51-59 epitope presentation pathway.
To test whether the classical antigen processing pathway is involved in CHIKV 6K51-59 epitope generation, we used the proteasome inhibitor lactacystin (LC) [38,39], and leucinthiol (Leu-SH), which has activity against ERAP and other metallo-aminopeptidases [40] (Table 1). Both LC (83 ± 3%) and Leu-SH (91 ± 13%) blocked specific recognition of rVACV-CHIKV-infected target cells by CHIKV 6K51-59-specific CD8+ T cells (Fig 10). In contrast, in the same experiment, these drugs had a lesser effect on VACV D12I251-259 epitope presentation (Fig 10).
CHIKV 6K51-59 epitope presentation to a specific T cell line was partially blocked by dec-RVKR (43 ± 20%), PURO (47 ± 21%) or both (66 ± 6%) (Figs 7, 8 and 9), whereas CHIKV 6K51-59 recognition by these CD8+ T cells was strongly inhibited by LC (83 ± 3%) and Leu-SH (91 ± 13%) (Fig 10). These differences were statistically significant (Table 3), which suggested that the CHIKV 6K51-59 epitope is generated by two distinct pathways, the classical antigen processing pathway and a second antigen presentation pathway that includes the four proteases (dec-RVKR, PURO, LC and Leu-SH).
In this study, we undertook identification of HLA-A*0201 epitopes from the CHIKV 6K protein and explored their antigen presentation pathways. Our results define several CHIKV 6K protein restricted epitopes, being to our knowledge the first time that epitopes from CHIKV are defined associated to human MHC class I molecules. Extended epitope prediction using the SYFPEITHI tool suggests that ligands of this small viral protein could be presented by a notable proportion of the HLA class I alleles tested (12 of 30; 40%, S1 Table). According to the Immune Epitope Database (IEDB) population coverage tool (http://tools.iedb.org/population/), these class I molecules are present in 86% of the human population (S2 Table). The short viral CHIKV 6K protein is thus of interest for targeting the cellular immune system. Further studies are needed to analyze cellular immune responses in CHIKV-infected individuals.
Here we identified three HLA-A*0201-restricted epitopes in the CHIKV small 6K protein. Using several protease inhibitors (Table 1), we report that various proteolytic activities (probably in two distinct antigen processing pathways) are necessary to generate one of these epitopes, the CHIKV 6K51-59 epitope. These results are consistent with a model for CHIKV maturation and processing and, by extrapolation, that of other Alphavirus structural polyproteins (Fig 11). Although no furin cleavage motif was found in the 6K protein, 6K51-59 peptide presentation was dependent on dec-RVKR-sensitive proteases, which indicates that proprotein convertase activity is needed to generate this epitope. Like other host and viral proteases [41], furin are involved in processing structural polyproteins in all Alphaviruses to yield the mature structural proteins that will form the virion. Maturation of the CHIKV structural polyprotein thus affects antigen processing of the 6K51-59 epitope.
With regard to the CHIKV replication cycle, only limited information can be extrapolated from comparison between CHIKV structural proteins and those of other Alphaviruses. The translation order of these Alphavirus polyproteins is capsid, PE2 precursor (that includes envelope glycoproteins E3 and E2, 6K, and envelope protein E1) [41]. Immediately after the ribosome starts translation of the PE2 precursor, the capsid protein (whose C-terminal domain has protease activity) is released in the cytosol by autoproteolysis. The new N terminus of the polyprotein thus bears a signal sequence for translocation of the PE2 precursor across the ER membrane. Additional signal sequences in the C terminus of E2 and 6K proteins allow their translocation to the ER. In the ER, signal peptidase cleavage of the C terminus of both the PE2 precursor and the 6K protein releases three viral protein products (PE2, 6K, and E1). The PE2 precursor and E1 protein remain attached to the membrane by their C terminus, and 6K remains as a short transmembrane protein. In the Alphavirus Sindbis virus, E1 and PE2 glycoproteins form a heterodimer in the ER, and this interaction is sufficient for transport beyond this organelle [42]. The E1-PE2 heterodimer reaches the trans-Golgi network, but prior to the cell membrane, CHIKV PE2 is cleaved by furin and other proprotein convertases such as PC5A, PC5B, and PACE4 to generate the mature E2 and E3 proteins [43]. CHIKV 6K51-59 epitope-dependent presentation by dec-RVKR-sensitive proteases thus indicates that CHIKV envelope proteins are transported from the ER to the trans-Golgi network as heterotrimers that also include CHIKV 6K protein, as also described for Semliki Forest virus [44]. In Alphaviruses, this cleavage induces conformational changes in E1 and E2 proteins, thus promoting extensive contacts between these two proteins to yield the spike architecture of activated viral envelope complex in 1:1 stoichiometry [41]. The role of the E3 structural protein is unclear; E3 is associated with virions in Semliki Forest virus [45], but not in other Alphaviruses including CHIKV [46, 47].
In Semliki Forest and Sindbis viruses, substoichiometric amounts of 6K are incorporated into the virion [44,48]. Most of this small protein must thus be discarded in the infected cells, although the fate of the CHIKV 6K protein is nonetheless unclear. As both PURO and CQ impaired antigen recognition of target cells by CHIKV 6K51-59-specific CD8+ T cells, the lysosomal DPPII must have a role in processing this epitope. This data also indicated that at least a fraction of the CHIKV 6K protein must be degraded in the lysosomes.
DPPII-processed CHIKV 6K protein or a fragment that includes the viral epitope must be transported to the cytosol for proteasome processing, as indicated by LC inhibition of the CHIKV 6K51-59 antigen presentation. How these fragments reached the cytosol remains unclear, but the proteasome is involved in the generation of some epitopes of the Epstein-Barr virus (EBV) latent membrane protein 2 (LMP2) transmembrane nucleoprotein, albeit by uncharacterized mechanisms [49]. Host cell transmembrane protein processing might be involved in both CHIKV and EBV epitopes [50].
The block in CHIKV 6K51-59-specific recognition by Leu-SH, but not by two drugs that do not inhibit ERAP activity (the general metalloproteinase inhibitor PHE and the aminoprotease inhibitor BEST) [51,52], indicates that ERAP or a similar metalloproteinase produces the final CHIKV 6K51-59 epitope, probably in the ER, after transport of proteasomal products by TAP.
The statistically different percentages of inhibition by LC and Leu-SH inhibitors vs. dec-RVKR, PURO and CQ drugs also suggest a direct contribution of the classical antigen processing pathway, with proteasome degradation of DRiPs from viral polyprotein followed by ERAP trimming. The relative contribution of both pathways to antigen presentation was quantified using the same percentage of inhibition from one-third to half by the classical antigen processing pathway and half to two-thirds by the circular antigen presentation pathway. Our results show a broad diversity of proteases involved in a complex antigen presentation pathway to yield the viral CHIKV 6K epitope.
In addition to proteasome and ERAP, several proteases are implicated in processing endogenously synthesized HLA class I antigens (reviewed in [19]). Many proteases included here, such as signal peptidase [53,54], furin [55,56], and uncharacterized lysosomal CQ-sensitive enzymes [57,58], have been linked independently to the processing of several epitopes, although sequential activity of these peptidases to generate a specific HLA class I epitope has not been described. These proteases and the supplementary involvement of DPPII in CHIKV 6K51-59 antigen presentation define the most complex antigen processing and presentation pathway reported to date; this route begins in the ER and includes the trans-Golgi network, lysosomes, retrograde transport to cytosol, and ER re-entry.
Lastly, the results reported here also have implications for analysis of the cellular immune response. Only proteasome and ERAP, but not other protease inhibitors, are generally used to analyze antigen presentation of different HLA class I ligands or epitopes. Inhibition is normally sufficient to formally assign presentation of an epitope to the classical antigen processing pathway, excluding additional protease activities (Fig 10). In addition to the Alphavirus genus and the Togaviridae family, however, many viruses of different families and orders use host proteases from distinct subcellular locations to generate mature envelope and even nuclear proteins. In other viral epitopes, it would thus not be unexpected to find complex antigenic processing and presentation pathways similar to those reported here, if the antiviral cellular immune response was analyzed in depth with a broad spectrum of protease inhibitors as was carry out in the current investigation.
In conclusion, the results of the present report highlight the diversity of peptidases involved in HLA class I antigen presentation and expose the complexity of antigen processing pathways, as represented by the CHIKV 6K protein. Definition of the importance of this epitope in natural infection nonetheless awaits studies in CHIKV-infected individuals. This process could have broad implications when applied to other viral proteins.
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10.1371/journal.pntd.0002190 | Fluctuations at a Low Mean Temperature Accelerate Dengue Virus Transmission by Aedes aegypti | Environmental factors such as temperature can alter mosquito vector competence for arboviruses. Results from recent studies indicate that daily fluctuations around an intermediate mean temperature (26°C) reduce vector competence of Aedes aeygpti for dengue viruses (DENV). Theoretical predictions suggest that the mean temperature in combination with the magnitude of the diurnal temperature range (DTR) mediate the direction of these effects.
We tested the effect of temperature fluctuations on Ae. aegypti vector competence for DENV serotype-1 at high and low mean temperatures, and confirmed this theoretical prediction. A small DTR had no effect on vector competence around a high (30°C) mean, but a large DTR at low temperature (20°C) increased the proportion of infected mosquitoes with a disseminated infection by 60% at 21 and 28 days post-exposure compared to a constant 20°C. This effect resulted from a marked shortening of DENV extrinsic incubation period (EIP) in its mosquito vector; i.e., a decrease from 29.6 to 18.9 days under the fluctuating vs. constant temperature treatment.
Our results indicate that Ae. aegypti exposed to large fluctuations at low temperatures have a significantly shorter virus EIP than under constant temperature conditions at the same mean, leading to a considerably greater potential for DENV transmission. These results emphasize the value of accounting for daily temperature variation in an effort to more accurately understand and predict the risk of mosquito-borne pathogen transmission, provide a mechanism for sustained DENV transmission in endemic areas during cooler times of the year, and indicate that DENV transmission could be more efficient in temperate regions than previously anticipated.
| Mosquitoes in the wild are exposed to daily fluctuations in temperature, but in the laboratory, the effect of temperature on vector competence is generally assessed using constant temperatures. Recent studies demonstrate that realistic fluctuations in temperature around an intermediate mean (26°C) can alter life-history traits, population dynamics, and the ability of a mosquito to become infected with and transmit dengue virus (DENV). Here we tested how fluctuations around high and low mean temperatures influence vector competence and the extrinsic incubation period. Small fluctuations around a high mean temperature (∼8°C swings around 30°C) had no detectable effect on vector competence. Large fluctuations around a low mean (∼18°C swings around 20°C) demonstrate that only 18.9 days were required for 50% of DENV-exposed mosquitoes to develop a disseminated infection, compared to 29.6 days at constant 20°C. Twenty-eight days post-exposure to the infectious blood meal, 100% of mosquitoes tested had a disseminated infection under fluctuating temperatures, but under a constant temperature this proportion was only 42%. Reduced duration of extrinsic incubation increases the potential for pathogen transmission. Results indicate that the rate of dengue transmission by mosquitoes in temperate regions with natural fluctuations may be underestimated by experiments conducted under constant temperatures.
| The ability of Aedes aegypti to transmit viruses, in particular dengue viruses (DENV), has long been known to be influenced by temperature [1]–[6]. It is generally assumed that higher mean temperatures facilitate DENV transmission due to faster virus propagation and dissemination within the vector. Vector competence, the probability of a mosquito becoming infected and subsequently transmitting virus after ingestion of an infectious blood meal [7], is generally positively associated with temperature, whereas the duration of the virus extrinsic incubation period (EIP) associates negatively with temperature [6].
The norms of reaction (i.e., phenotypic variation across environmental variation) of vector competence and EIP have been well documented for a large range of temperatures for Ae. aegypti. At high temperatures (26°C and above), DENV dissemination and transmission can be observed in one week or less [5], [6], [8], [9]. Lower temperatures generally extend the duration of EIP [5], [6], [8]; at 21°C and below, the EIP for DENV can be in the order of several weeks [3], [4]. Despite these prolonged incubation periods, DENV-infected Ae. aegypti are capable of transmitting virus under laboratory conditions after incubation at temperatures as low as 13°C [4], and can become infective after incubation under temperature as low as 10°C [8]. Evidence to support an upper thermal threshold for DENV transmission is more limited. There is a well-established link between temperature and many of the life-history traits of Ae. aegypti, with a (population dependent) thermal optimum for development, reproduction and survival [10]. Beyond this, subsequent increases in temperature become detrimental for the mosquito; i.e., immature development rate slows as mortality increases, adult reproductive function is impaired in the high 30°'s, and adult survival declines as temperature continues to rise [11], [12]. Ae. aegypti vector competence for DENV has been detected up to a maximum of 35°C [6], but at temperatures in excess of this, accurately measuring vector competence indices before the mosquito dies is difficult.
What is much less well-documented is the influence of fluctuations in daily temperature on the norm of reaction of vector competence and EIP. Indeed, environmental temperature under natural conditions does not remain constant, but oscillates between a minimum at night and a maximum during daytime. Results from studies using realistic fluctuating temperature profiles support the notion that fluctuating temperatures may alter estimates of both life history traits and vector competence of mosquitoes [9], [12]–[16], with the magnitude of the diurnal temperature range (DTR) associated with the degree of response observed. Vector competence of Ae. aegypti for DENV examined under fluctuating temperatures, indicated that a large DTR of ∼20°C around an intermediate mean of 26°C (i.e., ∼16°C to 36°C; temperatures representative of conditions mosquitoes in central west Thailand would be exposed to in the low DENV transmission season) reduced the proportion of Ae. aegypti females with a midgut infection and reduced female survival. At a mean of 26°C, EIP did not vary if temperature fluctuations were symmetric whereas EIP tended to last longer under more natural asymmetric fluctuations [9], [13].
While the effects of realistic temperature fluctuations on Ae. aegypti vector competence and EIP for DENV at an intermediate mean temperature (26°C) have recently been described [9], [13], [14], the impact of fluctuations at the upper and lower thermal limits are unknown. Short periods of the day spent at extreme temperatures may affect key steps of the mosquito infection process. Evidence suggests that DENV transmission may be more limited by lower daily temperatures [17], as opposed to average daily temperatures.
In this study we investigated whether fluctuations at high and low mean temperatures alter adult survival, vector competence and/or EIP of Ae. aegypti for DENV serotype-1 (DENV-1), compared to constant temperatures. We then explored how this might affect the geographical range of DENV in light of our understanding of the thermal limits of DENV transmission. Based on theoretical predictions [9], we expect that large fluctuations at low temperatures will enhance transmission (increase infection/dissemination probability and reduce the EIP of the virus) because of time spent under warmer (more optimal) conditions, whereas fluctuations at high temperatures will have a negative effect because of time spent at elevated temperatures detrimental to the vector and/or the virus. To test this hypothesis, we exposed mosquitoes to high and low temperatures with and without fluctuations across two experiments, and assayed mosquitoes for virus infection.
We determined the effect of constant and fluctuating temperature regimes at both high and low mean temperatures, on the survival and vector competence of Ae. aegypti for DENV-1. Over the course of two experiments we tested seven temperature regimes. At the low temperatures, we exposed mosquitoes to three constant temperatures (16°C, 20°C and 26°C) and one fluctuating temperature regime (a DTR of 18.6°C around a mean of 20°C). The minimum programmed temperature for the fluctuations was 11.7°C and the maximum was 30.3°C. Given the low temperatures in this experiment and the associated uncertainty of whether we would identify any infection, we included the 26°C treatment as a control temperature, knowing we could detect DENV infected females at this temperature. At the upper end of the temperature scale, we tested two constant temperature regimes (30°C and 35°C), and one cyclic temperature regime with a DTR of 7.6°C. Temperatures fluctuated between 27.1°C and 34.7°C, around a mean of 30°C. We included the 35°C constant temperature treatment to ensure that the peak temperature was not a limiting factor of infection potential.
The magnitude and asymmetrical shape of the temperature profiles were based on temperature recordings from Central West Thailand where DENV is endemic [14]. Fluctuating temperature regimes followed a sinusoidal progression during the day, and a negative exponential decrease at night, with minimum and maximum temperatures reached at 06:00 and 14:00 respectively. A 12∶12 hr light∶dark cycle was used, with the light schedule changing at 08:00 and 20:00. Experimental mosquitoes were housed in KBF115 incubators (Binder, Tuttlingen, Germany) that maintained climatic conditions. HOBO data loggers (Onset, Cape Cod, MA) recorded temperatures on an hourly basis in the two incubators with fluctuations. Actual air temperatures within the incubators followed the programmed temperature profile closely. There was an average of <0.3°C difference between the daily programmed temperature and the actual air temperature inside the incubators across treatments. Relative humidity was maintained between 70% and 80% across all treatments, and was also recorded by data loggers.
Ae. aegypti used in our experiments were collected from Kamphaeng Phet Province, Thailand as pupae during January 2011 and sent to UC Davis as F1 eggs. After eggs were received, they were hatched and reared at a low density (1.3 larvae/10 mL) in 24 cm×29 cm×5 cm containers with 1.5 L of deionized water. Colony maintenance was conducted under standard insectary conditions (constant 28°C±2°C, 70–80% RH) and a 12∶12 hr light∶dark cycle, with >500 females per generation. Larvae were fed a 1∶1 mix of bovine liver powder and puppy chow, with 0.1 g per 200 larvae each day for the first four days, 0.2 g on the fifth day, 0.3 g on the sixth, and then 0.2 g on the remaining two days, at which time most larvae had pupated. Generation F4 mosquitoes used in experiments.
When females were 4–5 days old, access to sucrose was removed for 24–36 hr, after which time females were fed defibrinated sheep blood (QuadFive, Ryegate, MT), mixed with DENV-1 freshly grown in cell culture prior to mosquito exposure, using an artificial feeding system. Virus supernatant was harvested after scraping and then separating all cells by centrifugation. Mosquitoes were fed through a desalted porcine intestinal membrane stretched over the bottom of a warm water-filled jar to maintain a temperature of 37°C. The viral isolate used, SV2951 obtained from Ratchaburi, Thailand, had been passaged at 28°C seven times in Ae. albopictus C6/36 cells prior to use in this study. While this is potentially sufficient time for adaptation to cell culture temperatures, we do not consider it likely that this would influence our results as 28°C is not deemed as a stressful temperature for DENV. Confluent cultures of C6/36 cells grown in 25-cm2 flasks were inoculated at a virus multiplicity of infection of 0.01 and left to grow for 10 days at 28°C in 5% CO2. The infectious blood meal consisted of 50% defibrinated sheep blood (Quadfive, MT), 45% viral supernatant harvested at Day 10, and 2.5% sucrose solution (diluted 1∶4 in water) and 2.5% adenosine triphosphate disodium salt (Sigma-Aldrich, MO) at a final concentration of 5×10−3 M.
We prepared one blood meal for each experiment. The blood meal for the low temperature experiment was calculated to contain 5.86×105 focus forming units (FFU)/ml of DENV-1. The calculated titer for the high temperature experiment was 7.89×105 FFU/ml. Mosquitoes in both experiments were limited to 35 min feeding, to minimize the effect of virus degradation in the infectious blood meal. Mosquitoes were allowed 2–3 hr to begin digestion after the blood meal. We subsequently sedated them using CO2 and retained only fully engorged females to set up experimental groups. For the low temperature experiment, forty-four replicate 1-pint paper cartons (Science Supplies WLE, NJ) with mesh tops, each containing 20 engorged females were set up. Twelve cartons were placed into each of the experimental temperature regimes, and eight cartons into the control 26°C incubator. For the high temperature experiment, we tested 28 replicate cartons each containing 16 females. Nine cartons were placed into the constant temperature incubators, and 10 into the 30°C plus fluctuation incubator.
We assessed vector competence at 7, 14, 21 and 28 days post exposure (DPE) to the infectious DENV-1 blood meal (i.e., days of EIP) for mosquitoes in the low temperature experiment. At each time point, we sampled three replicate cartons of mosquitoes from each experimental temperature, and two from the control 26°C treatment. At the high temperatures, mosquitoes were sampled at 3, 6 and 9 DPE. Three cartons were randomly removed from each incubator at each time point. The additional carton in the 30°C fluctuation treatment was also tested at 9 DPE. Because the course of DENV infection in the mosquito is faster at higher temperatures than at lower ones [4], [6], we sampled mosquitoes more frequently in the high temperature experiment to improve our statistical power of identifying differences among treatments.
For all surviving mosquitoes in each carton, we measured two components of vector competence, midgut infection and virus dissemination from the midgut in infected females, using a qualitative indirect fluorescence assay (Q-IFA). Virus EIP measurements were based on detection of a disseminated DENV infection in the mosquito.
We separated and tested bodies (comprising of the thorax and abdomen) for midgut infection and heads for disseminated infection, independently. Samples were placed into 1 mL viral transport medium (VTM; 77.2% low glucose DMEM, 18.5% heat-inactivated fetal bovine serum, 3.8% penicillin/streptomycin, and 0.15% gentamycin and nystatin) with approximately ten 2 mm glass beads (Fisher Scientific, Pittsburg, PA) in a screw-top plastic vial. Following collection, all samples were frozen at −80°C for later analysis by Q-IFA. We also collected the whole bodies (without separation of heads) of dead females daily and tested them for infection status. Results from analysis of dead mosquitoes were included in our survival analyses.
All data was analyzed using JMP software, version 10 (SAS Institute Inc., NC). Vector competence was analyzed by nominal logistic regression of the infection or dissemination status as a full-factorial function of temperature and DPE, and carton nested within temperature and DPE. Records of survival for individual females exposed to the infectious blood meals were kept throughout the duration of the both experiments. Female survival was analyzed using a Kaplan-Meier (log-rank) analysis, with females that were sacrificed on scoring days right-censored. We tested for differences in survival curves between different temperature regimes and infection status of recently dead mosquitoes. We corrected for multiple comparisons between treatment groups for our logistic regression and Kaplan-Meier analyses using a Bonferroni correction.
We used an infectious fluorescent focus assay [18] to titrate virus in blood meals offered to the mosquitoes. One-day old confluent monolayers of Vero (green monkey kidney) cells in 8-well chamber slides (Nunc, Rochester, NY) were inoculated with serial 10-fold dilutions of virus and blood meal samples. Dilutions were prepared in 2% FBS media in duplicate and inoculum was allowed to infect the cells for 1 hr at 37°C. A negative control (the media used for the dilutions) was included in all titrations. The overlay applied to the cells after the incubation was made of a 1∶1 mix of 2% FBS media∶carboxymethyl cellulose (CMC; 2% in PBS). We allowed 2 days for virus to replicate in the monolayer, then the media was removed and the cells washed carefully. In each washing step, PBS was added to cells three times, allowed to rest for 3–5 min, before PBS was again removed. The cells were fixed with 3.7% formaldehyde for 30 min, then washed and stained with 75 µL 1∶250 dilution of primary mouse anti-DENV monoclonal antibody (MAB8705; Millipore, MA) at 37°C for 1 hr. The cells were again washed to minimize background fluorescence, and then stained with 75 µL 1∶85 dilution FITC-conjugated secondary goat anti-mouse antibody (AP124F; Millipore, MA) for 30 min, which was used to detect and count the number of fluorescent foci under an FITC-fitted fluorescent microscope at 20× magnification.
To test mosquito samples for the presence of infectious DENV, we used a qualitative fluorescence assay. We homogenized the tissue samples for 4 min in a Retsch Mixer Mill 400, at 30 Hz. We filtered 300 µL of the sample through 0.22 µm cellulose acetate centrifuge filters (Costar Spin-X, Corning, Japan) and 50 µL of the filtered supernatant was inoculated in duplicate onto a 1-day old confluent monolayer of Vero cells, seeded at a density of 2.5×105 cells/well in a 96-well culture plate. The inoculum was allowed to infect the cells for 1 hr at 37°C, before a standard maintenance media containing 2% FBS overlay was applied to the cells in each well, and the plate was incubated 37°C for 4 days. Positive and negative controls were used in each plate. We then removed the overlay, washed and fixed the cells in 3.7% formaldehyde for 20 min. The washing and staining steps that followed were exactly the same as for the FFA, except that the volumes used for antibody staining were 50 µL for each of the primary and secondary antibodies. We viewed cells under FITC-fitted fluorescence microscope at 10× to screen for the presence or absence of green fluorescence, which was indicative of a sample being either infected or uninfected by DENV, respectively.
Compared to a constant temperature, large diurnal temperature fluctuations at a mean of 20°C reduced the EIP50 for Ae. aegypti with a disseminated DENV-1 infection by approximately 36%, from 29.6 to 18.9 days. These results indicate a greater potential for DENV transmission at cool temperatures with natural fluctuations, and at an accelerated rate compared to what would be predicted by analysis of a 20°C constant temperature regime. Nevertheless, low intrinsic mortality under each of the low temperatures (those below 26°C) supports the potential for a mosquito to complete virus EIP at low temperatures, allowing for subsequent transmission following a protracted incubation period.
Females exposed to a large DTR around a 20°C mean were more likely to have detectable disseminated DENV-1 after 28 days compared to those reared under a constant, control temperature (100% vs. 41.7% dissemination). Whether fluctuations at 20°C also increased the maximum proportion of infected females with a disseminated infection compared to 20°C constant cannot be ascertained from our data. We did not see dissemination at the constant 20°C temperature plateau or reach maximal levels in our 28 day experiment. It is possible that dissemination levels could have reached 100% if we had held mosquitoes for a longer time. Regardless, the accelerated EIP under the cyclic temperature compared to the equivalent constant temperature indicates the potential for laboratory experiments using constant temperatures to significantly underestimate the duration of EIP in nature. Relatively low mortality rates under the three cooler temperatures (<20% after 28 days) compared to 26°C constant (∼30%) suggest that lifespan will not be a limiting factor in transmission potential during cooler times of the year or in more temperate environments. Epidemiologically, this substantial reduction in the EIP of DENV at low temperatures with fluctuations, in combination with low mortality rates, would be expected to increase vectorial capacity, and thus virus transmission potential, compared to constant temperatures. It would be useful in future experiments to improve temporal resolution by increasing sampling between the intervals we used, and allow mosquitoes at cooler constant temperatures longer to complete the EIP to identify maximum dissemination levels.
We observed a very low proportion of DENV-1 infected females held at 16°C constant. The youngest of the three infected females identified was found dead at 4 DPE, while the remaining two females were collected at 7 and 21 DPE during our weekly sampling. Due to slow digestion at such a low temperature, it is possible that the 4 and 7 DPE mosquitoes retained some infectious blood from the blood meal several days earlier. Although it is possible for a mosquito to become infected with DENV at 16°C, as shown by a single individual with a body infection at 21 DPE, this low temperature sharply reduced vector competence for DENV in Ae. aegypti. While we did not observe any mosquito with dissemination at 16°C, Ae. aegypti exposed to DENV and held at temperatures as low as 13°C for 32 days have previously been demonstrated to be capable of transmission [4]. We did not examine mosquitoes after 28 DPE and thus it is possible we did not allow enough time to observe transmission (as estimated by dissemination) under the 16°C treatment, and/or the mosquitoes used differed in their susceptibility to DENV infection [19], [20].
There was no detectable effect of the small fluctuations around a high mean of 30°C in the proportion of females with a midgut infection or disseminated virus, or in the duration of the EIP compared to the constant temperature control. The entire temperature profile (∼27°C to 35°C) falls within limits known to be highly conducive to DENV transmission, therefore, the lack of observable change is possibly due to the magnitude of the DTR not being large enough to produce a detectable response given our sample size. We did not test the large DTR around a mean of 30°C because there are few locations that we are aware of that have such large amplitude fluctuations at high temperatures. We therefore restricted our use of the large DTR to lower temperatures. Our cyclic low temperature treatment was derived from ambient conditions in dengue-endemic northern Thailand between December and January [21].
Results from previous studies indicate that midgut infection levels were lower under fluctuating temperature regimes with a mean of 26°C compared to constant temperatures, leading to reduced transmission potential [9]. Conversely, in the present study we observe that fluctuating temperatures at a lower mean lead to positive changes in the probability of virus dissemination from the midgut, consequently increasing transmission potential. Lambrechts et al. [9] predicted infection and dissemination probabilities of females infected with DENV and the duration of the EIP under various magnitudes of DTR. Their theoretical model predicted ∼50% of Ae. aegypti would become infected at both a constant 20°C and 20°C with large fluctuations. Although observed infection levels in our experiments under both temperature profiles were lower than that predicted we did not detect a statistical difference between these two temperature regimes, in agreement with the model. A mean of 18°C was predicted to be a pivotal mean temperature, above which fluctuations would decrease dissemination probability and below which they would enhance dissemination. Our results on dissemination rates imply that this predicted pivotal temperature rather lies between 20°C and 26°C. We hypothesize that the opposite effects of these two temperatures is due to differences in rates of viral growth/replication at different temperatures experienced by the mosquitoes. At a mean of 26°C, viral replication rates at the lower extreme of the temperature profile (∼18°C) might slow the virus more than it accelerates it at the upper end of the scale (∼36°C), resulting in a net deceleration compared to the rate at a constant 26°C. Conversely at a mean of 20°C, where replication is already slow, the low temperatures experienced by mosquitoes at the bottom of the fluctuating temperature profile lower the rate of replication to zero, but the relative increase in replication as the temperature rises to ∼30°C at the peak of the profile during the day will increase replication far more than it is decreased overnight, leading to a net acceleration.
Lambrechts et al. [9] did not model the effect of DTR above a mean of 28°C, although according to their predictions, small fluctuations are expected to result in close to a 100% midgut infection, and 80% dissemination, with a virus EIP shorter than 10 days. Observed dissemination results and estimates of EIP in our study are not in disagreement with this prediction, although again infection levels were lower. Midgut infection, dissemination and EIP estimates to produce the model were obtained from multiple experimental mosquito-flavivirus infections (not including DENV), and as a result, this discrepancy between the predictions and observed results may be a result of differences between vector-virus systems. The low infectious titers used in these two experiments are likely responsible for the low proportion of infected individuals obtained. Despite this, such titers fall within the reported range of viremia observed in humans [22], [23].
Although we used only a single serotype (DENV-1) to test the hypothesis that fluctuations at high and low mean temperatures would alter mosquito vector competence, the EIP of the virus, and adult survival, cumulative results from our group [9], [13] demonstrate consistency between results from similar experimental temperature regimes, despite using two mosquito populations, two serotypes (DENV-1 and DENV-2), two virus strains within one of these serotypes, and different infectious titers of the blood meals. We are therefore confident that the present study reveals another level of complexity in the interaction between the vector, viral pathogen and temperature.
Our results indicate that the effect of fluctuations around a low mean temperature markedly reduce EIP, which has important implications for determining DENV transmission risk at the northern and southern edges of DENV's geographic range, areas with a mean temperature that would normally be considered too low for DENV transmission to occur. Additionally, seasonal variation in DENV transmission, which is a common feature of DENV transmission dynamics [6], [24], can be associated with changes in mean temperature and DTR [9], [25], [26]. Conditions similar to the low temperature fluctuating profile used in this study (e.g., a mean below 22°C and DTR greater than 15°C), are observed in the low DENV transmission season throughout many parts of South East Asia, including areas in northern Thailand, Myanmar and central/northern India [21]. Each of these countries lie within the top 20 countries reporting the largest number of dengue cases annually [27], and despite low mean temperatures due to northern latitudes and often altitude, according to the World Health Organization, seasonal DENV transmission still occurs annually in such areas. Studies in Anopheles stephensi indicate a similar response to cyclic temperatures. A DTR at low temperatures enhances malaria transmission, while at higher temperatures equivalent DTRs reduced transmission potential [16]. Another recent study on arboviruses examined the interplay between temperature and EIP in Culex pipiens infected with West Nile virus [28], demonstrating that environmental conditions could enhance transmission of one variant over another. In this study however, realistic temperature fluctuations were not considered. An improved understanding of pathogen transmission across more realistic environmental conditions will allow for greater accuracy in modeling efforts to aid vector control and disease prevention in the future. It is, therefore, important that in future studies when researchers test mosquitoes at lower temperatures, realistic conditions are considered.
Similar responses to temperature changes have been reported for life-history trait estimates of Ae. albopictus and Ae. aegypti [10], [29]–[32]. It is likely that their responses to fluctuations in temperature would be comparable. Ae. albopictus often display a generalist blood feeding behavior [33], and is a competent vector of DENV [34]–[36]. Importantly, the species inhabits both tropical and temperate climates [37]. It is significantly more tolerant to cooler conditions than Ae. aegypti [38] and, therefore, poses a risk for arbovirus transmission in more temperate regions (e.g., Europe) [39]. As such, similar experiments on Ae. albopictus are warranted to better understand virus transmission potential in more temperate environments.
We observed limited mortality throughout the duration of both experiments, and identified females with a disseminated infection in six of the seven temperature treatments tested (all but 16°C). Mosquitoes were raised under conditions with optimal nutrition and were maintained in an environment with limited risk of death other than intrinsic factors and temperature. We do not know the maximum potential lifespan of mosquitoes exposed to each of these temperature regimes. We planned the experimental duration to be long enough for mosquitoes of each temperature to discern the duration of the EIP under each treatment, but did not attempt to estimate longevity. Epidemiologically, although these estimates represent a conservative estimate of the number of mosquitoes that might survive to such a time in order to transmit DENV, the high survival estimates compared to the duration of the EIP indicate that a relatively large proportion of infected mosquitoes in both experiments were capable under laboratory conditions of surviving to an age where they could transmit DENV to a susceptible host.
Similar to previous studies [40], [41], we observed reduced mortality in virus-infected females as opposed to those that were exposed, but uninfected. We observed this result, however, only at mean temperatures of 30°C or above. One hypothesis for this result is that mounting an immune response against the virus is more energetically costly than allowing the virus to establish infection [41]. Contrary to the results previously reported, we did not observe a significant difference between the survival of uninfected and infected females at low temperatures [40], [41]. This apparent interaction between temperature and infection could be due to the rapid proliferation of the virus at higher temperatures inducing a stronger immune response, where as at low temperatures, virus replication is slower and the immune response is, therefore, milder. Had we assessed survival for longer than 28 days, we may have detected a response when survival rates started to decline.
Our results indicate that the use of constant temperature experiments to assess Ae. aegypti vector competence for DENV at low temperatures underestimate the potential rate at which transmission may occur under more natural, fluctuating temperature profiles. Low intrinsic mortality at low temperatures with fluctuations similarly favors increased potential for virus transmission. Our results, therefore, provide a mechanism for sustained DENV transmission in endemic areas during cooler times of the year and indicate that transmission could be more efficient in temperate regions than previously anticipated.
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10.1371/journal.pmed.1002151 | Obstetric Facility Quality and Newborn Mortality in Malawi: A Cross-Sectional Study | Ending preventable newborn deaths is a global health priority, but efforts to improve coverage of maternal and newborn care have not yielded expected gains in infant survival in many settings. One possible explanation is poor quality of clinical care. We assess facility quality and estimate the association of facility quality with neonatal mortality in Malawi.
Data on facility infrastructure as well as processes of routine and basic emergency obstetric care for all facilities in the country were obtained from 2013 Malawi Service Provision Assessment. Birth location and mortality for children born in the preceding two years were obtained from the 2013–2014 Millennium Development Goals Endline Survey. Facilities were classified as higher quality if they ranked in the top 25% of delivery facilities based on an index of 25 predefined quality indicators. To address risk selection (sicker mothers choosing or being referred to higher-quality facilities), we employed instrumental variable (IV) analysis to estimate the association of facility quality of care with neonatal mortality. We used the difference between distance to the nearest facility and distance to a higher-quality delivery facility as the instrument.
Four hundred sixty-seven of the 540 delivery facilities in Malawi, including 134 rated as higher quality, were linked to births in the population survey. The difference between higher- and lower-quality facilities was most pronounced in indicators of basic emergency obstetric care procedures. Higher-quality facilities were located a median distance of 3.3 km further from women than the nearest delivery facility and were more likely to be in urban areas.
Among the 6,686 neonates analyzed, the overall neonatal mortality rate was 17 per 1,000 live births. Delivery in a higher-quality facility (top 25%) was associated with a 2.3 percentage point lower newborn mortality (95% confidence interval [CI] -0.046, 0.000, p-value 0.047). These results imply a newborn mortality rate of 28 per 1,000 births at low-quality facilities and of 5 per 1,000 births at the top 25% of facilities, accounting for maternal and newborn characteristics. This estimate applies to newborns whose mothers would switch from a lower-quality to a higher-quality facility if one were more accessible. Although we did not find an indication of unmeasured associations between the instrument and outcome, this remains a potential limitation of IV analysis.
Poor quality of delivery facilities is associated with higher risk of newborn mortality in Malawi. A shift in focus from increasing utilization of delivery facilities to improving their quality is needed if global targets for further reductions in newborn mortality are to be achieved.
| Large increases in access to health facilities in many low- and middle-income countries (LMIC) have not produced equivalent gains in newborn survival.
Little data or evidence on the quality of delivery care in LMICs is currently available, and even less is known on the mortality impact of facility quality.
We designed and implemented a study of neonatal mortality in Malawi, where facility delivery is highly prevalent and a quality assessment of all health facilities was conducted in 2013–2014.
We quantified quality of maternal care at all delivery facilities based on 25 quality characteristics and classified the top 25% of facilities as higher quality. Average quality was low, with particular gaps in infrastructure and performance of basic emergency obstetric care procedures.
We linked a nationally representative sample of 6,686 births between November 2011 and March 2014 to their delivery facility and used multivariable linear regression models to estimate the impact of quality on neonatal mortality. To overcome selection issues, we used the relative proximity of higher-quality facilities as an instrument for facility quality.
Our empirical results suggest that delivering at a higher-quality facility is associated with a reduction of 23 deaths per 1,000 live births.
Expanded availability of health facilities does not guarantee access to essential elements of quality maternal and neonatal care.
Improvements in facility quality could reduce newborn deaths substantially among women who would receive higher-quality care if it were more accessible.
| Eliminating preventable infant mortality is a global health priority, reaffirmed in Sustainable Development Goal 3.2, which aims to reduce neonatal mortality to 12 per 1,000 live births by 2030 [1]. This is an ambitious goal: currently, over 2.5 million infants die each year in the first month of life [2]; neonatal mortality rates are estimated at 29 deaths per 1,000 live births in sub-Saharan Africa [3]. Globally, reductions in deaths within 28 days of birth have lagged decreases in postneonatal mortality. As a result, neonatal mortality now accounts for the largest share (44%) of under-5 mortality [2,4]. Achieving global targets in infant and child survival requires a redoubled focus on deaths in the first month of life.
Malawi was one of the few low-income countries to achieve the Millennium Development Goal (MDG) for child survival [5], a testament to high-level policy commitment to child health, donor-support for strengthening of health workforce capacity, and expanded maternal and newborn care [6]. Facility delivery rates increased from 53% in 2000 to 90% in 2014 [5], heavily influenced by a 2007 ban on deliveries with traditional birth attendants [7]. Although child mortality declined by more than 5% annually from 2000, newborn mortality declined less rapidly (3.3% per year) and remains 23 deaths per 1,000 live births. In response, the government of Malawi has recently adopted an Every Newborn Action Plan to end preventable newborn deaths [8].
Neonatal survival depends in large part on rapid and competent care during labor and delivery [6]. Basic neonatal resuscitation could avert as many as 30% of intrapartum-related newborn deaths [9]. An estimated 40% of deaths due to sepsis and tetanus could be prevented with infection control and hygienic cord care [10], and kangaroo mother care for low birth weight (LBW) infants should reduce neonatal mortality in these high-risk babies by half [6,11]. All of these interventions require qualified health workers as well as facility infrastructure and resources [6,12]. Simply delivering in a health facility does not guarantee care of sufficient quality to prevent newborn deaths [13–15]. A recent meta-analysis of 192 Demographic and Health Surveys (DHS) found inconsistent links between institutional delivery coverage and neonatal mortality [16]. Similarly, case studies in Rwanda and Malawi found no evidence of decreased neonatal mortality following large increases in facility-based delivery [7,17].
Research on the relationship between facility quality and mortality outcomes has been challenging not only because of generally scarce quality data in high-mortality settings but also because of the highly nonrandom selection of mothers with health complications into better-equipped referral facilities [16].
The aim of this study is to measure the association of quality of delivery care with neonatal mortality in Malawi. Malawi provides an ideal setting to test this association both because nearly all women deliver at a facility and because all health facilities in the country were recently assessed by a health facility census. The census allows us to identify all potential delivery locations for mothers and to construct relative distance measures. These measures enable us to employ instrumental variable (IV) estimation to better approximate the causal effect of facility quality on neonatal mortality. Determining whether facility quality is a barrier to reducing neonatal mortality in Malawi can inform policy there and in similar settings of persistently high neonatal mortality.
The original survey implementers obtained ethical approvals for data collection; the Harvard University Human Research Protection Program deemed this analysis exempt from human subjects review.
Data on health facilities were obtained from the 2013 Service Provision Assessment (SPA), a census of health facilities conducted by the DHS program. The SPA includes an audit of facility resources, surveys on clinical practices, and direct observation of delivery in larger facilities.
Data on child survival were obtained from the 2013–2014 MDG Endline Survey (MES), a multiple indicator cluster survey (MICS) conducted in collaboration between the Malawi government and the United Nations Children’s Fund (UNICEF). The MES is a nationally representative household survey that employed a multi-stage stratified sampling strategy to identify households within enumeration areas (EAs) drawn from the 2008 census.
Spatial locations of all EAs in the MES were obtained from the Malawi National Statistical Office. We grouped facilities based on type and management authority in the SPA survey to create categories matching response options to the MES question on delivery location. We linked all women delivering in institutions to the nearest facility of the type in which she delivered (e.g., government hospital) by direct distance from the geographic centroid of her EA. Based on prior studies suggesting women are unlikely to deliver far from home [18–20], we excluded women matching to facilities over 50 km away, as these women were likely in another area for childbirth.
Neonatal mortality was defined as death within the first 28 days of life [2] among all children born in the two years prior to interview date.
We reviewed the framework of quality of care for pregnant women and newborns endorsed by the World Health Organization (WHO) [21] and identified domains characterizing provision of care at the ultimate delivery facility: infrastructure, human resources, essential supplies, and evidence-based practices in routine and emergency care. We then used the WHO Safe Childbirth Checklist in combination with existing evidence on interventions most likely to avert maternal and neonatal death [11,15,22,23] to identify 25 quality criteria available in the SPA survey (listed in Fig 2). In keeping with prior research [24], the overall quality score was based on the proportion of criteria met, with missing items excluded from the calculation of the score for that facility. Facilities were missing data for only two items: staff training (15% missing) and partograph use (1.9%). We classified a facility as a “higher-quality facility” if it met more than 18 of 25 criteria, corresponding to the 75th percentile of the quality score distribution for all delivery facilities.
We created an alternative quality metric for sensitivity analyses. For the subset of facilities with clinical observations, we combined the 25-item quality index with a validated metric of quality of process of intrapartum and immediate postpartum care from direct observation of deliveries (45 items total) [25].
We obtained data on socioeconomic status (household wealth index, educational attainment above secondary), maternal demographics (age, marital status), and pregnancy characteristics (parity, maternal age under 18, receipt of any antenatal care [ANC], and receipt of the minimum recommended four ANC visits) for each mother from the MES [26]. We also included other variables that have been shown to be associated with increased mortality risk: male gender, multiple birth, and LBW (defined as ≤2.5 kilograms or very small by maternal report if weight not available).
We identified the SPA survey and MES sample in Malawi as a unique combination of data that permitted us to directly link facility quality to a population-representative sample of births. To address likely biases resulting from the nonrandom and unmeasured selection of more complicated deliveries into referral facilities, we selected IV analysis as the appropriate empirical strategy. We chose relative distance to quality care as the instrument based on existing health systems research in high-income settings [27–30]. Key domains of maternal care quality were identified from global guidelines following prior analytic work [24]; we refined this index after receiving the data based on the specific indicators available in the Malawi SPA survey. We prespecified an additive summary measure, as is standard practice in this field [31], and focused on a binary quality indicator for simplicity in our main empirical model. Given that clear and objective thresholds for sufficient quality are not currently available, we classified the top 25% of all facilities in our sample as higher-quality in our initial model and then explored two alternative cutoffs as well as the continuous quality score. We conducted an exploratory assessment of the shape of the relationship between quality and mortality, defining higher quality as the top 75%, top 50%, and top 10% of facilities in turn.
We present separate descriptive statistics for delivery facilities and births. Delivery facilities were defined as SPA facilities offering delivery services with at least one birth in the MES sample. Maternal and infant characteristics were weighted by the MES women’s sampling weight, rescaled to the analytic sample. We describe mortality by region and facility type and assess significance using an F-test corrected for clustering.
We first modeled mortality against delivering in a higher-quality facility in unadjusted linear regression. As we anticipated unmeasured selection of complicated deliveries into referral facilities would bias the relationship between delivery in a higher-quality facility and newborn survival, we employed IV analysis using the difference between distance to the nearest delivery facility and distance to a higher-quality facility as the instrument. We selected this instrument on the assumption that, for a given level of remoteness from the health system, the relative location of a higher-quality facility is random. By using differential distance rather than direct distance to quality care, we explicitly account for systematically higher health risks related to living in areas with limited access to the health system.
To be a valid instrument, differential distance must relate to mortality only through facility quality and not through a direct causal link or any shared common causes. Based on the distribution of measured confounders across contextual factors, we identified urban location and health system density as key control variables to eliminate other possible links between differential distance and neonatal outcomes. Health system density was defined as the natural log of one plus the number of health facilities within 20 km of the center of the EA.
The IV analysis estimates a local average treatment effect (LATE), i.e., the effect of delivering in a higher-quality facility among women whose choice is affected by relative distance [32]. We present further details on the causal model, an assessment of the underlying assumptions, falsification tests [33], and estimation of bounds for the LATE estimate if assumptions do not hold in the Supporting Information (S1–S3 Texts).
We plotted predicted probability of delivering in a higher-quality facility and of neonatal mortality against differential distance using a fractional polynomial plot to visualize the relationships among distance, quality, and mortality. We used two-stage least squares to fit a linear probability model of mortality on delivering in a higher-quality facility; linear probability models are standard in IV analysis [29]. In addition to urban residence and density of the health system, we controlled for maternal socioeconomic status and maternal and infant characteristics associated with mortality to increase precision in the estimate [33]. Observations with missing covariates (18, 0.3%) were excluded from the analysis. All analyses accounted for stratified sampling and clustering within EAs.
We performed several robustness checks on the measurement of quality. To assess sensitivity to the threshold chosen for high quality, we (A) increased the threshold to an absolute standard of 0.80 of 1.00 score on the quality index, (B) lowered the threshold of high quality to include the top tertile of facilities, and (C) employed the continuous standardized quality index in place of the binary indicator of high quality. To check the measure construction, we applied principal components analysis (PCA) to create a weighted summary of the 25 items. To validate the content used to construct the quality metric, we employed the composite index described above that included direct observation of deliveries, the gold standard of clinical quality measurement. This analysis was limited to the facilities where observations occurred.
We conducted two additional analyses to assess whether simpler measures of quality would show the same relationship as the facility quality index. We used hospital delivery as the exposure and differential distance to nearest hospital as an instrument. Secondly, we measured overall facility capacity using seven indicators of scope of services available [7] and used this index to define higher-quality facilities (top 25%) and to calculate differential distance to such facilities. We repeated all analyses using a probit model, which bounds the outcome between 0 and 1, to compare with the findings of the linear probability model.
The SPA assessed 977 of 1,066 health facilities in Malawi (92.2% response rate); 3% of facilities refused assessment, while the remainder were closed, empty, or inaccessible. Delivery services were provided by 540 facilities in total. The MES interviewed 24,230 of 25,430 eligible women (95.3% response rate), 7,576 of whom reported giving birth in the two years preceding the survey. Fig 1 shows the distribution of MES clusters and health facilities throughout Malawi; EAs are by construction small, with a target population of approximately 1,000 and an average size of 6.7 km2. Most women (6,935, 91.5%) reported a facility-based delivery; of these, 160 reported a facility that could not be matched to the SPA facility types, 102 lived in EAs that we were not able to match to census EAs, and 138 were matched to delivery facilities over 50 km away. The analytic sample comprised 6,535 women with live births (6,686 neonates with twins) matched to 467 unique delivery facilities; 6,668 neonates with complete data on covariates were retained in regression analyses.
Table 1 provides characteristics of delivery facilities. The majority of delivery facilities were health centers or clinics, with medical assistants and clinical technicians most likely to be the highest qualified clinician. One hundred thirty-four facilities met the threshold of higher quality (top 25% of the total 540 delivery facilities, equivalent to at least 18 of 25 items fulfilled). This included the majority of hospitals but only 16% of health centers; higher-quality facilities had larger (average of 73 clinical personnel versus 19) and more highly trained staff. The average quality score at the top 25% of facilities was 0.80 compared to 0.56 at all other facilities.
Fig 2 details the performance of delivery facilities on the facility quality index. The average facility achieved approximately 16 of the 25 items on the quality index (63%), with notable deficiencies in key infrastructure as well as selected supplies. Facilities commonly reported routine clinical practices (immediate breastfeeding, partograph use, and full infant exam all >90%), although vitamin K injections were rare. Nearly all facilities reported performing at least one basic emergency procedure in the past three months. As shown in Table 1, the difference between higher and lower quality was most pronounced in performance of basic emergency obstetric care, with a difference of over 40 percentage points.
Table 2 presents the women’s study sample: most women were rural dwellers, married, and with basic education (19.0% secondary education or more). Access to the health system was high: 99.5% of women attended at least one ANC visit, the average number of facilities within 20 km was 24.3 (median 13), and the average distance to matched delivery facility was 8.4 km (median 6.0 km). Women with greater educational attainment, primiparous women, and women carrying multiple infants or LBW infants were more likely to deliver in higher-quality facilities. A total of 115 neonatal deaths were reported, with higher mortality rates at higher-quality facilities. Mortality rates were similar between urban and rural areas (17.9 versus 18.3 deaths per 1,000) as well as at public and private facilities (18.7 versus 14.9 deaths per 1,000) but significantly higher in hospitals than non-hospitals (24.2 versus 14.3 deaths per 1,000).
Higher-quality delivery care was less accessible than any delivery care: the closest higher-quality facilities were on average 6.2 km (median 3.3 km) farther from households than the nearest delivery facility of any quality. Differential distance to a higher-quality facility was strongly negatively associated with delivery at a higher-quality facility. As shown in Fig 3A, the probability of delivery at a high-quality facility declined from 75% for women where the closest facility was a higher-quality facility (1,623 of 2,152) to 7% for women with a higher-quality facility more than 30 km more distant than the closest low-quality facility (7 of 105). The probability of neonatal mortality increased as the additional distance to higher-quality care increased, although considerable uncertainty exists above 20 km (Fig 3B).
The unadjusted regression model suggested a 0.6% point linear increase (95% CI -0.1%, 1.3%) in the probability of neonatal death for delivery in higher-quality facilities (Table 3). This estimate of nonsignificantly increased risk for infants born at better facilities applies to the full population of facility births but does not account for maternal factors or selection into such facilities. Although not statistically significant, this positive association is consistent with risk selection, in which sicker women and neonates are referred to better facilities. In the fully adjusted IV analysis, the estimated impact of delivering at a high-quality facility on neonatal mortality was -0.023 (95% CI -0.046, 0.000, p = 0.047). The predicted prevalence of neonatal mortality was 28.3 deaths per 1,000 (95% CI 16.8, 39.8) in lower-quality facilities compared to 5.2 deaths per 1,000 (95% CI -0.7, 17.4) for delivery in higher-quality facilities, holding all covariates at their mean values. The IV estimate improves on the regression estimate by accounting for confounding between facility quality and mortality; it applies to the subset of women who would receive higher-quality care if it were more accessible.
Tests of IV assumptions are reported in detail in the Supporting Information. Differential distance was strongly associated with quality of delivery facility (S1 Table). Infant and maternal risk factors were relatively evenly distributed across the range of differential distance (S2 Table), and falsification tests did not reject differential distance as a valid IV (S3 Table), lending support to the exclusion restriction and assumption of no unmeasured confounding of instrument and outcome. However, estimation of bounds around the IV estimate, should identifying assumptions not be met, showed a high degree of uncertainty, inclusive of the null (S4 Table).
Robustness results are shown in Table 3. In all specifications, including the main model, differential distance was strongly associated with delivering in a higher-quality facility, well above minimum thresholds for instrument strength [34,35]. Altering the threshold for higher quality using a continuous quality metric or calculating a weighted summary for the quality metric did not change the results (Models 1–4). Combining the facility quality index with a validated metric of quality of the process of care as directly observed resulted in a weaker association with mortality, -0.016 (95% CI -0.038, 0.005), although this analysis was limited to a smaller, higher-quality set of facilities.
Additional analyses employing simpler quality metrics resulted in estimates of association near -20 deaths per 1,000 with wide CIs inclusive of the null, suggesting such metrics are too coarse to fully capture meaningful variation in quality (S5 Table). In exploratory assessment of linearity of the relationship between quality and mortality, there were no significant protective associations of more lenient definitions of higher quality; the protective association obtained in the main model held true using a stricter categorization of higher quality (S6 Table). Results for the main model and all sensitivity analyses were unchanged in probit models (S4 Text).
This study is, to our knowledge, the first to link nationally representative data on births to detailed data of delivery facility quality in a sub-Saharan African setting. Our results suggest that delivery facilities in Malawi are both accessible and highly utilized, but that facility quality falls substantially short of global standards of evidence-based care. We found that higher-quality facilities, in the top 25% of our quality scale, were associated with 23 fewer neonatal deaths per 1,000 live births than other facilities in Malawi. This suggests improvements in facility quality could reduce mortality substantially among women who would deliver in higher-quality facilities were such facilities available.
Even though large improvements in neonatal survival seem plausible with high-quality care, the estimated reduction in mortality is large and may not necessarily be generalizable to other settings, including the full population of Malawi. The IV estimates shown represent LATEs, i.e., the causal effect (if all assumptions are met) of getting access to high-quality care in the subpopulation of women prevented from using such facilities by the relative distance. Large relative distances are more likely in rural and less developed areas, where baseline mortality is higher and potential improvements more substantial. The average population effect of quality will likely be smaller than the association estimated here, particularly as some women will always deliver in higher-quality facilities, whether by choice or referral.
Quality of care is increasingly recognized as central to the post-MDG global health agenda [36]. However, few prior studies have been able to move beyond access to care to systematically quantify quality of care [19]; most prior research on quality of care and maternal and neonatal outcomes consists of evaluations of specific quality improvement interventions [11]. This study extends existing knowledge by considering quality of delivery care at the facility level for the entire health system and by assessing the relationship of quality to mortality rather than intermediate health indicators.
A key strength of this study was the ability to link detailed data on health facility quality with population-representative mortality data. The detailed spatial location data from both surveys allowed us to construct relative distance instruments, which provided a means of estimating the causal relationship between quality and mortality despite salient selection concerns. Our main findings were robust in multiple sensitivity analyses. Finally, we found that although simpler quality indicators supported the generally protective association of quality, they did not capture the full variability of delivery care that may be important to newborn survival.
The study had several limitations. Women could have been matched incorrectly with facilities based on error in facility classification or location data. However, few women were matched to facilities implausibly far from their location, strengthening the credibility of the match. The small size of EAs mitigates the magnitude of misclassification due to displacement between a woman’s home and the EA center. Any misclassification that did occur would likely introduce greater error in estimation and bias results towards the null. A second potential limitation is the high variability in results of IV analyses; based on guidance in the literature, the sample size and strength of the instrument in this analysis should have been sufficient for the IV to be less biased than linear regression on average [37]. In addition, IV analysis depends upon assumptions, such as the exclusion restriction and lack of unmeasured confounding, that can be falsified but never fully verified. Extensive testing of the instrument provided support for the analysis while indicating that the resulting estimates depend critically on these assumptions. Fourth, our analysis did not address interpersonal quality of care, which could shape women’s choice of delivery facility [18,38]. Finally, an alternative to the facility quality index incorporating direct clinical observation showed a weaker association with mortality, as did analyses with coarser quality measures. Given the smaller sample sizes with the larger quality scale, it is hard to directly compare these estimates; the lack of significance in models with a larger number of items could reflect insufficient power or the diminished variation in quality among facilities with more extensive assessments.
Further research is needed to affirm and extend these findings. Validated, efficient metrics of facility quality are essential to strengthen and extend this area of inquiry. Identification of the minimum quality of care sufficient to ensure health outcomes is a particularly critical need in global health research. Replication in countries with higher mortality burdens and different health system capacities would strengthen the generalizability of these results. Such an undertaking is potentially feasible where detailed facility assessments have occurred prior to population health studies that include location data. Multiple tools for facility assessment have been employed throughout sub-Saharan Africa [39] in addition to the more commonly used population health surveys, yet their use for research has been limited to date. In general, linking facility surveys to population outcomes is complicated by the random sampling used for facility assessments and by displacement of household locations to preserve individual anonymity [40]. Full national facility censuses with quality assessments like the one conducted in Malawi would allow more research linking household heath behaviors and outcomes to facility indicators.
What do these findings imply for policy? Malawi is a leader in sub-Saharan Africa in implementing evidence-based policies to improve maternal and child health; the recently adopted Every Newborn Action Plan explicitly identifies improving facility quality as one means towards reducing newborn mortality [8]. This study provides strong and direct empirical support for such a policy and should galvanize targeted quality improvement interventions to extend child survival gains to newborns. Critical infrastructure and performance of basic emergency obstetric care functions may be priority areas for improvement. Neonatal mortality rates vary widely by district from under 15 to over 40 per 1,000 live births in urban versus rural districts [8]. In this context, the findings suggest targeted interventions at facilities in areas with no high-quality facilities, particularly in high-mortality districts, may be a starting point for quality improvement efforts. The exploration of associations at lower and higher thresholds of quality provides initial evidence that quality improvements are needed at most facilities; targeting only the lowest-performing facilities is unlikely to affect mortality. However, evidence for interventions that can rapidly improve quality of delivery care at scale is limited to date [41]. Given that larger facilities and hospitals had better quality performance, one strategy for providing women with better care is regionalizing delivery care to highest-quality centers while improving transport for women to reach these facilities [42].
Beyond Malawi, these results argue for pivoting from a focus on access to delivery facilities to measuring and improving quality of these facilities in the pursuit of reduced neonatal mortality. Although access to care is essential, ambitious global targets for newborn and child survival can be met only if the care that women receive is of sufficient quality.
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10.1371/journal.pntd.0006508 | MicroRNA and cellular targets profiling reveal miR-217 and miR-576-3p as proviral factors during Oropouche infection | Oropouche Virus is the etiological agent of an arbovirus febrile disease that affects thousands of people and is widespread throughout Central and South American countries. Although isolated in 1950’s, still there is scarce information regarding the virus biology and its prevalence is likely underestimated. In order to identify and elucidate interactions with host cells factors and increase the understanding about the Oropouche Virus biology, we performed microRNA (miRNA) and target genes screening in human hepatocarcinoma cell line HuH-7. Cellular miRNAs are short non-coding RNAs that regulates gene expression post-transcriptionally and play key roles in several steps of viral infections. The large scale RT-qPCR based screening found 13 differentially expressed miRNAs in Oropouche infected cells. Further validation confirmed that miR-217 and miR-576-3p were 5.5 fold up-regulated at early stages of virus infection (6 hours post-infection). Using bioinformatics and pathway enrichment analysis, we predicted the cellular targets genes for miR-217 and miR-576-3p. Differential expression analysis of RNA from 95 selected targets revealed genes involved in innate immunity modulation, viral release and neurological disorder outcomes. Further analysis revealed the gene of decapping protein 2 (DCP2), a previous known restriction factor for bunyaviruses transcription, as a miR-217 candidate target that is progressively down-regulated during Oropouche infection. Our analysis also showed that activators genes involved in innate immune response through IFN-β pathway, as STING (Stimulator of Interferon Genes) and TRAF3 (TNF-Receptor Associated Factor 3), were down-regulated as the infection progress. Inhibition of miR-217 or miR-576-3p restricts OROV replication, decreasing viral RNA (up to 8.3 fold) and virus titer (3 fold). Finally, we showed that virus escape IFN-β mediated immune response increasing the levels of cellular miR-576-3p resulting in a decreasing of its partners STING and TRAF3. We concluded stating that the present study, the first for a Peribunyaviridae member, gives insights in its prospective pathways that could help to understand virus biology, interactions with host cells and pathogenesis, suggesting that the virus escapes the antiviral cellular pathways increasing the expression of cognates miRNAs.
| Oropouche Virus causes typical arboviral febrile illness and is widely distributed in tropical region of Americas, mainly Amazon region, associated with cases of encephalitis. 500,000 people are estimated to be infected with Oropouche worldwide and some states in Brazil detected higher number of cases among other arboviruses such as Dengue and Chikungunya. As much as climate change, human migration and vector and host availability might increase the risk of virus transmission. Despite its estimated high prevalence in Central and South America populations, the literature concerning the main aspects of viral biology remain scarce and began to be investigated only in the last two decades. Nonetheless, little is known about virus-host cell interactions and pathogenesis. Virus infection regulates cellular pathways either promoting its replication or escaping from immune response through microRNAs. Knowing which microRNAs and target genes are modulated in infection could give us new insights to understand multiple aspects of infection. Here, we depicted candidate miRNAs, genes and pathways affected by Oropouche Virus infection in hepatocyte cells. We hope this work serve as guideline for prospective studies in order to assess the complexity regarding the orthobunyaviruses infections.
| Oropouche Virus (OROV) is the etiological agent of Oropouche fever, an arthropod-borne viral disease characterized by symptoms like fever, headache, myalgia, arthralgia, malaise, photophobia, nausea, vomiting, dizziness, skin rash, and in few cases encephalitis and meningitis [1–7]. It was first described in Trinidad in 1955 [8] and the first Brazilian strain was isolated from a dead pale-throated three-toed sloth (Bradypus tridactylus) near a highway construction campsite in Belém, Pará state, northern Brazil [9]. It is estimate that more than 500,000 people were infected in at least 30 outbreaks in South and Central America between 1961 and 2009 [8, 10, 11, 12], placing Oropouche fever as one of the most prevalent arboviral disease in some states of Brazil, after Dengue, Chikungunya and Yellow Fever. However, the virus pathogenesis is still obscure, and Oropouche fever is still considered a neglected disease. During urban outbreaks, the virus is mainly transmitted by its major transmission vector, the midge Culicoides paraensis [3, 9, 13]. Other insect species, like mosquitoes of the genus Aedes and Culex, might also be potential vectors [9]. OROV is classified in the order Bunyavirales, Peribunyaviridae family, Orthobunyavirus genus, as Bunyamwera Virus, La Crosse Virus and the recently discovered Schmallenberg Virus [14]. The order Bunyavirales is the largest virus order, containing several viruses implicated in the etiology of relevant human diseases, such as La Crosse Virus (LACV) and Oropouche Virus (Orthobunyavirus), Rift Valley Fever Virus (RVFV) (Phlebovirus), Crimean-Congo Fever Virus (CCFV) (Orthonairovirus) and the rodent-borne Hantaan Virus (HTNV), Andes Virus (ANDV) and Sin Nombre Virus (SNV) (Orthohantavirus). OROV has a tri-segmented negative strand RNA genome with a small segment (S) that encodes the nucleocapsid protein N and a non-structural protein NSs; a medium (M) segment that encodes the glycoproteins Gc and Gn and another non-structural protein, NSm, and a large (L) segment that encodes the viral RNA-dependent RNA polymerase (RdRP) [15]. Despite its relevance as a human pathogen and its high prevalence in South America, little is known about OROV replicative cycle, pathogenesis and virus-host interactions. A recent study demonstrated that the OROV entry in HeLa cells is dependent on clathrin-coated pits [16]. Another report showed the relevance of MAVS, IRF-3 and IRF-7, components of the innate immune response, in restricting OROV infection in knockout mice models and non-myeloid cells [17]. Despite that, the virus pathogenesis and the cellular pathways regulated by OROV infection are not known in detail. Gene expression and post-transcriptional regulation is mediated by short non-coding RNAs (microRNAs, miRNAs or miR) that plays important roles during virus replication.
MicroRNAs span between 19–22 nucleotides in length and their first description was made in nematodes [18, 19], though now they have been identified in several phyla of plants and animals [20], and even in viral genomes [21]. In mammals, they can be generated from intronic and exonic regions of protein-coding genes or intergenic regions [22]. They can be found as single miRNA genes or in clusters that encodes long precursor molecules, the pri-miRNA, ranging from hundred to thousand nucleotides in length [23, 24]. Pri-miRNAs begins to be edited in the nucleus by the enzyme Drosha into pre-miRNAs, shorter 70 nucleotides long molecules with hairpin structures [25, 26]. Those pre-miRNAs are exported from the nucleus into the cytoplasm by proteins such as exportin 5 and RAN-GTP [27], and are further processed by Dicer into a 22 nucleotides long double-stranded RNA (commonly referred as miRNA:miRNA*) [28, 29]. The double-stranded RNA is loaded into an Argonaute-driven RNA induced silencing complex (RISC), which selects one strand and binds to a target mRNA (commonly in the 3’-untranslated region, or 3’-UTR region) [30, 31] by base complementarity. The miRNA interaction with its target mRNA induces gene silencing by degradation (when full complementarity between the miRNA and the target sequence occurs) [32], or translational inhibition (in case of partial complementarity) [33, 34]. Since the seed sequence (the minimal complementarity site between miRNA and mRNA) is usually 7–8 nucleotides long, a single miRNA could regulates expression of several genes, as well as a single gene could be regulated by many miRNAs [35, 36].
MiRNAs have already been described influencing disease progression, pathogenicity and replicative cycle of several viruses, being either inhibitory or stimulatory of the infection [37, 38]. The liver-specific miRNA-122 stimulates HCV translation, stabilizing and protecting the 5’-UTR of viral RNAs from degradation, leading to an accumulation of the same in the cytoplasm [39–42]. In resting CD4+ T lymphocytes, HIV-1 viral production is impaired by cellular miRNAs that contribute to establish the viral latency [43]. Another miRNA, miR-29a, targets HIV-1 RNA to accumulate in RNA processing bodies (P-bodies), inhibiting virus infection through translation suppression [44]. Even different strains of the same virus can elicit different miRNA regulation responses, as demonstrated for the highly-pathogenic avian-derived Influenza A H7N7 strain and the low-pathogenic swine-derived Influenza A H1N1 strain [45], suggesting that miRNA signature profiles could raise clues about pathogenicity variation.
Concerning miRNA regulation by bunyaviruses, a study with pathogenic and non-pathogenic strains of hantaviruses demonstrated the variation on miRNA profile among the different specie-specific viruses and cell types [46]. Another study with the Hantavirus Respiratory Syndrome (HPS)-causing agent, Andes Virus (ANDV), identified down-regulation of miR-126 expression, a miRNA that acts as regulator of SPRED1 [47]. Increased expression of SPRED1 was suggested to be one of the mechanisms that augment endothelial cells permeability, leading to HPS. A recent study with PBMC of patients presenting acute hemorrhagic fever caused by the Crimean-Congo Hemorrhagic Fever Virus (CCHFV) showed the deregulation of several miRNAs, some of them associated with innate immunity and viral escape mechanisms [48]. The only study with phleboviruses described the association between miR-142-3p and the endocytic vesicle protein VAMP3, suggesting a control mechanism for the intracellular trafficking of Uukuniemi Virus (UUKV) [49].
Due to the scarcity of information regarding the regulation of bunyaviruses by miRNA and the increasing necessity of better understanding of virus-host interactions of relevant emerging pathogens, we aimed to evaluate and identify the cellular miRNA profile and target genes induced by OROV infection in vitro. We demonstrated that miRNAs miR-217 and miR-576-3p, differentially expressed during infection, could be regulating crucial pathways, like innate immunity response, mainly in upstream proteins of interferon-β induction pathway (adaptor and kinase proteins, as well as transcription factors), protein shutoff and apoptosis.
Cell lines Vero (ATCC, CCL-81), U87-MG (ATCC, HBT-14) and HeLa (ATCC, CCL-2) were maintained in DMEM (Gibco) supplemented with 10% v/v Fetal Bovine Serum (FBS) (Gibco) and 1% v/v of penicillin-streptomycin (10.000 U/ml-10.000 μg/ml) (Gibco) at 37°C and 5% CO2. HuH-7 cells were maintained in DMEM without sodium pyruvate (Gibco) supplemented with 10% v/v HyClone serum (GE Life Sciences), 1% v/v antibiotics, 1% 200 mM L-Glutamine (Gibco) and 1% v/v non-essential aminoacids (Gibco) at 37°C and 5% CO2. Jurkat (ATCC, TIB-152) and THP-1 (ATCC, TIB-202) were maintained in RPMI-1640 medium (Gibco) supplemented with 10% v/v FBS, 1% v/v antibiotics and 1% v/v sodium bicarbonate (Gibco) at 37°C and 5% CO2. OROV strain BeAn19991 was originally obtained from the Evandro Chagas Institute and propagated by serial passages in Vero cells by routine methods using DMEM. The OROV stock used in the present experiments was propagated in HeLa cells and titrated to 2 x 106 PFU/ml. Infections were performed at MOI 1 during 1 h at 37°C and 5% CO2 in medium without FBS, under biosafety level 3 conditions at a BSL-3 laboratory at Universidade Federal do Rio de Janeiro.
Virus titration was performed by plaque assay in Vero cells plated at 3 x 105 cells/well in 12 well plates 1 day prior to infection. After 1 h incubation with the virus, cells were replenished by DMEM supplemented with 1% v/v FBS, 1% v/v antibiotics and 1% v/v carboxymethyl cellulose (CMC) (Sigma-Aldrich), and incubated at 37°C and 5% CO2 during 4 days. Cells were fixed with 4% formaldehyde for 20 min at room temperature, washed in Phosphate Buffered Saline (PBS) (Gibco) and stained with 20% v/v ethanol-violet crystal solution for 15 min.
In order to induce monocyte-to-macrophage differentiation, THP-1 cells were stimulated with 100 nM phorbol 12-myristate 13-acetate (PMA) (Sigma-Aldrich) in standard RPMI medium for 24 h or 3 days followed by 5 days incubation at RPMI medium without PMA. Fresh RPMI medium was provided to cells after treatment and before infections. THP-1 derived macrophages cells were infected as described above, at 24 h or 8 days post PMA treatment.
Cells (105 cells/sample) were fixed with 4% paraformaldehyde for 20 min and permeabilized in 1% v/v Triton X-100 PBS solution. Blocking was performed in 5% v/v Donkey Serum (Sigma-Aldrich) PBS solution for 1h at 37°C. OROV infected and uninfected cells were incubated with mouse polyclonal anti-OROV antibody at 1:300 dilution in blocking solution at 37°C for 30 min. Cells were then washed thrice in PBS and incubated with 2 μg/ml Donkey anti-mouse AlexaFluor 488 secondary antibody (Thermo Fisher Scientific) at 37°C for 30 min. After incubation with the secondary antibody, cells were washed and resuspended in PBS. Flow cytometry was performed in Accuri C6 cytometer (BD Biosciences). At least 10,000 gated events were counted per experimental replica at FITC channel.
HuH-7 were plated at 2 x 104 cells/well density on 96-well plate and incubated at 37°C and 5% CO2 for 12 h. After that, cells were infected as described above. Cell viability was evaluated by CellTiter-Blue (Promega) according to manufacturer’s instructions. The fluorescence was measured at SpectraMax Paradigm Multi-Mode Detection Platform (Molecular Devices).
Total cellular RNA for microarray and target mRNA RT-qPCR analysis was isolated using MirVana kit (Thermo Fisher Scientific) according to manufacturer’s instructions. RNA quantification and quality was assessed by 2100 Bioanalyzer using RNA 6000 Nano kit (Agilent Technologies). Only samples with a RNA Integrity Number (RIN) ≥ 9.0 were used for microarray. Extraction of RNA for miRNA validation with specific primers was performed using PureLink RNA Mini Kit (Thermo Fisher Scientific) and quantification and integrity were assessed in NanoVue Spectrophotometer (GE Life Sciences). All RNAs were treated with DNase (TURBO DNA-free Kit, Thermo Fisher Scientific) before RT-qPCR experiments to avoid DNA contamination.
In order to evaluate the expression profile of miRNAs, an array using Taqman chemistry was performed as follows: 12 h after infection, six independent replicas of mock-infected or Oropouche infected (4 x 106 cells/replica at MOI 1) HuH-7 cells were trypsinized (Trypsin 0.25%, Gibco) and the total cellular RNA was extracted and quantified as described above. cDNA was generated using TaqMan MicroRNA Reverse Transcription Kit (Thermo Fisher Scientific) with 100 ng of RNA per sample according to manufacturer’s instructions. The cDNA was preamplified using Megaplex PreAmp Primers (Thermo Fisher Scientific) and Taqman PreAmp Master Mix (Thermo Fisher Scientific) as instructed by manufacturer. The qPCR reaction was performed using Taqman OpenArray Human MicroRNA Panels (Thermo Fisher Scientific), Taqman OpenArray Real-Time Master Mix and the OpenArray Accufill system OpenArray real-time robotics (Thermo Fisher Scientific). This platform is able to quantify 754 human inventoried miRNAs.
R statistical language [50] was used for background correction and data exploratory analysis (Rn intensity cumulative curve and High Resolution Melting—HRM graphs) for each RT-qPCR reaction. For relative expression quantification, a four parameters sigmoidal curve adjustment was done using the qpcR functions in R language [51]. Quantification cycle (Cq) was determined as the relative cycle to second derivative maximum point of adjusted sigmoidal curve (cpD2). The amplification efficiency was determined at the exponential amplification region, at the mean point between relative cycles to the first derivative maximum point and second derivative maximum point of adjusted sigmoidal curve [expR = cpD2-(cpD1-cpD2)], and calculated as the ratio between the expR corresponding cycle fluorescence and the prior cycle fluorescence. For each miRNA, the amplification efficiency was determined as the mean of efficiencies calculated for the corresponding miRNA. Endogenous small-nucleolar RNAs RNU 44, RNU 48 and U6 RNA were candidates for normalization controls selected by the geNorm method [52]. As an alternative normalization method, the normalization factor was calculated by the geometric mean of all miRNA expressed in each sample [53]. For normalized expression comparison between two sample groups, we performed a non-parametric T-test with 1,000 permutations [54]. For three or more groups comparison we used a one-way non-parametric ANOVA with unrestricted permutation (n = 1,000) followed by a non-parametric pairwise T-test mean comparison with permutation (n = 1,000) followed by Bonferroni correction [54]. Results were presented as mean ± S.E.M (standard error mean). Two-tailed p-values in sample groups’ comparison lower or equal to 0.01, 0.05 or 0.1 were considered as highly significant, significant and suggestive, respectively. The relationship between sample profiles was investigated by Bayesian Infinite Mixtures Model cluster analysis [55] and represented by 2D heatmap with dendrograms (bi-cluster). For the purpose of display in the heatmap, k-nearest neighbors method (k = 5) was performed to predict the missing values in uninfected cells for miR-217, miR-26a-2-3p and miR-92a-5p. After imputation of the missing values, a scaled (Z-score) normalization was performed (subtracted miRNA mean divided by miRNA standard deviation).
Reverse transcription was performed using miRNA 1st-Strand cDNA Synthesis Kit (Agilent Technologies) and qPCR reactions were made with High-Specificity miRNA QPCR Core Kit (Agilent Technologies) and forward specific primers for each miRNA investigated. Human U6 RNA forward primer (Agilent Technologies) was used as normalization control. All the experiments were done in four independent replicas for each time point and sample group. The qPCR reaction was performed in 7500 Real-Time PCR System (Applied Biosystems). The cycling parameters were set for standard SYBR Green method according to manufacturer’s instructions as follow: 95°C– 10 min and 95°C– 10 sec, 60°C– 15 sec, 72°C– 20 sec for 40 cycles. The miRNA forward primers sequences are depicted in S1 Table. Statistical analysis was performed using non-parametrical Mann-Whitney tests.
We only consider a putative target for differentially expressed miRNA (miRNA:mRNA interaction) the ones predicted in at least 3 out of 6 public databases as follows: TargetScan, (available at http://www.targetscan.org/index.html) miRTarget2 (available at http://mirdb.org/miRDB/), PicTar (available at https://pictar.mdc-berlin.de/), miRBase (available at http://www.mirbase.org/), TarBase (available at http://carolina.imis.athena-innovation.gr/diana_tools/web/index.php?r=tarbasev8%2Findex) and miRanda V3.3a. Interaction network tree was designed using Cytoscape v3.2.1 software (Cytoscape Consortium).
Ontology enrichment analysis [56] was performed for the predicted targets of the differentially expressed miR-217 and miR-576-3p. The ontologies were enriched mainly to biological processes, molecular function, cellular components, and gene interaction/regulation pathways. Only genes predicted in at least 3 out of 6 databases were considered candidate targets. Gene Entrez id for the predict ontologies were used in the Gene Ontology Database (GO, available at http://www.geneontology.org/), KEGG (available at http://www.genome.jp/kegg/) and REACTOME (available at http://www.reactome.org/PathwayBrowser) for this purpose. Only genes over represented in hypergeometric tests with p-value ≤ 0.001 were considered.
HuH-7 cells were seeded (105 cells/replica) in triplicate into 24 wells plate overnight. Negative control inhibitor, miR-217 inhibitor and miR-576-3p inhibitor (Integrated DNA Technologies) were transfected at a final concentration of 75 nM using 2 μl of Lipofectamine 2000 (Thermo Fisher Scientific) per replica. Green fluorescent short RNA siGLO (Dharmacon, GE Life Sciences) was used to assess transfection efficiency and establish the miRNA inhibitor concentration for inhibition experiments. 3 h post-transfection, cells were infected with OROV at MOI 1 and RNA were extracted for miRNA quantification (6 h post-infection) or target gene and OROV RNA quantification (18 h post-infection) by RT-qPCR. OROV segment S RNA was quantified using primers and probe [57] with Taqman 2x Universal PCR Master Mix (Thermo Fisher Scientific) and normalized by GAPDH using PrimeTime primers and probe mix (Integrated DNA Technologies).
Cells were seeded (106 cells/sample) and infected with OROV at MOI 1. The RNA was extracted at 12 h post infection and reverse transcription was performed using High-Capacity cDNA Reverse Transcription Kit (Thermo Fisher Scientific) and 1 μg of RNA. Quantitative PCR was done in six replica per condition using 50 ng/well of cDNA on Custom Taqman Array Fast plates (96 well) (Thermo Fisher Scientific) using specific primers and probes and Taqman Fast Universal PCR Master Mix (Thermo Fisher Scientific) according to manufacturer’s instructions on 7500 Fast Real-Time PCR System (Thermo Fisher Scientific). Statistical analysis was done as described for microarray using endogenous 18S, GAPDH, HPRT1 and GUSB as normalization genes. For target kinetics SYBR Green PCR Master Mix (Applied Biosystems) and pre-designed PrimeTime primers (Integrated DNA Technologies) were used according to manufacturer’s instruction (for primers sequences see S1 Table).
In order to expand the knowledge on the range of OROV-permissive cells, blood and hepatocyte cell lines were used to evaluate in vitro infection (Fig 1A). T CD4+ lymphocytes (Jurkat), monocytes (THP-1) and hepatocytes (HuH-7) cell lineages were infected with OROV at MOI 1 and, at 12 h post infection, infectivity was assessed by immunofluorescence using specific antibodies against OROV proteins and virus-positive cells were counted by flow cytometry. At indicated time points, 21% of Jurkat cells were infected, while THP-1 presented no susceptibility to the OROV infection. THP-1 cells can be induced to differentiate into macrophage by PMA treatment, becoming permissive to some viral infections, as described elsewhere [58–61]. In order to assess if THP-1 cells differentiated into macrophages were permissive to OROV infection, THP-1 cells were treated with PMA for 24 h or for 3 days, followed by incubation in medium without PMA for 5 more days. Differentiation of THP-1 into macrophage-like phenotype was accompanied by microscopy and attachment. At 12 h post infection, 31% and 50% of THP-1 treated with PMA for 24 h or 8 days, respectively, were infected with OROV, suggesting an increasing permissiveness to OROV infection as the cells shift from monocyte to macrophage-like phenotypes (Fig 1A). At the same MOI, HuH-7 cells showed to be more permissive to OROV infection, presenting 90% infected cells at 12 h post-infection (Fig 1A). Based on this result with HuH-7 cells, and considering previous demonstrations that the liver is an important replication site during experimental OROV infection in hamster [62, 63] and mouse [64], we chose the hepatocyte cell line HuH-7 as our in vitro model for further experiments.
To assess the most suitable conditions to ensure that most cells would be infected at indicated time points, HuH-7 cells were infected with different MOIs and the infectivity was measured by flow cytometry (Fig 1B). We reached 30% of infectivity at MOI 0.1 with a plateau of 90% in higher concentrations of virus (MOIs 1, 5 and 10), with no further increase of infectivity levels (Fig 1B). To assure that cells were still viable for further experiments, we assessed the cytopathic effect at 6, 12, 18 and 24 h post infection with MOI of 1 using Cell Titer-Blue (Fig 1C). We did not detect cell death associated to the OROV infection at least 18 h post infection. However, only 44% of cells were viable at 24 h post infection. We also quantified the virus titer generated in those cells by plaque assay and at 6 h post-infection, the titer in the supernatant was 6 x 103 PFU/ml (Fig 1C, gray line). As the infection progressed, a peak of 8.6 x 105 PFU/ml could be detected at 18 h post infection, reaching a plateau with no further increase in viral titer at 24 h post infection (Fig 1C, gray line). Based on these data, we proceeded using HuH-7 cells infected with MOI 1 in subsequent experiments to evaluate the virus-host interactions.
MiRNAs can be informative of cellular targets modulated by virus infection. In order to identify candidate cellular pathways differentially expressed in OROV infected cells, we performed an exploratory screening of 754 human miRNAs through probe-based RT-qPCR. MiRNAs expression was evaluated in four uninfected (control) and five OROV infected biological replicas at 12 h post infection. We found thirteen miRNAs differentially expressed upon OROV infection in HuH-7 cells with statistical significance: twelve up-regulated after infection and only one down-regulated (miR-450b-5p) (Fig 2 and Table 1). The reproducibility of effects in miRNAs was indicated by small variance noted among biological replicas, as depicted in the heat map hierarchical dendrogram (Fig 2).
The differential expression of the miRNAs in OROV infected cells was classified into three groups: up-regulated, down-regulated and infection-dependent miRNAs (selectively expressed miRNAs). MiRNAs miR-324-3p (1.73x), miR-1227 (1.95x), miR-362-3p (1.85x), miR-99b-3p (2.21x), miR-19b-1-5p (4.11x), miR-628-3p (2.77x), miR-26a-1-3p (42.47x), miR-576-3p (2.49x) and miR-27a-5p (108x) were up-regulated, in OROV-infected cells relative to uninfected cells. MiR-450b-5p was down-regulated 4.65 times in infected cells compared to uninfected cells. The induction of miR-26a-2-3p and miR-217 were inconsistent and observed only in three out of five infected replicas. From the thirteen selected miRNAs from the screening, only miR-576-3p and miR-26a-1-3p sustained significance (p ≤ 0.05, p ≤ 0.01, respectively) after Bonferroni correction according to the method used in this study. Nonetheless, some miRNAs presented borderline limits of significance (p = 0.0595), namely, miR-1227, miR-19b-1-5p and miR-450b-5p (Table 1).
In order to validate the miRNAs that were significantly deregulated in the array (miR-26a-1-3p and miR-576-3p) and to verify the expression of the miRNAs only detected in infected cells in the expression profile array (miR-217, miR-26a-2-3p and miR-92a-1-5p), we designed specific primers for each miRNA and checked its expression by RT-qPCR (Fig 3). Our validation experiments showed the same tendency of the miRNAs panel with an increasing expression of both miR-217 (Fig 3A) and miR-576-3p (Fig 3B) during infection, reaching a peak of expression at 6 h post-infection (about 5.5 fold increase for both miRNAs). The kinetics of expression of miR-217 suggests an early induction during infection compared to miR-576-3p, since miR-217 was up-regulated 2.26 times as early as 3 h post-infection while miR-576-3p was only up-regulated 1.44 at the same point. However, at later stages of infection, miR-217 expression was already closer to uninfected expression levels (up-regulated only 1.7 at 12 h post-infection), whereas miR-576-3p was still up-regulated 2.83 times in infected cells, indicating a slightly different kinetics for those miRNAs.
To confirm the robustness of our analysis, we further validated the expression of three other less stable star miRNAs: the highly significant miRNA miR-26a-1-3p and two miRNAs detected only upon infection, miR-26a-2-3p and miR-92a-1-5p (Fig 3C). Those three miRNAs were up-regulated 5.3, 4.5 and 6.3 fold, respectively, at 12 h post-infection in comparison with uninfected cells (p ≤ 0.01). Altogether, these results with specific primers to each miRNA corroborate with our large-scale panel data, identifying miRNAs that are modulated during OROV infection showing the same tendency with different approaches. Star miRNA nomenclature corresponds to passenger strands less favorable to processing by RISC with lower likelihood to regulate gene expression [65, 66]. As most of those miRNAs are previously annotated as star miRNAs (as example of miR-26a-1-3p previously annotated as miR-26a-1*) and some prediction database algorithms use proved interaction as criteria for prediction, we only selected miR-217 and miR-576-3p, both mature strand miRNAs, for further target prediction analysis (one detected only in infected cells and the other one up-regulated significantly upon infection in the array, respectively, and both validated).
To investigate possible pathways regulated by miR-217 and miR-576-3p during OROV infection, we performed target prediction using TargetScan, miRTarget2, PicTar, miRBase, TarBase and miRanda databases. Target genes predicted by at least 3 out of 6 of those databases were considered candidates. We predicted 195 cellular genes to interact with miR-217, miR-576-3p, or both, using that criterion (S2 Table). We used enrichment analysis with GO, KEGG and REACTOME to identify cellular pathways affected by the predicted targets identified with our selection criteria. Our analysis showed the enrichment of cellular pathways related to regulation of cell metabolic processes, cell cycle and differentiation, chromatin stability and RNA metabolism and expression, suggesting that OROV infection possibly affects cell basic processes and RNA-related regulation processes, as expected for a RNA virus (Fig 4). This can be confirmed by the increasing numbers of observed genes (gray columns) compared with the expected numbers (black columns) for each cellular pathway analyzed (Fig 4). All the analysis showed very significant statistical levels with p values < 0,0001.
We selected 95 target genes, which were either in the group of 195 predicted target genes (92 genes) and/or either were already published as target genes for those miRNAs, to evaluate their expression through RT-PCR in OROV infected hepatocyte cells. The selection criterion was based on the function described in the literature or involvement in relevant biological pathways related to RNA viruses such as: intracellular trafficking, apoptosis, innate immunity, gene expression regulation, antiviral restriction factor, protein synthesis regulation and intracellular signaling. The predicted selected targets and their association with miRNAs are depicted in the Fig 5 interaction network (see also S3 Table for a brief description of targets function).
From the 95 selected genes tested by RT-qPCR analysis we showed only the 18 genes that were differentially expressed 12 h post infection in comparison with uninfected cells (Fig 6A). The majority (16 genes) were down-regulated, corroborating with the opposite up-regulation trend of the related miRNAs during infection. The gene expression of membrane anchor protein ADAM9, the component of SCF (SKP1-CUL1-F-box protein) E3 ubiquitin-protein ligase complex F-box Only protein 11 (FBXO11), the TNF Receptor Associated Factor 3 (TRAF3), the Mitogen-Activated Protein Kinase 1 (MAPK1) and the Mitochondrial Antiviral-Signaling protein (MAVS), all had a trending of (0.05 ≤ p ≤ 0.1) down-regulation. They were 3.9, 3.9, 1.59, 1.39 and 1.26 fold (ADAM9, FBXO11, TRAF3, MAPK1 and MAVS, respectively) less expressed in infected cells 12 h post infection. On the other hand, the pro-inflammatory chemokine C-X-C motif Ligand 2 (CXCL2) had a trending 3.5 fold up-regulation (0.05 ≤ p ≤ 0.1). The Cytochrome C Oxidase assembly subunit 18 (COX18) was the only significantly up-regulated transcript (p ≤ 0.05) with a fold increase of 21.5 times. The significantly down-regulated transcripts include the Decapping Protein 2 (DCP2), Fibronectin Type III Domain Containing 3B (FNDC3B) protein, the chaperone protein Chaperonin Containing TCP1 Subunit 6B (CCT6B) and glutamate transporter Solute Carrier Family 1 Member 2 (SLC1A2) (2.78, 5.47, 17.21 and 39.56 times in infected cells, respectively). The Neurofibromin 1 (NF1), the FYVE, RhoGEF And PH Domain Containing 4 (FGD4), the transcription factor Nuclear Factor I A (NFIA), the Cardiotrophin-Like Cytokine Factor 1 (CLCF1), the Stimulator for Interferon Genes (STING) and the structural component of caveolae invaginations Caveolin 2 (CAV2) were down-regulated (12.57, 11.16, 8.95, 7.96, 7.16 and 6.59 times, respectively) with the same significance (p ≤ 0.01).
In order to evaluate if target regulation could present a higher effect in a later point of the infection, we selected two predicted and published targets for miR-217 and three for miR-576-3p to assess their expression 24 h post infection (Fig 6B). As it was demonstrated that apoptosis is regulated by OROV replication [67], we selected the Mitogen-Activated Protein Kinase 1 (MAPK1) for being a known miR-217 target that regulates apoptosis [68]. Although MAPK1 was not significantly deregulated at 12 h post infection it showed a 2.23 fold down-regulation at 24 h post infection (p ≤ 0.05). The three selected and unpredicted miR-576-3p targets, MAVS, TRAF3 and STING, are known to be important genes in the regulation of IFN-β response in viral infected cells [69]. MAVS and TRAF3 did not presented a significant down-regulation at 12 h post infection (Fig 6A); however, both presented significantly down-regulation at 24 h post infection (2 and 7.4 fold, respectively). STING, the only one of the three selected targets of miR-576-3p that already demonstrated a significant down-regulation at 12 h post infection, showed an even higher down-regulation at 24 h post infection (39 fold down-regulation at 24 h post infection compared to 7.16 at 12 h post infection). The Silent Information Regulator 1 (SIRT1), a histone deacetylase known to be involved in stress-responsive pathways as inflammation [70, 71, 72], was a miR-217 target that did not show significant differential expression relative to uninfected cells 12 h post infection but presented a significant down-regulation at 24 h post infection (3.45 fold down-regulated), what reinforces that different target genes of the same miRNA have different dynamics of regulation. Although not proved as a miRNA target yet, we included DCP2, the only selected miR-217 target already significantly down-regulated at 12 h post infection, because of its relevance as a restriction factor for other bunyavirus [73]. In our model, DCP2 kept a decreasing expression in infected cells, being 20 fold significantly down-regulated at 24 h post infection (p ≤ 0.001).
Overall, we confirmed the modulation of target genes transcription in the opposite direction of its cognate miRNA, showing that miRNA screening is very informative to predict cellular host genes modulated by virus infection.
The type I interferon response is an important canonical innate immunity response mechanism to viral infection. As STING, MAVS and TRAF3 were demonstrated to be key factors in regulation of that response [69], we aimed to quantify the variation in IFN-β transcripts in response to the infection. The IFN-β mRNA levels increased until 12 h post-infection, when it began to drop abruptly, reaching lower levels at 24 post infection (Fig 7A). Those results are consistent with interferon immune response being triggered at early stages of virus replicative cycle. Virus RNA secondary structures are recognized by RIG-I-like receptors (RLR) or toll like receptors members at early stages of virus replication. However, as the infection proceed, the virus induces the miR-576-3p expression promoting the down regulation of its target genes STING and TRAF3 (Fig 7B and 7C)). We hypothesize that OROV try to escape IFN-β response reducing the levels of STING and TRAF3 through miR-576-3p induction (Fig 7D).
In order to assess if miR-217 and miR-576-3p were playing a role in OROV infection, we aimed to evaluate their impact in OROV replication using specific anti-miRNAs (Fig 8).
To accomplish that, HuH-7 cells were transfected with non-human negative control miRNA inhibitor, miR-217 inhibitor, miR-576-3p inhibitor or both miRNA inhibitors and infected with OROV 3h post-transfection. At least 70% of cells were efficiently transfected with negligible cytotoxicity at the concentration tested (S1 Fig). Both miRNA presented a 3-fold decrease in the presence of its respective inhibitor in comparison with negative inhibitor control (Fig 8A). Nonetheless, the predicted target genes for miR-217 and miR-576-3p, DCP2 and STING, respectively, recovered to similar levels to non-infected cells in the presence of miRNA inhibitors 18 h post-infection (Fig 8B). DCP2 RNA levels were slightly above of those in non-infected cells (up to 0.5 fold) whereas STING mRNA levels did not recovered completely but presented a lower decrease compared to the positive control (1.9 and 7.4 fold decrease, respectively).
To further confirm if the miRNA inhibition would influence OROV replication, we measured the intracellular viral RNA levels 18 h post-infection in the presence of miRNA inhibitors (Fig 8C). Inhibition of miR-217 led to a 2.3 fold decrease in viral RNA replication, while inhibition of miR-576-3p led to a 7.7 fold decrease. The highest reduction was observed with inhibition of both miRNAs (8.3 fold), but was not significantly lower than miR-576-3p inhibition alone (Fig 8C). Finally, reduced viral titers confirmed the diminished replication, as a 3-fold decrease was observed in the same time point using miR-217 and miR-576-3p inhibitors (Fig 8D). Altogether, those data demonstrate that inhibition of miR-217 and miR-576-3p is a prospective approach to restrict OROV replication in HuH-7 cells.
In this study, we aimed to identify miRNAs and the target genes regulated in OROV infected hepatocyte cell lines. We demonstrated that miR-217 and miR-576-3p were up-regulated during infection and that their cognate targets were down-regulated. Gene targets related to apoptosis, type I interferon-mediated response and antiviral restriction factors were associated with those miRNAs, suggesting a post transcriptional modulation of those pathways by OROV infection, giving new insights about virus-host interactions.
We initially investigate the susceptibility of human cell lines to the viral infection (Fig 1). Lymphocytes T CD4+ cells (Jurkat) demonstrated low permissiveness to OROV, though being susceptible to infection in vitro, as denoted by the 20% of infected cells (Fig 1A). On the other hand, the monocyte cell line THP-1 was not infected in the same conditions. Activation with PMA leads to progressive differentiation of THP-1 into macrophage-like phenotype, as demonstrated elsewhere [58,59,60]. We observed an increase in THP-1 infected population under two different PMA treatment conditions, suggesting a higher susceptibility of those cells as they shift to macrophage phenotype. Indeed, a recent case report detected OROV in peripheral blood mononuclear cells of two patients [74], sustaining the possibility of blood cells playing a role in OROV pathogenesis in humans. In mouse models, however, macrophages only sustained viral replication in immune-compromised individual with deletions in IFN genes [17]. The human hepatocyte cell line HuH-7 was highly permissive to OROV infection, corroborating with previous data that suggest a sustainable liver tropism for OROV [17, 62, 63]. HuH-7 presented a 90% rate of infected cells with no associated cytopathic effect until 18 h post-infection at MOI 1 (Fig 1C), leading us to choose it as our cell model.
We initially found 13 miRNAs differentially expressed in infected cells relative to uninfected cells (Fig 2, Table 1). Some of them were induced while others were modulated upon infection, in agreement with the complexity of miRNA regulation network. MiR-217 was already described in cancer cells involved in tumor migration suppression [75, 76]. Expression kinetics showed a peak at 6 h post-infection for this miRNA (Fig 3A). Regarding the predicted target genes, ten of them were significantly down-regulated 12 h post-infection in RT-qPCR screening (Fig 6A). SLC1A2, also known as Excitatory Amino Acid Transporter 2 (EAAT2) is a glutamate transporter in astrocytes. Lower expression of this transporter was associated with neuropathogenesis outcomes in HIV-1 [77] and Human Herpesvirus 6 (HHV-6) infected cells [78]. In hepatocytes, an increased expression was associated to cholestasis outcome [79]. It is unclear how this transporter could affect OROV infection in hepatocytes, but considering the neurotropism of OROV infection in vivo, an investigation of its role in neuropathogenesis should be considered. Another possible cellular factor related to neuropathogenesis of OROV is NF1. The regulation of NF1 by other miRNAs was already demonstrated in neurons and other tissues [80], and is considered a mechanism of fine-tuning in neurological disorders such as neurofibromatosis. The transcription factor NFIA was recently demonstrated to be a novel factor that is negatively regulated by miR-373 [81]. As a consequence, IFN-β response is down-regulated, facilitating Porcine Reproductive and Respiratory Syndrome Virus (PRRSV) replication. The data suggest that both miR-217 and miR-576-3p could act synergistically to inhibit IFN-β antiviral response. CLCF1 is a member of Interleukin-6 (IL-6) family and play a dual role as pro-inflammatory and anti-inflammatory cytokine. FNDC3B has a role in cell migration and invasiveness in hepatocellular carcinoma [82] and glioblastoma cells [83]. In the second case, it was shown that FNDC3B could be down-regulated by miR-129-5p. Likely, FNDC3B could be one of many targets which down-regulation leads to an apoptotic state in OROV infection. The chaperone protein CCT6B as well as its relevance in OROV infection remains elusive. The assembly factor COX18 is a key component for cytochrome oxidase complex works properly [84]. Although its regulation showed an opposite trend, we speculate that this phenomenon could be a collateral effect of the apoptosis state, with cells trying to increase cytochrome oxidase efficiency due to a leaking of cytochrome c to cytoplasm.
We further assessed the expression in a later point of infection (24 h) of three targets of miR-217 during OROV infection: DCP2, MAPK1 and SIRT1 (Fig 6B). DCP2 is a decapping protein involved in mRNA decay. It was recently demonstrated that bunyaviruses compete for the same cellular capped mRNAs that DCP2 targets for degradation in a process known as “cap-snatching” [73]. Bunyaviruses need to snatch capped cellular mRNAs in order to replicate the virus RNA genome; therefore, DCP2 is a direct competitor for bunyaviruses replication. Our results suggested that the down-regulation of DCP2 by miR-217 could explain OROV sustained replication. The kinase MAPK1 and protein SIRT1 both presented a significant lower transcription only 24 h post infection (Fig 6B), when most living cells presumably are in apoptosis process.
The miR-576-3p expression peaked at 6 h post-infection and presented a kinetic similar to miR-217 (Fig 3B). The expression levels at 12 h post-infection were consistent in both quantitative assays (e.g. miRNA array and validation RT-qPCR), corroborating our findings (Table 1 and Fig 3B). At the same time point, two candidate targets, FGD4 and CAV2 were down-regulated, confirming the inverse trend of miR-576-3p (Fig 6A). FGD4 is a protein involved in regulation of actin cytoskeleton and cell migration. Another miRNA, miR-155, was associated to reduced FGD4 levels, resulting in impaired neutrophil migration in myelodysplastic syndromes [85].
CAV2 is a protein component of caveolae structures. A recent study demonstrated that the caveolae and, therefore, its components could act as restriction factor for Tiger Frog Virus (TFV) release in late steps of viral cycle [86] in another hepatocyte cell line, HepG2. As OROV entry is mediated by clathrin-endocytosis [16], we speculate that caveolae could be a restriction site for viral budding/release; therefore down-regulation of a structural component could favors viral release.
MiR-576-3p was recently proposed as a key miRNA in feedback regulation of IFN-β pathway in response to viral infections [69]. Our results regarding down-regulation of STING and TRAF3 corroborated that hypothesis (Fig 6). Moreover, IFN-β transcription regulation correlated with miR-576-3p, STING and TRAF3 transcription dynamics (Fig 7), implying in a temporal feedback mechanism in response to OROV infection, as suggested for other viruses. Based on our data and in conclusions of other group [69], we proposed the following dynamics in antiviral response (depicted in Fig 7D): upon viral entry and uncoating, double-strand viral RNA triggers the IFN-β signaling pathway through STING, MAVS and TRAF3 action, leading Interferon Responsive Factor 3 (IRF3) to activate INF β transcription. Concomitantly, miR-576-3p transcription is also activated by the transcription factor IRF3 and the miRNA accumulation increases until peak 6 h post-infection (Fig 3B). When enough miR-576-3p accumulates in cytoplasm (6 h post-infection) the target mRNA levels, mainly for TRAF3 and STING, begin to fall progressively (Fig 7B and 7C)). At 12 h post-infection, as result of the decrease of STING and TRAF3 mRNA levels, the IFN-β response begins to be relieved, starting a feedback mechanism that leads to a halt in antiviral response and sustaining viral replication.
The miR-576-3p is a primate specific miRNA that was conserved along the evolution, presumably, to avoid tissue damage derived from an excessive inflammatory response due to an infection. Indeed, in mice, OROV infection can be successfully controlled by IFN pathway in immune competent individuals. On the other hand, immune compromised mice (e.g. deleted for genes of IFN pathway) have high mortality rates and fast disease progression with notable liver damage [17]. Our results suggested that, unlike mice, the presence of miR-576-3p in primates and repression of INF-ß rendered them more susceptible to OROV infection.
Furthermore, the inhibition of miR-217 and miR-576-3p partially restricted viral replication, as demonstrated by a decreasing in both viral RNA and titer in the presence of miRNA inhibitors (Fig 8). Those results are in accordance with previous data for miR-576-3p inhibition in other viral infections [69]. We speculate that the restriction is a consequence of a longer sustained innate immune response due to lower suppression of IFN-β pathway signaling cascade in a miRNA inhibition scenery, since both miRNAs might regulate target genes of that pathway.
Finally, as NSs protein has been demonstrated elsewhere to be a candidate viral protein that regulates host innate immune response in other bunyaviruses [87]. A recent study demonstrate that a mutant NSs-deleted OROV induces a strong IFN-α production in opposition to the virus with functional NSs [88]. However, sensitivity to IFN-α treatment was not related to the presence of NSs, as both viruses presented similar sensitivity. It was also demonstrated that OROV is more resistant to IFN-α in comparison to BUNV. Although NSs alone seems to be a candidate viral protein to modulate IFN pathway, we cannot exclude the role of other viral or cellular proteins, as well as viral secondary RNA structures in this conundrum.
We focused our analysis on miR-217 and miR-576-3p given the aforementioned reasons; nonetheless, we cannot exclude the possibility that the other miRNAs identified could be playing a role in OROV infection, as we could validated some of them (Fig 3C). As most of them were star miRNAs and could not be properly investigated by our methodology, a different approach would be necessary to further evaluate if they regulate target genes. Recently, new methodologies to investigate miRNA-mRNAs interactions have been proposed [89, 90] and could be an alternative for future studies. We chose the hepatocyte cell line HuH-7 as our model for an initial, representative study. However, it is also possible that at different time points or using different cell models we could identify different miRNA signatures. It would be interesting to compare the miRNA signature among other permissive cells and using different OROV strains to investigate unique and common miRNA responses to the viral infection. Although the targets validated by RT-qPCR are a good indicative of regulation, those assumptions must be considered with caution, as only RNA down-regulation not necessarily reflects a decrease in protein expression coded by the RNA. Protein quantification (e.g. western blot) would be necessary to assure that the final products of those genes are indeed being regulated. We limited the present study to identify miRNAs and their targets regulated during OROV infection, however, the mechanism by which that regulation occurs remains elusive. A further functional study with expression and knockout of viral proteins could shed a light on the role of viral proteins in this mechanism.
To our knowledge, this is the first study to identify candidate miRNAs that could modulate infection of a member of Orthobunyavirus genus, the most representative genus from Peribunyaviridae family. Taken together, the data obtained in this study hint at pathways that could impact OROV infection, replication and pathogenesis, and expand the knowledge of the complex interactions in bunyavirus infections.
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10.1371/journal.pgen.1000533 | Evolution of Mutational Robustness in the Yeast Genome: A Link to Essential Genes and Meiotic Recombination Hotspots | Deleterious mutations inevitably emerge in any evolutionary process and are speculated to decisively influence the structure of the genome. Meiosis, which is thought to play a major role in handling mutations on the population level, recombines chromosomes via non-randomly distributed hot spots for meiotic recombination. In many genomes, various types of genetic elements are distributed in patterns that are currently not well understood. In particular, important (essential) genes are arranged in clusters, which often cannot be explained by a functional relationship of the involved genes. Here we show by computer simulation that essential gene (EG) clustering provides a fitness benefit in handling deleterious mutations in sexual populations with variable levels of inbreeding and outbreeding. We find that recessive lethal mutations enforce a selective pressure towards clustered genome architectures. Our simulations correctly predict (i) the evolution of non-random distributions of meiotic crossovers, (ii) the genome-wide anti-correlation of meiotic crossovers and EG clustering, (iii) the evolution of EG enrichment in pericentromeric regions and (iv) the associated absence of meiotic crossovers (cold centromeres). Our results furthermore predict optimal crossover rates for yeast chromosomes, which match the experimentally determined rates. Using a Saccharomyces cerevisiae conditional mutator strain, we show that haploid lethal phenotypes result predominantly from mutation of single loci and generally do not impair mating, which leads to an accumulation of mutational load following meiosis and mating. We hypothesize that purging of deleterious mutations in essential genes constitutes an important factor driving meiotic crossover. Therefore, the increased robustness of populations to deleterious mutations, which arises from clustered genome architectures, may provide a significant selective force shaping crossover distribution. Our analysis reveals a new aspect of the evolution of genome architectures that complements insights about molecular constraints, such as the interference of pericentromeric crossovers with chromosome segregation.
| Sexual life cycles constitute a costly alternative to vegetative modes of reproduction. Two categories of hypotheses seek to explain why sexual life cycles exist: those investigating the selective advantages that have driven the evolution of individual parts of this life cycle and those rationalizing the advantages sexual life cycles may offer as a whole, e.g., in extant species. Sex and recombination can be understood as efficient ways to interact with mutations and their consequences. Mutations occur at random and are mostly either deleterious or neutral. A prominent hypothesis suggests that sex and recombination are advantageous since they enhance the purging of such deleterious mutations and create individuals with a lower than average deleterious load. Deleterious mutations should co-determine the parameters that govern recombination of genomes in meiosis. Using an evolutionary computer simulation of diploid, unicellular sexual populations, we show that recessive lethal mutations can drive the evolution of chromosome architectures, in which essential genes become genetically linked into clusters. Evolved architectures exhibit structural properties and fitness similar to digitized yeast chromosomes and provide mutational purging capabilities superior to those of randomly generated or unclustered architectures. Our study demonstrates the importance of sexual cycles in the context of lethal mutations.
| Mating and meiosis are the masterpieces of an evolutionary invention thought to meet the challenges of changing environmental conditions that need to be solved by mutational inventions. Among the many hypotheses that govern the various benefits of mating and meiosis [1], two main hypotheses stand out: enhanced purging of deleterious mutations [2] and the combination of beneficial alleles into one genome [3]. It remains a matter of discussion, however, which of these advantages constitutes the main reason for the evolution of sexual recombination and, furthermore, its continuing prevalence in most eukaryotic life forms [4],[5].
Mutations take the form of DNA lesions that are caused by environmental factors, e.g. radiation, but they are also a natural byproduct of DNA replication. Genotypes that exhibit an elevated mutation rate are frequent in nature and can be induced in studies on experimental evolution. The complex interplay of factors that govern the adaptive significance of “mutator alleles” (i.e. alleles that cause higher mutation rates) has been studied in experimental and theoretical work in unicellular organisms [6]–[8] and during cancer progression [9],[10]. In asexual yeast populations, a selective advantage of mutator alleles has been demonstrated, serving as a prerequisite for expanding the spectrum of mutations typically not accessible in non-mutator genotypes [11]. In this study with yeast, the mutator advantage was found to be more prominent in diploid rather than haploid cells, which can be explained by the presumed dominance of beneficial mutations [11],[12] and by the recessive nature of most deleterious mutations in yeast [13]. Furthermore, the accumulation of recessive deleterious mutations in yeast may not significantly decrease the fitness of the genotype or population growth rates, as long as the diploid nuclear condition is maintained [14].
S. cerevisiae and many other yeast and fungal species seek to maintain the diploid state of the genome, if possible; after meiosis this usually happens by immediate mating of the gametes following germination. Mating occurs mostly between closely related spores, either among products of the same meiosis, or between spores from related cells according to population structure [15]. Outcrossing between unrelated strains [16] and even between closely related species of the sensu stricto yeast group does occur [17]; these events appear to be extremely rare, but they might be important for generating new persisting lineages. However, whether these rare events suffice to create a selective pressure towards maintaining a sexual cycle is doubtable.
Upon inbreeding, high rates of homozygotisation occur at various loci. This is reduced for loci linked to the MAT locus, since mating type heterozygosity is the prerequisite for a mating event. A MAT-linkage to centromeres is frequently observed in yeast and other fungi [18]. In S. cerevisiae, the genetic distance between the two loci is in the range of 18–30 cM [19], which is much smaller than expected from the physical length and caused by a region intervening these two loci that is cold for meiotic recombination [20]. In Neurospora tetrasperma MAT linkage to the centromere is enforced by crossover suppression in a long region of the chromosome, which correlates with an extensive unpaired region at pachytene [21]. Reasoning for such arrangements is provided by population genetic models that suggest a selective advantage arising from shielding of recurrent deleterious load via linkage to the MAT locus [22]. Population genetic modifier models that investigate alteration of the inbreeding frequency predict the evolution of high inbreeding rates, in particular for spores from the same tetrad (i.e. automixis), and the linkage of load loci to the MAT locus [23],[24]. These MAT linked chromosomal recombination abnormalities are believed to have initiated the evolution of sex chromosomes, which was then continued by expansion of the recombination suppressed region through the recruitment of other sex-related factors [25],[26].
Non-random distribution of meiotic recombination throughout the genomes into cold and hot regions has been reported for many species [27], but the molecular mechanisms as well as the selective forces that generate these patterns are still not fully understood.
Similarly, the distribution of genes along chromosomes appears to be non-random, and in many species a significant clustering of essential genes (“housekeeping genes”) has been reported [28],[29]. In budding yeast, a prominent genome-wide enrichment of essential genes has been observed in regions that are cold for meiotic recombination [30]. This finding is consistent with the observation of a slight enrichement of essential genes near the centromeres [31], which are known to be cold for meiotic recombination [32],[33]. Current models speculate about mechanisms of co-expression and reduction of gene expression noise as the driving force that shaped these patterns [29], [30], [34]–[36]. However, the selective advantage of such a scenario has not been demonstrated, and proof that single rearrangements associate with an advantage sufficient for their selection has not been provided.
An alternative mechanism that may pool essential genes into clusters with a reduced probability of disruption by frequent crossovers could be a selection based on their common denominator. This appears to be their essential nature only, as no functional correlation of essential genes within the same cluster has been observed [30].
Meiotic recombination is favorable for purging deleterious load from populations. We hypothesize that clustering of essential genes may further enhance purging efficiency of lethal load from sexual populations, since non-uniform distributions of essential genes and crossover sites change the global genetic linkage relationship of all essential genes (as compared to situations with uniform or random distributions). In the context of a scenario with more than one lethal mutation in the genome, this will influence the segregation of mutations during meiosis and subsequent mating.
In order to address this question, we computationally studied the correlation between non-random distributions of essential genes and meiotic recombination with lethal load affecting essential genes. A Monte-Carlo-simulation of breeding diploid yeast populations and chromosome architectures, termed S. digitalis, allowed us to investigate the fitness of any genome architecture upon exposure to lethal mutations. We find that several hallmarks of yeast chromosome crossing over during meiosis are consistent with natural selection imposed by recessive lethal mutations affecting essential genes (see Figure 1 for an overview of our approach and the results).
Our simulations imply that lethal phenotypes are frequently caused by single essential gene inactivation. Alternatively, lethal phenotypes may arise from genetic interactions between only weakly deleterious mutations. We explored both possibilities using a conditional yeast mutator strain and analyzed the causes of the accumulation of haploid lethal phenotypes. By determining the global effect on germination and mating, we tested whether the associated load is being transmitted into the next round of diploid growth.
Our combined results suggest an evolutionary history for yeast where sex and meiosis fulfilled a need for efficient purging of mutational load in important/essential genes.
We sought to assay the consequences of non-random distributions of essential genes and meiotic recombination hotspots for the fitness of populations upon frequent inactivation of essential genes by lethal mutations. This analysis requires a direct comparison of the fitness of yeast strains with the same genomic content but different arrangements of the genetic elements. Conducting an experiment such that this question can be addressed in isolation from the many other possible consequences of human-designed genomic architectures is far from trivial. Therefore, we developed a computer simulation of populations of digital genomes subjected to digital life cycles of mitosis, meiosis and mating, modeled according to a simple yeast life cycle (Figure 2A). We used this simulation to study the relationship between genome architecture and the fitness of populations as well as the evolution of genome architectures upon exposure of populations to essential gene inactivating mutations (Figure 2B).
Haploid lethal mutations are frequently observed in yeast and constitute approximately 40% of all deleterious mutations [37],[38] (see also below). The remaining fraction of deleterious mutations has been reported to exhibit a weak impact on fitness [37] and both types of mutations are shielded well in heterozygous diploids [39]. Therefore, we decided to focus on heterozygous lethal mutations, which confer a lethal phenotype either on the level of haploids or upon homozygotisation of mutations in diploids (Supplementary Figure 1A in Text S1).
S. digitalis simulates populations of diploid digital organisms with one chromosome, which consists of different building blocks: genes, intergenic elements, centromeres and mating type loci (MATa and MATα). The digital genes are either non-essential or essential. Each gene of the latter category carries a unique identifier. Genes are separated by intergenic elements (IE). IEs are either cold for crossing over ( = coldspots) or hot ( = hotspots). Each feature is represented by an element in the matrix of the population genome, and mutations only affect elements that represent essential genes (Figure 3A). Typical natural yeast chromosomes contain a few hundred genes, and so do our digital counterparts. Populations have a finite size and typically consist of a few hundred to ten thousand diploid individuals.
Mitosis yields a copy of the original genome. It differs from the template genome by mutations, which are introduced at random and lead to the inactivation of essential genes (Figure 3B). The statistical frequency of essential gene mutations per diploid genome and mitosis is given by the genomic recessive lethal mutation rate R [40] (for example, R = 1 corresponds to an average of one essential gene inactivation per diploid genome and mitosis).
Genomic rearrangements can be simulated. They manifest themselves either as positional swapping of genes and associated intergenic elements, or as segmental inversions (Figure 3B). Genomic rearrangements occur in mitosis and always affect both homologous chromosomes.
In meiosis, the genome duplicates and the homologous chromosomes undergo meiotic recombination (crossing over). The distribution of crossover sites considers meiotic recombination hotspots and crossover interference based on a genetic distance definition (hotspot distribution). The crossover frequency can be adjusted by the shape factor of the Erlang distribution that is used to describe crossover interference (Figure 3C). The four meiotic haploid progenitor genomes constitute a tetrad.
Mutations are allowed to occur in mitosis (see Text S1, section “Supplementary Results and Discussion” for an analysis of meiotic mutations). This implementation considers one single mitotic cycle between consecutive meiotic cycles. This mimics a situation in which many consecutive rounds of mitoses occur without exponential growth. This applies to scenarios where a high loss of individuals occurs (e.g. many individuals eaten by predators or washed away into non-fertile grounds) that keeps the size of a local population more or less constant. Under circumstances where deleterious mutations are recessive and do not influence the fitness of the cells (as indicated by literature [39]), this would lead to the accumulation of mutations during the vegetative period of the life cycle. This simplified scenario should come close to a realistic description of natural S. cerevisiae that is consistent with the absence of reports of large natural cultures of S. cerevisiae (outside of human-engineered fermentation processes). Moreover, this approximation allows us to simulate large numbers of complete life cycles, which would otherwise be inaccessible due to computational limitations.
Upon germination, the haploid genomes directly engage in mating with other haploid genomes (Figure 3D). Mating can occur between genomes from the same meiosis, which is called intratetrad mating and more generally referred to as automixis or inbreeding. Mating between haploid genomes from different tetrads can also occur, and is referred to as amphimixis or outbreeding. In this article, we use the terms inbreeding and outbreeding to distinguish between the two principal types of mating partner selection in the simulation: mating inside and outside the tetrad. Outbreeding events may nevertheless bring closely related genomes together, simply due to the finite size of the simulated populations. The total fraction of inbreeding matings per round of mating can be specified. Mating optionally considers mating types, of which two exist (MATa and MATα). The MAT locus can be placed anywhere on the chromosome. In this case, the modeled chromosome can be considered to be the sex chromosome. Alternatively, the simulation can employ a virtual second chromosome that contains the MAT locus next to its centromere. In intratetrad mating, this causes a linkage of the MAT locus to the centromeric region of the investigated chromosome [41].
Diploid genomes with different gene order belong to different species. Individuals from different species are not able to mate with each other. These species represent sub-populations, which emerge in simulation scenarios with genomic rearrangements. Alternatively, different sub-populations can be specified at the beginning of the simulation, e.g. in order to compare the fitness of different genome architectures (species) in survival competition assays.
Fitness of the individuals is assessed in the diploid stage before mitotic or meiotic cell division. We furthermore assumed that mating is not prevented by a haploid lethal mutation in an essential gene (this assumption was experimentally tested; see below, section “Mating rescues genomes associated with lethal mutations”). We decided to use a simple fitness denominator for individuals: a 1 is assigned for diploid genomes that contain at least one functional copy of each essential gene, while a 0 is assigned for genomes, in which both copies of at least one essential gene are non-functional. Individuals with a fitness of 0 are removed. Hence, the only criterion underlying the loss of an individual due to mutations is the homozygotisation of a mutated essential gene. This can occur in two different ways: a new mutation inactivates the second wild type copy or a mating event brings together two chromosomes that both contain a mutated allele at the same position.
Populations were limited in size according to a defined maximum (the population size cap). Excess individuals are removed at random before the next round of mitotic or meiotic division. This simulates limited availability of nutrients. As a result, a selective pressure is introduced that has the potential of driving the evolution of species ( = different genome architectures) that are better adapted to handle lethal mutations. Supplementary Figure 1A and 1B in Text S1 provides an overview of the mechanisms of the simulation.
The simulation provides modules for different types of experiments, including mutational robustness benchmarks of populations with specific genomic architectures, selection advantage assessments with two or more isolated populations that compete for nutrients and evolution experiments of large in- and out-breeding populations that constantly undergo genomic rearrangements (see Text S1). Detailed descriptions of all simulation modules and the implementation of yeast and model chromosomes are provided in Text S1.
Using S. digitalis we first assessed the maximum mutation rate R populations with random distributions of genes and recombination hotspots can resist before becoming extinct (the mutational robustness Rmax). We compared the results with the mutational robustness obtained for the S. cerevisiae chromosome IX architecture, which deviates significantly from a random arrangement of essential genes and meiotic recombination hotspots distribution [30]. This revealed a superior mutational robustness of the yeast chromosome architecture for the entire spectrum of inbreeding fractions (Supplementary Figure 4A; Supplementary Figure 2A in Text S1). We obtained the same result when allowing mutations to occur in meiosis only, or both in mitosis and meiosis (Supplementary Figure 2B and 2C in Text S1).
Using a survival competition assay, we directly compared the persistence of populations with random chromosomes and of populations with yeast chromosome IX at different mutation rates and for different population sizes. The competition experiments revealed a clear selective advantage of the yeast chromosome IX architecture for most regions of the investigated parameter space (Figure 4B). A stalemate situation was only observed at low mutation rates R<0.01 and for extreme inbreeding fractions (i = 0 and i = 1) (Figure 4B and 4C). We performed a control experiment to demonstrate that the quantitative outcome of the survival competition assay is unaffected by the choice, in which life cycle state mutations are simulated (mitosis and/or meiosis) (Figure 2D in Text S1).
An analysis of the recently recorded distribution of 4,300 single crossover events in 50 meioses of yeast [42] indicated non-random distributions of crossovers and essential genes for the entire yeast genome. We found that the resulting average level of clustering is more than 2.5σ higher than the level expected for random distributions (Table 1 in Text S1). Using S. digitalis, we obtained comparative data by subjecting digitalized implementations of all chromosomes (Text S1) to a survival competition against randomly generated chromosomes. The simulation outcome attests a superior fitness to almost all of the yeast chromosomes (Figure 4D).
Taken together, our data suggest that yeast-like chromosome architectures contain evolved features consistent with selection imposed by lethal mutations.
The fitness advantage for the structure of chromosome IX relative to randomly structured chromosomes may result from essential gene clusters that are either centromere-linked or peripheral to the chromosome, or cumulatively from both. We designed synthetic chromosome architectures to discern the potential impact of these structural relationships. First, in simulations without MAT loci, the highest average Rmax were obtained for synthetic chromosomes, in which essential genes were distributed in a few large clusters (Figure 5A). Moreover, the variability of Rmax as a function of the inbreeding ratio was larger in the case of the chromosomes with less essential gene clustering. The number of clusters providing the best performance depends on the population size: the larger the population the smaller the optimal number of clusters the pool of essential genes must be distributed to (Figure 5B). For clustered architectures, the introduction of a MAT locus (which constitutes an obligatory heterozygosity) into one of the clusters increased the persistence compared to genotypes without a MAT locus (Figure 5A, inset; and Supplementary Figure 3 in Text S1). Using survival competition assays in genomes with MAT-linked clusters, we compared the fitness of genotypes with peripheral essential genes either in clusters or in random distributions. We obtained a fitness advantage of peripheral clustering for a wide range of inbreeding ratios and mutation rates (Figure 5C). Thus, MAT-centromere-linked clusters and peripheral essential gene clusters provide cumulative fitness benefits.
The tight physical linkage of all essential genes into a single, large cluster is not a very likely configuration for natural genomes. However, achiasmate meiosis (the absence of meiotic crossovers) results in a comparable situation, since it genetically links together all essential genes on a chromosome. In the following, we will use the word “achiasmate” to denote the absence of meiotic crossing over between all essential genes present on a chromosome. Meiosis without crossovers has been reported for several species [43] and was also suggested to occur in the hemiascomycete yeast Saccharomycodes ludwigii [44],[45]. Using our survival competition assay we found that achiasmate meiosis exhibits a high mutational robustness Rmax (Supplementary Figure 3 in Text S1, genomes with one essential gene cluster and with mating types, +MAT) and provides a particularly strong fitness advantage in the entire investigated parameter space (considering the inbreeding fraction i and the mutation rate R) when a MAT was present (Figure 6A, top panel).
Generally, no advantage of achiasmate meiosis would be expected for pure outbreeding. The advantage of achiasmate meiosis observed in our simulations in the pure outbreeding domain can be explained by the finite population size, which implies that all individuals are related to a certain degree.
Without MAT linkage to the essential gene cluster, the advantage is reduced, but significant for mutation rates R<1.5 and non-extreme inbreeding fractions (0<i<1) (Figure 6A, lower panel). Using direct competition, we found a strong advantage of achiasmate meiosis over random chromosomes for mutation rates R between 10−4 and 1, which further increases with increasing population size (Figure 6B and 6C). In achiasmate meiosis, linkage to the MAT locus either occurs physically (on the chromosome where the MAT locus is located) or via the centromeres (for all other chromosomes due to intratetrad mating). This preserves heterozygosity at autosomal centromeres (see Figure 6D). The resulting selection advantage may provide population-genetic reasoning for the secondary loss of meiotic crossing over in Saccharomycodes ludwigii.
Our experiments demonstrated a fitness advantage of clustered chromosome architectures when exposed to deleterious mutations. This fitness advantage is the result of the cumulative effects arising from pre-existing essential gene clusters, but it does not allow us to deduce whether clustered genomes can evolve from unclustered or random architectures solely due to the exposure to lethal mutations. For example, alternative and potentially synergistic mechanisms are conceivable (see Discussion). In order to constitute a driving force, the presence of deleterious load would have to cause the emergence of clusters in a self-organized manner, exclusively based on the rules that govern the evolutionary process of genomes in the context of unicellular breeding populations subjected to deleterious mutations.
In our investigation of the in silico evolution of clustered genomes, we first dissected the complementary process: the maintenance of essential gene clusters in the context of chromosomal rearrangements and the linkage of the MAT locus to a gene cluster. We designed an initial genome that contained all essential genes in one large cluster and all meiotic recombination hotspots outside this cluster. Scenarios with and without MAT loci were considered in an inbreeding-only domain (full intratetrad mating), which is least favorable to successful persistence of the lineage in the situation without a MAT (see Figure 6A). For +MAT scenarios, the MAT locus was placed outside the essential gene cluster. Populations were evolved using different rearrangement rates (r). For rearrangement rates r<10−4, simulated architectures both with and without MAT loci preserved highly significant levels of essential gene clustering and anti-correlated meiotic crossover distributions over periods of at least 150,000 generations (Figure 7A and 7B, Video S1). Without MAT, high preservation of essential gene clustering was only observed in a relatively narrow range of mutation rates (0.6<R<1.4). In the presence of MAT loci, however, clustering was well maintained over a significantly broader range of mutation rates (R≥0.2) and also for higher rearrangement rates (Figure 7A). We found that the mating type locus had always relocated to a position inside the cluster by stochastic rearrangement (usually within the first 1,000 generations, n = 20 experiments) and had remained in the cluster afterwards. This experiment demonstrates that a significant level of clustering can be preserved in the presence of lethal mutations. Less well-clustered architectures that arise in the presence of destructive forces (genomic rearrangements) are quenched due to a selective pressure towards the better-performing clustered architectures, as long as the rearrangement rate is not too high.
In the next step, we investigated the de novo evolution of MAT-linked essential gene clusters in small model genomes containing five essential genes, five non-essential genes and four recombination hotspots (Figure 7C). We found that genomes evolved MAT linked essential gene clusters over a broad range of mutation rates and rearrangement rates, which is consistent with the fact that inbreeding preserves MAT-linked heterozygosity (Supplementary Figure 7D; and Supplementary Figure 4 in Text S1). Architectures with a single cluster (2+1+1+1, 3+1+1, 4+1 or 5 genes) were consistently favored over multi-cluster architectures (2+2+1 or 2+3 genes) (Figure 7C and 7D; grey rectangles indicate single-cluster architectures in Figure 7D).
In order to investigate the evolution of large yeast-like chromosome architectures we switched to domains with 50% inbreeding, since no advantage of chromosome-peripheral clustering was apparent for the extreme breeding domains at i = 0 and i = 1 (Figure 4 and Figure 5C). For this series of experiments, we implemented a species barrier in the simulation (see also first section of Results, and Text S1, section “Supplementary Results and Discussion”). Thereby, each genomic rearrangement leads to the formation of a new species. This scenario mimics reproductive isolation due to meiotically incompatible chromosomes. A new species might eventually dominate the population or become extinct depending on the reproductive success arising from the fitness (dis-)advantage of its particular genome architecture. We found that the simple life cycle of mitosis, meiosis and mating was sufficient to reproducibly evolve genomes with MAT-linked as well as non-random peripheral essential gene distributions (Figure 8A and Video S2). In order to obtain good statistics on this phenomenon, we parallelized the assay by using grid computing, which allowed us to simultaneously evolve many unrelated populations (n = 3,000 experiments). On average after 15,000 generations, high-R +MAT populations reproducibly evolved a level of clustering 2σ above the mean level encountered in random architectures. The evolution of the same level of clustering in low-R +MAT populations and in −MAT populations required a 2–3 fold longer period. Importantly, all high-R +MAT populations and even a small fraction of the other populations eventually arrived at the level of clustering of the natural yeast chromosome IX (emerging after 30,000–70,000 generations, statistics are provided in the legend of Figure 8A). Genomes with one MAT-linked cluster dominated at high mutation rates (R = 1), whereas genomes with several clusters, one of which associated with the MAT locus, typically evolved at lower mutation rates (R = 0.1).
We must point out that the parameter space explored in the evolution of clustering constitutes a compromise enforced by limitations in computation time. The computation of all events during one generation requires approximately ten seconds of CPU time in a population of 4,000 individuals. In order to be able to perform a statistically meaningful number of experiments under different conditions, we applied relatively high rearrangement rates close to a “destructive regime”, in which any emerging cluster quickly became scrambled. This setting allowed us to simulate genomic restructuring as would quantitatively occur over long evolutionary timescales using reasonable amounts of computation time. However, up-scaling the rearrangement rate also necessitates up-scaling the mutation rate, in order to arrive at a selective pressure on par with the potential destructive force introduced by the random rearrangements. If computation time was unlimited, we would also expect a qualitatively comparable outcome for lower values of R in the context of lower rearrangement rates.
We were able to qualitatively reproduce our results with respect to the evolution of essential gene clustering in three additional series of experiments. In these experiments, we provided the simulation framework with unclustered architectures assembled from the genetic building blocks of S. cerevisiae chromosomes VI, VII and X (Supplementary Figure 5A in Text S1 and data not shown). Even in large chromosomes with chromosome VII- and X-like sizes, highly significant levels of essential gene clustering were reproducibly established. Moreover, similar results were obtained when using an externally linked mating type locus as well as when using lower rearrangement rates over longer evolution periods (r = 10−5 for 200,000 generations, Supplementary Figure 5A in Text S1).
Taken together, these in silico experiments demonstrate that deleterious mutations inactivating important genes can provide a sufficient driving force to reproducibly evolve chromosome architectures resembling their natural counterparts with respect to essential gene distributions, meiotic recombination hotspot distributions and MAT-centromere linkage. Therefore, a recurrent exposure to lethal mutations can select for genome architectures in order to account for the associated load in the context of a sexual cycle.
Any evolutionary process, when successful, should generate individuals that perform better under the conditions of their evolution than their ancestors. In order to determine the level of success of our evolutionary simulation, we performed survival competition experiments between the evolved genomes discussed above and random chromosomes or yeast chromosome IX. We observed a strong fitness advantage of +MAT genomes evolved at R = 1.0 when competing with random chromosomes and yeast chromosome IX (Figure 8B). As expected from the presence of large MAT-associated essential gene clusters in these genomes, the fitness advantage results for a wide range of mutation rates (R≥10−3) and for the entire inbreeding/outbreeding domain. The genomes of +MAT populations evolved at R = 0.1 also performed significantly better than random architectures, but only slightly better than the chromosome IX architecture, with the exception of mixed breeding ratios and high mutation rates (R = 1.0), for which chromosome IX performed better. In the statistical average, low-R +MAT populations exhibited almost the same overall performance as the chromosome IX architecture (Figure 8C).
To further expand this analysis, we also performed competitions of the chromosome X-like products of the evolution experiment shown in Supplementary Figure 5A in Text S1 with random architectures as well as with the actual S. cerevisiae chromosome X, using a chromosome description derived from [42]. The results of the competition experiments were qualitatively comparable to those obtained for chromosome IX (Supplementary Figure 5B and 5C in Text S1). The reproduction of our results in the context of chromosome X is particularly striking, since the digitalized chromosome X architecture exhibits the highest level of essential gene clustering of all sixteen yeast chromosomes (see Table 1 in Text S1) and therefore constitutes a particularly challenging opponent for the evolution products in the survival competition.
We conclude that, in the context of our reference life cycle, architectures with chromosome IX-like purging evolve in the regime characterized by the lower mutation rate. The high-R regime promotes the evolution of single large gene clusters, the extreme of which represents achiasmate meiosis.
So far, we have investigated the correlation between lethal mutations in essential genes and the parameters that govern population fitness with respect to crossing over and population genetics via simulations of simple life cycles. There are also other processes that may influence the lethal load present in populations (see also Discussion). In particular, mating type switching followed by mating of daughter cells (termed “haplo-selfing”) with their respective mothers is a prominent feature of the life cycle of S. cerevisiae (but not of all yeasts, see Discussion). Mating type switching leads to a homozygous diploid, but only if it involves a haploid genome that is free of lethal mutations. Hence, if occurring at significant rates, haplo-selfing would be expected to decrease the lethal load in populations. However, if the mutagenic load in the population is too high, there is only a small probability of generating viable diploids by haplo-selfing. Comparing pre-loaded chromosomes with and without haplo-selfing revealed a sharp transition of the competitive advantage in favor of non-switching populations for a load higher than two lethal mutations per diploid (Supplementary Figure 6 in Text S1). In this regime, already a small percentage of diploidisation via haplo-selfing (2%) constituted a strong disadvantage.
To assay the impact of haplo-selfing on the fitness of different chromosome configurations, we performed several analyses. First, we compared the mutational robustness (Rmax) of chromosome IX and of random chromosomes for different levels of haplo-selfing. A high haplo-selfing rate of 50% leads to a significant increase in the mutational robustness Rmax, both for random architectures and for chromosome IX. At the lower rate of 10% haplo-selfing, the advantage of the chromosome IX architecture remained, but the mutational robustness decreased as compared to the situation without haplo-selfing (see Supplementary Figure 2A and 2E in Text S1). We further performed competitive advantage experiments of chromosome IX vs. random chromosomes at 10% haplo-selfing and for mutation rates between 10−3 and Rmax. In the absence of a mutagenic pre-load, we noticed the emergence of a phase transition in parameter space at high mutation rates, indicating a region that is dominated by random architectures (Figure 9). However, even in this extreme scenario the chromosome IX architectures, on average, still performed better than the random architectures (57% competition wins of chromosome IX vs. 43% competition wins of random architectures).
Using S. digitalis, we determined the influence of the meiotic crossover rate of chromosomes on fitness and on the ability to purge mutational load. In yeasts, crossover rates vary considerably, from 0 (achiasmate meiosis) up to approximately 20–40 crossovers per chromosome in S. pombe. The genomic mean for all S. cerevisiae chromosomes is 5.6 crossovers per meiosis (derived from the genetic map, www.yeastgenome.org) [46]. This value was confirmed by the direct assessment of crossover frequency and distribution [42]. The chromosome-specific number of crossovers scales linearly with the number of genes (R2 = 0.89), in a range of 2.5–9 crossovers for individual chromosomes (Supplementary Figure 7 in Text S1). We used random genomes of different sizes (250–1,500 genes) to assess the mutational robustness Rmax as a function of crossover frequency and inbreeding fraction. This revealed an increase of Rmax with increasing crossover rates, reaching saturation levels (95–99.5%) in the range of 4.5–8 crossovers. The observed variability of Rmax as a function of the inbreeding fraction was minimal in the 95–99.5% saturation interval (Figure 10A). A slight dependency on chromosome length was apparent (Figure 10A, inset). Direct competition of chromosome IX populations subjected to the natural crossover rate with chromosome IX populations at modified crossover rates demonstrated that crossing over rates higher or lower than the naturally observed average lead to a decrease in the fitness advantage (Figure 10B; see Supplementary Figure 8 in Text S1 for a plot of the average performance). Moreover, when assessing competition experiments of chromosome IX versus random architectures as a function of the crossover rate, chromosome IX performed best in a regime of yeast-like crossing over rates (Figure 10C; see Supplementary Figure 8 in Text S1 for a plot of the average performance). This suggests that the natural rates of meiotic crossovers in S. cerevisiae are adapted to handle deleterious load.
Haploid lethal phenotypes may be caused by mutational inactivation of one essential gene, or they may arise as a consequence of cumulative effects of non-lethal mutations in essential and non-essential genes. Dissecting synthetic lethal relationships demonstrated a 0.8–4% chance of lethality when two non-essential genes are deleted [47]; a simple extrapolation predicts a 50% chance for a lethal phenotype upon inactivation of approximately 7–14 non-essential genes, assuming scalability (and ignoring the possibility of non-linear network properties, such as positive epistasis [48]). In this case, however, most of the cells would have died before reaching such a high load, due to inactivation of one of the 19% essential genes. This rather empirical analysis might indicate that many haploid lethal phenotypes are caused by inactivation of a single essential gene, as we hypothesized for the purpose of our investigation.
In order to test this hypothesis directly, we generated a diploid strain containing MSH2 under control of the weak and fully repressible GalS promoter in order to perform mutation accumulation experiments. Deletion of the mismatch repair gene MSH2 leads to greatly elevated levels of point mutations [49],[50]. Additionally, reduced sequence specificity for homologous recombination was observed [51],[52], leading to increased levels of recombination between similar sequences at separate loci (ectopic recombination). Mismatch repair deficient strains accumulate high levels of lethal mutations, which are however not associated with an increase in gross chromosomal rearrangements [53].
Growth of this conditional mutator strain on glucose-containing medium led to depletion of Msh2 from the cells (Figure 11A and 11B) and to the accumulation of mutant phenotypes with reduced viability. In order to restrict the accumulation of mutations to vegetative growth, we shifted the cells to galactose-containing medium to induce MSH2 expression prior to sporulation (Figure 11A), which also prevents aberrant post-meiotic segregation events and ectopic recombination associated with the MSH2 deletion [54]. When grown exclusively on galactose-containing medium, the GalS-MSH2 strain exhibited wild type spore viability (Figure 11C). Upon mutation accumulation for approximately 30–36 generations (three growth periods of one day each), tetrad analysis (n = 400) revealed little 3-spore or 1-spore viability (8% and 15.5%). The majority of tetrads with unviable spores contained two viable spores (36%), which could be caused by single locus events leading to haploid lethality. Alternatively, some 2-spore viability may have resulted from meiosis I non-disjunction. We tested this option by measuring the linkage of the lethal load in 2-spore viable asci to the heterozygous leu2/LEU2 locus, which itself is centromere-linked (5 cM), and found that the lethal load exhibited on average only partial linkage to leu2 (27 cM). Since the load locus is different in each analyzed tetrad, only in approximately 25% of all cases crossing over between the centromere/leu2 locus is prevented (see Text S1, section “Supplementary Results and Discussion”), either due to tight centromere linkage of the load or as the consequence of homologous non-disjunction in meiosis I. Therefore the prominent fraction of tetrads with two viable spores is likely to be caused by freely segregating single mutagenic events affecting an essential function/gene. Importantly, the frequency of tetrads with only one dead spore was low, indicating low cumulative lethal effects of non-lethal mutations recombined into a haploid genome during meiosis. Taken together, these data suggest that losses of genomes associated with random mutagenic events are frequently associated with single events leading to a lethal haploid phenotype.
In this scenario, any linked mutation providing an advantage is lost as soon as the lethal mutation becomes exposed, e.g. by haploid growth following meiosis or homozygotisation. However, the genome with the lethal mutation may be preserved via mating with a spore containing the wild type allele. In order to test whether haploid lethal mutations have a high or a low chance to render spores unable to mate, we investigated the rates of diploid colony formation of single dyads using fluorescence activated cell sorting (FACS) after different mutation accumulation periods (Supplementary Figure 9 in Text S1). The observed frequency of diploid colonies did not change significantly as a function of accumulated mutations, when compared to the wild type. If spores associated with lethal mutations were frequently unable to mate – either due to the lethal mutation itself or due to co-accumulated non-lethal mutations – a greater than two-fold decrease of diploid colonies during the course of the experiment would have been expected (Figure 11C). Consistently, we found that only a very minor fraction of viable spores were impaired in mating and that dead spores would still germinate and often form microcolonies (see Text S1, section “Supplementary Results and Discussion”; and Supplementary Figure 10 in Text S1 for a histogram of the colony sizes). This latter result is also consistent with our observation that the majority of lethal phenotypes are not caused by meiosis I non-disjunction, as spores that lack entire chromosomes usually fail to germinate. Taken together, our results show that the majority of randomly occurring lethal mutations did not prevent the spores from mating.
We investigated the influence of deleterious mutations on the evolution of genome architectures. In breeding unicellular diploid organisms subjected to high mutation rates, the frequent inactivation of important genes selects for non-random distributions of important/essential genes and meiotic recombination hotspots. In simulated experiments architectures evolve, which bear a striking resemblance to the architectures of natural yeast chromosomes and provide a competitive advantage over a wide range of mutation rates.
The selective advantage of clustered genomes arises from multiple effects that cumulatively improve the fitness of such genomes. Initially, the average mutational load () in a simulated population subjected to a mutation rate R increases until an architecture-dependent equilibrium is reached. In this equilibrium, the influx of new stochastic mutations is equal to the outflux associated with loss of individuals from the population (Figure 2B). Upon elimination of an individual, all mutations associated with its genome are also removed from the population (Supplementary Figure 1A in Text S1). Within the simulation framework, the loss of individuals occurs via two factors. First, the random removal of excess genomes due to the population size cap is entirely neutral, since it affects all evolved sub-populations with the same probability. Second, and most importantly, genomes are lost due to homozygotisation of a mutant locus. The probability of a homozygotisation via a new mutation during the vegetative division is proportional to the mutational load present in a genome, irrespective of the genome architecture. A higher mutational load in a population therefore lowers its fitness in mitosis due to the increased sensitivity to new mutations, even if the heterozygous load is entirely neutral. Therefore, the only process whose outcome can be improved is meiosis and the loss of individuals due to homozygotisation after mating. Since homozygotisation of any single recessive lethal mutation removes the whole genome from the population, including the associated mutations, the effect of clustering can be explained generally by a reduction of the total number of individuals that are lost after mating. This maximization of mutation purging is caused by the evolutionary optimization of the genome in the context of a sexual lifestyle. Supplementary Figure 11 in Text S1 illustrates a simple scenario.
Clustering increases the level of genetic load that can be maintained in the genome. In order to obtain a positive overall effect, clustering needs to compensate for increased mitotic losses, which in simulations of random chromosome architectures constitute approximately 20% of all homozygotisation losses during one life cycle. In those simulation scenarios, in which a large EG cluster evolved (R = 1.0), mitotic losses can reach the same magnitude as meiotic losses (Supplementary Figure 12 in Text S1).
In conclusion, the advantage of clustered genomes must arise from an increase in reproductive fitness in mitosis and meiosis. This results from an optimized balancing of all factors that influence the frequency, with which homozygotisation of a lethal load locus occurs (Supplementary Figure 1B in Text S1 and Supplementary Figure 11 in Text S1). In mitosis, homozygotisation of lethal mutations is dependent on new mutations and the global load in the population, while in meiosis, homozygotisation results after mating. This is influenced by crossover distribution and frequencies, the breeding behavior and the genetic linkage relationship between crossover sites and essential gene loci.
In several experiments, we applied mutation rates higher than the average deleterious mutation rate reported for some S. cerevisiae strains [37],[55],[56]. However, as noted in the description of the simulation (see Results, section “Simulation of digital yeast genomes with S. digitalis”), we did not assume an exponential growth of our populations, but rather implemented a scenario where species compete for a limited pool of nutrients. In this scenario our implementation of only one round of mitosis between consecutive meioses mimics a scenario of many mitoses without population growth. Although this may not properly describe the growth of a population in a local niche for a short period of time, it certainly does recapitulate longer evolutionary periods that include many consecutive sexual cycles. It is reasonable to assume that natural yeast populations undergo more mitoses than meioses. Hence, if the mutation rate is to be compared to the natural mutation rate, it must be divided by the average number of consecutive mitoses. This number, however, is currently not known.
Crossover suppression near centromeres may also be caused by the interference of pericentromeric crossovers with proper meiotic chromosome segregation [57]. Alternatively, it has been proposed that cold centromeres may be caused by a requirement to protect centromeric repeats [58]. These considerations relate to a general issue in evolutionary biology: frequently, several independent or interacting mechanisms exist or are at least conceivable to explain an observation. As a result, it is often difficult to precisely define the scenario in which one or the other mechanism is dominant and how different mechanisms interact with each other. The situation is further complicated, since there are usually exceptions to each explanation (e.g. species, in which certain aspects are different): yeast does not exhibit centromeric repeats; there are species, in which crossovers occur preferentially in centromere-proximal regions [59], etc. The fact that there are molecular mechanisms that provide support for different scenarios (crossovers close to or further away from centromeres) makes it difficult to deduce the causal connection.
Exploring whether a specific mechanism is singularly able to provide a valid explanation constitutes one strategy to tackling this issue. In the case of S. cerevisiae, our evolutionary scenario is able to recapitulate the evolution of crossover suppression and essential gene enrichment in pericentromeric regions. Our simulation provides evidence that lethal mutations and chromosome evolution interact. But, of course, there is no record for the true historical events that enforced this constellation. The complete answer could be given by exploring the (derived) genomes of all ancestors of S. cerevisiae along with the evolution of the molecular mechanisms that govern centromeric crossover protection (which is still subject to investigation).
While we took advantage of recent data from Mancera et al. [42] in many experiments involving the digitalization of natural yeast chromosomes, our digital implementation of chromosome IX was derived using the hotspot mapping data from Gerton et al. [32]. In the meantime, additional hotspot data sets have been published [60],[61]. Using data sets about double strand breaks to predict the distribution of crossovers is accompanied by the limitation that DSB frequencies do not translate linearly into crossover frequencies. In this sense the Gerton et al. data set (which was the only data set available at time we began our analysis) was very helpful [32], since it provided a correct description of cold centromeres, as confirmed by the yeast genetic map. Two more recent studies [60],[61] detected more DSBs near centromeres, which, however, do not convert into crossovers (www.yeastgenome.org, see Text S1, section “Supplementary Results and Discussion” for a quantitative assessment of the cM-to-kb relationship in pericentromeric regions). These studies do not deviate so much from the Gerton et al. study in the rest of the genome (except for the subtelomeric regions, which are not so relevant in our context).
Natural isolates of S. cerevisiae were reported to exhibit large diversity in terms of mutagenic load, yielding a significant fraction of isolates with low to extremely low spore viability. Some proportion of this load, which must have accumulated during mitotic growth and preserved during inbreeding [62],[63], may have been caused by mutator phenotypes. Widespread occurrence of natural variation that can give rise to mutator phenotypes has been reported [64],[65]; this or a similar type of variation may be the reason for the formation of natural yeast strains with highly unstable karyotypes [66]. These may be caused by impaired recombinational repair, as indicated by a certain dependency on Rad52 [67]. Interestingly, these strains can give rise to meiotic offspring with stable karyotypes.
Based on these considerations we speculate that genomic rearrangements, which are associated with the formation of new species or lineages, are frequently also associated with high mutation rates that occasionally – and in particular during the periods that shape the genome – exert a selective pressure by means of high levels of lethal mutations.
The yeast evolutionary history governs several hundred million years and includes many species with (at least nowadays) predominantly diploid life cycles [19],[68],[69]. Although the average frequency of meioses is unknown (from the general perspective as well as for individual species), a more than sufficient number of sexual cycles and genomic rearrangements must have taken place to allow for the maintenance and selection for particular genome architectures.
In yeast, mating is a highly favored process that occurs whenever two cells of opposite mating type meet − in dense populations or on the level of the spores of a tetrad [31],[70]. Even considering that S. cerevisiae sometimes aborts the formation of one to three spores during sporulation (due to limited availability of nutrients), the overall formation of spores with a mating partner available from within the same tetrad is well above 80% for a broad range of conditions [31]. This circumstance as well as the possibility of mating of spores from different tetrads indicates that mating type switching is most likely not the dominant way of diploidisation in S. cerevisiae. Thus, it appears safe to assume that the relative effects we observed at a level of 10% haplo-selfing represents an overestimation of the actual situation, rather than an underestimation.
Mating type switching was one of the key inventions of lifestyle variation that emerged around the time point of the whole genome duplication [68],[71]. One may wonder about the reasons for this evolutionary invention in the first place, and what causes its recurrent secondary loss (mating type switching deficient S. cerevisiae may constitute up to 10% of the strains isolated from natural wine fermentation, [72]). Lifestyles seem to evolve towards a preference for diploid stages [73], in which heterozygosity can be maintained. An associated need for efficient purging of load may have influenced the evolution of mating type switching. Occasionally, when mutation rates become too high or meiotic cycles too infrequent, a resulting high lethal load may also be able to select against mating type switching. Diploidy in the context of lethal load can enforce the evolution of very high levels of intratetrad mating in unicellular eukaryotes [23], as observed in the non-switching pre-WGD duplication yeast species Saccharomyces kluyveri [74] and Saccharomycodes ludwigii [45],[75]. These considerations together do not exclude additional or other roles for mating type switching and intratetrad mating, but they suggest that lethal mutations and mutagenic load may frequently accompany diploid life cycles in unicellular organisms.
Additional mechanisms exist that influence the lethal load in diploid populations. Loss of heterozygosity associated with mitosis acts in a position-specific manner with increased frequency further away from the centromere. It could therefore provide an additional reason for essential gene enrichment in pericentromeric regions. However, its frequency is low (approximately 0.5 to 10 per 10,000 cell divisions in young cells and 50 to 500 in old mother cells [76]) and it is therefore unlikely to significantly influence the global lethal load of the populations.
In a race for beneficial mutations upon exposure to new conditions, essential genes would effectively reduce genetic drift, if their inactivation caused a significant fitness reduction in the diploid organism. Consequently, the chance of acquiring new mutations not immediately accessible to the original genome would decrease, e.g. reducing the frequency of beneficial mutations that are accompanied by a mutation that causes the loss of an essential function.
The occurrence of deleterious mutations is predicted to select for the evolution of properties that increase robustness, even if the evolved genomes have a lower maximum fitness in the mutation-free environment. This is compensated by the higher mean fitness of the variants present in a mutant population, known as “survival of the flattest” [77]. Support is provided by studies of viruses and bacteria, but also by studies using digital organisms [78]. An important body of literature is summarized in Wilke and Adami (2003) [79]).
In yeast, several mechanisms exist that account for buffering mutational load. One is global buffering or positive epistasis of the fitness reduction in combinations of non-essential gene deletions [48]. Essential genes exhibit less expression noise than non-essential genes [80],[81]. Batada and Hurst [36] have proposed that the evolution of essential gene clustering was driven by their accumulation into chromosomal regions of low average nucleosome occupancy (open chromatin), which are domains with lower expression noise and which coincide with the domains of low meiotic recombination [82]. Although this idea is intriguing, no support has been provided that this scenario results in a fitness advantage sufficient to select for the relocation of an essential gene into a cluster. S. cerevisiae grows predominantly as diploid. Since the individual intrinsic noise from one copy of a gene is uncorrelated to the noise from the other copy, a lower total noise level is present in diploids as compared to haploids [83]. Selection of low noise for essential genes would thus be less effective in diploids than in haploids. Only about 9% of heterozygous deletion strains of essential genes exhibit haplo-insufficiency in S. cerevisiae [84], indicating that low noise may be particularly important in a diploid situation, where one copy of an essential gene is inactivated due to a mutation. This would minimize noise in the context of an overall reduced protein level. This result would suggest that a significant interaction may exist between the low noise model and purging of mutations from diploids, which could act synergistically towards improving the clustering of essential genes.
Proliferation of tumor cells depends on the inactivation of tumor suppressor genes by subsequent multiple mutations. Due to early-acquired mutations in DNA mismatch repair and other pathways required for genetic stability [10], [85]–[87], mutator phenotypes have the potential of accelerating the progression of cancer development by enhancement of the variability upon which Darwinian selection can act [9],[10],[88]. In this scenario, evolved robustness towards haploid lethal load is an important factor that has implications for the understanding of genetic instability in cancer development.
Purging of deleterious load depends on the tendency to accumulate load and on the presence of alleles that increase the rate at which mutations occur (i.e. mutator alleles). The robustness inherent to the genome architecture of yeast therefore indicates that high mutation rates and genetic load have played an important role during evolution, when the ability to deal with deleterious load co-evolved with better suited genome architectures. Altogether there is increasing evidence that deleterious load is common to evolving yeast populations and that many aspects of the cellular physiology evolved to allow survival as fit but “flat” species.
A diploid strain carrying chromosomal MSH2 under control of the inducible GalS promoter was constructed [89] in the well-sporulating SK1 genetic background [90]. The cells were constantly maintained under GalS inducing conditions (YP-galactose/raffinose (YP-Gal), see also Text S1, section “Supplementary Methods”) and exhibited wild type spore viability. In order to allow random mutations to occur, the GalS promoter was repressed using glucose while cells were grown for 24, 48 and 72 hours (24 hours≈10–12 generations) using serial dilution. At each time point, an aliquot of the cells (5·107 cells) was plated on YP-Gal for 16 hours. The cells were then washed off, plated on a sporulation plate (1% KAc, 0.02% raffinose, 0.02% galactose) and incubated for 40 hours at room temperature. Ascus formation occurred in all cases with frequencies >99%. After disrupting the asci, FACS sorting was used to spot single spores and dyads on large YP-Gal plates (1536 spores or 384 dyads per plate). Sorted spores and dyads were analyzed for colony formation (viability) and mating type (MATa, MATα and no mating type = diploid cells) using mating type tester strains and a halo assay (Text S1, section “Supplementary Methods;” and Supplementary Figure 9 in Text S1). The viability of the spores from 400 different tetrads was analyzed by tetrad dissection.
The source code of the simulation S. digitalis is provided as Protocol S1. A detailed description of the simulation is included in Text S1, section “The Computer Simulation S. digitalis;” and in Table 2 in Text S1.
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10.1371/journal.pntd.0001023 | Mining a Cathepsin Inhibitor Library for New Antiparasitic Drug Leads | The targeting of parasite cysteine proteases with small molecules is emerging as a possible approach to treat tropical parasitic diseases such as sleeping sickness, Chagas' disease, and malaria. The homology of parasite cysteine proteases to the human cathepsins suggests that inhibitors originally developed for the latter may be a source of promising lead compounds for the former. We describe here the screening of a unique ∼2,100-member cathepsin inhibitor library against five parasite cysteine proteases thought to be relevant in tropical parasitic diseases. Compounds active against parasite enzymes were subsequently screened against cultured Plasmodium falciparum, Trypanosoma brucei brucei and/or Trypanosoma cruzi parasites and evaluated for cytotoxicity to mammalian cells. The end products of this effort include the identification of sub-micromolar cell-active leads as well as the elucidation of structure-activity trends that can guide further optimization efforts.
| Diseases like malaria and sleeping sickness are caused by tropical parasites and represent a major cause of mortality and morbidity in the developing world. A pragmatic approach to discover new drugs for these diseases is to search for drug leads among existing small molecule collections generated in the for-profit pharmaceutical industry. In this study, we searched for new drug leads among a collection of small molecules donated by Celera Genomics. This collection of molecules was originally developed to inhibit a class of human enzymes (cathepsins) implicated in diseases like osteoporosis and psoriasis. Similar enzymes are also present in most tropical parasites, making this collection a logical place to search for new drug leads. The end result of this effort saw the identification of compounds that inhibit the growth of one or more tropical parasites and that will serve as good starting points for the development of new drugs for tropical parasitic diseases.
| There is a critical need for new drugs to treat neglected tropical diseases [1], [2], [3], [4]. Current therapies are limited by inadequate efficacy, drug resistance, or toxicity. Chagas' disease, for example, remains the leading cause of heart disease in Latin America, with between 8 and 12 million people currently infected, and over 90 million at risk (as reported in WHO fact sheet No. 340, June 2010). Current therapy for Chagas' disease consists of the nitro heterocycles nifurtimox and benznidazole. Both drugs have frequent and serious side effects [5] limiting their efficacy, and each requires an extended (60–120 days) course of therapy. Furthermore, resistance to nifurtimox is emerging. Drug choices for those suffering from human African trypanosomiasis (HAT, sleeping sickness) are similarly poor, with organoarsenic derivatives (e.g. melarsoprol) still employed in the treatment of CNS disease despite a ∼5% rate of drug-associated mortality [6]. In the treatment of malaria, the artemisinin-based combination therapies (ACT) [7] are currently effective and well tolerated, but resistance to the partner drugs [8] and possibly to artemisinins as well [9] is emerging.
One pragmatic strategy to develop new antiparasitic drug leads is to focus on targets that are shared by multiple pathogens. Cysteine proteases play essential roles in numerous protozoan and helminth parasites, and are therefore appropriate targets for antiparasitic chemotherapy [10]. The Clan CA [11] cysteine protease cruzain (cruzipain) has been advanced as a potential drug target in Trypanosoma cruzi, the parasite responsible for Chagas' disease [12]. Cruzain is highly expressed in the intracellular amastigote stage of the parasite that is responsible for human disease. Cruzain deficient strains of the parasite fail to establish infection in immune-competent experimental hosts [13]. Because genetic deletion of the cruzain gene is not feasible, the target has instead been validated with small molecules. Hence, irreversible inhibitors of cruzain produce a characteristic swelling of the parasite Golgi compartment in T. cruzi parasites, leading to subsequent dysmorphic changes in both Golgi and endoplasmic reticulum, and ultimately rupture of parasite cells [14]. A number of cysteine protease inhibitors have been shown to arrest T. cruzi development within mammalian cells in vitro, and some have been shown to arrest or cure infection in mouse models of disease [15]. In Trypanosoma brucei parasites, the cysteine proteases rhodesain (brucipain) and a cathepsin-B like protease (TbCatB) have been advanced as potential drug targets [16], [17].
The erythrocytic malaria parasite produces a variety of proteases that perform the essential function of hemoglobin hydrolysis, whereby the parasite acquires its principal source of amino acids [18], [19]. We and others have shown that cysteine protease inhibitors block the hydrolysis of hemoglobin by erythrocytic parasites, causing the food vacuole to fill with undegraded hemoglobin, preventing parasite development, and indicating that cysteine proteases play an essential role in hemoglobin hydrolysis [20]. Among the various proteases that mediate hemoglobin hydrolysis are the cysteine proteases falcipain-2 and falcipain-3. Disruption of the falcipain-2 gene leads to a block in trophozoite hemoglobin hydrolysis [21]; the falcipain-3 gene could not be disrupted, strongly suggesting that this protease is essential for erythrocytic parasites [22]. The inhibition of cysteine protease mediated hydrolysis of hemoglobin is therefore a promising and active area of drug discovery research [23], [24], [25].
A concern in targeting parasite cysteine proteases with chemotherapeutic agents is the potential for toxicity due to the existence of homologues such as cathepsin B, K, S, and L in the mammalian host. This fear has to some extent been alleviated by pre-clinical experience with cysteine protease inhibitors targeting plasmodium and trypanosome parasites. Even compounds that are not significantly selective at the biochemical level have proven to be well tolerated in animals. Why might this be so? One possibility is that whereas mammalian cysteine proteases are a redundant gene family in which many discreet catalytic types occur in the same lysosome or vacuolar compartment, parasite enzymes – although sometimes part of a small gene family – tend to be less redundant [26]. A second factor is that the host-origin lysosomal cysteine proteases are present in mammalian organelles at millimolar concentrations, much higher than is thought to be the case for the parasite proteases [27], [28]. As such, it would be difficult to completely eliminate host cathepsin activity in the context of a short course of therapy targeting an acute infection. Nevertheless, a team of academic and industry researchers recently reported a series of nitrile-based cruzain inhibitors that display excellent in vitro selectivity for the parasite protease over mammalian cathepsins [29].
The cysteine protease inhibitor library screened in this study is unique and not commercially available. It is composed entirely of compounds synthesized by medicinal chemists at Celera Genomics in the course of various discovery campaigns targeting human cathepsins, primarily cathepsins K, S, and B [30], [31], [32], [33]. The library members are generally peptidic in nature and nearly all possess an electrophilic “warhead” that engages the active-site cysteine thiol reversibly or irreversibly through the formation of a C-S bond. The topic of irreversible enzyme inhibition continues to generate healthy debate, with reversible (even if covalent-reversible) modes of inhibition generally viewed as being preferable [34]. This position seems prudent in the case of chronic conditions where long-term safety in very large patient populations is of paramount importance (e.g., in osteoporosis). In the case of an acute infection however, irreversible inhibition is arguably advantageous, a view that has been advanced in recent review articles [35], [36]. In any event, the focus of the Celera group on chronic disorders like osteoporosis is reflected in the constitution of the inhibitor library, which is heavily biased towards reversible-covalent type inhibitors, most notably aminoacetonitriles and heterocyclic ketones. The library contains a much smaller number of irreversible inhibitors, such as fluoromethylketones [37] and vinylsulfones [30]. The objective of the study described herein was to mine this library of ∼2100 unique cathepsin inhibitors to identify target- and parasite-active lead compounds. This discovery paradigm seeks to leverage chemical entities already in existence for a new purpose, much akin to the re-purposing of approved drugs. An important advantage of re-purposing at the lead stage, however, is that it allows for subsequent optimization of drug properties specific to the antiparasitic indication.
Recombinant cruzain, rhodesain, TbCatB, falcipain-2 and falcipain-3 were expressed as described previously [38], [39], [40], [41], [42]. Inhibitor stock solutions were prepared in DMSO and screened at 0.1 µM for cruzain and rhodesain, 1 µM for TbCatB and 50 nM for falcipain-3 (0.5% DMSO in assay). For IC50 determinations, compounds were serially diluted in DMSO in the range of 25 µM – 0.001 µM for cruzain, rhodesain and TbCatB; and 5 µM – 0.06 nM for falcipain-2 and falcipain-3. Protease inhibition assays for cruzain and rhodesain were carried out in 96 well plate format as described previously [43]. Cruzain (4 nM), rhodesain (4 nM) or TbCatB (258 nM) was incubated with test compound in 100 mM sodium acetate, pH 5.5, containing 5 mM DTT and 0.01% Triton X-100 (buffer A), for 5 min at room temperature (the higher concentration of TbCatB was required due to its lower intrinsic activity against the fluorogenic substrate). Then buffer A containing Z-Phe-Arg-AMC (Bachem) was added to enzyme-compound mixture to give 10 µM substrate in a final assay volume of 200 µL. The rate of free AMC release was measured at excitation and emission wavelengths of 355 and 460 nm, respectively, with a microtiter plate spectrofluorimeter (SpectraMax M5, Molecular Devices) for 3 min. Percentage inhibition of test compound was calculated relative to the DMSO control (0% inhibition control). IC50 values were determined with Prism 4 software (GraphPad, San Diego, CA) using sigmoidal dose-response variable slope model.
Protease inhibition assays for falcipain-2 and falcipain-3 were carried out in 96 well plate format as described previously [42], [43] with modification for end-point readout. Falcipain-2 (21 nM) or falcipain-3 (78 nM) was incubated with test compound for 10 min at room temperature in 200 mM sodium acetate, pH 6, 10 mM DTT and 3.6% glycerol and 0.01% Triton X-100 (buffer B). Then buffer B containing Z-Leu-Arg-AMC (Bachem) was added to the enzyme-compound mixture to give 25 µM substrate in a final assay volume of 200 µL. After 15 min, 50 µL of 5 M acetic acid was added to each well to stop the reaction. End-point fluorescence was read in a spectrofluorimeter as above. The 0% inhibition control wells contained DMSO while 100% inhibition control wells contained 50 µM E-64 (Sigma). Percentage inhibition of test compound was calculated relative to the controls and IC50 curve fitting was performed with Prism 4 software as above.
The enzyme inhibition assay was carried out in 96 well plate format as described previously [44] with modification in the sequence of substrate addition. Trypanothione reductase (3 mU/mL) was incubated with 10 µM test compound (1% DMSO in assay) for 30 min at room temperature in the presence of 50 µM DTNB (5,5′-Dithiobis(2-nitrobenzoic acid), 6 µM trypanothione, 40 mM Hepes pH 7.4 and 1 mM EDTA. Then 150 µM NADPH was added to give a final assay volume of 100 µL. The rate of TNB (2-nitro-5-mercaptobenzoic acid; yellow color) formation was measured in absorbance at 412 nm with a microtiter plate spectrofluorimeter (Flexstation, Molecular Devices). Percentage inhibition was determined with respect to the 0% inhibition control (1% DMSO).
The growth inhibition assay for T. brucei brucei was conducted as described previously [45]. Bloodstream forms of the monomorphic T. brucei brucei clone 427-221a were grown in complete HMI-9 medium containing 10% FBS, 10% Serum Plus medium (Sigma Inc. St. Louis Mo. USA), 50 U/mL penicillin and 50 µg/mL streptomycin (Invitrogen) at 37°C under a humidified atmosphere and 5% CO2. Inhibitor stocks were prepared in 100% DMSO and screened at 5 µM for percent inhibition values or serially diluted from 25 µM to 0.04 µM in 10% DMSO for IC50 determinations. 5 µL of each dilution was added to 95 µL of diluted parasites (1×104 cells per well) in sterile Greiner 96-well flat white opaque culture plates such that the final DMSO concentration was 0.5%. The 0% inhibition control wells contained 0.5% DMSO while 100% inhibition control wells contained 50 µM thimerosal (Sigma). After compound addition, plates were incubated for 40 hours at 37°C. At the end of the incubation period, 50 µL of CellTiter-Glo™ reagent (Promega Inc., Madison, WI, USA) was added to each well and plates were placed on an orbital shaker at room temperature for 2 min to induce lysis. After an additional 10 min incubation without shaking to stabilize the signal, the ATP-bioluminescence of each well was determined using an Analyst HT plate reader (Molecular Devices, Sunnyvale, CA, USA). Raw values were converted to log10 and percentage inhibition calculated relative to the controls. IC50 curve fittings were performed with Prism 4 software as above.
Jurkat cells (clone E6-1) were grown in complete RPMI-1640 medium containing 10% FBS, 50 U/mL penicillin and 50 µg/mL streptomycin (Invitrogen) at 37°C under a humidified atmosphere and 5% CO2. For cytotoxicity testing, cells were diluted to 1×105 per ml in complete RPMI-1640 medium. Test compound stocks were prepared in 100% DMSO and screened at 10 µM against cells (1×104 cells per well) in sterile Greiner 96-well flat white opaque culture plates (0.5% final DMSO concentration). The 0% inhibition control wells contained 0.5% DMSO while 100% inhibition control wells contained 40 µM staurosporine. After compound addition, plates were incubated for 40 hr at 37°C. At the end of the incubation period, 50 µL of CellTiter-Glo™ reagent (Promega Inc., Madison, WI, USA) was added to each well and plates were placed on an orbital shaker at room temperature for 2 min to induce lysis. After a further 10 min incubation without shaking to stabilize the signal, the ATP-bioluminescence of each well was determined using an Analyst HT plate reader (Molecular Devices, Sunnyvale, CA, USA). Calculations of percentage inhibition and IC50 were performed similar to those in the T. brucei brucei assay.
The growth inhibition assay for P. falciparum was conducted as described previously [43]. W2-strain P. falciparum parasites (1% parasitemia, 2% hematocrit) were cultured at 37°C in human red blood cells under the atmosphere of 3% O2, 5% CO2 balance N2 in 0.2 mL of per well in medium RPMI-1640 supplemented with 10% human serum in duplicate 96-well flat bottom culture plates in the presence of inhibitors. Tested compounds were serially diluted 1∶3 in the range 10,000 – 4.6 nM, with maximum DMSO concentration of 0.1%. Following 48 hours of incubation, the medium was removed and the cells were fixed in 2% formaldehyde in PBS. Parasite growth was evaluated by flow cytometry on a FACsort (Becton Dickinson) equipped with AMS-1 loader (Cytek Development) after staining with 1 nM DNA dye YOYO-1(Molecular Probes) in 100 mM NH4Cl, 0.8% NaCl. Parasitemias were determined from dot plots (forward scatter vs. fluorescence) using CELLQUEST software (Becton Dickinson). IC50 values for growth inhibition were determined from plots of percentage control parasitemia over inhibitor concentration using GraphPad Prism software.
The growth inhibition assay for the intracellular T. cruzi was conducted in 96-well plate format as described previously [28]. Briefly, bovine embryo skeletal muscle (BESM) cells (2×103/well), seeded overnight, were infected with CA-I/72 T. cruzi trypomastigotes (2×103/well). Sixteen hours post-infection, test compounds (serial dilution concentrations starting from 20 µM) were added and assay plates were incubated for an additional 72 hours at 37°C with 5% CO2. Cells were then washed once in PBS, fixed with 4% paraformaldehyde and stained with DNA fluorescent dye DAPI (4′ 6-diamidino-2-phenylindole). The assay plates were then imaged in the IN Cell Analyzer 2000 (GE Healthcare) with the excitation and emission filters set of 350 nm and 460 nm, respectively, to detect DAPI signals. The feature extraction module in IN Cell Developer Toolbox 1.7 software was used to count host nuclei and parasite kinetoplasts by size difference.
The library was donated by Celera Genomics to the Sandler Center for Basic Research in Parasitic Disease at UCSF. The structural constitution of the library is summarized below (Figure 1). The vast majority of the compounds contain a biaryl or biheteroaryl ring system at the P3 position, a natural or unnatural amino acid at P2, and a thiol-reactive warhead function with or without a P1 substituent. Nearly three quarters of the library members are of the aminoacetonitrile warhead type and lack any P1 side chain; around 100 nitrile analogs possess a geminal (cyclic) substituent at P1. Other abundant inhibitor chemotypes include ∼170 ketobenzoxazoles, ∼50 ketooxadiazoles, and ∼80 α-hydroxy/alkoxy ketones. Around 50 irreversible inhibitors of the vinylsulfone type are present, as are a smaller number of other warheads. The majority of ketone and vinylsulfone warheads also contain a P1 side chain, and the majority of these are unbranched aliphatics of between two and four carbons, or are phenethyl.
The varieties of P2 side chains present in the library reflect the substrate specificity of the proteases being targeted and are heavily biased towards leucine and phenylalanine type side chains. A large number of analogs contain more extended P2 side chains (e.g., homo-phenylalanine or benzyl-cysteine), branching substituents and/or halogenations on the aromatic ring. About 60 analogs contain a geminal disubstituted (cyclic) moiety at P2. The majority of inhibitors are capped on their N-termini with benzoyl, acyl, carbamoyl (e.g., Cbz), or urea functions. Both aryl and heteroaryl P3 groups are represented, and many are highly substituted or bear an additional pendant aryl or heteroaryl ring substituent. Around 500 analogs contain such a biaryl or heterobiaryl P3 moiety and para-substituted biaryls are greater in number than meta- or ortho-substituted biaryls.
Evaluation of the cathepsin inhibitor library was envisioned to begin with a single-concentration screen which would be followed by analysis of actives in full dose-response and against cultured parasites. The determination of inhibition kinetics was deemed impractical given the size of the library. Still, we recognized the perils of comparing IC50 values between reversible and irreversible inhibitors, since inhibition by the latter is expected to be time dependent. We therefore first evaluated time-dependence of inhibition for a small but representative subset of the library (Table 1). Test compounds were pre-incubated with falcipain-3 for 0, 10, or 60 minutes prior to addition of a fluorogenic protease substrate. As expected, inhibitor types thought to act irreversibly (vinylsulfones, fluoromethylketones) showed time-dependent inhibition while those thought to confer reversible inhibition (e.g., nitriles) showed no time dependence. Even so, the irreversible inhibitors showed at most a ∼10-fold shift in IC50 value at the longer incubation times. We selected a 5 minute pre-incubation time for profiling the entire library, reasoning that comparisons of reversible and irreversible inhibitor potency in vitro is most meaningful with short incubation times, during the pre-covalent stage of inhibition by irreversible inhibitors.
Single-point protease inhibition data were determined for the entire 2157 member library against the proteases cruzain, rhodesain, TbCatB, and falcipain-3. Falcipain-2 was not screened against but used only to test falcipain-3 hits. Protease inhibition was determined using standard functional assays [42], [43] employing fluorogenic substrates (Z-Phe-Arg-AMC or Z-Leu-Arg-AMC) and inhibitor concentrations of 50 nM (falcipain-3), 100 nM (cruzain and rhodesain), or 1 µM (TbCatB). To control for possible non-specific electrophilic inactivation of enzyme by test compounds, we also screened the library against trypanothione reductase, a non-protease that nonetheless contains catalytic active-site cysteine functionality. This latter assay was carried out according to an established method [44] but at a relatively high inhibitor concentration (10 µM) due to the expectation that the protease-targeted compound library would not potently inhibit trypanothione reductase.
Single-point screening data are presented as scatterplots and color/shape-coded according to warhead/P2 chemotype, as SAR at these positions provide the most useful information about substrate/inhibitor specificity (Figure 2). Hence, the cathepsin L-like proteases rhodesain and cruzain show a similar substrate-mimetic specificity, with the data points falling roughly along the diagonal of the scatterplot (Figure 2, panel A). The most potent inhibitors of these enzymes were vinylsulfones possessing Phe or similar (e.g. napthylalanine) P2 substituents. Also notable are a small number of rhodesain-selective analogs, many of which are characterized by the presence of more extended P2 side chains. The inhibition profile of falcipain-3 and TbCatB are distinct from that of either rhodesain or cruzain (panels B and C). Most of the potent inhibitors of falcipain-3 possess smaller Leu-like P2 moieties rather than the larger P2 side chains preferred by rhodesain (panel B). A small number of primarily ketone-based inhibitors were identified that significantly inhibit both proteases (panel B, green needles and green squares). Only a handful of library compounds inhibited TbCatB to a significant extent but strikingly, many of those that did possess large P2 moieties such as halogenated Phe/Tyr or napthylalanine (panel C, circles and needles). Furthermore, whereas the P2 halogenated Phe/Tyr analogs were selective for TbCatB over rhodesain, the P2 napthylalanine analogs inhibited both proteases potently. Conversely, P2 phenylalanine analogs bearing a vinylsulfone warhead proved highly selective for rhodesain over TbCatB (panel C, red triangles). Few inhibitors of trypanothione reductase were identified (even at 10 µM) and, as expected, no correlation between protease inhibition and inhibition of trypanothione reductase could be discerned (panel D). Thus, the single-concentration screening data provided useful insight into the substrate/inhibitor preferences of the various parasite proteases and set the stage for more careful profiling of specific compounds from the library.
The most potent inhibitors identified in the single-point screen (3–6% of the total library depending on enzyme) were subsequently evaluated in full dose-response to generate IC50 values for rhodesain, cruzain, TbCatB and falcipain-3. All falcipain-3 inhibitors were also tested against falcipain-2. The results are presented graphically in Figure 3, where it can be seen that a significant number of potent falcipain-2/3 and rhodesain/cruzain inhibitors, but many fewer potent inhibitors of TbCatB, were identified. Falcipain hits narrowly clustered at sub-micromolar IC50 with nitrile P1 warhead and leu-like P2 chemotypes predominating, while rhodesain/cruzain hits were more distributed in activity and chemical class. Accordingly, while more than one hundred falcipain-2/3 inhibitors were tested against cultured W2 P. falciparum parasites, only around thirty inhibitors of the trypanosome proteases were judged suitable for testing against T. brucei brucei and/or T. cruzi parasites. The entire library was examined for cytotoxicity to a mammalian cell line (Jurkat) at a concentration of 10 µM, and those compounds studied in the T. cruzi assay were also evaluated for toxicity (at 20 µM) toward the host BESM cells used in that assay. Parasite growth inhibition (GI50) and Jurkat/BESM cell cytotoxicity data are presented graphically in Figure 4. Nearly half of the falcipain inhibitors examined were found to inhibit parasite growth at low or sub-micromolar concentrations and without apparent cytotoxicity to Jurkat cells. A much smaller number of inhibitors were effective against T. brucei brucei or T. cruzi parasites and many of these were also cytotoxic to either Jurkat or BESM cells. The most promising parasite-active lead compounds are discussed in more detail below.
The parasite-active and non-cytotoxic analogs identified in the study can be considered as good candidates for future lead optimization efforts targeting parasitic indications. As starting points for such optimization, these compounds are likely more ‘advanced’ than compounds identified from typical screening libraries, as they were synthesized in the course of lead optimization campaigns targeting cathepsins. More than 85% of the library compounds have one or zero violations of Lipinski's ‘rule-of-five’ [46] and can therefore be regarded as having reasonable prospects for oral bioavailability. The structures and associated data are provided for ∼25 such lead compounds (Figures 5–6; Tables 2–5). The data presented in the tables includes historical inhibition values (Ki) against human cathepsin L and cathepsin B (data provided by Celera Genomics). As noted above, a significant number of compounds were identified that possess low- or sub-micromolar activity against cultured P. falciparum parasites. That so many plasmodium-active compounds were identified can be attributed to the known substrate preference of falcipains for leucine-like P2 side chains [47] which are very highly represented in the library (Figure 1). In fact, a leucine or leucine-like P2 side chain is found in nearly all of the anti-malarial lead compounds identified (compounds 14–21, Figures 5 & 6).
Among the antimalarial lead compounds identified, a notable subset was found to possess a thiophene ring at the N-terminal (P3) position (Table 2). These analogs (14a–d) are substituted on the thiophene ring with one (14b–d) or two (14a) aryl or heteroaryl rings. Enzyme potencies for these analogs were in the mid-low nanomolar range while GI50 values were low single-digit micromolar. The structurally related thiophene analogs 15a–b demonstrated potencies similar to 14a, but while compound 15a with a trifluoromethyl substituent was similarly effective against cultured P. falciparum parasites, the analogous phenyl substituted congener 15b surprisingly was not. Inhibitors 14a–d and 15a–b were not cytotoxic to Jurkat cells at 10 µM, and inhibited human cathepsin L and B about 10 and 1000-fold less potently than the parasite proteases.
Analogs bearing para-biphenyl (16a–d) or meta-biphenyl (17a–c) P3 substituents represent a second class of anti-plasmodium lead compounds identified in this study (Table 3). The terminal aryl ring in these analogs appears to be insensitive to substitution pattern or type, with electron withdrawing or donating substituents well tolerated in various positions. Interestingly, the most cell-active analogs in this subset were 16a and 17a, both of which possess a methylsulfone substituent on the distal aryl ring. Indeed, analog 17a is perhaps the most interesting lead compound in this set, with a sub-micromolar GI50 value, selectivity over the human cathepsins, and lead-like properties (MW∼425, clogP∼1.25) that would leave sufficient room to maneuver in a lead optimization campaign.
Two of the most potent analogs identified were vinylsulfone-based inhibitors of the type represented by 18a–b (Table 4). In fact, compound 18a was previously reported to be an inhibitor of P. falciparum growth, and structure-activity studies of similar compounds have been published [48]. A series of ketobenzoxazole-based analogs (19a–c) were also identified as potent inhibitors of falcipain-2/3 and P. falciparum growth in culture. Aside from the P1/warhead moiety, ketobenzoxazole 19a is otherwise identical to the vinylsulfones 18a–b, and indeed these compounds exhibit similar IC50 values against falcipain-2/3. Despite these similarities, the irreversible inhibitors 18a and 18b inhibited parasite growth at ∼100-fold lower concentrations than did the reversible inhibitor 19a. It is not clear whether this effect is due to the nature of inhibition or other factors such as cell permeability, warhead inactivation (e.g. ketone reduction), or inhibition of other as-yet unidentified targets in the parasite.
Nitrile-based inhibitors 20 and 21 are also of significant interest as lead compounds as they represent the only reversible inhibitors identified that exhibited sub-micromolar GI50 values against P. falciparum parasites. Both inhibitors possess an aliphatic nitrile warhead geminally di-substituted at the P1 position and are thus structurally distinct from the aromatic nitriles (cyanopyrimidines) reported recently as potent inhibitors of falcipains [25]. The introduction of cyclic P1 moieties (as in 20 and 21) has been reported previously [49] as a strategy to counter peptidase-mediated drug degradation in vivo and it seems plausible that these analogs may have been originally synthesized with such an objective in mind. Both analogs 20 and 21 have leucine or leucine-like P2 side chains and extended biphenyl moieties at P3, compound 21 possessing an interesting heterocyclic surrogate for the P2/P3 amide bond. The combination of reversible inhibition, low cytotoxicity, and nanomolar potency against falcipains 2/3 and whole parasites, recommend compound 20 in particular for further lead optimization studies.
Whereas a number of promising antimalarial lead compounds were identified in this study, very few compounds of clear merit were uncovered as leads for trypanosomal disease. In fact, the most potent antitypanosomal compound identified (22) appears not to be a protease inhibitor at all – having no apparent warhead function. While compound 22 did not inhibit the growth of Jurkat cells at 10 µM, it did significantly inhibit the growth of BESM cells (∼90% inhibition at 20 µM). It therefore seems likely that compound 22 confers its antitrypanosomal effect via a mechanism other than protease inhibition. While this is not in itself problematic, a mechanism of drug-like action (as opposed to non-specific toxicity) would need to be established before further pursuing compounds like 22.
With respect to likely cysteine protease targets in T. brucei, RNAi knockdown of TbCatB but not rhodesain was shown to confer a phenotype similar to treatment with irreversible cysteine protease inhibitors [17]. Recently published data for small molecule inhibitors of TbCatB [50] also suggest that TbCatB is the more important drug target in T. brucei. Of significant interest then are ketobenzoxazole analogs like 23a and 23b which are potent (low-mid nM) inhibitors of TbCatB but only weak inhibitors of rhodesain (Table 5, Figure 6). Both compounds indeed inhibit the growth of T. brucei brucei parasites, although only at concentrations at least 100-fold greater than their in vitro IC50 values against TbCatB (low µM GI50 values vs. mid-low nM IC50 values). Ketooxadiazole analog 24 possesses excellent in vitro potency against both TbCatB and rhodesain, and like 23a/b is effective against cultured T. brucei brucei parasites, but also at relatively high concentrations (GI50 = 7.0 µM). Ketobenzoxazole analogs 19a and 19b have quite the opposite selectivity profile of 23a/b and are ∼1000 fold more potent inhibitors of rhodesain than TbCatB. That these compounds were not effective against T. brucei brucei parasites even at 25 µM is consistent with the hypothesis that TbCatB is the more important target in T. brucei. Thus, ketone-based inhibitors such as 23a–b and 24 represent promising leads with regard to in vitro potency and selectivity (for 23a–b) but would require further optimization for enhanced activity in cells. Possibly poor permeability and/or instability of the ketone warhead function in these compounds can explain their relatively modest activity against whole parasites.
A selection of potent cruzain inhibitors with diverse warhead types were selected for study in T. cruzi infected BESM cells using a microscopy-based high-content assay (Figure 7). Interestingly, the ketobenzoxazole analogs described above as active in T. brucei brucei were also among the most effective compounds against T. cruzi parasites. This is perhaps not surprising, since nearly all of these analogs are low nanomolar inhibitors of cruzain in vitro (Table 5). The most effective inhibitors were 23b and 24, but as was the case in T brucei, these compounds exert their effects on parasites only at much higher concentrations than are required to inhibit the target enzyme in vitro. As noted above, further chemical optimization of these leads for improved permeability and/or stability will likely be required to realize their full potential to affect parasite growth in vitro and in vivo. Also in favor of these compounds is their reversible-covalent nature of inhibition, which might be preferable to irreversible inhibitors with respect to the potential for in vivo toxicity and/or immunogenicity.
Comparisons of in vitro potency in biochemical assays with parasite growth inhibition is always problematic since factors such as cell permeability, compound stability, and sub-cellular localization are not always well understood and can vary with structure in unpredictable ways. Neither is it possible to rule out a role for as yet unidentified protease or non-protease targets as contributing to the observed parasite growth inhibition, since it is very likely that none of the protease inhibitors discussed herein is perfectly selective for the intended target(s). We have generally limited direct comparisons of potency to congeneric chemical series that inhibit by similar mechanisms (i.e., have similar warheads). For example, our discussion of protease targets in T. brucei is focused on reversible ketone-based inhibitors because the in vitro selectivity of such compounds is more likely to translate to the intracellular context than would selectivity data for irreversible inhibitors that exhibit time-dependent protease inhibition. We have similarly chosen not to make overreaching conclusions about the relative potency of compounds against parasite and human proteases, as the latter data were generated at an earlier time in a different laboratory (and furthermore were determined as Ki values rather than the IC50 values provided for the parasite proteases). We nonetheless felt it was valuable to include this historical cathepsin data as it provides some qualitative indication of the potential selectivity of the analogs described.
The market forces that spur new pharmaceutical development in the developed world are typically absent in the case of tropical parasitic diseases, despite indisputable medical need. One strategy to address this discrepancy is to focus on anti-parasitic drug targets that are homologous to other targets that are being actively pursued in the industry. As demonstrated here, this approach can produce potent and cell-active lead compounds directly out of a primary screening campaign. Many of these compounds are already good candidates for initial evaluation in animal pharmacokinetic and pharmacodynamic studies. Progress towards the identification of viable clinical candidates would require a robust lead optimization campaign to improve in vivo efficacy and address any safety issues that emerge. This work is far from trivial and will require significant resources in synthetic chemistry and animal pharmacology and toxicology. Providing encouragement for such work, however, is the knowledge that nitrile- and ketone-based cathepsin inhibitors not unlike those described herein have been successfully progressed into human clinical trials for other therapeutic indications.
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10.1371/journal.pgen.1002929 | Convergence of the Transcriptional Responses to Heat Shock and Singlet Oxygen Stresses | Cells often mount transcriptional responses and activate specific sets of genes in response to stress-inducing signals such as heat or reactive oxygen species. Transcription factors in the RpoH family of bacterial alternative σ factors usually control gene expression during a heat shock response. Interestingly, several α-proteobacteria possess two or more paralogs of RpoH, suggesting some functional distinction. We investigated the target promoters of Rhodobacter sphaeroides RpoHI and RpoHII using genome-scale data derived from gene expression profiling and the direct interactions of each protein with DNA in vivo. We found that the RpoHI and RpoHII regulons have both distinct and overlapping gene sets. We predicted DNA sequence elements that dictate promoter recognition specificity by each RpoH paralog. We found that several bases in the highly conserved TTG in the −35 element are important for activity with both RpoH homologs; that the T-9 position, which is over-represented in the RpoHI promoter sequence logo, is critical for RpoHI–dependent transcription; and that several bases in the predicted −10 element were important for activity with either RpoHII or both RpoH homologs. Genes that are transcribed by both RpoHI and RpoHII are predicted to encode for functions involved in general cell maintenance. The functions specific to the RpoHI regulon are associated with a classic heat shock response, while those specific to RpoHII are associated with the response to the reactive oxygen species, singlet oxygen. We propose that a gene duplication event followed by changes in promoter recognition by RpoHI and RpoHII allowed convergence of the transcriptional responses to heat and singlet oxygen stress in R. sphaeroides and possibly other bacteria.
| An important property of living systems is their ability to survive under conditions of stress such as increased temperature or the presence of reactive oxygen species. Central to the function of these stress responses are transcription factors that activate specific sets of genes needed for this response. Despite the central role of stress responses across all forms of life, the processes driving their organization and evolution across organisms are poorly understood. This paper uses genomic, computational, and mutational analyses to dissect stress responses controlled by two proteins that are each members of the RpoH family of alternative σ factors. RpoH family members usually control gene expression during a heat shock response. However, the photosynthetic bacterium Rhodobacter sphaeroides and several other α-proteobacteria possess two or more paralogs of RpoH, suggesting some functional distinction. Our findings predict that a gene duplication event followed by changes in DNA recognition by RpoHI and RpoHII allowed convergence of the transcriptional responses to heat and singlet oxygen stress in R. sphaeroides and possibly other bacteria. Our approach and findings should interest those studying the evolution of transcription factors or the signal transduction pathways that control stress responses.
| Transcriptional responses to stress are critical to cell growth and survival. In bacteria, stress responses are often controlled by alternative σ factors that direct RNA polymerase to transcribe promoters different from those recognized by the primary σ factor [1], [2]. Therefore, identifying the target genes for a particular alternative σ factor can help identify the functions necessary to respond to a given stress. For example, the transcriptional response to heat shock in Escherichia coli uses the alternative σ factor σ32 to increase synthesis of gene products involved in protein homeostasis or membrane integrity [3]. From available genome sequences, proteins related to E. coli σ32 are conserved across virtually all proteobacteria. This so-called RpoH family of alternative σ factors is characterized by a conserved amino acid sequence (the “RpoH box”) that is involved in RNA polymerase interactions [4], [5]. RpoH family members also possess conserved amino acid sequences in σ factor regions 2.4 and 4.2 that interact with promoter sequences situated approximately −10 and −35 base pairs upstream of the transcriptional start sites, respectively [6]. However, the definition of functional promoters for this family of alternative σ factor using only the presence or the extent of sequence identity for the predicted −10 and −35 binding regions is not a sufficient predictor of transcription activity [7].
While bacteria often possess many alternative σ factors, they usually possess only one member of the RpoH family. However, several α-proteobacteria, including Brucella melitensis [8], Sinorhizobium meliloti [9], [10], Bradyrhizobium japonicum [11], [12], Rhizobium elti [13] and Rhodobacter sphaeroides [14], possess two or more RpoH homologs. In some cases, one or more of these RpoH homologs completely or partially complement the phenotypes of E. coli ΔrpoH mutants, suggesting that these proteins can functionally interact with RNA polymerase and recognize similar promoter elements [8]–[11], [14], [15]. However, in the nitrogen-fixing plant symbiont Rhizobium elti, the ΔrpoH1 mutant was sensitive to heat and oxidative stress while the ΔrpoH2 mutant was sensitive to osmotic stress [13]. Therefore, the additional members of the RpoH family in α-proteobacteria may have roles in other stress responses.
Previous work demonstrated that either R. sphaeroides RpoHI or RpoHII can complement the temperature sensitive phenotype of an E. coli ΔrpoH mutant; that singly mutant R. sphaeroides strains lacking either rpoHI or rpoHII are able to mount a heat shock response; and that RNA polymerase containing either RpoHI or RpoHII can initiate transcription from a common set of promoters in vitro [14]–[16]. Combined, these observations suggest that RpoHI and RpoHII have some overlapping functions in R. sphaeroides. On the other hand, in vitro transcription assays identified promoters that were selectively transcribed by either RpoHI or RpoHII [14], [15]. Moreover, rpoHII is under direct transcriptional control of RpoE, a Group IV alternative σ factor that acts as the master regulator of the response of R. sphaeroides to singlet oxygen stress [17]–[19]. These later results and the recent observation that a ΔrpoHII mutant is more sensitive to singlet oxygen stress than the wild-type strain [15], [17] suggest that RpoHI and RpoHII also have distinct functions in R. sphaeroides. Finally, global protein profiles of R. sphaeroides mutants lacking rpoHI, rpoHII, or both genes, suggested that RpoHI and RpoHII have distinct and overlapping regulons [15], [17], [20]. However, the extent of genes that are direct targets for RpoHI and RpoHII is still unknown because past studies have been unable to distinguish direct from indirect effects on gene expression or identify all the direct targets for either of these σ factors.
In this study, we characterized the RpoHI and RpoHII regulons using a combination of expression microarrays, chromatin immunoprecipitation and computational methods which have been previously been shown to predict correctly direct targets for other alternative σ factors or DNA binding proteins [19], [21]. We found that the genes predicted to be common to the RpoHI and RpoHII regulons function in protein repair or turnover, membrane maintenance, and DNA repair. Genes specific to the RpoHI regulon encode other proteins involved in protein maintenance and DNA repair, whereas genes specific to the RpoHII regulon include proteins involved in maintaining the oxidation-reduction state of the cytoplasmic thiol pool. We used information on the members of each regulon to generate and test hypotheses about DNA sequences that determine promoter specificity of these two RpoH homologs. The observed properties of these two R. sphaeroides RpoH homologs illustrate how duplication of an alternative σ factor and subsequent changes in promoter recognition could have allowed convergence of transcriptional responses to separate signals. In the case of R. sphaeroides, we predict that these events allowed convergence of the transcriptional responses to heat shock and singlet oxygen stresses to be under control of these two RpoH paralogs.
To define members of the RpoHI and RpoHII regulons, we monitored transcript levels and protein-DNA interactions in R. sphaeroides strains ectopically expressing either RpoHI or RpoHII. To generate these strains, we constructed low copy plasmids carrying rpoHI or rpoHII under the control of an IPTG-inducible promoter [22] and conjugated them into R. sphaeroides mutant strains lacking rpoHI [16] or rpoHII [15], respectively. To induce target gene expression, we exposed exponentially growing aerobic cultures to IPTG for one generation before cells were either harvested to extract total RNA for analysis of transcript levels or treated with formaldehyde to prepare samples for chromatin immunoprecipitation on a chip (ChIP-chip) assays. The Western blot analysis used to measure levels of these alternative σ factors demonstrates that cells ectopically expressing RpoHI and RpoHII contained each protein at levels comparable to those following either heat shock or singlet oxygen stress (Figure 1). Thus, these strains can be used to characterize members of the RpoHI and RpoHII regulons.
As controls for this experiment, we measured the abundance of individual RpoH proteins and a control transcription factor (PrrA) [23], which is not known to be dependent on either alternative σ factor for its expression, when wild type cells were exposed to either heat or singlet oxygen stress. This analysis showed that RpoHI is detectable prior to heat stress, but its levels increase 10 and 20 minutes after the shift to increased temperature (Figure 1A). RpoHI levels remain elevated after the temperature shift but they decline within 60 minutes after heat shock, suggesting that as in the case of E. coli σ32, there is an initial rise in RpoHI levels immediately on heat shock before they return to a new steady state level at elevated temperature [24]. RpoHII was also detected prior to exposure to singlet oxygen and within 10 minutes of exposure to this reactive oxygen species, levels of this protein were increased (Figure 1B). Levels of RpoHII found within 20 minutes after exposure to singlet oxygen remained relatively constant over the time course of this experiment, suggesting a continuous requirement for RpoHII during this stress response (Figure 1B). The abundance of the control transcription factor PrrA did not follow these same trends, suggesting that the observed increases in individual RpoH proteins was associated with these stress responses. In addition, the abundance of individual RpoH proteins did not increase significantly to both stress responses, as expected if these increases were not due to a general increase in protein levels in response to different signals.
To identify transcripts that were increased in abundance as a result of RpoHI or RpoHII activity, we compared mRNA levels of cells expressing RpoHI or RpoHII ectopically to those of control cells lacking either rpoHI or rpoHII. We selected differentially expressed genes with a significance level set for a false discovery rate ≤5% and that displayed at least 1.5-fold higher transcript levels in cells expressing either RpoH family member. This analysis revealed that transcripts from 241 and 186 genes were increased by expression of RpoHI and RpoHII, respectively (Figure 2). These two sets of differentially expressed genes have 60 genes in common.
We recognize that some of these differentially-expressed transcripts might be not be direct targets for RpoHI and RpoHII. Therefore, to determine which of the above genes were directly transcribed by RNA polymerase holoenzyme containing either RpoHI or RpoHII, we performed ChIP-chip assays from comparable cultures to map direct interactions of RpoHI or RpoHII with genomic DNA. We were able to raise specific antibodies against RpoHII that performed well for the ChIP-chip assay, but repeated attempts to raise suitable antibodies against RpoHI failed. Therefore, we placed a FLAG polypeptide tag [25] at the N-terminus of the RpoHI protein sequence and used anti-FLAG monoclonal antibodies to perform the ChIP-chip assay. As a control we tested and showed that addition of the polypeptide tag did not alter the activity and specificity of RpoHI by comparing the mRNA level profiles of cells expressing the tagged version of RpoHI with cells expressing wild-type RpoHI (Figure S1). In addition, other control experiments showed there was no detectable cross-reaction between FLAG-RpoHI and the antibody used to precipitate RpoHII, and vice versa (data not shown). From the ChIP-chip analysis we identified 812 and 1353 genomic regions enriched after immunoprecipitation with antibodies against RpoHI and RpoHII, respectively, using a significance level set for a false discovery rate ≤5%. Because the signal from a single σ factor binding site extends on average over a 1 kb region, some enriched regions may contain multiple binding sites. To increase the resolution of the putative RpoHI and RpoHII binding sites, we identified the modes of the ChIP-chip signal distributions within each enriched region. This adjustment increased the number of putative binding sites for RpoHI and RpoHII to 1085 and 1765, respectively.
We then identified all the annotated genes that contained a ChIP-chip peak within 300 base pairs upstream of their start codons as a way to define candidate genes or operons in the RpoHI or RpoHII regulons. Included in this list of potential regulon members were genes that are predicted to be co-transcribed using a previous computational analysis of R. sphaeroides operon organization (http://www.microbesonline.org/operons/) [26]. Therefore, by these criteria, the upper limits of the total numbers of genes potentially regulated by RpoHI or RpoHII are 1120 and 1616, respectively (Figure 2). We recognized that a significant number of the putative RpoHI or RpoHII promoters may not be assigned from the ChIP-chip dataset alone, especially because promoter orientation needs to be considered and that because σ factor or RNA polymerase binding events do not always promote transcription. Therefore, we refined the respective RpoHI and RpoHII regulons by intersecting the lists of target genes identified from the ChIP-chip analysis with the lists of candidate genes identified from the expression profiling analysis. After this intersection, we predict that the RpoHI regulon contains 175 genes and the RpoHII regulon contains 144 genes with 45 genes common to both regulons (Figure 2).
Upon examining the annotations of these predicted target genes, the 45 genes that are members of both the RpoHI and RpoHII regulons are predicted to encode mainly for functions related to the electron transport chain, protein homeostasis, and DNA repair (Table 1 and Table S1). The 130 predicted members of the RpoHI regulon also encode functions in these three groups, but with a larger representation for functions associated with protein homeostasis. The 99 predicted members of the RpoHII regulon include fewer proteins predicted to play a role in protein homeostasis and a larger number of proteins predicted to help maintain the oxidation-reduction state of the cytoplasmic thiol pool. However, a large number of genes in both the unique and overlapping RpoHI and RpoHII regulons are annotated as having no predicted functions. Overall, this analysis revealed that RpoHI or RpoHII activate a large set of distinct and overlapping sets of target genes.
Previous work indicated that RpoHI and RpoHII can recognize and initiate transcription from similar promoter sequences [14], [15], [20]. The characterization of their respective regulons also suggests that some promoters can be transcribed by both σ factors while others are specific to either RpoHI or RpoHII. Therefore, we hypothesized that while the promoter sequences of the two σ factors may be similar, different sequence-specific interactions of RpoHI or RpoHII with promoter elements are the basis of promoter specificity for transcription initiation by RNA polymerase.
To overcome the limited resolution of the ChIP-chip experiment and predict determinants of promoter specificity for RpoHI or RpoHII, we searched the regions upstream of genes in each regulon for conserved sequence elements (137 sequences for RpoHI and 120 sequences for RpoHII). The conserved sequence elements we identified mapped to putative promoter elements that were within 100 bp of the coordinates of the modes of the distributions of the ChIP-chip signal. Thus, the predictions of these searches identified conserved sequence elements that were in agreement with the experimental data. In addition, even though we analyzed the individual RpoHI and RpoHII regulons independently for these motifs, the sequence alignment algorithm converged to the same sequence elements for promoters that were predicted to be recognized by both RpoHI and RpoHII. This result is not surprising given that both σ factors have similar amino acid sequences in their DNA recognition regions and are thus expected to recognize similar promoter sequences. However, this observation supports the hypothesis that RpoHI and RpoHII recognize common promoter sequences in their respective target genes as opposed to distinct promoters.
To predict specificity sequence determinants for each RpoH paralog, the putative distinct and overlapping promoter sequences were sorted into three groups according to the expression profiling and ChIP-chip data sets and converted into sequence logos (Figure 3, Table S2). The sequence logos derived from the three groups include: two groups that are preferentially or selectively bound and transcribed by either RpoHI or RpoHII and one group that is bound and transcribed by both σ factors. As noted above, some promoters appear to be bound by RpoHI or RpoHII without inducing detectable changes in transcript levels. We aligned these promoters separately to determine if they possessed unique characteristics, but no significant differences were detected (data not shown).
The conservation of a TTG motif in the −35 region in all three logos is consistent with the importance of this triplet in a previous analysis of at least one promoter known to be recognized by both RpoHI and RpoHII [27]. However, there was also evidence for sequence-specific elements in the logos for each RpoH paralog. In the logo for the RpoHI-dependent promoters, a cytosine is overrepresented at position −37 and a thymine is overrepresented at position −9. In the logo for RpoHII-dependent promoters, cytosine and thymine are overrepresented at positions −14 and −13, respectively.
Overall, the comparison between RpoHI and RpoHII-specific promoter logos allowed us to identify significant differences in the promoter sequences that may be used to adjust promoter selectivity and strength for RpoHI or RpoHII. In addition, the predicted sequence elements for RpoHI or RpoHII promoters are not mutually exclusive. Rather, it appears that promoter specificities for RpoHI or RpoHII are distributed along a gradient using a combination of specific bases at various positions of the −35 or −10 promoter elements.
To test predictions about specificity determinants derived from these logos, we cloned several putative promoters upstream of a lacZ reporter gene and integrated these into the genome of a R. sphaeroides ΔrpoHI ΔrpoHII mutant [15] via homologous recombination. The activity of each promoter was measured by assaying β-galactosidase activity in these R. sphaeroides reporter strains ectopically expressing either RpoHI or RpoHII (Figure 4) at levels comparable to those found during a stress response (see above and Figure 1). The RSP_1173, RSP_1408, and RSP_1531 promoters (which were either predicted to be members of the RpoHI regulon or, in the case of RSP_1173, known to be heat inducible and transcribed by RpoHI [16], had significant activity in the strain expressing RpoHI, but not when the same strain expressed RpoHII (Figure 4). In contrast, the RSP_2314, RSP_2389, and RSP_3274 promoters (which were either predicted to be members of the RpoHII regulon by our analysis or known to be induced by conditions that generate singlet oxygen [17], [18], [20]) showed activity in the presence of RpoHII but not RpoHI (Figure 4). Finally, the RSP_1207 and RSP_2617 promoters (which were predicted to be transcribed by both RpoH proteins and, in the case of RSP_1207, known to be transcribed by RNA polymerase holoenzyme containing either RpoH homolog [15] showed activity in cells containing either RpoHI or RpoHII (Figure 4). Overall, these results support predictions about members of the RpoHI or RpoHII regulons derived by combining the transcription profiling, ChIP-chip and computational analyses.
To test the predictions about the contributions of individual bases to promoter recognition, we measured the activity of R. sphaeroides RpoHI with an existing library of mutant E. coli groE promoters fused to a lacZ reporter in an E. coli tester strain [7]. The data from this analysis revealed that base substitutions in the TTG motif of the −35 region of this RpoH-dependent promoter (positions −36, −35, and −34) reduced its activity by at least 80% with RpoHI (Figure 5A), as expected from the predictions of promoter logo. We also found a slight increase in promoter activity when position −32 was changed to a cytosine, even though the C-32 is not conserved in RpoHI promoters. This observation is consistent with the results of a previous mutational analysis showing that E. coli σ32 prefers a cytosine at position −32 when the alanine at position 264 of its amino acid sequence is substituted to an arginine (corresponding to R267 of RpoHI) [28], but also suggests that the −32 position is not utilized to distinguish between RpoHI- and RpoHII-specific promoters. In the −10 region of the groE promoter, substitutions of the cytosine at position −14 for an adenine or guanine, the cytosine at position −13 for an adenine, or substitution of the thymine at position −11 for a cytosine, each reduced RpoHI-dependent promoter activity. In addition, a substitution of the adenine at position −12 for a cytosine or changing the thymine at position −9 for any other base reduced RpoHI-dependent activity by >90%. These observations are consistent with the conservation of a thymine at position −9 of the derived RpoHI promoter logo (Figure 3).
To test the predicted requirement of RpoHI for a thymine at position −9, we also analyzed the properties of two R. sphaeroides promoters in this E. coli tester strain. Activity of the RpoHI-dependent RSP_1531 promoter was reduced by 90% when the thymine at position −9 was changed to a cytosine, whereas the RpoHII-dependent RSP_2314 promoter had higher RpoHI-dependent activity when a thymine was placed at position −9 (Figure 5B). Therefore, this analysis confirmed that position −9 plays a critical role in promoter specificity for RpoHI. In conclusion, the measured effects of mutations in the E. coli groE promoter on RpoHI-dependent transcription confirmed that our models captured elements that are critical for promoter recognition by RpoHI.
We were unable to test activity of R. sphaeroides RpoHII against this groEL promoter library in the same E. coli tester strain (data not shown). Instead, we generated a small set of point mutations in the P1 promoter of the R. sphaeroides cycA promoter (Figure 3) which was previously shown to be transcribed by both RpoHI and RpoHII [27] and measured activity from single-copy fusions of these mutant promoters to lacZ in cells that either lacked both RpoH homologs or that contained a single rpoH gene under control of an IPTG-inducible promoter (Materials and Methods).
By analyzing this promoter library, we found that a G to T mutation at position −36 of cycA P1 (G-36T) increased its transcription by both RpoHI and RpoHII (Figure 5C). This result is consistent with the high predicted information content for T at this position for both RpoHI and HII (Figure 3), as well as the previous observation that the overall increase in activity of cycA P1 is caused by the G-36T mutation [27]. While our RpoHI and RpoHII promoter models (Figure 3) predict that a C could be allowed at position −36, a G-36C mutation lowered activity with RpoHII and had no positive impact on transcription by RpoHI (Figure 5C). Due to the significantly increased in activity from the G-36T mutation in cycA P1, all of the other promoter mutations we tested were generated in this background. Mutations we tested in the −35 region, T-35C and G-34C, resulted in virtually complete loss of cycA P1 activity with either RpoHI and RpoHII when compared to their G-36T parent promoter (Figure 5C), indicating that these bases are essential for transcription initiation by both RpoH homologs. Based on the relatively low information content predicted by our models for other positions in the −35 element (Figure 3), we did not test the effects of other mutations in this region on promoter selectivity by RpoH homologs.
In the predicted −10 region, A-12 has very high information content for both RpoHI and RpoHII, but the sequence logo suggests a T at this position might allow selective recognition by RpoHI (Figure 3). Indeed, a promoter containing a T at position −12 is still active only with RpoHI, suggesting that A-12 is essential for RpoHII activity but not RpoHI activity. The T at position −9 of cycA P1 is also predicted to have significantly higher information content for RpoHI than RpoHII, while a C at this position should have more information content for RpoHII than RpoHI (Figure 3). As predicted, we found RpoHII retained significant activity after placing a T-9C mutation in the context of the G-36T cycA P1 promoter. Furthermore, we found that this mutation completely abolished its activity with RpoHI, illustrating the high information content of a T at this position for transcription by this RpoH homolog. The importance for a T at the analogous position was also observed when testing activity of mutant E. coli groE promoters with RpoHI (T-9C mutation Figure 5A) or assaying function of the R. sphaeroides RSP_1531 promoter (which contains a T, Figure 5B) that is only transcribed to a detectable level by RpoHI (Figure 4). Finally, we also replaced the A at position −10 of the cycA P1 promoter with a G, as the sequence logo suggests there to be little information content at this position for either RpoHI or RpoHII (Figure 3). As predicted, there is little impact of the A-10G mutation on promoter function, though activity with RpoHII is more significantly reduced than that with RpoHI activity (Figure 5C).
When organisms encounter environmental or internal stress they often increase the transcription of genes encoding proteins that help mitigate damage to cellular components. Therefore, identifying functions that are involved in transcriptional stress responses is critical to understand both the nature of the damage caused to cellular components and how organisms respond to these challenges. Singlet oxygen and increased temperature are very different phenomena, but in R. sphaeroides the transcriptional responses to these two stresses involve two alternative σ factors, RpoHI and RpoHII, that each belong to the RpoH family [15], [16], [18]. Several other α-proteobacteria contain two or more members of the RpoH family that appear to control different stress responses [13], [29], [30]. However, as it is the case in R. sphaeroides, little is known about the target genes for these multiple RpoH homologs. In this work, we characterized genes that are directly transcribed by R. sphaeroides RpoHI and RpoHII to gain a better understanding of the biological response to heat shock and singlet oxygen stresses. We found that each of these RpoH paralogs control transcription of over 100 genes, suggesting that each of these phenomena lead to large changes in gene expression. However, we also found that there is significant overlap in the RpoHI and RpoHII regulons, creating an unexpectedly extensive connection between the transcriptional responses to these two signals. In addition, we investigated the characteristics of RpoHI- and RpoHII-dependent promoters. This effort allowed us to identify sequence elements that define promoter specificity for each σ factor, thereby allowing cells to selectively partition target genes for each RpoH paralog into different stress responses.
This work revealed a surprisingly extensive overlap of the RpoHI and RpoHII regulons even though these two homologs activate transcriptional responses to different signals in R. sphaeroides. This suggests that genes activated by these two pathways of the transcriptional regulation network play a role in the physiological response to both these, and even possibly, other stresses. Indeed, the genes regulated by both RpoHI and RpoHII encode known or annotated functions involved in protein homeostasis, DNA repair, and maintenance of cell membrane integrity (Table 1). These types of functions are central to cell viability and may be relevant for the physiological responses to multiple stresses that can have broad primary and secondary effects on cells. Indeed, the predicted functions of the overlapping members of the RpoHI and RpoHII regulons encode functions that are also part of the general stress response regulons for σS in E. coli or σB in Bacillus subtilis [31], [32]. Interestingly, σS homologs are mostly present in β- and γ-proteobacteria, but to date absent from sequenced genomes of α-proteobacteria like R. sphaeroides (http://img.jgi.doe.gov/) [33]. Thus, it is possible that the set of genes controlled by both RpoHI and RpoHII is part of a general stress response that is common to the heat shock, singlet oxygen and possibly other uncharacterized signals in R. sphaeroides [14], [15], [17], [18], [20]. This hypothesis is supported by the observation that R. sphaeroides and R. elti strains lacking both RpoHI and RpoHII are more sensitive to several conditions than strains lacking only one of these proteins [13], [15], [20].
In considering the scope of functions that are regulated by both RpoHI and RpoHII, it is also important to note that this set of genes may be larger than the one we characterized because some promoters known to be transcribed by both σ factors were only marginally affected by ectopic expression of either RpoHI or RpoHII. For example, the RSP_2310 (groES) promoter was shown to be transcribed by both RpoHI and RpoHII in previous in vitro experiments [14] and was detected by our ChIP-chip experiment to be bound by both RpoHI and RpoHII, but did not meet all the criteria of our analysis. Thus, the groES promoter, like other promoters, may be subject to complex regulation in vivo.
Our data also significantly extend the number and types of functions that are specifically controlled by RpoHI or RpoHII (Table 1). We expected to find specific sets of target genes because strains lacking either RpoHI or RpoHII displayed different phenotypes [14], [15], [17], [20]. While previous results indicated that accumulation of ∼25 proteins was dependent on RpoHII [17], our data indicate that some 150 genes are directly controlled by each R. sphaeroides RpoH paralog.
Genes in the direct but RpoHI-specific regulon encode functions that are involved in protein homeostasis, maintaining membrane integrity, and DNA repair, as is found for the E. coli σ32 regulon [3] (Table 1) The RpoHI specific regulon is also predicted to encode cation transporters and proteins in the thioredoxin-dependent reduction system (Table 1). Ion transporters can aid the heat shock stress response since exporting cations like iron, which may be released by thermal denaturation of damaged iron-sulfur or other metalloproteins, decreases secondary effects caused by formation of toxic reactive oxygen species [34]. The thioredoxin-dependent reduction system reduces disulfide bonds and peroxides, which are created by protein oxidation, and thereby helps maintain cytoplasmic proteins in a reduced state [35]. Inclusion of these functions in the RpoHI regulon suggests that oxidative damage may be an important secondary effect of heat shock, perhaps caused by protein denaturation or permeabilization of the cell envelope. Overall, these results support the hypothesis that the function of RpoHI in R. sphaeroides is similar to that of σ32 in E. coli for the response to heat shock stress. In addition, it is also possible that RpoHI plays a role in the R. sphaeroides response to other forms of stress. There is precedent for roles of σ32 homologs in other stress responses by other bacteria since the activity of RpoH in Caulobacter crescentus is increased by heavy metal stress [36].
In contrast, rpoHII transcription is under direct control of a Group IV alternative σ factor (RpoE) that serves as the master regulator of the singlet oxygen stress response [18]. In addition, an R. sphaeroides ΔrpoHII mutant is more sensitive to singlet oxygen than a wild-type or ΔrpoHI strain [15], [17]. Therefore, members of the direct RpoHII-specific regulon might be expected to play an important role in the response to singlet oxygen stress. Among the genes in the RpoHII-specific regulon are others predicted to function in maintaining membrane integrity and performing DNA repair, both potential targets for damage by singlet oxygen. However, the RpoHII–specific regulon contains fewer genes encoding functions related to protein homeostasis than found in the RpoHI regulon (Table 1). Other functions apparently unique to the RpoHII regulon include the glutathione-dependent reduction system, which like the thioredoxin-dependent system repair oxidized protein residues and maintain a reduced cytoplasm (Table 1). Even though the thioredoxin- and gluthatione-dependent reduction systems serve similar cellular functions, they are apparently under the control of different RpoH-dependent transcriptional networks in R. sphaeroides. Thus, it is possible that the thioredoxin- and gluthatione-dependent reduction systems preferentially function on different oxidized substrates. Glutathione-dependent reduction systems are known to function on lipids or other types of protein oxidative damage that might be experienced by the cell following singlet oxygen damage [35]. We also found that the RpoHII-specific regulon includes the multi-subunit NADH:quinone oxidoreductase and genes encoding enzymes in heme and quinone biosynthesis (Table 1). Each of these functions are critical for the respiratory and photosynthetic electron transport chains of R. sphaeroides and are known or predicted to contain one or more oxidant-sensitive metal centers. Thus, placement of these genes in the RpoHII-specific regulon suggests that these membrane or bioenergetic functions are damaged by and need to be replaced in the presence of singlet oxygen. Overall, our data indicates that the RpoHII-specific regulon controls expression of functions in the repair of oxidized proteins and replacement or assembly of critical electron transport chain components. Furthermore, the different types of repair functions found in the RpoHII regulon predict that singlet oxygen can damage numerous cellular components.
Our global gene expression data, results from analysis of gene fusions, as well as previously reported in vitro experiments [14], [15] all indicate that RNA polymerase containing either RpoHI or RpoHII can recognize some promoters in common. This observation is not surprising considering that RpoHI and RpoHII have similar amino acid sequences in their respective promoter recognition regions and are each able to rescue growth of E. coli σ32 mutants [14]–[16]. Likewise, the sequence logos derived here revealed that the promoter sequences recognized by each of the R. sphaeroides RpoH homologs are similar to both each other and to that recognized by E. coli σ32 [37].
Our experiments provide definitive evidence that some promoters are transcribed either exclusively or predominantly by RpoHI or by RpoHII. We were also able to predict and confirm the importance of bases for activity with individual RpoH homologs (particularly those in the −35 element). We have computational and experimental observations that can explain some aspects of promoter selectivity by RpoHI and RpoHII. For example, our experiments identify T-9 and other positions in the −10 element as potential candidates in this discrimination, as one or more substitutions have larger effects on activity with individual RpoHI homologs. Mutation of T-9 to any other base reduced RpoHI-driven expression of GroE promoter by more than 90%, and this same effect was observed using an authentic RpoHI promoter from R. sphaeroides. Importantly, changing the −9 position of an RpoHII R. sphaeroides promoter to T permitted expression by RpoHI. Together, these data suggest that T-9 is either required for or significantly enhances expression of RpoHI promoters, but is likely to be less important for expression of RpoHII promoters, as there is only weak conservation of -9T in RpoHII promoters. Our data also predict that other bases, which are overrepresented in the RpoHII promoters, could be critical for expression by that σ factor. As is the case with E. coli σ32 there are likely to be specificity determinants that lie outside the canonical −35 and −10 elements [7], [37]. Thus, additional in vivo and in vitro experiments with a larger suite of mutant promoters and a library of mutant RpoH proteins are needed to better define the determinants of promoter selectivity by RpoHI and RpoHII.
In conclusion, our results suggest that, at least in R. sphaeroides, RpoHI controls functions that are necessary for maintenance of protein homeostasis and membrane integrity after temperature increase and other cytoplasmic stress, similar to the well-characterized role of E. coli σ32 in the heat shock response [3]. However, we propose that, in R. sphaeroides, some RpoHI-regulated functions are also useful for survival in the presence of other forms of stress because these target genes also contain promoters that are recognized by RpoHII. We propose that the duplication of an ancestral RpoH protein to create a second homolog of this alterative σ factor provided R. sphaeroides the opportunity to connect stress response functions to another stimulus. In this model, rpoHII was placed under the control of the master regulator of the singlet oxygen stress response and the two RpoH proteins evolved to recognize somewhat different but compatible promoter elements to assure the optimal regulation of distinct but overlapping stress regulons. As a result of these events, the transcriptional responses of R. sphaeroides to heat shock and singlet oxygen stress were separable but allowed to converge and contain a common set of functions. It will be interesting to identify and examine other examples of such convergence across bacteria and other organisms that possess multiple homologs of RpoH or other transcription factors.
E. coli strains were grown in Luria-Bertani medium [38] at 30°C or 37°C. R. sphaeroides strains were grown at 30°C in Sistrom's succinate-based medium [39]. E. coli DH5α was used as a plasmid host, and E. coli S17-1 was used as a donor for plasmid conjugation into R. sphaeroides. The media were supplemented with kanamycin (25 µg/ml), ampicillin (100 mg/ml), chloramphenicol (30 mg/ml), spectinomycin (50 mg/ml), tetracycline (10 mg/ml for E. coli and 1 mg/ml for R. sphaeroides), trimethoprim (30 µg/ml), or 0.1% of L-(+)-arabinose when required. Unless noted, all reagents were used according to the manufacturer's specifications. The list of bacterial strains and plasmids used in this study are summarized in Table S3.
Plasmids for ectopically expressing RpoHI or RpoHII were constructed by separately cloning the rpoHI or rpoHII genes downstream of the IPTG-inducible promoter in pIND4 [22]. DNA fragments containing rpoHI or rpoHII were amplified from R. sphaeroides 2.4.1 genomic DNA using oligonucleotides containing BsrDI and BglII restriction sites (for RpoHII, RSP_0601_BsrDI_F GTAGCAATGCATGGCACTGGACGGATATACCGATC, RSP_0601_BglII_R GTAAGATCTTCATAGGAGGAAGTGATGCACCTCC, and for RpoHI, RSP_2410_BsrDI_F GTAGCAATGCATGAGCACTTACACCAGCCTTC, and RSP_2410_BglII_R GTAAGATCTTCAGGCGGGGATCGTCATGCC). These resulting fragments were digested with BsrDI and BglII and ligated into pIND4 that was digested with BseRI and BglII to create pYSD40 (rpoHI) and pYSD41 (rpoHII), respectively. The pYSD42 plasmid expressing the FLAG-tagged version of RpoHI was constructed following the same procedure but with an oligonucleotide primer containing a sequence encoding for three consecutive copies of the FLAG epitope (DYKDDDDK) at the N-terminus (RSP_2410_3FLAG_BsrDI GTAGCAATGCATGGACTACAAGGACCACGACGGCGACTACAAGGACCACGACATCGACTACAAGGACGACGACGACAAGAGCACTTACACCAGCCTTCCCGCTC). pYSD40, pYSD41, and pYSD42 were conjugated into R. sphaeroides ΔrpoHI [16] and R. sphaeroides ΔrpoHII respectively.
To monitor levels of RpoHI and RpoHII after heat shock, exponential phase aerobic cultures (69% nitrogen, 30% oxygen and 1% carbon dioxide) of wild type R. sphaeroides strain 2.4.1 grown at 30°C, were transferred to a 42°C warm bath with samples collected before heat treatment and at 10 min time intervals after heat shock, up to 60 min. To assess induction resulting from singlet oxygen stress, similarly grown wild type cells were treated with 1 µM methylene blue and exposed to 10 W/m2 incandescent light with samples collected before treatment and at 10 min time intervals after treatment, up to 60 min. Exponentially growing aerobic cultures of R. sphaeroides ΔrpoHI and ΔrpoHII mutants carrying the pYSD40 or pYSD42 plasmids respectively, were treated with 100 µM IPTG for one generation and harvested. All cell samples were resuspended in 3 M urea containing 1× protease inhibitor cocktail (Thermo Scientific, Rockford, IL) and sonicated. Samples were centrifuged to remove debris and total protein concentration of the samples determined with a protein assay kit following the manufacturer protocol (Bio-Rad, Hercules, CA). An equal amount of total protein for each sample was loaded onto a NuPAGE acrylamide gel (Invitrogen, Carlsbad, CA) and run in 1× 4-morpholineethanesulfonic acid running buffer at 150 V for ∼90 min. Proteins were transferred to Invitrolon PVDF membranes (Invitrogen, Carlsbad, CA), which were subsequently incubated for 1 hr in 1× Tris-buffered saline, 0.1% Triton-X, and 5% milk protein. The membranes were incubated with rabbit polyclonal antibodies raised against either RpoHI, RpoHII or PrrA. Horseradish-peroxidase-conjugated goat anti-Rabbit IgG antibody (Thermo Scientific, Rockford, IL) was used as secondary antibody for detection with Super Signal West Dura extended duration substrate (Thermo Scientific, Rockford, IL).
Triplicate 500 ml cultures were grown aerobically with bubbling (30%O2, 69% N2, 1% CO2) until they reached early exponential phase (OD at 600 nm of 0.15). At this point IPTG (Isopropyl β-D-1-thiogalactopyranoside) was added to a final concentration of 100 µM to induce gene expression from the pIND4 derivatives. After 3 hours incubation (OD at 600 nm of 0.30), 44 ml of cell culture were collected and 6 ml of 5% v/v phenol in ethanol was immediately added. Cells were collected by centrifugation at 6,000 g and frozen at −80°C until sample preparation. RNA extraction, cDNA synthesis, labeling, and hybridization were performed as previously described on Genechip Custom Express microarrays (Affymetrix, Santa Clara, CA) [40]. Processing, normalization, and statistical analysis of the expression profile data were performed in the R statistical software environment (http://www.r-project.org/) [41]. Data were normalized using the affyPLM package with default settings [42]–[44]. The expression microarray data have been deposited in the NCBI's Gene Expression Omnibus [45] and are accessible through GEO Series accession number GSE39806 (http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE39806).
Cells were harvested at mid-exponential growth (OD at 600 nm of 0.30) from the same cell cultures used for the expression microarray experiment to prepare samples for a ChIP-chip assay [19]. RpoHI-FLAG was immunoprecipitated using commercial monoclonal antibodies against the FLAG polypeptide (DYKDDDDK ) (Sigma Aldrich, St Louis MO). RpoHII was immunoprecipitated with anti-R. sphaeroides RpoHII rabbit serum. Labeled DNA was hybridized on a custom-made tiling microarray, synthesized by NimbleGen (Roche NimbleGen Inc, Madison, WI), covering the genome of R. sphaeroides 2.4.1 [19]. Before data analysis, dye intensity bias and array-to-array absolute intensity variations were corrected using quantile normalization across replicates (limma package in the R environment) [46]. Regions of the genome enriched for occupancy by RpoHI or RpoHII were identified using CMARRT with a false-discovery rate ≤0.05 [47]. The ChIP-chip data have been deposited in the NCBI's Gene Expression Omnibus [45] and are accessible through GEO Series accession number GSE39806 (http://www.ncbi.nlm.nih.gov/projects/geo/query/acc.cgi?acc=GSE39806).
DNA sequences were manipulated using custom Python scripts. Operon structure predictions for R. sphaeroides 2.4.1 were obtained from VIMSS MicrobesOnline (http://www.microbesonline.org/operons/) [26]. The promoter sequences predicted to be recognized by RpoHI and RpoHII were discovered using Bioprospector [48] set to search for bi-partite conserved sequence motifs. The promoter sequence alignments were refined using HMMER 1.8.5 [49]. The logo representations of the promoter sequence alignment were generated using WebLogo (http://weblogo.berkeley.edu/) [50], [51].
To assay the in vivo activity of RpoHI and RpoHII at target promoters, β-galactosidase assays were conducted with R. sphaeroides ΔrpoHI ΔrpoHII mutant strains containing individual reporter gene fusions. To construct this set of reporter strains ∼350 base pair regions upstream of putative target genes: RSP_1173, RSP_1408, RSP_1531, RSP_2314, RSP_2389, RSP_3274, RSP_1207 and RSP_2617, were amplified from genomic DNA using sequence specific primers, with NcoI and XbaI restriction sites at the ends of the upstream and downstream primers respectively. The amplified DNA fragments were purified, digested with NcoI-XbaI and then cloned in a pSUP202 suicide vector containing a promoterless lacZ gene (pYSD51). These Tcr plasmids were then conjugated into an R. sphaeroides ΔrpoHI ΔrpoHII mutant [15], generating single copy promoter-lacZ fusions integrated in the genome. pYSD40, pYSD41 or pIND4 (empty vector) were then conjugated into each of these reporter strains. Exponential phase cultures of these strains, grown by shaking 10 mL in 125 mL conical flasks, were then treated with 100 µM IPTG for one generation and samples analyzed for β-galactosidase activity. β-galactosidase assays were performed as previously described [52]. The data, presented in Miller units, represents the average of three independent replicates.
To test bases that contribute to RpoHI and RpoHII promoter specificity, β-galactosidase assays were conducted in R. sphaeroides tester strains containing reporter gene fusions of the cycA (RSP_0296) P1 promoter with a variety of point mutations (see Results). These reporter strains were constructed as described above, with individual point mutations being generated by overlap extension PCR [53]. β-galactosidase assays were conducted as described above and the data represents the average of three independent replicates. Background LacZ activity from control strains for each promoter fusion containing only the empty pIND4 plasmid (i.e. not expressing either RpoHI or RpoHII) was subtracted from the measured LacZ activity for each mutant promoter.
The construction of the E. coli CAG57102 mutant strain, the promoter library, and the β-galactosidase assay used to test the activity of R. sphaeroides RpoHI in vivo on mutant promoters were described previously [7]. To express R. sphaeroides RpoHI the E. coli rpoH gene of pSAKT32 [7] was replaced with the R. sphaeroides rpoHI gene. At least triplicate assays for β-galactosidase activity were performed on all strains.
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10.1371/journal.pcbi.1003127 | Pathway-based Screening Strategy for Multitarget Inhibitors of Diverse Proteins in Metabolic Pathways | Many virtual screening methods have been developed for identifying single-target inhibitors based on the strategy of “one–disease, one–target, one–drug”. The hit rates of these methods are often low because they cannot capture the features that play key roles in the biological functions of the target protein. Furthermore, single-target inhibitors are often susceptible to drug resistance and are ineffective for complex diseases such as cancers. Therefore, a new strategy is required for enriching the hit rate and identifying multitarget inhibitors. To address these issues, we propose the pathway-based screening strategy (called PathSiMMap) to derive binding mechanisms for increasing the hit rate and discovering multitarget inhibitors using site-moiety maps. This strategy simultaneously screens multiple target proteins in the same pathway; these proteins bind intermediates with common substructures. These proteins possess similar conserved binding environments (pathway anchors) when the product of one protein is the substrate of the next protein in the pathway despite their low sequence identity and structure similarity. We successfully discovered two multitarget inhibitors with IC50 of <10 µM for shikimate dehydrogenase and shikimate kinase in the shikimate pathway of Helicobacter pylori. Furthermore, we found two selective inhibitors (IC50 of <10 µM) for shikimate dehydrogenase using the specific anchors derived by our method. Our experimental results reveal that this strategy can enhance the hit rates and the pathway anchors are highly conserved and important for biological functions. We believe that our strategy provides a great value for elucidating protein binding mechanisms and discovering multitarget inhibitors.
| Many drug development strategies focus on designing inhibitors for single targets. These inhibitors often lose potency owing to mutations in the protein binding sites and are ineffective for complex diseases. Multitarget inhibitors can decrease probability of drug resistance and enhance the therapeutic efficiency; however, identifying them is still a challenge because targets often have low sequence and structure similarities in their binding sites. Here we propose a pathway-based screening strategy that simultaneously screens proteins in a metabolic pathway for discovering multitarget inhibitors. Because these proteins interact with similar metabolites and modify them step-by-step, the proteins share similarities in binding sites. We developed pathway site-moiety maps that present the conserved binding environments of the proteins without relying on the sequence or structure alignment. Compounds that bind these conserved binding environments are often multitarget inhibitors. We applied this strategy to the shikimate pathway of Helicobacter pylori, and discovered two multitarget inhibitors (IC50<10 µM) for shikimate dehydrogenase and shikimate kinase. In addition, we found two selective inhibitors based on specific binding environments for shikimate dehydrogenase. Thus the pathway-based screening strategy is useful for identifying multitarget inhibitors and elucidating protein-ligand binding mechanisms and has the potential to be applied to human diseases.
| The concept of “one–disease, one–target, one–drug” has dominated drug development strategy for decades [1], [2]. Based on this strategy, many virtual screening methods have been developed and applied successfully for identifying specific inhibitors of a single target [3]–[5]. However, the hit rates of these screening methods are often low because they generally cannot identify the key features from a single protein for understanding biological functions or determining inhibitor activities. In addition, single-target inhibitors often lose their potency owing to a single residue mutation in the target binding sites, resulting in drug resistance. For instance, some influenza strains containing a single residue mutation are resistant to the drug oseltamivir [6]. Another well-known example is human immunodeficiency virus type 1, which has a high mutation rate and rapidly develops resistance to drugs [7]. Furthermore, the single-target inhibitors are often therapeutically inefficient for complex diseases that were caused by multiple targets (such as cancers). Therefore, an emerging strategy for drug discovery is to enrich the hit rate and identify multitarget inhibitors, decreasing the probability of drug resistance and enhancing therapeutic efficiency by inhibiting multiple targets.
Some proteins share similarities in physicochemical properties and shapes of their localized binding sites despite low sequence or low overall structural similarities. For example, the proteins in a metabolic pathway contain conserved binding environments where the product of one enzyme is the substrate of the next enzyme in a series of catalytic reactions. Using this property, it is possible to design a multitarget inhibitor to simultaneously inhibit multiple proteins in a disease pathway to increase the therapeutic effectiveness against the disease. Recently, the concept of polypharmacology has been proposed for drug design, which deals with drugs that bind multiple target proteins [8]–[10]. However, designing these inhibitors is a challenging task because the proteins in a pathway often lack structural and sequence homology [11], [12]. Therefore, a new strategy for extracting conserved binding environments from these proteins is needed for the discovery of multitarget inhibitors.
To address these issues, we propose a new strategy, called pathway-based screening by using pathway site-moiety maps (PathSiMMaps). The strategy is an extension of our previous studies, which described a site-moiety map of a protein and core site-moiety maps of orthologous proteins [13], [14]. The main concept of this strategy is the simultaneous screening of multiple target proteins in the same metabolic pathway that interact with compounds sharing similar common substructures. Our previous studies showed that a site-moiety map can identify the moiety preferences and the physico-chemical properties of a binding site for elucidating binding mechanisms [13], [14]. A site-moiety map contains several anchors, and an anchor has three essential elements: (1) conserved interacting residues of a binding pocket (i.e., a part of the binding site); (2) moiety preference of the binding pocket; and (3) the type of interaction between the moieties and the binding pocket. Here we have extensively enhanced and modified our previous works to develop PathSiMMaps of multiple proteins with low sequence and structural similarities using an anchor-based alignment method. PathSiMMaps represents the conserved binding environments (i.e., conserved anchors, called pathway anchors) of multiple proteins in a pathway. Pathway anchors often play key roles in the series of catalytic reactions, and therefore can be used for the discovery of multitarget inhibitors and to increase the hit rate.
The major enhancements in PathSiMMaps developed in the present study compared with the core site-moiety maps developed in our previous study are as follows. The PathSiMMaps are designed to identify multitarget inhibitors for multiple proteins lacking structural similarity and sequence homology. In contrast, the core site-moiety maps were designed for orthologous proteins, which often have the same functions and similar structures in their binding sites. An anchor-based alignment method was developed to identify pathway anchors without relying on sequence or structure alignments. The PathSiMMaps are also designed to find specific anchors and selective inhibitors for a specific protein. Furthermore, we developed a PathSiMMap-based scoring method to enrich the hit rate.
We have applied the pathway-based screening strategy to identify pathway anchors and multitarget inhibitors of shikimate dehydrogenase (SDH) and shikimate kinase (SK) in the shikimate pathway of Helicobacter pylori (H. pylori), which causes peptic ulcer disease [15], [16]. The shikimate pathway consisting of seven proteins is an attractive target pathway for drug development because it is absent in humans [17]. SDH and SK are among the seven proteins in the pathway. We first identified four pathway anchors of SDH and SK despite their low sequence identity (8.3%) and structure similarity (RMSD is 4.8 Å). Based on these pathway anchors, two multitarget inhibitors were successfully discovered for SDH and SK with IC50 of <10 µM. Experimental results show that pathway anchors and their residues are highly conserved and play important roles for studying biological functions and designing multiple-target inhibitors. Our screening strategy significantly enriches the hit rate based on multiple targets. These experimental results reveal that the pathway-based screening strategy can find pathway anchors and multitarget inhibitors of structurally dissimilar proteins. We believe that our strategy is useful for studying protein-inhibitor binding mechanisms and discovering multitarget inhibitors for human complex diseases.
The concept of the pathway-based screening strategy is to simultaneously screen multiple proteins in a pathway and extract conserved binding environments of these proteins to discover multitarget inhibitors (Fig. 1). The screening strategy relies on the following criteria: (1) the proteins are in the same pathway; (2) the proteins catalyze similar ligands with common substructures; and (3) the site-moiety maps of these proteins share comparable pathway anchors. The strategy can work efficiently when proteins in the same pathway perform a series of catalytic reactions to yield a product compound. The intermediates of these proteins often share common substructures and their binding sites may share similar physicochemical properties and shapes.
Seven proteins in the shikimate pathway catalyze several metabolites with similar substructures to synthesize chorismate [18] (Figs. 1A, 1B, and S1). The similarities of the substrates, products and cofactors were represented by MACCS-Tanimoto values obtained from OpenBabel (http://openbabel.org/wiki/Main_Page) (Figs. S1B and S1C). The similarity matrix showed that the substrates/products generally share similarities (average Tanimoto value: 0.61). After 3-Dehydroquinate synthase converts DAHP to DHQ by NAD+, the downstream substrates/products (3-dehydroshikimate, shikimate, shikimate-3-phosphate, EPSP, and chorismate) of the protein have relatively similarities (Tanimoto value: 0.75) because these substrates/products share similar scaffolds (blue part in Fig. S1A). The cofactors in this pathway also have similar scaffolds (Fig. S1C).
In this study, we selected two proteins as the test screening targets: SDH and SK. These proteins are the fourth and fifth enzymes, respectively, in the shikimate pathway. SDH converts 3-dehydroshikimate into shikimate using NADPH as a cofactor [18] (Fig. 1B). Then, SK converts shikimate into shikimate 3-phosphate by another cofactor, ATP [19]. The major scaffolds (blue part) of 3-dehydroshikimate, shikimate, and shikimate 3-phosphate are the same (blue part in Fig. 1B), which implies that SDH and SK have conserved binding environments for recognizing this common scaffold.
We used the anchors of site-moiety maps to describe the binding environments of protein binding sites. The anchors have three interaction types: electrostatic (E), hydrogen-bonding (H), and van der Waals (V) interactions. First, we docked 302,909 compounds collected from public compound databases to binding sites of SDH and SK using our in-house docking tool, GEMDOCK [20]. Our previous studies revealed that GEMDOCK has similar performance to other docking methods such as DOCK [21], FlexX [22], and GOLD [20], [23], [24]. Furthermore, we have successfully used GEMDOCK to identify novel inhibitors and binding sites for several targets [25]–[27]. Subsequently, the site-moiety maps of SDH and SK were established by statistical analysis of the top 6,000 docked compounds (approximately 2% of the 302,909 compounds) (Fig. 1C). We then developed an anchor-based alignment method to find pathway anchors that are conserved in SDH and SK, for constructing the PathSiMMaps (Fig. 1D). The pathway anchors of the PathSiMMaps reflect conserved interactions between binding pockets with specific physico-chemical properties and their preferred functional groups, all of which are essential for pathway functions (Fig. 1E). Finally, the compounds that simultaneously matched pathway anchors of multiple targets were selected for the bioassay (Fig. 1F).
The site-moiety map of SDH consisted of five H anchors (H1, H2, H3, H4, and H5) and four V anchors (V1, V2, V3, and V4) (Figs. 1C and S2). For each anchor, several residues comprising a binding pocket with specific physicochemical properties, moiety compositions, and interaction type were identified from the top-ranked compounds. The H1 anchor (Fig. S2C), consisting of three residues (T65, K69, and D105), prefers polar moieties such as carbonyl, amide, and nitro groups. The hydroxyl moiety of shikimate participates in the dehydrogenase reaction (Fig. 1B) and forms hydrogen bonds with the three residues of the H1 anchor (Fig. S2B). The two residues (K69 and D105) of this anchor are highly conserved and are responsible for transferring a hydride ion between NADPH and shikimate in SDH of Thermus thermophilus [28]. Three residues (H15, T65, and Y210), constituting the H2 anchor, form hydrogen bonds with amide, carbonyl, sulfonate, amide, and carboxylic acid groups of the top-ranked compounds. The H4 (S129, A179, and T180) and H5 (K69 and S129) anchors interact with NADPH and are composed of two polar binding pockets (Fig. S2B). The major interacting moieties of the H4 anchor are carboxylic acid amide, ketone, ether, and hydrazine derivatives. The H5 anchor favors carboxylic acid amide, ketone, sulfonate, and carboxylic acid groups. The H3 anchor, which is spatially distant from the shikimate and NADPH binding sites, consists of three residues (T180, D207, and L208), revealing an additional binding pocket for designing inhibitors. This binding pocket often forms interactions with nitro, ether, sulfonate, and ketone moieties.
Ring moieties are the major moiety types of the V1, V2, V3, and V4 anchors of SDH. Among the 6000 top-ranked compounds, the aromatic moieties of 1,879; 886; 745; and 1,454 compounds form van der Waals contacts with the residues of the V1, V2, V3, and V4 anchors, respectively. The number of compounds (3,509 of 6,000 compounds) interacting with the V1 anchor, formed by three hydrophobic residues (L66, L208, and A209), is higher than those for the other V anchors. Aromatic ring, phenol, alkene, and oxohetarene moieties are the major compositions of the V1 anchor. The ribose of the cofactor NADPH is located in the V1 anchor (Fig. S2B), suggesting the importance of the V1 anchor for maintaining the function of the protein. The V2 anchor constituted by three residues (L208, Y210, and Q237) forms van der Waals interactions with 1,933 docked compounds by bulky moieties such as aromatic and heterocyclic moieties. Furthermore, this anchor occupies the position of the pyridine ring of NADPH (Fig. S2B). The V4 and the V3 anchors are situated in the groove and close to the entrance of the NADPH binding site, respectively. The residues (L66, G127, and G128) of the V4 anchors often interact with aromatic ring, phenol, alkene, and oxohetarene moieties. The three residues (L184, A209, and Y210) comprise the V3 anchor, and their preferred moieties are aromatic ring, alkene, phenol, and oxohetarene moieties. These nine anchors (five H and four V anchors) describe binding environments that can be used to design SDH inhibitors that block the binding of shikimate or NADPH.
We have previously described the site-moiety map of SK [14]. Three anchors (E1, V2, and H3) are located at the shikimate binding site (Fig. S3). The E1 anchor pocket consists of two positively charged residues (R57 and R132) which are essential for shikimate binding [29]. The anchor prefers negatively charged moieties such as carboxyl, sulfonate, and phosphate groups. The V2 anchor residues (D33, F48, G80, and G81) form van der Waals interactions with the ring of shikimate (Fig. S3B). This pocket often interacts with aromatic rings, carboxylic acid amidine, oxohetarene, and alkene moieties. The polar pocket of the H3 anchor consists of three residues (K14, D33, and G80) which often form hydrogen-bonding interactions with polar moieties (carboxylic acid amide, ketone, sulfonate, and ether) of the docked compounds. The H1, H2, and V1 anchors are situated at the ATP site. The H1 (G11, S12, G13, K14, and S15) and H2 (S15, D31, and D33) anchors are involved in the Walker A motif (K14 and S15) and a DT/SD motif (D31 and D33), respectively, and bind the phosphate groups of ATP [29]. The two anchors favor similar polar moieties, such as carboxylic acid amide, ketone, and sulfonate. The V1 anchor (M10, G11, S12, G13, K14, and S15) is situated between the H1 anchor and the H2 anchors, and its frequently interacting moieties are aromatic groups, oxohetarene, phenols, heterocyclic groups, and alkenes.
SDH and SK have four pathway anchors identified by the anchor-based alignment method despite their low sequence and structure similarity (Figs. 2 and 3). The pathway hydrogen-bonding anchor 1 (PH1) was derived from alignment of the H1 anchor of SDH and the E1 anchor of SK. The interaction type of the PH1 anchor was assigned as the hydrogen-bonding type because the preferred moieties of the E1 anchor are able to participate in hydrogen bonding. The pathway hydrogen-bonding anchor 2 (PH2) was derived from the alignment of the H4 anchor of SDH and the H3 anchor of SK. The pathway van der Waals anchor 1 (PV1) was derived from the alignment of the V4 anchor of SDH and the V1 anchor of SK. The pathway van der Waals anchor (PV2) was derived from alignment of the H5 anchor of SDH and the spatially close V2 anchor of SK.
The PH1 anchor consists of residues T65, K69, and D105 for SDH and R57 and R132 for SK (Fig. 2). The PH1 anchor prefers polar moieties such as nitro and carboxylic acid groups and is involved in the dehydrogenase reaction for SDH and the binding of shikimate [29] (Figs. 2B and 3). Interestingly, the shikimates of SDH and SK consistently occupy the location of the PH1 anchor. This result indicates that the PH1 anchor is essential for catalysis and substrate binding of these two proteins in the shikimate pathway. For SDH, the residues (S129, A179, and T180) of the PH2 anchor interact with NADPH; similarly, the SK residues (K14, D33, and G80) of PH2 are involved in the Walker A motif and DT/SD motif, both of which are involved in shikimate and ATP binding to SK [19] (Fig. 3). This suggests that the PH2 anchor is involved in shikimate binding and the binding of cofactors such as NADPH of SDH and ATP of SK.
The interaction residues of the PV1 anchor (L66, G127, and G128 in SDH; M10, G11, S12, G13, K14, and S15 in SK) constitute a binding pocket that frequently yields van der Waals interactions with compound moieties (Fig. 2B). The major moieties of the PV1 anchor are aromatic ring (40%), alkene (18%), and phenol (8%). The high preference of the aromatic ring may derive from the long side chains of the residues (L66 in SDH; M10 in SK), which can form stable van der Waals interactions with the aromatic rings of the compounds. For the PV1 anchor of SDH, the anchor residues (L66, G127, and G128) interact with the phosphate group of NADPH through van der Waals interactions. In addition, the residue L66 yields van der Waals interactions with the adenosine ribose of NADPH, which may stabilize NADPH binding. Similarly, the anchor residues (M10, G11, S12, G13, K14, and S15) of the SK PV1 anchor surround the phosphate groups of ATP and provide van der Waals interactions with ATP. These observations showed that the PV1 anchor plays an important role in interacting and transferring the phosphate groups of ATP (SK) and NADPH (SDH) during catalytic reactions, despite the different functions of SDH and SK (Fig. 3).
For the PV2 anchor, the side chains of its interaction residues (K69 and S129 in SDH; D33, F48, G80, and G81 in SK) provide van der Waals contacts with alkene (22%), aromatic ring (17%), enamine (7%), and heterocyclic moieties (5%) (Fig. 2B). The aromatic ring composition of the PV2 anchor is lower than that of the PV1 anchor, which may have resulted from the less compact binding environment of the PV2 anchor comprising a relatively small residue number. For SDH, the van der Waals interactions are formed between the residues (K69 and S129) of the PV2 anchor and the pyridine ring of NADPH (Fig. 3). Moreover, the residue K69 is a catalytic residue for the dehydrogenase reaction based on the SDH structure of Thermus thermophilus [28]. The SK PV2 anchor is located at the shikimate binding site, and its residues (D33 and F48) make van der Waals interactions with the cyclohexene group of shikimate. D33A or F48A mutations result in a loss of SK activity [14], revealing the anchor is essential for the shikimate binding. Although SDH and SK have different residue compositions in their PV2 anchors, these residues interact with similar ring moieties (e.g., cyclohexene of shikimate and the pyridine ring of NADPH) during their catalytic processes.
We evaluated the pathway anchors by site-directed mutagenesis. A site-directed mutagenesis study on SDH of Escherichia coli showed that it lost substrate-binding activity when the residues were mutated at positions 67, 92, and 107 (T65, J69, and D105, respectively in SDH of H. pylori) [30]. Our previous study also showed that mutations in the pathway anchor residues (M10, S12, S15, D33, F48, R57, and R132 in SK) reduced the activity of shikimate kinase [14], [31]. These results suggest that the pathway anchors are essential for catalytic reactions and that the mutations on the pathway anchor resides often decrease enzyme activities of SDH and SK (Figs. 3C and 3D).
Three multitarget inhibitors that simultaneously inhibit SDH and SK were identified based on the PathSiMMap scores. Two inhibitors NSC45174 and NSC45611, match the four pathway anchors in both targets (Fig. 4) and their IC50 values were consistently <10 µM. The inhibitor RH00037 lacks a polar moiety near the PH1 anchor, resulting in poor IC50 values (24.8 µM for SDH and 23.8 µM for SK) (Fig. 4A). The sulfonate group of NSC45174 and the carboxyl group of NSC45611 form hydrogen bonds with the residues of the PH1 anchor in the same way as the hydroxyl group of shikimate in SDH and the carboxyl groups of shikimate in SK. The elimination of polar moieties in RH00037 causes an approximately 10-fold reduction in inhibitory ability, revealing the importance of the PH1 anchor for multitarget inhibitor design.
Although the urea moiety of NSC45174 is different from the azo moieties of NSC45611 and RH00037, these moieties consistently form hydrogen-bonding interactions with the pocket of the PH2 anchor (Fig. 4). NSC45174 uses naphthalene, whereas NSC45611 and RH00037 use aromatic moieties to make van der Waals contacts with the residues of the PV1 anchor. Similarly, NSC45174, NSC45611, and RH00037 use naphthalene, aromatic ring, and 9H-xanthene to make van der Waals contacts with the residues of the PV2 anchor, respectively. These ring moieties can consistently engage in van der Waals interactions with residues of PV1 and PV2 despite their differing moieties. In these inhibitors, the presence of different moieties with similar physico-chemical properties reveals the advantages of the PathSiMMaps for identifying diverse multitarget inhibitors and providing an opportunity for lead optimization.
We further carried out experiments to compare three dose-response curves (Fig. S4): (1) shikimate dehydrogenase (SDH) activity; (2) shikimate kinase (SK) activity; and (3) dual enzyme (SDH and SK) activity. The dual enzyme assay is based on the determination of the release of ADP from the substrate 3-dehydroshikimate in the presence of two enzymes. For the inhibitors (NSC45611 and NSC45174) that simultaneously blocked SDH and SK, it was interesting that the dual enzyme curve had the median effect. At inhibitor concentrations greater than the IC90 value, it was intriguing that the dual enzyme curves swiftly approached approximately 0, revealing the greater combined inhibitory effect. In contrast, there were nearly identical profiles for the SK-specific inhibitor (NSC162535 [14]).
The proteins share similarities in physicochemical properties and shapes of their localized binding sites, despite low sequence or low overall structural similarities. This provides an opportunity to design multitarget inhibitors or results in unexpected side effects. For complex diseases such as cancer, diabetes, and cardiovascular diseases, the inhibition of multiple proteins is necessary for efficient therapy. Current therapeutic strategies use drug combination for these diseases, which frequently results in unwanted side effects. Our studies reveal that the anchor-based alignment method can be applied to measure binding environment similarities between proteins instead of relying on sequence or structure alignments.
We further examined the pathway anchors with respect to residue conservation (Fig. 5). The residues of SDH and SK were classified into four groups: pathway anchor residues, anchor residues, binding site residues, and other residues according to the following rules. The residues of the pathway anchors were classified as pathway anchor residues. The residues that formed anchors but were not pathway anchor residues were classified as anchor residues. The residues of the defined binding sites that were neither pathway anchor nor anchor residues were classified as binding site residues. The remaining residues were classified as other residues. Each residue position was assigned an evolutionary conservation score according to the Consurf server [32]. For a query protein, the Consurf server provided a multiple sequence alignment of its homologous sequences for measuring the conservation degree of each residue position. The conservation degree was divided into nine grades. Residues with the highest conservation score, 9, represented the highly conserved positions, which often play important roles for maintaining protein functions/structure during the evolutionary process. The statistical results revealed that the pathway anchor residues are the most conserved among the four groups (Fig. 5A). The conservation score of 9 was observed for 81% of pathway anchor residues, 63% of anchor residues, 30% of binding site residues, and 5% of other residues. When we calculated an average conservation score for each anchor and pathway anchor, the pathway anchors proved to be more conserved than the anchors (Fig. 5B). For example, the conservation score for the PH1 anchor is 9, and the conservation score for each of its residues (T69, K69, and D105 in SDH; R57 and R132 in SK) is 9. The high conservation of the pathway anchors implies that they have been essential for a series of catalytic reactions during evolution owing to their importance for interacting with shikimate. This is based on structure complex observations (Fig. 3).
One of the advantages of the pathway-based screening strategy is to design multiple-target inhibitors that occupy the pathway anchors for reducing the probability of drug resistance. For multitarget inhibitors, the probability of simultaneously arising resistant mutations is exponentially lower than that of any single mutation. In contrast, the conventional strategy for developing drugs is easily susceptible to resistant mutations using a “one-disease, one-target, one-drug” strategy. The conventional strategy is ineffective against diseases with high mutation rates, such as influenza virus, cancers, and human immunodeficiency virus type 1 [7], [33], [34]. Therefore, the pathway-based screening strategy is useful for designing multitarget inhibitors for such diseases.
The alignment of the site-moiety maps of SDH and SK revealed a specific site for SDH despite many similarities shared by the two targets (Fig. 6A). The specific site consists of the H3, V1, and V3 anchors, which are not involved in the NADPH and shikimate binding sites. The specific site provided an opportunity to discover selective inhibitors for SDH. We evaluated this concept using two selective inhibitors (NRB03174 and HTS02873) that occupied three-specific anchors with high PathSiMMap scores (Fig. 6B). NRB03174 and HTS02873 inhibited SDH with IC50 values 9.7 µM and 4.9 µM, respectively, whereas they demonstrated no inhibitory effect at 100 µM for SK (Fig. 6B). NRB03174 interacts with the residues of the V1 and V3 anchors using the bromobenzene moiety (Fig. 6C); similarly, HTS02873 makes van der Waals contacts with the residues of the V1 and V3 anchors using the anisole moiety (Fig. 6D). Although no hydrogen-bonding interactions were observed in the specific anchors of SDH for the NRB03174/HTS02873 molecules, these two inhibitors formed hydrogen-bonding interactions with the anchor residues of the pathway anchors. For example, NRB03174 yielded hydrogen bonds with the anchor residues (L66, K69, S129, and A179), and HTS02873 made hydrogen-bonding interactions with the residues (S129, and A179).
Designing selective inhibitors for disease-specific proteins can prevent unexpected side effects that are major obstacles in clinical trials and often result in treatment failure. For example, more than 100 p38 MAP kinase inhibitors that were designed for treating inflammatory or cardiovascular diseases were suspended because of their serious side effects [35]. The above results suggested that specific anchors and the pathway anchors can be used to design selective inhibitors and multitarget inhibitors, respectively. Thus the concept of the pathway-based screening strategy can be further extended to design multitarget inhibitors of disease-specific proteins. By combining specific anchors and the pathway anchors of multiple disease-related proteins, it is possible to design multitarget inhibitors that bind disease-specific but not non-specific proteins. Such multitarget inhibitors can enhance therapeutic potency and minimize side effects.
The accuracy of the PathSiMMap was assessed using the hit rate and compared with site-moiety map and energy-based methods. The energy-based method used here was the piecewise linear potential (PLP) of GEMDOCK [20]. GEMDOCK is comparable to some docking methods (e.g., DOCK, FlexX, and GOLD) on the 100 protein-ligand complexes and has similar accuracy to some energy-based scoring functions in the prediction of binding affinities [20], [24]. During the docking process, GEMDOCK first assigned formal charge and atom type (i.e., donor, acceptor, both, or nonpolar) to atoms of compounds and proteins. Then, the GEMDOCK PLP measures intermolecular potential energy between proteins and docked compounds. The intermolecular potential energy includes electrostatic, van der Waals, and hydrogen-bonding interactions. The compounds can be ranked based on their intermolecular potential energy. The hit rate is defined as Ah/Th (%), where Ah is the number of active compounds among the Th highest-ranking compounds. For SDH, the active compounds used for verification were the three multitarget inhibitors and the two specific inhibitors (Ah = 5). For SK, the active compounds used for verification were the seven SK inhibitors [14] (Fig. S5), and three multitarget inhibitors (Ah = 10). The hit rate of the PathSiMMap was considerably better than that of other methods used for identifying inhibitors of SDH and SK (Fig. 7, Tables S1 and S2). Our pathway-based screening strategy can be used to enhance the hit rate because the pathway anchors are often highly conserved and important for biological functions (Figs. 3 and 5). This suggests that the pathway anchors often play important roles for ligand binding. Thus, the compounds that match the pathway anchors are often potential inhibitors of the target proteins. For example, for SDH, the ranks of NSC45174 were 3810 by the energy-based method, 177 by the site-moiety map, and 13 by PathSiMMap.
We selected 20 compounds (Tables S3 and S4) for bioassay based on their PathSiMMap scores, drug-like properties, availabilities, and domain knowledge. We performed the compound-anchor profile analysis to find why NSC45174 and NSC45611 were more potency than other top-ranked compounds (Fig. S6). This profile analysis showed that NSC45174 and NSC45611 simultaneously matched the four pathway anchors of SDH and SK (Fig. S6A) and inhibited them with IC50 values ≦10 µM. In contrast, most of the inactive compounds matched 2–3 pathway anchors of SDH and SK. For example, KM02359 has no polar moieties to yield hydrogen-bonding interactions with the PH1 anchor residues of SDH and SK (Figs. S6A and S6C). CD01870 lacks a polar moiety in the PH1 anchor and is unable to form hydrogen bonds with the anchor residues of SDH and SK (Figs. S6A and S6D).
We next analyzed the compound–residue interaction profiles to find why some compounds that matched the four pathway anchors were inactive for both SDH and SK (Fig. S6B). These profiles showed that NSC45174, NSC45611, and RH00037 maintained the conserved interactions (i.e., those commonly found with >50% of inhibitors) with the anchor residues of SDH and SK (e.g., K69, D105, G127, A179, L208, and S129 in SDH; M10, G11, S12, G13, K14, S15, D33, R57, G80, and R132 in SK). These conserved interactions of the pathway anchors may have accounted for the potency of NSC45174 and NSC45611. These profiles indicated that some compounds (e.g. HTS05470) with high PathSiMMap scores lacked several of the conserved interactions, which may have resulted in their inactivity. For example, HTS05470 lost the conserved hydrogen-bonding interactions with these residues (A179 and L208 in SDH; K14 and S15 in SK) (Figs. S6B and S6E). According to both compound-anchor profiles and compound–residue interaction profiles, these results showed that the compound often inhibits proteins when it highly matches the pathway anchors and keeps conserved interactions. In addition, we applied the pathway-based screening strategy for additional four pathways (Figs. S7, S8, S9, S10, and Text S1).
The pathway-based screening strategy to discover multitarget inhibitors relies on the following criteria: (1) the proteins catalyze ligands with common substructures, and (2) these proteins share conserved binding environments and comparable anchors in their site-moiety maps. We selected the other five proteins in the shikimate pathway of Helicobacter pylori to examine whether they share conserved binding environments (i.e. pathway anchors) with SDH and SK (Fig. S11). These proteins include DAHP synthase, 3-dehydroquinate synthase (3CLH), 3-dehydroquinate dehydratase (1J2Y), EPSP synthase, and chorismate synthase (1UM0). Because structures of DAHP synthase and EPSP synthase are unavailable, we obtained their structures using an in-house homology-modeling server [36]. First, the site-moiety maps of these five proteins were established. The anchor-based alignment method was then applied to identify the pathway anchors of these seven proteins. Among these proteins, 3-dehydroquinate synthase, SDH, SK, and EPSP synthase share the four pathway anchors (Fig. S11). The former three proteins have similar substrates (DAHP, 3-dehydro shikimate, and shikimate) and cofactors (NAD+, NADPH, and ATP) (Fig. S1). Conversely, the PEP, the cofactor of EPSP synthase, is much smaller than NAD+, NADPH, or ATP.
These four pathway anchors located across substrate and cofactor sites often play key roles in catalytic reactions and ligand bindings for 3-dehydroquinate synthase, SDH, SK, and EPSP synthase (Figs. 3 and S12). 3-dehydroquinate synthase converts DAHP into DHQ with the cofactor NAD+ (Fig. S1). The PH1 anchor of 3-dehydroquinate synthase is situated at the DAHP site (Fig. S12), while the PH2, PV1, and PV2 sit at the NAD+ site. Three polar residues (D126, K210, and R224) comprise the PH1 anchor. The carboxyl moiety of DAHP forms hydrogen-bonding interactions with the PH1 anchor residues (K210 and R224), involving in the catalytic reaction [37]. The nicotinamide moiety of NAD+ interacts with the PH2 anchor residue (D99) and the PV2 anchor residues (D126, K132, and K210) by hydrogen-bonding and van der Waals interactions, respectively. Two residues (G95 and L122) constitute the PV1 anchor and make van der Waals interactions with the tetrahydrofuran-3,4-diol moiety of NAD+. EPSP synthase catalyzes the conversion of shikimate-3-phosphate into EPSP with PEP (Fig. S1). The PH1 anchor of EPSP synthase consists of three residues (A154, S155, and K329). A hydrogen bonding network is formed between the anchor residues (S155 and K329) and the phosphate moiety of shikimate-3-phosphate. Three polar residues comprise (K11, T83, and D302) the PH2 anchor, and these residues yield hydrogen bonds with the phosphate moiety of PEP and the hydroxyl moiety of shikimate-3-phosphate. The PV1 anchor consists of three residues with long side chains, including K11, D302, and E330. The acrylic acid moiety of PEP is situated at this anchor, and makes van der Waals interactions with these residues. The cyclohexene moiety of shikimate-3-phosphate is sandwiched between the PV2 anchor residues (Q157, R182, and I301) and forms stacking interactions with them. These observations show the importance of these pathway anchors for performing biological functions of these proteins. In addition, although these four proteins have different functions, their pathway anchor residues have similar physicochemical properties for interacting their substrates and cofactors. For example, the PH1 anchor residues of 3-dehydroquinate synthase, SDH, SK, and EPSP synthase are polar and consistently form hydrogen bonding interactions with carboxyl, ketone, carboxyl, and phosphate moieties of their substrates, respectively.
We then docked the multitarget inhibitors of SDH and SK into 3-dehydroquinate synthase and EPSP synthase to examine whether these inhibitors match the pathway anchors of these two proteins. The docked poses show that NSC45174 matches the four pathway anchors in 3-dehydroquinate synthase, while NSC45611 and RH00037 match three pathway anchors (Fig. S13). The docked pose of NSC45174 in 3-dehydroquinate synthase is similar to those in SDH and SK. For example, the sulfonate moiety of NSC45174 is located at the PH1 anchor of these three proteins and consistently forms hydrogen bonds with the PH1 anchor residues (Figs. 4B, 4E, and S13A). Similarly, the naphthalene moiety of NSC45174 consistently sits at the PV2 anchor, and makes van der Waals interactions with the anchor residues. In contrast, these three compound match 2–3 pathway anchors in EPSP synthase. For instance, the sulfonate moiety of NSC45174 is located at the PV1 anchor and thereby is unable to form hydrogen-bonding interactions with the PH2 anchor residues (Fig. S13D). Next, we carried out experiments to determine IC50 values of the three compounds for 3-dehydroquinate synthase. NSC45174 inhibited 3-dehydroquinate synthase with an IC50 value 7.1 µM, while NSC45611 and RH00037 showed no inhibitions (Figs. S13G and S13I). NSC45174 is a novel multitarget inhibitor that simultaneously inhibited three proteins (SDH, SK, and 3-dehydroquinate synthase) of the shikimate pathway. These results reveal that the pathway-based screening strategy can identify multitarget inhibitors in a pathway.
Apo-form structures of SDH and SK were selected for virtual screening because the use of closed-form structures induced by bound ligands may limit the diversity of identified inhibitors. For defining binding sites, the apo-form structures of SDH (3PHG) and SK (1ZUH [19]) were aligned to their respective closed-form structures SDH (3PHI) and SK (1ZUI [19]), using a structural alignment tool [38]. The bound ligands (shikimate and NADPH for SDH and shikimate and phosphate for SK) were used to determine the binding sites of SDH and SK. The binding sites of these structures were defined by residues situated ≤8 Å from the bound ligands.
We selected compounds from two public databases, Maybridge and National Cancer Institute, to generate the PathSiMMaps and discover multitarget inhibitors because of their rapid availability and low cost. Compounds with molecular weight <200 or >650 daltons were not selected. The total number of compounds selected for screening was 302,909.
The 302,909 compounds were docked into the binding sites of SDH and SK using an in-house docking tool, GEMDOCK [20] (Fig. S14A) to establish the site-moiety maps of target proteins. Subsequently, the top 2% compounds (approximately 6,000) ranked by docking energy were selected to establish site-moiety maps. We inferred site-moiety maps to recognize interaction preferences between binding pockets and moieties using the top-ranked 2% compounds. First, protein-compound interaction profiles were generated based on the PLP calculated by GEMDOCK (Fig. S14B). The profiles described the interactions (i.e., E, H, and V interactions) between the compounds and the protein residues. Each profile can be represented by a matrix with size P×C, where P and C are the numbers of docked compounds and interacting residues of a protein. For the E and H profiles, the entry was set to 1 (green regions in Fig. S14B) if the compound forms electrostatic or hydrogen-bonding interactions with the residues such as T65, K69, and D105 in the anchor H1; otherwise, the entry was set to 0 (black regions). For the V profile, the entry was set to 1 if the V energy was less than −4 kcal/mol.
The consensus interacting residues (e.g., T65, K69, and D105) of the profiles recognized according to Z scores often play key roles in biological functions. For each profile, the Z score (Zi) of the protein residue i was computed by , where fi is the observed interaction frequency between compounds and residue i, and μ and σ are the mean and the standard deviation of interaction frequency derived from 1,000 randomly shuffled profiles. We considered the residue i to be a consensus interacting residue if its Z score was greater than 1.645, a common threshold used in statistics (corresponding to a 95% confidence level). Then spatially neighboring interacting residues and their interactive moieties with statistical significance were assigned as an anchor (Fig. S14C). Finally, the site-moiety map of each target was constructed (Fig. S14D).
Pathway anchors, which are conserved anchors among the target proteins, represent key features including consensus interactions between the compounds and the binding pockets in a pathway (Figs. 2 and 3). Identifying pathway anchors using a structural alignment tool is a challenging task because of low sequence identity (8.3%) and structure similarity (RMSD is 4.8 Å) between SDH and SK [38]. To address this task, we developed an anchor-based alignment method according to spatial arrangements, the interaction-type similarity, and the volume similarity of the aligned anchors (Fig. S15). Each aligned anchor pair x between SDH and SK site-moiety maps is assigned an anchor alignment score (AAS(x)), which is defined aswhere i is interaction-type similarity, V is anchor-volume similarity, and d is the distance between the aligned anchors. i is set to 1 if the aligned anchors have the same interaction type or to 0.5 when an E anchor is aligned to an H anchor because negatively/positively charged moieties of the E anchor are able to form hydrogen bonds as well as polar moieties of the H anchor; otherwise i is set to 0. V is defined as , where Vmax and Vmin are the respective volumes of the larger and the smaller anchor. Then the alignment was achieved by maximizing the similarity score (S) between the site-moiety maps of SDH and SK. The similarity score is defined as , where n is the number of the aligned anchors. The alignment of the two site-moiety maps was achieved by seeking the highest similarity score using exhaustively superimposing the anchors. The aligned anchors were considered to be the pathway anchors, and the center of the pathway anchor was defined as the geometric center of the two aligned anchors. These pathway anchors consisted of the PathSiMMaps of SDH and SK (Fig. S15C).
Compounds matching the pathway anchors were considered potential inhibitors for the shikimate pathway. For compound j at a binding site, the PathSiMMap score (PS), a measure of the inhibition capability, was calculated aswhere PASp(j) is the pathway anchor score of compound j in the pathway anchor p; ASa(j) is the anchor score of compound j in anchor a; P and A are the numbers of the pathway anchors and anchors, respectively. Here PASp(j) is set to 1 if the compound j matches the pathway anchor p and otherwise to 0. Similarly, ASa(j) is set to 1 if the compound j matches the anchor a. For example, P is 4, and A is 9 and 6 for SDH and SK, respectively. The screening compounds were ranked based on their PathSiMMap scores for SDH and SK. Then, the compounds were re-ranked by consensus rankings of SDH and SK PathSiMMap rankings for selecting potential multitarget inhibitors. Finally, the top-ranked compounds that were commercially available were selected for bioassay. In addition, for SDH, the top-ranked compounds derived from the specific anchor were selected for bioassay. These compounds were considered to be selective inhibitors for SDH.
The SK activity was measured by coupling the release of ADP from the SK-catalyzed reaction to the oxidation of NADH using pyruvate kinase (EC 2.7.1.40) and lactate dehydrogenase (EC 1.1.1.27) as coupling enzymes [31]. SDH activity was determined by monitoring the formation of NADPH. The initial rate of the reaction was measured by the increase in absorbance at A340 (ε = 6,200 M−1 cm−1) in the present of shikimate. The assay was performed at 25°C in a mixture of 100 mM Tris-HCl buffer, pH 8.0.
Both SK and SDH were used in a final enzyme concentration of 100 nM. For determination of IC50 for each inhibitor, the assay was initiated by the addition of shikimate (1.6 mM) after incubation in a buffer containing cofactor (2 mM ATP for SK or 2 mM NADP for SDH), enzyme, and inhibitor (dissolved in 5% dimethyl sulfoxide). All assays were conducted in a 96-well microplate and analyzed with a spectrophotometer (FLUOstar OPTIMA, BMG LABTECH). A dose-response curve was fitted using the non-linear regression function of GraphPad Prism®.
We performed ADP assay providing a direct method to analyze SDH-SK dual enzyme activity. The assay was initiated by addition of the 3-dehydroshikimate (2 mM) after incubating in a reaction mixture containing 2.5 mM ATP, 0.5 mM NADPH, 100 nM SDH, 100 nM SK enzyme, 50 mM KCl, 5 mM MgCl2 and different inhibitors. The reaction was carried out at 25°C in a mixture of 100 mM Tris-HCl buffer, pH 7.5 and terminated at 100°C for 5 mins in the reaction time of initial rate. The amount of ADP was measured by using ADP Colorimetric Assay Kit II (BioVision) according to the manufacturer's instruction. We also performed 3-dehydroquinate synthase inhibition assay. The reaction was comprised of 3-deoxy-D-arabinoheptulosonate 7-phosphate (1 mM) and NAD+ (0.5 mM). The amount of NADH was measured by using NAD+/NADH Quantification Kit (BioVision). All assays were conducted in a 96-well microplate and analyzed with a spectrophotometer (FLUOstar OPTIMA, BMG LABTECH). The dose-response curve was fitted using the non-linear regression function of GraphPad Prism. The IC90 values were computed from the IC50 and Hill slop.
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10.1371/journal.pcbi.1003211 | Structural Similarities and Differences between Amyloidogenic and Non-Amyloidogenic Islet Amyloid Polypeptide (IAPP) Sequences and Implications for the Dual Physiological and Pathological Activities of These Peptides | IAPP, a 37 amino-acid peptide hormone belonging to the calcitonin family, is an intrinsically disordered protein that is coexpressed and cosecreted along with insulin by pancreatic islet β-cells in response to meals. IAPP plays a physiological role in glucose regulation; however, in certain species, IAPP can aggregate and this process is linked to β-cell death and Type II Diabetes. Using replica exchange molecular dynamics with extensive sampling (16 replicas per sequence and 600 ns per replica), we investigate the structure of the monomeric state of two species of aggregating peptides (human and cat IAPP) and two species of non-aggregating peptides (pig and rat IAPP). Our simulations reveal that the pig and rat conformations are very similar, and consist of helix-coil and helix-hairpin conformations. The aggregating sequences, on the other hand, populate the same helix-coil and helix-hairpin conformations as the non-aggregating sequence, but, in addition, populate a hairpin structure. Our exhaustive simulations, coupled with available peptide-activity data, leads us to a structure-activity relationship (SAR) in which we propose that the functional role of IAPP is carried out by the helix-coil conformation, a structure common to both aggregating and non-aggregating species. The pathological role of this peptide may have multiple origins, including the interaction of the helical elements with membranes. Nonetheless, our simulations suggest that the hairpin structure, only observed in the aggregating species, might be linked to the pathological role of this peptide, either as a direct precursor to amyloid fibrils, or as part of a cylindrin type of toxic oligomer. We further propose that the helix-hairpin fold is also a possible aggregation prone conformation that would lead normally non-aggregating variants of IAPP to form fibrils under conditions where an external perturbation is applied. The SAR relationship is used to suggest the rational design of therapeutics for treating diabetes.
| IAPP, a 37 amino-acid peptide hormone belonging to the calcitonin family, is an intrinsically disordered peptide produced along with insulin by pancreatic islet β-cells in response to meals. In its functional form, IAPP acts as a synergic partner of insulin to reduce blood glucose. IAPP can, however, also play a pathological role, contributing to Type II diabetes (T2D). Knowledge of the structural nature of the physiological and pathological forms of IAPP will facilitate the rational design of novel drugs for therapeutic treatment of T2D. However, because IAPP does not fold to a single structure, but rather co-exists between multiple functional (and toxic) structures, it is extremely challenging for experimental methods to gain detailed structural information. Using a computational approach, we were able to obtain detailed structures of four IAPP variants and propose a novel structural hypothesis for the two opposing roles of this peptide.
| The Islet Amyloid Polypeptide/IAPP (also known as amylin) is coexpressed and cosecreted with insulin by pancreatic islet β-cells [1]–[3] and acts as a synergistic partner of insulin to limit after-meal glucose excursions [4]. IAPP belongs to the calcitonin (CT) family of peptides (See Table S1 in Text S1 ) [5]–[7]. The CT peptides function as hormones and are distributed in various peripheral tissues (the endocrine pancreas in the case of IAPP), and play important biological roles including reducing nutrient intake (IAPP), decreasing bond resorption (Calcitonin) and vasodilatation (CGRP/calcitonin gene-related peptide). These functions are fulfilled through hormone-receptor agonism in which the CT peptide binds to a signal transduction membrane protein complex and thereby induces cell response in peripheral tissues. In the particular case of IAPP, this peptide binds to multiple amylin-specific (AMY) receptor complexes [1], [7]–[10]. CT peptides show strong sequence homology in their two terminal regions (residues 1–19 and 30–37), and shares three features: [7] an N-terminal disulfide bond, an N-terminal amphipathic region, and C-terminal amidation.
In addition to playing an important physiological role as a CT hormone, IAPP can also play a pathological role. Indeed, the 37-residue long human form of IAPP is the major protein constituent of pancreatic islet amyloid deposits found in 95% of Type 2 Diabetes (T2D) patients [11]–[14]. Interestingly, the IAPP peptide is found in a number of animal species, with a few point mutation differences, yet not all of these animals develop T2D. The development of T2D appears to be directly linked with the inherent aggregation propensity of the peptide. A recent bioinformatics study [15] ranked the aggregation propensities of the IAPP variants using the AGGRESCAN program [16], with the pig sequence emerging as the least aggregation prone and the puffer fish as the most aggregation prone. Figure 1 lists the sequences of four IAPP species that we will consider in this study, and their aggregation propensities. Human and Cat (as well as Monkey and Dog) IAPPs all have high aggregation propensities, and all these species can develop T2D. Tellingly, species (such as rodents and pigs) that are well-known to tolerate excessive food intake without obvious health ramifications, have low aggregation propensities and are not known to develop Type II Diabetes. Transgenic rats, on the other hand, possessing the human variant of IAPP, spontaneously develop T2D when placed on a high calorie, sedentary diet [13], [17]–[21]. In all IAPP variants (Figure 1), the two terminal parts are conserved (namely residue 1–16 and residue 30–37, which we will refer to as “conserved region I and II”, respectively). These are the very regions that are conserved in all CT family peptides and play important biological functions, with conserved region I activating the receptor and conserved region II binding in an antagonistic manner [22]. The mutations that differentiate the different IAPP forms occur in the middle region (residues 18–29, hereafter referred to as “the mutation region”) and are responsible for the different aggregation propensities. The importance of this mid-region in governing aggregation is further highlighted by a familial form of T2D found in Japan that involves a single point mutation in this middle region (S20G). The S20G mutant aggregates more rapidly than its human wild type hIAPP counterpart [23], [24], and leads to β-cell death and early onset T2D [25], [26].
The above observations suggest a link between IAPP aggregation and the β-cell apoptosis occurring in T2D. Further support for a toxic role of IAPP aggregates include 1) the observation that human hIAPP amyloids play a deleterious role in transplanted islet tissue [27]–[30] and that aggregates of synthetic hIAPP induce apoptotic β-cell death in vitro [31]–[33]. 2) The recent experimental observation of two parallel pathways leading to β-cell apoptosis by hIAPP: one involving extrinsic death signals triggered by extracellular hIAPP aggregates [34], [35], and an intrinsic endoplasmic reticulum (ER) stress pathway linked to the presence of intracellular hIAPP aggregates [36]–[40]. 3) Experimental evidence of a membrane-damaging effect of hIAPP aggregates leading to β-cell dysfunction [41], [42]. 4) The fact that blocking aggregation of hIAPP through interaction with non-aggregating hIAPP mutants [43]–[46] as well as small molecules (e.g. EGCG [47], tetracycline [48] and resveratrol [49]) can reduce hIAPP-induced toxicity. A summary of a putative scheme embodying the functional and pathological roles of IAPP is given in Figure 2.
The fact that some IAPP variants aggregate while others do not raises the question as to whether the small sequence differences between species (Figure 1) could lead to structural differences in the monomeric forms of these peptides that would lead one species to favor aggregate prone conformations. This question has been examined both experimentally and computational (by our group and by others) through a study of the rat and human IAPP sequences. The study of IAPP is particularly challenging because the peptide is intrinsically disordered, in other words, it does not populate a single well-defined three-dimensional structure, but rather interconverts between a number of co-existing conformations. While not readily amenable to traditional ensemble-averaging experimentally (for instance CD studies [50]–[53] simply report that both human and rat IAPP variants are globally disordered and mainly adopts random coiled structures), recent experimental advances in studying IDPs have revealed subtle differences between IAPP variants. For instance, NMR studies looking at secondary chemical shifts [54]–[60] showed that IAPP variants are partially disordered but that the N-terminal part adopts helical structure, and that this structuring is significantly more pronounced in rIAPP than in hIAPP. A study combining data from CD, fluorescence dye binding, atomic force microscopy/AFM and electron microscopy/EM [61] showed that hIAPP adopts two distinct conformers containing both β-sheet and α-helix structural motifs. More recently, ion-mobility mass spectrometry studies [62], [63] and 2D-IR spectroscopy analysis [64], [65] showed evidence of the presence of β-structure in hIAPP monomeric and oligomeric samples. We recently explored the conformational space sampled by hIAPP and rIAPP using replica exchange molecular dynamics simulations (REMD) with an Amber force field ff96 and an implicit solvent (igb5). Our simulations showed that both peptides were flexible, and populated over 10 structural families (see supporting material of ref. [62]) even when using a very large dissimilarity measure (i.e. Cα-RMSD cutoff of 3 Å). Despite this flexibility, we were able to identify some very interesting structural similarities and differences between these two sequences. Whereas rIAPP was found to populate only helix-coil conformations, hIAPP populated both helix-coil conformations and β-rich conformations including a helix-hairpin and an extended β-hairpin. The structures obtained from simulation were a good match for the collision cross-sections obtained experimentally using ion-mobility mass spectrometry [62], [63]. Using GBSA implicit solvent model and Amber99SB force field, Murphy et al identified partially structured conformational states of the hIAPP monomer [66]. Using an explicit solvent and the Gromos96 53a6 force field, Reddy et al. independently obtained similar structures for both rIAPP and hIAPP, consistent with their 2D-IR data [64], [65]. Using the modified TIP3P water model and the CHARMM27 force field, Liang et al. [67] probed sequence-induced differences in structural stability between hIAPP and rIAPP from preformed monomer to pentamer, which is based on strand-loop-strand scaffold. Their simulations showed rIAPP adopt less β-sheet-rich structure and a disturbed U-shaped topology than hIAPP.
In the present paper, we perform an extensive investigation of the conformational space of two additional IAPP variants, the non-amyloidogenic pig variant (pIAPP) and the amyloidogenic cat variant (cIAPP). Additionally, we extend our simulations of the rat and human forms to match the simulations lengths (600 ns/replica) that we use in this study. To our knowledge, there are no published studies of the pig and cat IAPP structures. By enlarging our dataset of IAPP variants and using available peptide-activity data, we are now able to formulate a novel structure-activity relationship that rationalizes the dual function (pathological and physiological) of IAPP.
Details of the methodology are given in the Methods section. We used the replica exchange molecular dynamics protocol (REMD), with the Amber ff96 force field coupled with the implicit igb5 solvent model to sample to conformational space of IAPP. REMD simulations were performed for each IAPP variant (pIAPP, rIAPP, cIAPP and hIAPP), initiated from an extended conformation and run for 600.0 ns per replica, leading to 9.6 µs for each variant (16 replicas per variant). Block analysis was used to ensure convergence (see Table S2 in Text S1), and analysis was performed on the last three blocks of the trajectory (100.0 ns per block) at temperature of 300 K.
The secondary structure propensities for the four peptides are shown in Figure 3. While all peptides show a high fraction of turn and coil structures (from 0.49 for hIAPP to 0.57 for rIAPP), consistent with the natively disordered structural nature of IDPs, there are nonetheless some striking differences between the amyloidogenic (human, cat) and non-amyloidogenic (rat, pig) sequences. In particular, while helicity is present in each peptide, the degree of helicity is much more pronounced for the non-amyloidogenic sequences (∼0.4 for pIAPP and rIAPP vs. ∼0.1 for cIAPP and hIAPP ). The trend for sheet structure is reversed, with low β-sheet content (∼0.03) for pIAPP and rIAPP and significantly larger content (∼0.4) for cIAPP and hIAPP.
The location of the secondary structure elements on a per-residue level is shown in Figure 4. Overall, the two non-amyloidogenic sequences (pIAPP and rIAPP) share similar secondary structural profiles, while the amyloidogenic sequences (cIAPP and hIAPP) share a different pattern. For the non-amyloidogenic sequences, conserved region I (residues 1–16) consists primarily of helical structure, whereas the mutation region (residues 18–29) and conserved region II (residues 30–37) consist primarily of turns and coils, with a modest amount of helix and sheet structure. For the amyloidogenic sequences, the conserved region I (residues 1–16) shows both helical and sheet elements, with the sheet contribution much more pronounced. The mutation region (residues 18–29) now show a turn at residues 18–22, linking the N-terminal (residues 5–18) strand to a C-terminal (residues 22–33) strand (the latter located in conserved region II (residues 30–37)), indicative of the presence of β-hairpin population.
From our clustering analysis (described in the Methods section), a large number of diverse structural families were identified. The centroid structures of the top 15 most populated structural families (≥1% of total structure population) from the last 100.0 ns of simulation are shown in Table S3 of Text S1 for each IAPP sequence. The structural families were then further merged into several super structural families based on similarity in the molecular topology. A representative structure and the abundance for each super structural family are presented in Figure 5.
The non-amyloidogenic pIAPP and rIAPP structural ensembles contain two super families: a helix-coil super family (structures A and C in Figure 5) and a helix-hairpin super family (structures B and D in Figure 5). In the helix-coil fold, the peptide adopts a short turn-coil (residues 1–7), a short helix (residues 8–17) and a long turn-coil (residues 18–37). In the helix-hairpin fold, the peptide adopts a short turn-coil (residues 1–7), a short helix with a kink (residues 8–17) and a short β-hairpin close to the N-terminal (β-strands: residues 25–27 and 33–36, loop: residues 28–31). The helix-coil supper family (∼87%) is more abundant than the helix-hairpin super family (∼13%). Although pIAPP differs from rIAPP by 9 out of 37 residues, their tertiary structure ensembles are very similar (as are their secondary structural profiles, as seen in Figure 4).
The cIAPP and hIAPP structural ensembles contain three super families: a β-hairpin super family (structures E and H in Figure 5) (a β-strand 9–17, turn 18–22, and another β-strand 23–33), a helix-coil super family (structures F and I in Figure 5), and a helix-hairpin super family (structures G and J in Figure 5). The first two super families are very similar to the two super families adopted by pIAPP and rIAPP, but occur in different abundance. The difference is particularly striking in the case of the helix-coil super family, with a population of ∼28% for cIAPP and ∼15% for hIAPP, dramatically less than the ∼87% seen for pIAPP and rIAPP. The helix-hairpin super family population is modest for all sequences (∼25% for cIAPP, ∼9% for hIAPP, and ∼13% for pIAPP and rIAPP). The β-hairpin fold is only seen for the amyloidogenic sequences, and occurs with large population (∼47% for cIAPP and ∼75% for hIAPP).
Although the cIAPP structural ensemble is quantitatively similar to that of hIAPP, we identified a number of quantitative differences. In particular, the population of the hairpin super family is ∼28% less for cIAPP than for hIAPP, and the population of helix-coil super family of cIAPP is correspondingly larger (by ∼13%). Subtle differences (for instance, a shift or difference in the strand length) are observed (see Table S3 of Text S1 in which the top 15 structural families are shown).
Peptide solubility is an important component in the aggregation process. We computed the GBSA solvation energy for each of the IAPP super families (Table 1). The absolute GBSA solvation energies of these ionic peptides are large (<−554 kcal/mol) as a result of the charges (+4 of pIAPP and +3 of rIAPP, cIAPP and hIAPP) carried by the four peptides. Based on the order of the GBSA solvation energy, we find that the β-hairpin is the least soluble motif, the helix-coil the most soluble with the helix-hairpin lying in the middle. When the relative GBSA of the four IAPP variants is considered (using hIAPP as the zero scale reference), the order of solubility is rIAPP (−97.1 kcal/mol)>pIAPP(−90.7)>cIAPP (−74.3)>hIAPP (0.0 kcal/mol). This order correlates with the aggregation ability order of the four IAPP variants, with the non-amyloidogenic sequences being more soluble than the amyloidogenic ones.
Since IAPP is an IDP, and, as such, populates partially structured conformers, it is important to consider the structural flexibility of the conformations identified in simulation. Our structural ensembles enable us to directly characterize this feature for the folds of the four IAPP variants by calculating their RMSF (see Methods section). These results are reported in Figure 6. The helix-coil fold of all four IAPP variants show much smaller structural fluctuation in the N-terminal part (residues 1–17, where the helix is located) (RMSF of ∼5 Å) than in of the C-terminal part (residues 18–37), with an approximately linear increase from ∼5 Å to ∼20 Å. The flexibility of the N-terminal region may be required for the hormone function of IAPP (i.e. interacting multiple membrane receptors). In the case of the helix-hairpin fold of the four IAPP variants, the N-terminal part (residues 1–22) has comparable fluctuations to the same region in the helix-rich fold, but the C-terminal part (residues 30–37) is slightly more rigid than the corresponding part in the helix-coil fold (by ∼2 Å). In the case of the β-hairpin fold seen only in the amyloidogenic cIAPP and hIAPP sequences, the N-terminal part (residues 1–22) has comparable fluctuations to the same region of their helix-coil fold, but the C-terminal part (residues 23–37) is significant more rigid than the corresponding part of the helix-coil fold (by ∼5 Å).
All IAPP sequences play the same physiological role in reducing post-meal blood glucose [2], however, some sequences are capable of aggregating into pathological structures. In this paper, we used all-atom REMD simulations coupled with an implicit solvent model to thoroughly sample the conformations adopted by two amyloidogenic sequences of IAPP (cat and human) and two non-amyloidogenic sequences (rat and pig). We wished to examine whether structural similarities existed between non-amyloidogenic and amyloidogenic forms of IAPP that could explain the dual functional/pathological roles that certain IAPP variants play. The similarity in functions suggests a possible similarity in structure. On the other hand, the fact that a few point mutations lead to enhanced aggregation tendencies suggests that these mutations may lead to dramatic conformational changes at the monomeric level, shifting the population from functional to pathological.
Our simulations revealed that all four peptides populated helix-coil and helix-hairpin conformations, but that the amyloidogenic sequences populated in addition a β-hairpin conformation. The helix-coil structure was the dominant fold for the non-amyloidogenic structures, and the second dominant fold for the amyloidogenic sequences (after the hairpin). We propose that this fold corresponds to the physiologically relevant fold. The helical region in this fold is located in the N-terminal region, a region conserved in all CT peptides. NMR studies on rat and human variants support the presence of helicity in the N-terminal region [54], [56], [59], [68]. We found that this helical region was the most rigid region of all folds. Intriguingly, the C-terminal (turn/coil) region, corresponding to conserved region 2, is the most flexible part. Both conserved regions 1 and 2 are involved in receptor binding, and it is possible that this dual rigid/flexible architecture may play a role in enabling IAPP to bind to multiple AMY1–3 receptors [7], [22]. Given the lower abundance (∼22%) of the helix-coil conformation adopted by amyloidgenic peptides (cIAPP and hIAPP) relative to that (∼87%) of non-amyloidogenic peptides (pIAPP and rIAPP), we would expect hIAPP and cIAPP to have slightly reduced normal hormone function relative to pIAPP and cIAPP. Indeed, Young et al.[2] have shown in a preclinical rat study that hIAPP has slightly lower binding affinities for amylin, CGRP, and calcitonin receptors, and induces slightly weaker responses in isolated muscle (Table 2).
In contrast, the β-hairpin structure, present only in the amyloidogenic sequences, is a possible candidate for an amyloid-competent structure. β-hairpin structures have been found in molecular dynamics simulations in a number of amyloidogenic peptides, most notably in fragments of the Alzheimer Amyloid Aβ peptide [69]–[83] and the prion protein [84], [85].Simulations of oligomerization of the Aβ(25–35) peptide indicate that hairpins play an important role in initiating aggregation and in stabilizing the growing front of the fibril [83]. This may also be the case for IAPP. The hairpin structure that we find in simulation shares important similarities with the structure of hIAPP in the context of a fibril. The solid state NMR [86] structure of the hIAPP fibril consists of a strand-loop-strand topology (related to the strand-turn-strand hairpin by a 90° rotation), with the loop (residue 18–27) located at the turn region of our hairpin. By having the correct strand placement (as in the fibril), the hairpin structure could facilitate nucleation and subsequent fibril growth. Further support for the notion of this structure as a key player in aggregation comes from the work of Kapurniotu and co-workers who identified through fragment binding affinity studies a number of “hot-spot” regions responsible for inter-peptide interactions in aggregation [87]. These hot-spots correlate with the β-strand regions of the hairpin seen in our simulations [62].
The β-hairpin structure is less soluble and less flexible than the helix-coil fold based on our analysis of the GBSA solvation energy and Cα-RMSF, features that make it a good candidate for β-sheet formation. Indeed, this increased rigidity might contribute to the fast association of β-hairpins into β-sheet rich oligomers. Our recent dimer simulations of hIAPP and rIAPP [63] support this picture: the β-rich monomers have strong tendencies to form β-rich dimers, while the helix-coil rich monomers form, if anything, only loosely bound, disordered complexes with much lower binding energies. Formation of β-rich hIAPP dimers was also found in atomistic, explicit solvent simulations [65] and both hIAPP dimers with moderate β-content and rIAPP dimers with no β-content were observed in a Hamiltonian-Temperature-REMD simulations using a coarse grained protein force field (OPEP) [88].
The notion of a hairpin as an important player in the aggregation process can explain a number of experimental observations. For instance, within this framework, the increased aggregation rates of the S20G IAPP mutation [25], [26] can be explained by an increased propensity to form a β-turn (glycines are turn promoters and residue 20 that is involved in this mutation lies right in the turn region of our hairpin (residues 18–22)). In addition, the observed inhibition effects of a non-aggregating form of hIAPP with two N-methylations at positions G24 and I26 (hIAPP-GI) could be explained by blocking inter-strand β-sheet formation [43], [89], [90]. In other words, the β-strand of hIAPP-GI could bind to the β-strand of hIAPP, forming a complex that now has a face with exposed N-methyl groups that is unable to hydrogen bond with another hIAPP peptide, thus blocking further growth.
cIAPP is less amyloidogenic than hIAPP [15] and, consistent with our hypothesis of a role of the hairpin in facilitating aggregation, our structural data shows that cIAPP has slightly lower β-sheet content (β-hairpin) than hIAPP. We speculate that the decrease in hairpin population is due to the S29P mutation that differentiates cat from human. Proline is indeed known to be a β-sheet breaker. Along similar lines, other disorder-promoting substitutions (e.g. P, R and K) [91] may further lead to a diminished propensity for hairpin formation, eventually leading, in the case of rIAPP (with key mutations A25P, S28P and S29P) and pIAPP (S20R and N31K) to the complete disappearance of the β-hairpin population. It is interesting to note that the drug PRAMLINTIDE (symlin) with same sequence as hIAPP, but containing the 3 proline substitutions of rIAPP, shows a very weak tendency to aggregate [58]and has proven to be an efficacious agent that takes over the physiological role of hIAPP, acting as a synergistic partner to insulin [2], [92].
Interestingly, we find that all four sequences adopt a helix-hairpin fold, although in lower amounts than the helix-coil fold (for pIAPP and rIAPP) and hairpin (for hIAPP and cIAPP). This fold is more soluble than the β-hairpin fold, but less soluble that the helix-coil fold. We speculate that this motif may act as an on-pathway intermediate leading to β-rich conformations (β-hairpin or β-sheet) and thus amyloid fibrils under certain conditions (e.g. the presence of an interface, peptide-peptide interaction, solvent effect etc.). Indeed, rIAPP, although commonly thought of as a non-aggregating species, has been observed to aggregate into fibrils under specific non-physiological conditions [93]–[95]. Indeed β-sheet-rich fibrils of rIAPP were seen to form at a liquid-solid interface [93], mixing rIAPP with hIAPP lead to a templating of rIAPP onto hIAPP fibrils [94], and placing rIAPP in Tris-HCl buffer and sonicating lead to fibril formation [95].
We note that it is also plausible, as proposed by Miranker and coworkers [54], Eisenberg and coworkers [57] and Raleigh and coworkers [96], that early oligomerization may be initiated by helix-helix association, with β-structure emerging later in the aggregation process. It is of course difficult to tell whether experimentally observed helix-rich oligomers are on or off-pathway to fibrils. Likewise, the hairpins that we see in simulation may be on-route to fibrils formation, or may as well lead to off-pathway aggregates. However, small hairpin oligomers, even if not directly on-route to fibril formation, may play an important role in toxicity.
Small oligomers are also increasingly being associated with hIAPP cytotoxicity [35], [37]. In particular, membrane pores, formed by small amyloidogenic oligomers, have been suggested as a means of toxicity of hIAPP [97]. These pores can be formed by helical conformers of hIAPP, as supported by experiments on the 1–19 fragment of IAPP. Indeed, the human and rat IAPP(1–19) fragments can adopt helical conformation in membrane mimics [56] and have been shown to be toxic to cells, with hIAPP(1–19) being more toxic than rIAPP (the latter differ by an H to R substitution) [98]. We note that it is possible that IAPP pores can also be formed from β-rich conformation, as in the case of the β-rich annular-like channel proposed for Aβ and other amyloid peptides [99], [100], based on molecular dynamics simulations, atomic force microscopy and channel conductance measurements. A similar β-rich annular-like channel model for hIAPP has recently been proposed using molecular dynamics simulations [101]. The hairpin structures that we observe are reminiscent of the cylindrin structures discovered by Eisenberg and co-workers, cylindrical barrels of β-hairpins that may interact with membranes and constitute a generic architecture for toxic amyloid oligomers [102]. There is at present no experimental data for a cylindrical barrel model for hIAPP, but such a structure is plausible given the observed β-hairpin in simulations and the observation of β-barrel type of ion channel for other amyloid systems. Finally, another toxicity mechanism of hIAPP may be associated with membrane fragmentation due to the growth of amyloid fibrils [103].
In summary, there are compelling genetic, biochemical, cellular and animal data to support both natural biological functions for hIAPP and a toxic role of hIAPP leading to β-cell death [14], [34], [37], [104]–[107]. Combining these functional data with our structural models of four IAPP variants, we put forth the structure-activity relationship (SAR) that the helix-coil conformations are responsible for the normal hormone function of IAPP; and that β-rich conformations of IAPP may be linked to β-rich aggregation and contribute, along with other mechanisms, to the toxicity of IAPP. While the former might be realized by binding of the helix-coil conformers to AMY receptor, the latter might be due to the formation of toxic β-rich oligomers and amyloid fibrils leading to β-cell death. This SAR scheme is summarized in Figure 7. Our SAR can give insights into the rational design of drugs to combat Type II Diabetes, with drugs that either destabilize the pathological conformations and/or promote the formation of the physiologically active conformations. These drugs could come in the form of small molecules, or be peptide based. Ideally, one could design an IAPP variant that retains the functional role of hIAPP, but does not have the same tendency as hIAPP to misfold and aggregate (such as pramlintide), and that furthermore inhibits the aggregation of wild type hIAPP. This drug would not only enhance insulin-sensitivity (like pramlintide), but also preserve β-cells by preventing aggregation.
The AMBER 8 [108] simulation suite was used in replica exchange molecular dynamics (REMD) [109] simulations. The four IAPP variants were modeled using the AMBER all-atom point-charge protein force field, ff96 [110]. Solvation effects were represented by the implicit solvent model (IGB = 5) (96) plus the surface term (gbsa = 1, 0.005 kcal/Å2/mol) with an effective salt concentration of 0.2 M. Studies in the Dill group have examined a number of force field/implicit solvent combinations [111], [112] and have concluded that this ff96/IGB5 offers a good balance between helical and sheet propensities. This combination, in conjunction with REMD simulation yielded impressive results in the folding of both small α, β and α/β proteins with a well defined native fold [111], [113]–[115] and natively unfolded peptides [62], [84] including hIAPP and rIAPP, and in predicting correct inter-domain orientation of a large multi-domain protein (CheA) [116].
The simulation protocol closely followed the one described in the Methods section of reference [62] and the salient points are highlighted here. A minimized extended-conformation of each IAPP variant was used as the input for each set of REMD simulations. 16 replicas were set up with initial temperatures exponentially spaced from 270 to 465 K, which were optimized by the algorithm described in reference [117]. Initial atom velocities for each replica system were generated according to the Maxwell-Boltzmann distribution corresponding to the initial temperature of that replica. The first 1.0 ns of molecular dynamics simulation was performed without replica exchanges to equilibrate the system at its target temperature. After the equilibrium phase, exchanges between neighboring replicas were attempted every 2000 MD steps (3.0 ps) and the exchange rate was ∼20% in the production phase. SHAKE [118] was applied to constrain all bonds linking to hydrogen atoms and a shorter time step of 1.5 fs rather than the typical 2.0 fs was used to circumvent the occasional SHAKE failure, probably caused by large atomic displacements at the high temperatures used in our simulations (up to 450 K leading to high kinetic velocities). In order to reduce computation time, non-bonded forces were calculated using a two-stage RESPA (reference system propagator algorithm approach) [119] where the fast varying forces within a 12 Å radius were frequently updated (e.g. every step) and those beyond 12 Å were updated every two steps. Langevin dynamics was used to control the target temperature using a collision frequency of 1.0 ps−1 (a low collision frequency is used for better conformational sampling). The center of mass translation and rotation were removed every 500 MD steps (0.5 ps). Each replica was run for 600.0 ns giving a cumulative simulation time of 9.6 µs for each IAPP system The snapshots in the replica trajectories were saved at 45.0 ps intervals for further analysis.
Because experiments are typically performed around 300 K, our data analysis was focuses on the replica at 300 K. The convergence was rigorously checked by a block analysis: the total 600.0 ns sampling at 300 K was equally divided into six blocks, and structural properties was calculated for each block. For the four sets of REMD simulations, a good convergence was found during the last half of the trajectory (see for example, the secondary and tertiary structure data of the four IAPP variants in Table S2 of Text S1). Thus, the standard deviations of the structural properties presented in the main text were calculated from the last three blocks (i.e. the last 300.0 ns).
The STRIDE program of Frishman and Argos [120] was used to obtain secondary structure propensities. For tertiary structure analysis, the structural ensembles from simulations were classified into structural families using the GROMACS clustering protocol [121], in which the structure similarity metric is based on a pair wise Cα- RMSD (root mean square deviation) cutoff of 3.0 Å, the neighboring structures are identified for every structure using the RMSD similarity cutoff, the structure having the most neighbors (called as the centroid structure) is removed together with its neighbors to form a structure family, and the process continued for the remaining structures until all structures have been assigned into the structural families. The centroid structure serves as the representative structure of the structural family. For example, the centroid structures of the top 15 populated structural families (≥1% of total structure population) from the last 100.0 ns for each IAPP sequence are shown in Figure S3. Next, the structural families were further merged into three super-families (helix-coil, helix-hairpin and β-hairpin) based on the secondary structure type of both the N-terminal part (residues 1–17) and the C-terminal part (residues 18–37): First, it belongs to a helix-rich fold if the N-terminal part contains more helix than β-sheet, otherwise it belongs to β-hairpin super family; Second, a helix-rich fold belongs to the helix-hairpin super family if the C-terminal part contains more than four sheet-residues coil (i.e. a minimal 2∶2 β-hairpin), otherwise it belongs to the helix-coil super family.
Dynamic fluctuations of each residue was characterized by calculating the Root Mean Square Fluctuation (RMSF) of its Cα atom from the structural ensemble. Because the N-terminal part (residues 1–17) of these IAPP peptides is more rigid than its C-terminal part (residues 18–37), a superposition of the C-terminal part was carried out prior to the RMSF calculation. Also because the four IAPP variant contains two or three types of folds (helix-coil, helix-hairpin and β-hairpin), the RMSF was calculated separately for each one.
The absolute solubility of the four IAPP variants can be estimated from their solvation free energies. When the solute conformational entropies of the IAPP variants are comparable, the relative solvation free energy can be estimated from the relative GBSA solvation energy. A recent benchmark study [122] has shown that GBSA models give reliable results when only the relative solvation energy is considered. We obtained the statistics of GBSA solvation energy for each IAPP fold from its last 300.0 ns of simulation data at 300 K.
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10.1371/journal.ppat.1006359 | Genetically-barcoded SIV facilitates enumeration of rebound variants and estimation of reactivation rates in nonhuman primates following interruption of suppressive antiretroviral therapy | HIV and SIV infection dynamics are commonly investigated by measuring plasma viral loads. However, this total viral load value represents the sum of many individual infection events, which are difficult to independently track using conventional sequencing approaches. To overcome this challenge, we generated a genetically tagged virus stock (SIVmac239M) with a 34-base genetic barcode inserted between the vpx and vpr accessory genes of the infectious molecular clone SIVmac239. Next-generation sequencing of the virus stock identified at least 9,336 individual barcodes, or clonotypes, with an average genetic distance of 7 bases between any two barcodes. In vitro infection of rhesus CD4+ T cells and in vivo infection of rhesus macaques revealed levels of viral replication of SIVmac239M comparable to parental SIVmac239. After intravenous inoculation of 2.2x105 infectious units of SIVmac239M, an average of 1,247 barcodes were identified during acute infection in 26 infected rhesus macaques. Of the barcodes identified in the stock, at least 85.6% actively replicated in at least one animal, and on average each barcode was found in 5 monkeys. Four infected animals were treated with combination antiretroviral therapy (cART) for 82 days starting on day 6 post-infection (study 1). Plasma viremia was reduced from >106 to <15 vRNA copies/mL by the time treatment was interrupted. Virus rapidly rebounded following treatment interruption and between 87 and 136 distinct clonotypes were detected in plasma at peak rebound viremia. This study confirmed that SIVmac239M viremia could be successfully curtailed with cART, and that upon cART discontinuation, rebounding viral variants could be identified and quantified. An additional 6 animals infected with SIVmac239M were treated with cART beginning on day 4 post-infection for 305, 374, or 482 days (study 2). Upon treatment interruption, between 4 and 8 distinct viral clonotypes were detected in each animal at peak rebound viremia. The relative proportions of the rebounding viral clonotypes, spanning a range of 5 logs, were largely preserved over time for each animal. The viral growth rate during recrudescence and the relative abundance of each rebounding clonotype were used to estimate the average frequency of reactivation per animal. Using these parameters, reactivation frequencies were calculated and ranged from 0.33–0.70 events per day, likely representing reactivation from long-lived latently infected cells. The use of SIVmac239M therefore provides a powerful tool to investigate SIV latency and the frequency of viral reactivation after treatment interruption.
| Elucidation of HIV dynamics is essential for a thorough understanding of viral transmission, therapeutic interventions, pathogenesis, and immune evasion. The complex dynamics of reservoir establishment and viral recrudescence upon therapy removal present the primary obstacles to developing a functional cure. We sought to develop a virus model system for use in nonhuman primates that allows for the genetic discrimination of nearly 10,000 otherwise isogenic clones. This “synthetic swarm” adds a genetic component to viral dynamics where individual viral lineages can be tracked and monitored during infection. Here we utilized this model to identify the dynamics of viral reservoir establishment and rebound. We found that after 300 or more days of therapy, between 4 and 8 distinct viral lineages could be detected upon therapeutic intervention. Using the relative proportion of each distinct genetic barcoded virus and the overall viral load curve, we could estimate the time and rate of reactivation from latency. On average, we found 1 reactivation event every 2 days with reactivation of the first rebounding variant within days of therapeutic interruption. This virus model will be useful for testing various approaches to reduce the latent viral reservoir and to molecularly track viral dynamics in all stages of infection.
| A major obstacle to developing a cure for HIV is the establishment in early infection of long-lived viral reservoirs, defined as sources of virus that can persist over extended periods despite seemingly effective suppressive combination antiretroviral therapy (cART), that can cause recrudescent viremia if cART is interrupted. While multiple anatomic sites and cell compartments likely act as viral reservoirs, it has been argued that latently infected resting CD4+ T cells represent the most significant long-lived viral reservoir for HIV-1 [1–7]. During latency, these reservoirs are unrecognized by host immune responses and cells containing integrated latent proviruses are unaffected by current cART, which acts only by blocking new rounds of infection. For patients to safely stop treatment, the immune system must be able to control rebound infection (sustained cART free remission or functional cure), or all reactivatable replication-competent virus must be completely eradicated.
Numerous studies are in progress to test therapies designed to decrease viral reservoir size and prolong ART-free remission. A critical element for evaluating the effectiveness of these therapies is an accurate measurement of reservoir size before and after treatment. These assessments have typically involved ex vivo estimates and have been based on total cell-associated viral DNA (CA-vDNA) measurements [8–10], stimulation of PBMCs or enriched CD4+ T cells to measure the frequency of cells producing viral RNA (vRNA induction assay or TILDA) [11–14] or the frequency of cells harboring replication competent virus (quantitative viral outgrowth assays, QVOA)[1, 3, 13, 15, 16]. However, each method for estimating reservoir size has shortcomings. Ho et al. demonstrated that the QVOA tends to underestimate the amount of replication competent virus present in any sample, as not all latent proviruses will reactivate after a single stimulation event [16]. Additionally, QVOA requires large source specimens, is time and labor intensive, and has limited precision and dynamic range. On the other hand, PCR detection of viral DNA tends to greatly overestimate the reservoir size, as much of the viral DNA detected in these assays does not encode full length replication competent virus due to large deletions or APOBEC mediated mutations. While the vast majority of intact, APOBEC mutation-free genomes are replication competent and could contribute to rebound viremia [16] identifying and quantifying these genomes requires near-full genome sequencing which is time consuming and expensive, necessarily precluding its use in large cohorts of patients. While accurate assessment of the size of the viral reservoir is central to the evaluation of HIV cure strategies, none of the ex vivo assays directly assess the size of the viral reservoir that can lead to recrudescent viremia after cART interruption.
Most studies evaluating the effects of novel therapies on viral reservoir size are dependent on these ex vivo assays, however due to sample size and assay sensitivity issues, “undetectable” viral measurements do not necessarily indicate an absence of reactivatable virus, so as experimentation progresses, ultimately these treatments still require testing in HIV+ patients with the eventual discontinuation of cART to test for functional cure. In these instances, time to rebound after treatment interruption is considered the most direct measure of cure intervention treatment efficacy [17]. This approach might be effective for revealing large differences between treatment groups, which cause significant differences in time to detectable rebound viremia, however effects of treatments that result in small but potentially meaningful changes in reservoir size may be too subtle to be detected with this approach [17]. This will be particularly true for individuals with large reservoirs where even large differences between treatment groups will be difficult to detect using only time to rebound, and in groups with highly divergent interpatient reservoir size which will affect time to detectable rebound viremia. Therefore, alternative approaches for evaluating the functional reservoir size (i.e. the cells that can contribute to systemic viremia once therapy is removed) and the effects of new therapeutic interventions on the reservoir size are needed.
AIDS virus infected non-human primates (NHPs) represent useful models to study viral reservoir establishment and to evaluate changes in reservoir size with novel interventions. Until recently, consistent and complete viral suppression was difficult to achieve in SIV-infected rhesus macaques with cART regimens developed for HIV-1 infection in humans. However, there are now several classes of drugs, including nucleos(t)ide reverse-transcriptase inhibitors, protease inhibitors, and integrase inhibitors that have been evaluated and shown to be effective for suppression of SIV and SHIVs in infected macaques. Recently, cART regimens have been developed that can effectively, durably and sustainably reduce plasma vRNA to clinically relevant levels (below 15–50 copies per mL) [18–22]. These regimens result in similar viral suppression dynamics to those observed in humans. Additionally, drugs are typically administered daily without any “drug holidays” or accidental missed doses. Frequent blood sampling and standardized assays provide assurances of successful suppression. Finally, NHPs may be removed from cART without the ethical implications involved in removing HIV-1 infected humans from treatment.
To more fully realize the potential of NHP models for evaluation of candidate cure approaches, we developed a novel, barcoded virus system that allows for a deep genetic assessment of the number of rebounding viruses, in conjunction with time to rebound viremia measurements. This novel barcoded virus is fully and stably replication competent in vitro and in vivo and can be used to establish infection with a large number of otherwise sequence identical viral clonotypes bearing unique barcode sequences. Following cART treatment and interruption, the number and relative proportion of each rebounding clonotype can be measured with next generation sequencing of the barcodes, using high template input that allows for the discrimination of individual rebounding clonotypes. By combining viral growth rates (the rate at which the virus grows once achieving detectable systemic infection) and the relative proportion of each rebounding clonotype, the frequency of rebound of each clonotype can be estimated in each animal. This approach is likely more sensitive than measuring time to detectable viremia alone because it is less affected by natural variation among individual animals, and consequently requires smaller group sizes to distinguish statistically significant differences in reservoir size. This approach allows for detection of both small or large changes in the viral reservoir population, a distinction which may be critical for evaluating interventions resulting in real, but only modest changes in reservoir size. Our use of this system in initial in vivo studies demonstrates that the time of initiation and duration of cART administration in NHPs can alter the size of the reservoir, allowing for tightly controlled experimental design and execution, an idea also introduced by Whitney et al (19). These data will help inform HIV cure research by providing a basic understanding of the biology of latency establishment, maintenance, and reactivation and will facilitate evaluation of potential therapies intended to reduce reservoir size.
Twenty-six purpose-bred Indian-origin male rhesus macaques (Macaca mulatta) weighing on average 7kg (range 5-9kg) were housed at the National Institutes of Health (NIH) and cared for in accordance with the Association for the Assessment and Accreditation of Laboratory Animal Care (AAALAC) standards in an AAALAC-accredited facility and all procedures were performed according to protocols approved by the Institutional Animal Care and Use Committee of the National Cancer Institute (Assurance #A4149-01). Animals were maintained in Animal Biosafety Level 2 housing with a 12:12-hour light:dark cycle, relative humidity 30% to 70%, temperature of 23 to 26°C and all animals were observed twice daily by the veterinary staff. Filtered drinking water was available ad libitum, and a standard commercially formulated nonhuman primate diet was provided thrice daily and supplemented 3–5 times weekly with fresh fruit and/or forage material as part of the environmental enrichment program. Environmental enrichment: Each cage contained a perch, two portable enrichment toys, one hanging toy, and a rotation of additional items (including stainless steel rattles, mirrors, and challenger balls). Additionally, the animals were able to listen to radios during the light phase of their day and were provided with the opportunity to watch full-length movies at least three times weekly. At the start of the study, all animals were free of cercopithecine herpesvirus 1, simian immunodeficiency virus (SIV), simian type-D retrovirus, and simian T-lymphotropic virus type 1. All animals were treated with enrofloxacin (10 mg/kg once daily for 10 days), paromomycin (25 mg/kg twice daily for 10 days), and fenbendazole (50 mg/kg once daily for 5 days) followed by weekly fecal culture and parasite exams for 3 weeks to ensure they were free of common enteric pathogens. At least a 4-week post-treatment period allowed time for stabilization of the microbiome prior to use in this study.
Primers were designed to introduce an MluI restriction site between the vpx and vpr accessory genes. These primers contain regions complementary to either the vpx or vpr genes with the MluI restriction site appended to the 3’ end of each primer. Amplicons were generated from SIVmac239 template using a generic primer upstream of SbfI and downstream of the EcoRI restriction site. The vpx-containing fragment was digested with MluI and SbfI, and the vpr-containing fragment was digested with MluI and EcoRI. SIVmac239 plasmid digested with SbfI and EcoRI was ligated with the two digested amplicons overnight at 16°C using T4 DNA ligase (NEB). 5μL of the ligation reaction was transformed into Stbl2 cells (Invitrogen) and plated on agar plates containing 100μg/mL ampicillin. Resulting colonies were checked for correct assembly and insertion of the MluI site. This clone was termed SIVmac239-Mlu.
The barcode insert was synthesized as single stranded forward and reverse barcoded templates (IDT) that were comprised of 10 random bases, flanked on either end by a stretch of bases complementary to the same region on the opposite primer to function as a molecular “clamp” with MluI sticky ends on both ends of the dimer (Fig 1A). To generate primer dimers, the forward and reverse barcode primers were mixed in equal proportion and heated to 95°C. The temperature was slowly lowered at a rate of 1.5°C/min to allow primer pairs to anneal.
SIVmac239-Mlu was digested with MluI and the DNA was purified with a Qiaex II kit. The digested SIVmac239-Mlu and primer dimers were mixed and ligated at 16°C overnight. The primer sequence was designed such that upon ligation of the primer into the MluI site of the SIVmac239-Mlu, the MluI site would be destroyed. Thus, the ligation product was digested again with MluI to linearize any genome not containing a primer dimer insert, and the digestion product cleaned and purified with a Qiaex II kit. The eluted product was transformed into Stbl2 cells, and transformants were grown up in LB amp overnight. The plasmid library was extracted from the bacterial preparations using the Qiagen MaxiPrep kit.
Virus was prepared in HEK-293T cells transfected with the prepared SIVmac239M plasmid library using Mirus Trans-IT 293 transfection reagent as described by the manufacturer. Culture medium was changed at 24hr post-transfection, and culture supernatants were collected at 48hr. Supernatants were passed through a 0.45μm filter and stored at −80°C in 0.5 or 1 mL aliquots. Viral infectivity was determined using TZM-bl reporter cells (reference no. 8129; NIH AIDS Research and Reference Reagent Program), which contain a Tat-inducible luciferase and β-galactosidase gene expression cassette. Infectivity was determined by assessing the number of β-galactosidase expressing cells present after infection with serial dilutions of viral stocks. After dilution correction, wells containing blue cell counts falling within a linear range were averaged and used to determine the titer of infectious units (IU) per mL in the viral stock as previously described [23].
RNA was isolated from plasma or viral stock using QIAamp Viral RNA mini kit per manufacturer’s instructions. RNA was eluted from the column with 65μL elution buffer. cDNA was synthesized from the extracted DNA using Superscript III reverse transcriptase (Invitrogen) and a reverse primer (Vpr.cDNA3: 5’-CAG GTT GGC CGA TTC TGG AGT GGA TGC-3’ at position 6406–6380). The reaction mixture was prepared as previously described with initial incubation at 50°C for one hour then increased to 55°C for an additional hour. Temperature was increased to 70°C, and the reaction incubated for 15 minutes. Each reaction was then treated with RNaseH and incubated at 37°C for 20 minutes. qRT-PCR was used to quantify the cDNA synthesized in the previous step using the primers VpxF1 5’-CTA GGG GAA GGA CAT GGG GCA GG-3’ at 6082–6101 and VprR1 5’-CCA GAA CCT CCA CTA CCC ATT CATC-3’ at 6220–6199.
PCR was used to amplify the cDNA and add MiSeq adaptors directly onto the amplicon. Reactions were prepared using High Fidelity Platinum Taq per the manufacturer’s instructions, using primer VpxF1 and VprR1 combined with either the F5 or F7 Illumina adaptor sequence containing a unique 8 nucleotide index sequences. Template input values ranged from 5x103 copies to 1x106 copies. Reaction conditions used are as follows: 94°C, 2min; 40x [94°C, 15sec; 60°C, 1:30min; 68°C, 30sec]; 68°C, 5min.
Following PCR, 10μL from each reaction was pooled and purified using the QIAquick PCR purification kit. The resulting eluted DNA was quantified using the QuBit. The combined DNA sample was diluted to 3.0nM and 5μL of this diluted sample was placed in a new tube and denatured with 5μL 0.2N NaOH. This sample was vortexed and centrifuged at 280xg for 1 minute. The sample incubated at room temperature for 5 minutes, and 990μL of chilled HT1 buffer added. This sample was then diluted to 12.5pM.
The control PhiX library was treated similarly. 2μL of the PhiX library was combined with 3μL Tris-HCl pH 8.5, 0.1% tween-20. 5μL of 0.2N HCl was added to the library, and the sample vortexed and centrifuged at 280xg for 1 minute. The sample was incubated at room temperature for 5 minutes, and 990μL of chilled HT1 buffer added. Multiplexed samples and PhiX library were then loaded on the MiSeq reagent tray, and the run initiated.
For low-template samples, we used single genome amplification (SGA) followed by direct Sanger sequencing to assess the frequency and number of unique barcodes. cDNA synthesis and PCR was performed as described above but using a limiting dilution of cDNA prior to PCR amplification. This method provides representative proportionality and excludes PCR-induces errors [24].
Samples that were multiplexed were separated into individual samples using Geneious software and the unique 8-nucleotide index. After barcode splitting, individual barcoded clonotypes were identified by sequencing the first 50 bases of vpr. The 34 bases immediately upstream of this alignment were extracted and presumed to encode the inserted barcode region. Sequences obtained from infected animals were identified by comparison to all barcodes identified in the stock (14,357). Only identical matches to a defined barcode were counted as an authentic input sequence. Given the short duration of infection prior to initiation of cART and the limited size of the insert, the vast majority of sequences were identical to a known barcode and were thus identified.
Some identified barcodes contained 1 or more deletions in the insert region. This deletion resulted in 1 or more bases of vpx necessarily included in the extracted “barcode”. Although these unique but shorter barcodes represent only 0.7% of all inserts observed, they were included in the comprehensive tally of all barcodes because they remain a unique and genetically identifiable insert.
Since all samples were quantified by real-time PCR, the theoretical limit of detection was estimated for each sample as the minimum number of sequences that would result from a single copy of an input template. Sequences below this threshold were discarded.
Viral replication curves were prepared by culturing CD8+ T-cell-depleted Indian-origin rhesus macaque peripheral blood mononuclear cells (PBMCs) (CD8+ depletion performed using Miltenyi Biotec CD8+ microbeads) in RPMI supplemented with 10% fetal bovine serum (FBS), 2mM l-glutamine, and 100U/mL penicillin and 100μg/mL streptomycin (RPMI-complete), stimulated for 3 days with 5μg/mL phytohemagglutinin (PHA) and IL-2 (100U/mL). Stimulated PBMC cultures were infected with SIVmac239 or SIVmac239M at an MOI of 0.01 or 0.001 (as determined by TZM-bl). 24hr post-inoculation, cell cultures were washed with phosphate buffered saline (PBS) twice and once with RPMI-complete to remove excess virus. Viral replication was monitored over 14 days by detection of supernatant SIV p27 antigen in an enzyme-linked immunosorbent assay (ABL) according to the manufacturer’s provided protocol.
In total, 26 animals were intravenously infected with 2.2x105 IU (1mL) of transfection produced SIVmac239M. All 26 animals were used to enumerate the number of detectable barcodes measured during primary infection. Of these 26 animals, two animals were followed for over 3 months to assess early viral replication kinetics (peak and set point viral load) of SIVmac239M. Four of the other infected animals began antiretroviral treatment beginning at day 6 post infection and continued for 82 days. Each animal received a combination antiretroviral therapy (cART) regimen comprising a co-formulated preparation containing the reverse transcriptase inhibitors tenofovir (TFV: (R)-9-(2-phosphonylmethoxypropyl) adenine (PMPA), 20 mg/kg) and emtricitabine (FTC; 50 mg/kg) administered by once-daily subcutaneous injection, plus raltegravir (RAL; 150-200mg) given orally twice daily. At the time of interruption from cART, three animals were infused at the day of cART interruption with autologous CD8+ T cells transduced with an anti-SIV Gag T-cell receptor (animals MK9 and KTM) or with an irrelevant receptor (animals KMB and KZ2) plus daily subcutaneous injections of IL-2 at 10,000 IU/kg for 10 days. The total number of infused cells ranged from 4.6 to 6.4x109 cells with <1% of the cells CD4+. In these animals, infused cells did not traffic to lymphoid or GI tissues and persistence of the cells was poor. Animal KTM died due to procedural complications at the time of cART interruption and was therefore excluded from subsequent analyses. In a separate cohort of 6 animals (study 2), therapy was initiated on day 4 post-infection with the same therapeutic regimen (TFV, FTC, RAL) described above with the addition of the protease inhibitor indinavir (IDV; 120mg BID) and ritonavir (RTV; 100mg BID) for the first 9 months. In study 2, cART treatment was continued for 305, 374, or 482 days, with two animals discontinuing therapy at each time point. The remaining 14 animals were used to enumerate the number of replicating clonotypes during primary infection.
Whole blood was collected from sedated animals. Plasma for viral RNA quantification and PBMCs for proviral DNA assays were prepared from blood collected in EDTA Vacutainer tubes (BD). Following separation from whole blood by centrifugation, plasma aliquots were stored at 80°C. PBMCs were isolated from whole blood by Ficoll-Paque Plus (GE Healthcare) gradient centrifugation.
Plasma viral load determinations for SIV RNA were performed over the duration of the study using quantitative real-time PCR as described previously [25]. The limit of detection of this assay is 15 vRNA copies/mL.
Quantitative assessment of cell-associated viral DNA and RNA in PBMC pellets was determined by the hybrid real-time/digital RT-PCR and PCR assays essentially as described in Hansen et al. [26] but specifically modified to accommodate cell pellets. 100μL of TriReagent (Molecular Research Center, Inc) was added to cell pellets in standard 1.7mL microcentrifuge tubes and the tubes sonicated in a Branson cup horn sonicator (Emerson Electric, St. Louis) for 15 seconds at 60% amplitude to disrupt the pellet. Additional TriReagent was added to a final volume of 1mL and the remainder of the protocol was carried out as described previously [26]. Limit of detection is evaluated on a sample by sample basis, dependent on the number of diploid genome equivalents of extracted DNA assayed.
SIVmac239M viral stock was randomly distributed into 168 aliquots with 5,000 viral cDNA templates per aliquot. After next-generation sequencing of the barcode region, a bimodal frequency distribution of the number of copies of a given sequence in a single aliquot was observed. Many sequences were present at very low copy number, likely representing erroneous sequences generated during the PCR amplification and/or sequencing process. By contrast, sequences present at high copy numbers (representing authentic ‘input’ barcode sequences) were also observed in each aliquot. A mixture model approach was used to model the frequency of both the erroneous and input sequences. If X is a random variable corresponding to the number of copies of an individual sequence, then the distribution of X in an aliquot f(x) can be modeled as a mixture distribution of X for the erroneous sequences fE(x) and the authentic barcode input sequences fI(x). This can be written as:
f(x)=pfE(x)+(1−p)fI(x)
(1)
where p is the proportion of erroneous sequences in an aliquot, and (1−p) is the proportion of input sequences in an aliquot. Based on observed sequences, we fitted a model where the number of copies of the input sequences follows a lognormal distribution, while the erroneous sequences follow a power law distribution. The above distribution function is fitted to the number of copies of each unique sequence in an aliquot, using the function mle from MATLAB (R2014b). An optimal cutoff number of copies of a sequence for each aliquot was determined as the value where the theoretical distribution in the mixture model reaches a minimum. The sequences above the cutoff were designated putative input sequences and the sequences below the cutoff putative erroneous sequences. Moreover, the percentage of input sequences classified as erroneous and the number of erroneous sequences classified as input was estimated.
The method above identifies the number of putative input sequences in each aliquot, however we also estimate that 2–5% of these are actually erroneous sequences that are classified as input barcodes. Since generation of PCR/sequencing error is likely a random event in a given aliquot, we might expect that most erroneous sequences will be confined to one or a few of the 168 aliquots. However, since we expect ≈10,000 total input sequences in the stock, and observe around 2000 sequences per aliquot, then we should see most input sequences in many aliquots. Based on this observation, the probability of observing a sequence in n aliquots is given by the mixture of binomial distributions:
p(n)=fBin(N,p1)+(1−f)Bin(N,p2)
(2)
in which p1 is the probability of observing the erroneous sequences, p2 is the probability of observing an input sequence, and f is the proportion of erroneous sequences. The above distribution function is fitted to the histogram of the number of aliquots each sequence is observed in (using the function mle in MATLAB v. R2014b). Using the fitted distribution function, we could find a cutoff value that can be used to determine the total number of input sequences across all aliquots. We can also estimate the false positive and false negative rates around this cutoff. Additionally, we also tested for a binomial model with non-constant proportion in the input sequences. However, allowing for a distribution in the proportion of input sequences did not yield a better fit (p = 0.78, likelihood ratio test), hence we found no evidence for a distribution in clone size of the input sequences.
In order to estimate frequency of reactivations, we assumed exponential viral growth at the earliest stage of infection.
The time between ith and (i + 1)th reactivations, Δi = ti+1 − ti, can be estimated from ratios Ri=ViVi+1=eg(ti+1−ti), i = 1,…,n − 1 of rebounders as shown by the following formula:
Δi=lnRig.
(4)
In order to find the growth rate g of each rebounder (assumed to be the same), we assume that reactivation occurs in average every Δ days. Thus the total viral load (i.e.: the sum of all variants) at time t after treatment can be expressed by formula:
V(t)=V0eg(t−t0)+V0eg(t−t0−Δ)+V0eg(t−t0−2Δ)…+V0eg(t−t0−(n−1)Δ),(n−1)=⌊(t−t0)/Δ⌋.
(5)
Taking into account that (e−gΔ)m, m = 0,…,n − 1, is a geometric progression, we can reduce the function (5) so it will take the form:
V(t)=V0eg(t−t0)1−e−gΔ(⌊t−t0Δ⌋+1)1−e−gΔ,
(6)
where ⌊x⌋ is the largest integer not greater than x.
The function (6) has discontinuity that may create some obstacle in finding the global minimum during fitting. Thus, for the purpose of fitting we removed the discontinuities in (6) by substituting ⌊(t − t0)/Δ⌋ with (t − t0)/Δ and rewrite the expression (6) for the log of viral load:
lnV(t)=lnV0+ln(eg(t−t0)−e−gΔ)−ln(1−e−gΔ)
(7)
In order to use average time between reactivations that can be obtained from the ratios of founder virus data, as it was described above, we substitute Δ in (7) by the estimate of the mean, Δ¯=L¯g, where L¯=1(n−1)∑i=1n−1lnRi. Thus, we obtain the formula:
lnV(t)=lnV0+ln(eg(t−t0)−e−L¯)−ln(1−e−L¯),
(8)
where n is the number of founder viruses in the dataset. Model was fitted (using Prism 6.07, GraphPad Software Inc. San Diego, Ca, USA) to exponential phase of growth of virus in monkeys having V0 as a shared parameter.
We reasoned that a molecularly barcoded SIV clone would have great utility for studies of HIV/SIV latency, viral reservoir establishment and maintenance, and viral rebound upon therapeutic interruption. To generate this barcoded virus, the MluI restriction recognition sequence (ACGCGT) was introduced into the SIVmac239 infectious molecular clone (IMC) between the stop codon of vpx and the start codon of vpr. A genetic cassette consisting of 10 random bases with 7 complementary bases flanking each end was ligated into the SIVmac239 clone using the introduced MluI restriction site (Fig 1A). Importantly, the genetic insert is bidirectional, effectively doubling the discriminating power of the barcode. Following ligation, a large bacterial plasmid library was generated and was then used for large-scale virus production via transfection of HEK-293T cells. All produced virus was collected, pooled, and aliquoted, such that single aliquots contain a representative sampling of all genetic variants generated. Thus, the generated virus stock contained variants of SIVmac239 that differed only within a 34-nucleotide insertion harboring a 10 base-stretch of random nucleotides in an otherwise genetically clonal genome. These 34 bases comprise the viral barcode and the virus stock was designated SIVmac239M.
The goal of generating SIVmac239M was to produce a phenotypically homogeneous viral population with extensive diversity contained entirely within a small region of the genome suitable for deep sequencing and with a known distribution of the genetically distinct viral barcodes (or viral clonotypes). Therefore, it was necessary to determine the genetic diversity and abundance of each clonotype in the virus stock. When sequencing such a large potential number of genetic variants, it can be difficult to discern between sequences arising from PCR or sequencing error and those representing true input viral clonotypes. To distinguish these sequences, viral RNA was extracted, synthesized into cDNA, and distributed into 168 aliquots each containing 5,000 viral templates. Following PCR and Illumina-based sequencing of each aliquot, the number of unique sequences was compared to the total sequence count. Using a limited template input with massive oversampling of sequencing (at least 100-fold over-sequencing per template), we found a clear bimodal distribution of both PCR-induced errors (power-law distributed, with a high proportion of single sequences), and authentic clones (log-normally distributed, with sequences present at high frequency) (Fig 1B). We then identified the threshold number of copies separating the erroneous from the authentic barcode sequences in each aliquot. Using this approach, we detected a total of 14,357 unique clonotypes across the 168 aliquots. These clonotypes were then rank ordered by the number of replicate aliquots in which each was found. Of the 5,021 sequences found in only one aliquot, we estimated that only approximately 100 of these were likely to be authentic input barcodes based on the distribution of the 168 aliquots (Eq 2). Therefore, the vast majority of input barcodes were contained within the top 9,336 sequences.
Phylogenetic analysis of these 9,336 identified clonotypes was performed to determine the genetic relatedness of each barcoded clone (S1 Fig). Of the 9,336 identified barcodes, 5,519 were inserted in one direction, and 3,817 were inserted in the inverse direction. To quantify genetic relatedness between barcodes, we performed pairwise comparisons of each barcode (S2 Fig). We found two distinct populations (representing the two barcode orientations), with an average nucleotide difference of 7 bases. Genetic analysis also revealed 105 barcodes with one or more base pair deletions generating slightly smaller barcodes. These short inserts are likely due to errors in the molecular generation of the barcoded clone and although these barcodes are truncated, they retain their usefulness because they can still be genetically distinguished from the rest of the variant pool. Overall, these data support the conclusion that we have generated a genetically diverse, synthetic viral population approaching 10,000 individual viral clonotypes.
Prior to use in nonhuman primates, viral infectivity and replication of SIVmac239M was assessed using TZM-bl reporter cells and primary rhesus lymphocytes, respectively. This stock contained 2.2x105 IU/mL, which was equivalent to the infectious titer of a stock of parental SIVmac239 produced using the same approach. To assess the replication capacity of SIVmac239M, CD8+ T-cell-depleted PBMCs were inoculated with equivalent infectious units of SIVmac239M or the parental SIVmac239 and samples were collected every 2–3 days (Fig 2A). SIVmac239M displayed peak virus replication levels on day 7, corresponding to a detected 1.8ng of reverse transcriptase (RT)/mL of culture supernatant. The viral growth curves were comparable between SIVmac239M and parental SIVmac239, which also peaked on day 7 with 2.0ng of RT/mL. These results demonstrate that the insertion of the barcode into the viral genome did not have a measurable deleterious effect on either infectivity or replicative capacity in vitro.
To confirm that SIVmac239M did not have any replication defects, we assessed its replication capacity in vivo in rhesus macaques. Two rhesus macaques (ZK37 and ZK56) were infected with 2.2x105 IU of SIVmac239M via intravenous injection. Plasma viral loads were monitored regularly using qRT-PCR. SIVmac239M displayed viral replication kinetics comparable to wild type SIVmac239 [27] resulting in peak viremia in both SIVmac239M infected animals at day 13 with viral RNA copies at 5.0x107–1.0x108 copies/mL (Fig 2B). Viral RNA set-point was reached by day 49 with titers at ~106 copies/mL, which was similar to parental SIVmac239 [27]. These data indicate that SIVmac239M is fully functional in vivo with viral kinetics indistinguishable from wild-type SIVmac239 and suggest that the insertion of the barcode did not result in impaired infectivity or replicative function. Furthermore, sequence analysis through 3 months post infection revealed no loss of detected barcodes or accumulated changes that precluded barcode tracking and variant enumeration. Sequence analysis of plasma during chronic viremia revealed all viral genomes contained a barcoded insert. It was also observed that while mutations did occur, they were uncommon in the 34 bases of the barcode insert. On occasions when mutations did affect the barcoded region, the parental barcode was identifiable phylogenetically.
The underlying design premise in using SIVmac239M for studies of viral reservoirs was to establish a disseminated systemic infection with a large number of sequence-discriminable viral variants that are isogenic outside of the barcode and biologically equivalent. As each different variant represents the progeny of a distinct chain of infection events, barcode sequence analysis in SIVmac239M infected animals undergoing viral recrudescence after cART discontinuation should allow for unprecedented facility and depth of analysis while limiting confounding diversification and differences in viral replication capacity or other biological properties that can accumulate over time in the infected host prior to initiation of cART. To achieve reservoir seeding with numerous barcode variants, we employed a relatively high dose intravenous infection, while allowing limited time for viral replication before initiation of cART to control the size of the reservoir. The design premise of this study was to mimic the diversity of viral variants capable of seeding persistent viral reservoirs in chronic HIV infection without the attendant biological variability. It was therefore necessary to determine whether SIVmac239M could establish a genetically diverse infection in rhesus macaques. To assess the number of detectable barcodes in plasma during primary infection, plasma from day 4 to day 14 post-infection was obtained from 26 animals infected with SIVmac239M (including ZK37 and ZK56). Sequence analysis revealed an average of 1,247 clonotypes per animal (244–4800). The number of barcodes identified for individual animals varied based on duration of infection prior to treatment. Of the 9,336 confirmed barcodes in the stock, 7,991 (85.6%) were identified in at least one animal. Furthermore, each barcode was identified in a mean of 5.2 out of 26 animals, with an interquartile range of 1–8 animals. Because the large majority of barcodes from the stock could also be detected in animals, we conclude that at least 85.6% of the barcodes in our total stock are both infectious and functional.
Because variation in the proportion of each clonotype within the stock was observed, the correlation between the frequency of the clonotype in the stock and frequency of detection of the clonotype during acute infection in animals was determined. Comparing the number of viral stock replicates in which a particular clonotype was identified (out of 168 total replicates) against the number of animals in which each clonotype was found yielded a linear correlation with an R2 value of 0.77 (p<10−5 Pearson/Spearman, Fig 3A). Thus, clonotypes that were identified frequently in the 168 replicates in the stock were also found in more animals after in vivo challenge. Importantly however, clonotype abundance in each animal (i.e., the relative number of copies of each clonotype) was only weakly correlated to their abundance in the viral stock (Spearman p-values ranging from 0.07–0.18), suggesting that although clonotypes found more frequently in the stock were more likely to infect an animal, they did not necessarily become the dominant clonotypes within the animal. These results highlight the biologically consistent nature of the different clonotypes and a lack of negative impact on viral fitness due to a barcode insertion. Further analysis of the distribution of barcodes in animals revealed an inverse relationship between the number of animals in which a barcode was found (i.e. animals sharing a common clonotype), and the number of common barcodes found in that number of animals (Fig 3B). That is, individual barcodes were found most frequently in only 1 animal, and least frequently in all 26 animals. The mean relative frequency of each clonotype was calculated by averaging the proportional abundance at which it was found in each individual animal. This value was then correlated with the number of animals in which that clonotype was identified. The number of animals in which each clonotype was found appears to be largely independent of its mean relative frequency in these animals, and each clonotype was found at nearly equal proportions in each animal with no single clonotype dominating the population. The fact that a barcode was found in many animals was therefore not due to greater fitness, as barcodes found in more animals did not represent larger proportion of the total sequences than barcodes observed in only a few animals. (Fig 3C). Thus, while the proportion of each clonotype in the stock correlates with the likelihood of establishing systemic infection, there is no indication that clonotypes differ in their replicative capacity.
Our major goal in generating a barcoded virus was to facilitate the ability to discriminate between individual rebound events contributing to viremia following treatment interruption. A pilot study using short-duration cART treatment was initiated to test the feasibility of using the barcoded virus model system to discriminate distinct viral lineages following cART interruption (study 1). Here, 4 rhesus macaques (MK9, KMB, KZ2, and KTM) were each infected intravenously with 2.2x105 IU of SIVmac239M followed by daily cART (TFV/FTC/RAL) administration from day 6 to day 88 post-infection, at which time cART was discontinued (Fig 4). Cell associated-viral RNA (CA-RNA) levels in PBMC peaked on day 6 post-infection at an average of 7.6x105 copies/106 cells (S3A Fig). CA-RNA levels dropped dramatically on cART to an average of 6.3 copies/million cells at time of interruption. CA-viral DNA (CA-DNA) also peaked on day 6 at an average of 2.0x104 copies/106 cells (S3B Fig). These DNA levels declined over the course of cART treatment, but more gradually than CA-RNA, reaching an average of 5.5x102 copies/ 106 cells at the time of interruption. The plasma SIV RNA viral load was below the assay quantification limit (15 copies/mL) from day 68 to treatment interruption for 3 of 4 animals (MK9, KMB, and KTM). Viral rebound was detectable in plasma 1–2 days following cART interruption, and plasma viral loads at rebound peak were between 1.2x105–9.2x106 copies/mL. Animal KZ2 never achieved full viral suppression, and had a detectable viral load (40 SIV RNA copies/mL) on the day therapy was interrupted, which rapidly increased thereafter, highlighting the lack of full suppression in this cohort. KTM died due to procedural complications at the time of cART interruption and was therefore excluded from subsequent analyses. These data reveal typical early replication dynamics, with a greater than 5-log reduction in plasma vRNA and CA-RNA during cART treatment and a less than 2-fold decrease in the CA-DNA levels during therapy. These animals displayed rapid rebound kinetics following cART interruption.
Sequence analysis just prior to initiation of therapy identified 1,872 distinct clonotypes in animal MK9, 1,815 in animal KMB, and 3,739 in animal KZ2. To assess the number of detectable rebounding clonotypes, next generation sequence analysis was performed 7 days post-interruption. In contrast to the pre-treatment viral diversity, following cART interruption, recrudescent rebound viremia contained only 118 distinct clonotypes for MK9, 136 for KMB and 87 for KZ2 (S4 Fig). Therefore, despite the limited duration of therapy, the number of detectable clonotypes in plasma was greatly reduced from the pre-therapy time point. For animal KZ2, which had detectable virus at the time of interruption, of the 87 detectable rebounding variants, one clonotype was found at 2 logs higher proportion than the next detectable clonotype. The relative proportion of all other clonotypes were within half a log of its nearest neighbor. Overall, these results indicate that individual clonotypes can be detected in plasma viremia immediately following cART interruption and, with sufficient template input, the relative proportion of each clonotype may be accurately assessed across 5 logs.
To confirm the reproducibility of the MiSeq sequencing to consistently provide proportional representation of virus populations and to determine if additional barcodes could be identified with a larger template input, barcode sequencing of rebound plasma viremia was repeated for MK9 but with a starting template input 10-fold higher than first assayed (total 1x106 vRNA template copies assessed). Upon comparison of the relative abundances of sequenced clonotypes, nearly identical proportions of detected clonotypes were observed (S5 Fig). Furthermore, 32 additional barcodes were identified below the previous lower-limit of detection of 0.001%. These results confirm the reproducibility of our sequencing approach and that template input quantity determines the lower limit of detection.
This short-term cART treatment study demonstrated that SIVmac239M could be used to enumerate transmitted/founder variants before cART and the rebound/founder variants following cART interruption. However, 82 days of therapy was insufficient for full suppression and was likely not long enough for the decay of all short-lived viral reservoirs, therefore, a longer-term cART suppression study was conducted (study 2). Six rhesus macaques infected intravenously with SIVmac239M were given cART (TFV/FTC/RAL/IDV/RTV) starting on day 4 post-infection. Viral load measurements revealed rapid acute phase kinetics with viral load measurements ranging from 3.3x104–9.1x105 copies/mL at day 4 post-inoculation. Plasma viremia decreased over the next 9 weeks, and by day 67, all animals were suppressed to below 15 copies/mL (Fig 5A). Plasma viral loads were maintained below 15 copies/mL apart from one viral blip (30 copies/mL) in animal H105 at day 269 post-infection, which happened to coincide temporally with a diagnosis of dermatitis and associated topical antibiotic treatment.
At day 305 post-infection, therapy was discontinued in 2 animals (DEJX and DFGV). Viremia was first detected at day 9 post-cART for DEJX and day 16 for DFGV. Peak post-cART viral loads of 2.3x105copies/mL for DEJX and 2.7x106 copies/mL for DFGV were measured at day 16 and day 23, respectively. At day 374 post-infection, therapy was discontinued in an additional 2 animals (H090 and DEJW). Viremia was detected at days 7 and 9 post-cART with peak viral loads of 3.9x105 copies/mL on day 15, and 3.5x105 copies/mL on day 29 for H090 and DEJW, respectively. The final two animals (H105 and DEPI) discontinued treatment on day 482. Viremia was first detected on days 7 and 11 with peak rebound viral loads of 1.5x106 copies/mL on day 18 for H105, and 4.9x105 copies/mL on day 18 for DEPI. In this study, the time to detectable viremia ranged from 7 to 16 days post-cART, which was significantly longer than animals in study 1 (p = 0.004, Log-rank Mantel-Cox test). Notably, the time to rebound is much faster in these studies than has been reported for human studies [28]. However, there was no significant difference in peak rebound viremia between study 1 (mean 3.4x106 copies/mL) and study 2 (mean 9.4x105 copies/mL) (p = 0.26).
CA-RNA and DNA were isolated from PBMCs collected regularly over the course of the study and quantified using real-time PCR/RT-PCR. For animals in study 2, CA-RNA levels peaked at 3.0x103–2.5x105 copies/106 cells on day 4 (immediately prior to the initiation of therapy) (Fig 5B). CA-RNA fell below 10 copies/106 cells by day 53 in all animals and remained at or below the limit of quantification until interruption from cART. CA-DNA levels peaked at 1.3x102–1.4x103 copies/106 cells immediately prior to the initiation of suppressive therapy which diminished more slowly than CA-RNA levels, but reaching 10 copies/106 cells by day 283 (Fig 5C). Animal H105 showed a spike in CA-DNA on day 313, but was again near the limit of quantification by day 430 and remained suppressed for the duration of therapy. This CA-DNA spike was preceded by a blip in plasma viral load at day 269. No corresponding increase in CA-RNA was observed. The peak levels of CA-DNA and CA-RNA at cART initiation and the levels at interruption were markedly higher in animals in study 1 where treatment began on day 6 and lasted for only 82 days compared to those in study 2 in which treatment began on day 4 and lasted for more than 300 days (p<0.001, t-test). These differences in viral DNA and RNA levels highlight the importance of the dynamics of acute infection and timing of treatment initiation on the establishment of the viral reservoir (19, 37) and provide a means to control the reservoir size based on time to cART initiation.
Sequence analysis of the clonotypes detected in plasma prior to cART treatment in study 2 revealed patterns similar to those seen prior to therapy in study 1. The average number of detectable barcodes at day 4 (peak pre-therapy) was 1,274. Post-rebound sequencing was performed on samples obtained from ramp-up to early set-point viremia to identify the number of rebounding clonotypes and assess their relative abundance over time (Fig 6). Across all six animals, we detected a total of 34 unique rebounding clonotypes ranging from 4–8 variants per animal (overall mean of 5.7), with a maximum of 7 clonotypes detected at any given time, with no clear difference in animals that discontinued therapy on day 305 (mean of 5.5 clonotypes), day 374 (mean of 5.0 clonotypes), or day 482 (mean of 6.5 clonotypes). For animals DEJX, DFGV, H090, DEPI, and H105, the proportions of variants remained substantially consistent over time in the initial weeks after cART discontinuation, with only the minor clonotypes showing any notable variation. Interestingly, for animal DEJW, the dominant clonotype during rebound (clone 4886) was replaced at day 35 post interruption by the second most dominant clonotype (clone 997). Overall, these data are consistent with previous work showing a stable viral population over time with limited changes in variant proportion once those proportions are established [27].
Next, each rebounding clonotype was compared to its relative proportion in the same animal prior to cART initiation and to the SIVmac239M stock (S6 Fig). Of the 34 total detected rebound clonotypes, 27 were identified in the pre-therapy sample from the same animal. We considered whether some clonotypes may be more fit than others, despite their inherent clonality. While this was not directly measurable, it was noted that if some clonotypes were more fit, they would presumably emerge as rebounders in most or all animals used in this study. In fact, in study 2, no rebounding clonotype was observed in more than one animal, and even in study 1, in which we counted an average of 114 rebounding clonotypes per animal, less than 25% were identified in more than one animal, and only 1 was found in all three. Additionally, in both study 1 and 2, the clonotypes most abundantly represented prior to therapy were more likely to be detected following cART interruption, presumably because they were more likely to seed a larger number of cells capable of harboring stable residual virus (S7 Fig). We find significant correlation between the pre-therapy and post-rebound clonotypes in study 1 (p<0.001), but no significance in study 2, likely due to the limited number of rebounding genomes in this study. These observations are key as they demonstrate that it is possible to enumerate and identify clonotypes both before and after ART treatment. The observed clonotypes following ART interruption represent progeny from the activation of a long-lived viral reservoir.
A major goal in generating a barcoded virus was to establish a model in which both the number of sources of recrudescent virus and the dynamics of each rebounding variant could be directly estimated from the number and relative proportion of clonotypes detected in plasma. We propose that each reactivation event leading to systemic viremia can be identified using the relative proportion of each rebounding variant and the overall slope of the rebounding viral load curve. Assuming equivalent replication of each clonotype, the differences in the relative proportion of each detectable barcode can be used to infer the rate of reactivation from latency for each animal. The dynamics of viral recrudescence leading to measurable plasma viremia and the relative abundance of each clonotype sequence detected in plasma during acute recrudescence (time point highlighted by asterisk in Fig 6) were used to calculate the estimated frequency of reactivation from latency (or reactivation rate) (Eq 8). In animals in study 2 treated beginning at day 4 post infection for 305, 374, or 482 days, the estimated reactivation rates per day averaged 0.64, 0.59, and 0.41 respectively with an overall average of 0.54 –that is roughly one reactivation event every 2 days. In study 1, while the calculated reactivation rate was 16–31 events per day (mean 22.7, p = 0.02, Mann-Whitney Test), these rates likely reflect the residual presence of virus that was actively replicating or being actively produced during the relatively brief duration of cART prior to discontinuation, and thus may not represent reactivation from long-lived, latent cells. This interpretation is supported by the detectable viral load values for animal KZ2 prior to interruption from cART. Therefore, we cannot distinguish between plasma virus representing reactivating latently infected cells and residual viremia that, upon drug levels reaching a minimum threshold concentration, was immediately available to initiate a spreading infection.
Using the proportion of the total viral load represented by each clonotype, and the exponential growth rate in plasma viremia, we extrapolated the slope of the viral load curve of each animal from study 2 to below the limit of detection for each detected clonotype to the theoretical concentration of a single virion in the total blood volume, calculated using a standard clinical volume of plasma in a rhesus macaque (54mL plasma/kg body weight) which equates to 2.6x10-3 viral RNA copies/mL in a typical 7kg animal (Fig 7). The reactivation rate was plotted across the x-axis, starting at time 0 post interruption from cART, indicating the estimated average time interval between each reactivation event within each animal. Although reactivation of a latent provirus in an individual cell is stochastic in nature, we found most reactivations occurred within the predicted window of time (i.e., within the time-frame predicted by the calculated reactivation rate). In 3 animals, we hypothesized that a theoretical reactivation event occurred within the first window of reactivation (DEJX, H090, and H105), and posited that reactivation occurred shortly after therapy was discontinued with limited time for drug washout. For animal DEJW, an activation event likely did not occur within the first predicted reactivation window, but afterwards all three detectable clonotypes fit within the inferred reactivation windows. For animal DEPI, a reactivation event did not occur in the first 2 reactivation windows, but did for subsequent windows. The time to rebound in animal DFGV was the most delayed and we estimate that the first 6 reactivation windows were missed prior to a robust reactivation of many viral lineages. The theoretical initial reactivation events occurred in animals DEJX, DFGV, DEJW, H090, DEPI, and H105 on days 1.8, 7.3, 3.2, 0.5, 4.3, and 0.3 days after treatment interruption.
Accurately assessing the size and nature of the residual viral reservoir that can give rise to recrudescent viremia is essential for studies focused on prolonging cART-free remission. In addition to various ex vivo assays, all of which have significant limitations, many current assessments of reservoir-targeting therapeutic strategies include in vivo testing in HIV+ individuals, followed by cART interruption and monitoring of time to detectable viremia. However, treatment interruption likely causes reseeding of the viral reservoir during viral recrudescence off therapy, regardless of how short a time interval the interruption lasts, and the potential consequences of this must be carefully considered. Using treatment interruption and time-to-detection of virus to measure changes in the frequency of reactivation and then extrapolating this frequency to estimate changes in reservoir size after reservoir-targeting therapeutic strategies has low statistical power to detect the effects of therapy [17]. Moreover, if reactivation rates are high, or highly variable, then this will further reduce the probability of successfully assessing new interventions [29]. In the absence of any identified strategies that robustly diminish the size of the residual virus pool on cART by multiple logs, it will be important to develop approaches that allow for the identification of those therapies that have a more modest effect on the size of the viral reservoir but that may be improved upon or useful in combination with other agents. Nonhuman primates offer useful models for the assessment of intervention strategies as reservoirs can be established to recapitulate those established in HIV-1 infected patients, and interruption can be studied without risk to patients. For studies of reservoir targeting strategies, it is advantageous for the size of the viral reservoir to be controlled and normalized between experimental groups. This kind of experimental stringency is possible in NHP models in a way that is impossible in humans. Here we demonstrate a barcoded virus model to assess reservoir size which, when taken together with time to rebound measurements, provides a more sensitive and robust assessment of therapeutic changes to the latent reservoir.
Viral RNA collected from human plasma following rebound has been sequenced to estimate the number of latent cells reactivating [17], as the diverse viral quasi-species in chronic HIV infection allows for enumeration and characterization of viral reservoirs and recrudescence. While this previous work has shown that multiple viral lineages can contribute to rebounding viremia (28–31), they are limited in the depth of sequencing, the small region of viral sequence analyzed, and limited overall viral diversity in HIV infection to distinguish individual reactivation events. One of the possible problems with attempting to use this method with human samples is that sequencing of env or pol requires SGA to evaluate the genetic diversity of each sample [30–32], analysis that is labor intensive and can feasibly only yield a maximum of approximately 100 sequences. Analysis of 100 sequences from a patient with viral loads greater than 104 copies/mL will only allow for the detection of lineages representing the largest proportions of that population. An alternative approach would be to preselect individuals with greater overall viral heterogeneity and use a diverse region of the genome to identify the relative proportion of variants [17, 33]. While the relative proportions of each lineage can still be estimated with these approaches, utilizing current technology, they lack sufficient depth and dynamic range to accurately assess reactivation rates in most HIV-1 infected individuals. By contrast, our clonal barcoded virus is genetically homogeneous, with only the short stretch of 34 bases harboring the entire genetic disparity between clonotypes. Because only this short portion of the genome requires analysis to distinguish between lineages, we are able to use next generation sequencing, a method that reads tens of thousands of individual sequences, allowing for detection of rare clonotypes and consistent evaluation of the relative proportions of rebounding lineages. When evaluated in this manner, the proportion of clonotypes is reflective of the timing of the individual reactivation events that led to each viral lineage. This measured rate may therefore be used to assess the successful intervention of novel therapeutics that can reduce the overall viral reservoir size. This type of evaluation is entirely dependent on the assumption that each individual clonotype replicates at an identical rate, emphasizing the utility of our clonal virus model, and further highlights the complication of utilizing this approach in HIV-1 infected patients. Admittedly, despite the clonal nature of SIVmac239M, one factor potentially complicating analyses based on both time to detection and reactivation rates is the anatomic location of each rebounding lineage. Reactivation at sites with a limited number of target cells available for rapid viral expansion might alter the inferred reactivation rate. However, this error is minimized when using reactivation rates as a measure of reservoir size because the rates are based on an average of multiple reactivation events and variation in growth rate of a few rebounding clonotypes will not alter the calculated rate. By contrast, if using only time to detection as an estimate of reservoir size, the variation in target cell availability would significantly impact the time to viral detection, because this assessment is based only on a single measurement (i.e., the time needed for the first reactivating latent reservoir to produce detectable virus).
In order for a barcoded virus to allow the evaluation of the latent reservoir, each viral variant must be functionally equivalent. With only a 34-nucleotide cassette inserted between the vpx and vpr genes, there is no apparent effect on in vitro or in vivo infectivity or replication capacity of the virus. Of 9,336 distinct, apparently biologically equivalent clonotypes present in the stock, 105 clonotypes were found to bear truncated barcode inserts, representing 1.1% of the total stock population. Hypothesizing that these smaller barcodes might confer some fitness advantage compared to clonotypes with the full 34 base insertion and that resultant viral populations might show bias for these clonotypes, we examined all pre- and post-therapy populations to determine the relative contribution of these clonotypes to the total viral load. We found that these barcodes do not overwhelm the pre-therapy population nor do they prevent full length clonotypes from also establishing infection and reservoirs. Even the presence of wild-type SIVmac239 and the SIVmac239 with only the MluI restriction site but no barcode did not cause a disproportionate bias in the population. This emphasizes the fact that the insertion of the genetic cassette has no discernable negative effects on successful replication of the virus. Additionally, stochastic mutations elsewhere in the genome could potentially allow for selection, however in this study, animals were placed on suppressive therapy on days 4 and 6, which limited the time for a fitness-conferring mutation to arise, and therefore upon therapy release, no genome was advantageously poised to dominate the population.
One of the most common methods for evaluating the relative size of the latent reservoir in HIV+ individuals is through the quantification of CA-DNA in PBMCs. In our study, early treatment appears to have constrained the level of CA-DNA in the animals, with all of the animals receiving long-term therapy in study 2 having fewer than 10 copies of CA-DNA per million cells by day 53 post-infection, and even the animals receiving short-term therapy in study 1 reaching minimum levels of only 387–650 CA-DNA copies per million cells. These low levels unfortunately prevented the collection of a meaningful number of CA-DNA and RNA sequences to compare with rebounding clonotypes in the plasma. More extensive sampling and analysis in future studies may allow assessment of potential correlations between various estimates of reservoir size based on ex vivo assays and based on the method presented here. Early treatment also likely prevented elevated levels of immune activation/inflammation that persist in cART suppressed individuals and animals initiating cART during chronic infection [34, 35].
One of the major advantages to using NHP models for reservoir research is the ability to manipulate reservoir size and the resultant number of rebounding variants following treatment interruption by controlling standardized inocula, timing of cART initiation, and duration of treatment. In our study, the animals that received cART beginning on day 6 post-infection for 82 days (study 1) had approximately 10 times higher frequency of rebounders than animals started on cART beginning on day 4 for 305–482 days (study 2). There may be two explanations for this. First, these animals were treated on day 6, two days later than the day 4 treated animals in study 2, therefore viral seeding, which expands logarithmically during the acute (pre-peak viremia) phase of infection, reached higher levels prior to suppression. This hypothesis is supported by the fact that the CA-DNA levels just prior to cART initiation and just prior to cART discontinuation are approximately 2 logs higher in the animals in study 1 (averaging approximately 103 copies/106 cells), compared to the animals in study 2 (averaging approximately 10 copies/106 cells). Because the seeded latent reservoir is larger, this could explain why both the rate of reactivation and the number of rebounding clonotypes is significantly higher (p = 0.01, Mann-Whitney Test). An additional factor potentially contributing to the increased number of detectable rebounders is that the duration of suppressive therapy was much shorter in the animals in study 1, and it is likely that cells actively producing replication competent virus have not had sufficient time to decay down to minimal quantities. This would dramatically increase the apparent size of the reservoir, and result in a much greater rate/number of reactivating cells. Additionally, full viral suppression was likely not achieved in these animals, as evidenced by the detectable viral load of KZ2 on the day of therapy interruption. This suggests the presence of residual production of virus in the animal which, upon degradation/metabolization of cART, could potentially initiate recrudescent infection, given susceptible target cells. As such, there is no way to distinguish between those clonotypes that arose from spread of variants present as residual viremia and those that resulted from activation of latently infected cells. These confounding factors likely contribute to rebound viremia in the animals in study 1, whereas by the time of cART interruption in the animals in study 2, these shorter-lived reservoirs likely had decayed or been eliminated, and residual viremia cleared from the system. Furthermore, animals DEJX, H090, and H105 were also detectable quickly within the first few days, suggesting drug washout is rapid in these animals. Interestingly DFGV had a similar rate of reactivation to the other animals in study 2, but had a considerable delay in time to first detectable viremia likely due to variability in drug washout kinetics. This animal to animal variation might be important for studies that utilize only time to detection as the measure of viral reservoir size.
To estimate the decrease in reactivation rate in animals in study 2, a decay curve was fitted to our data (y = y0ekt, where y is the reactivation rate, and t is the duration of treatment). We find that time required for the reactivation frequency to decrease by 50% is 299 days. Ultimately, it is most likely that both peak viral load and duration of therapy account for the differences in the number of rebound variants we observe in the two studies. This then illustrates a major advantage of an animal infection model paired with our barcoded virus, namely, that we may control the timing of cART initiation and treatment duration to modulate reservoir size to fit the desired parameters of the study. Most animal studies conducted to measure reservoir size allow infection to reach chronic phase prior to initiation of therapy, and then utilize time to detection of viral load to identify changes in reservoir size. Time to detection is a valid method for monitoring changes in reservoir size, however it requires large sample group sizes to detect the effects of reservoir reduction [17, 29]. Our model adds power and depth to the traditional time to detection measurement. By using a barcoded virus and next generation sequencing, we greatly increase the sensitivity of detecting changes in the reservoir size by increasing the amount of information that can be derived from each animal. The measured number of rebounding clonotypes and the corresponding reactivation rates are reflective of the functional reservoir size, and thus may be used as a direct measurement of the latent reservoir that effectively contributes to rebound viremia.
When this study was initiated, it was theorized that early treatment itself could limit the viral reservoir and prevent rebounding viremia [36]. One sobering observation made here was that even after very early treatment (day 4 post-infection) and over a year of suppressive therapy, viremia rapidly recrudesced after treatment interruption. Despite the early treatment start date, a time frame which is virtually impossible to achieve for newly infected humans, we still detected viral rebound within eleven days of treatment interruption. Although delayed when treated early, previous studies in both humans [28, 37] and NHPs [19] demonstrate similar findings: that once viremia is detectable, despite early treatment, viral reservoir is irreversibly established and causes recrudescent viremia. These studies and our findings highlight the urgency of developing novel therapeutics to target the reservoir directly. Furthermore, it remains to be determined if post-rebound control of viremia can be augmented by some intervention strategy, thereby providing a functional cure if elimination of viral reservoirs cannot be obtained.
There are likely numerous applications for a barcoded virus. This would be an ideal system for sensitive detection of minor changes in reservoir size induced by latency reversing agents or other adjunctive therapies. Additionally, this barcode could be introduced into other lentiviruses used in nonhuman primate research, including SHIV clones and minimally chimeric HIV. It may also be useful if introduced into HIV-1 clones for in vitro testing and in humanized mice. This approach is also not limited to lentiviruses, and might be useful for other viruses that would tolerate a small genetic barcode. Furthermore, this approach might extend to other replicating biological systems that could benefit from genetic tracking, including bacteria and fungi. A major advantage of the model is that because the genetic insert is small, it reduces the probability of exerting any inhibitory effect on growth or infectivity and is it unlikely to be extruded from the genome.
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10.1371/journal.pbio.1001750 | The Velvet Family of Fungal Regulators Contains a DNA-Binding Domain Structurally Similar to NF-κB | Morphological development of fungi and their combined production of secondary metabolites are both acting in defence and protection. These processes are mainly coordinated by velvet regulators, which contain a yet functionally and structurally uncharacterized velvet domain. Here we demonstrate that the velvet domain of VosA is a novel DNA-binding motif that specifically recognizes an 11-nucleotide consensus sequence consisting of two motifs in the promoters of key developmental regulatory genes. The crystal structure analysis of the VosA velvet domain revealed an unforeseen structural similarity with the Rel homology domain (RHD) of the mammalian transcription factor NF-κB. Based on this structural similarity several conserved amino acid residues present in all velvet domains have been identified and shown to be essential for the DNA binding ability of VosA. The velvet domain is also involved in dimer formation as seen in the solved crystal structures of the VosA homodimer and the VosA-VelB heterodimer. These findings suggest that defence mechanisms of both fungi and animals might be governed by structurally related DNA-binding transcription factors.
| In many fungi, developmental processes and the synthesis of nonessential chemicals (secondary metabolites) are regulated by various external stimuli, such as light. Although fungi employ them for defensive purposes, secondary metabolites range from useful antibiotics to powerful toxins, so understanding the molecular processes that regulate their synthesis is of particular interest to us. In the mold Aspergillus nidulans the main regulators of these processes are the so-called “velvet” proteins VeA, VelB, and VosA, which share a 150-amino acid region known as the velvet domain. Velvet proteins interact with each other, alone (“homodimers”), in various combinations (“heterodimers”), and also with other proteins, but the molecular mechanism by which these proteins exert their regulatory function has been unclear. In this work we show that velvet proteins form a family of fungus-specific transcription factors that directly bind to target DNA, even though analysis of their amino acid sequence does not reveal any known DNA-binding domains or motifs. We determined the three-dimensional structure of the VosA-VosA homodimer and the VosA-VelB heterodimer and found that the structure of the velvet domain is strongly reminiscent of the N-terminal immunoglobulin-like domain found in the mammalian transcription factor NFκB-p50, despite the very low sequence similarity. We propose that, like NFκB, various homo- or heterodimers of velvet proteins modulate gene expression to drive development and defensive pathways in fungi.
| The fungal and the animal kingdom are related as they both belong to the ophistokonts with a common ancestor existing about 1 billion years ago [1],[2]. Animals have evolved with an elaborate inflammation and immune system for self-defence. Inflammation, the immune system, and animal development are controlled by various mono- and multiprotein assemblies of RHD-containing proteins. Among others, one family, named NF-κB, consists of five members, which respond to external stimuli [3],[4]. In contrast to animals, fungi are normally secured by a thick cell wall and had been misclassified as plants for centuries due to their loss of motility and the establishment of a cell wall. In addition, in response to various abiotic or biotic signals, filamentous fungi produce small signalling and/or defensive bioactive molecules [5],[6]. These secondary metabolites range from antibiotics such as penicillins to mycotoxins such as aflatoxins, affecting everyday life of animals and human beings [7]. Regulation of the secondary metabolism as well as the control of growth and differentiation of the model mold Aspergillus nidulans are coupled by a family of fungal regulators, the velvet proteins (Figure S1) [6],[8]. These velvet regulators are present in most parts of the fungal kingdom from chytrids to basidiomycetes. The velvet proteins share a homologous region comprising about 150 amino acids, which lack significant sequence homology to any other known proteins (Figure S2).
In A. nidulans the four velvet proteins VeA, VelB, VelC, and VosA have been identified and characterized. They can interact with each other and also with non-velvet proteins resulting in complexes, which link morphological and chemical development of fungi [9]. The regulation of sexual development and secondary metabolism has been shown to be a light-regulated process coordinated by the heterotrimeric complex, consisting of the velvet proteins VeA, the VeA-like protein B (VelB), and the putative methyltransferase LaeA. The heterotrimeric VelB/VeA/LaeA-complex activates secondary metabolism and sexual development. The ΔlaeA and ΔveA mutant strains are unable to produce hardly any sterigmatocystin, the penultimate precursor of aflatoxins. Similarly ΔveA and ΔvelB strains do not form any sexual fruiting body [9]. Notably, in the dark VeA is predominantly found in the nucleus, whereas it is mostly in the cytoplasm in the light. VeA contains an N-terminally located nuclear localisation signal (NLS) recognized by the nuclear import factor KapA mediating the transport from the cytoplasm into the nucleus [10], once it becomes accessible by a yet unknown factor or mechanism.
VosA contains an N-terminally located velvet domain and is required for the transcription of several genes essential for spore viability [11]. Deletion of vosA results in a severe down-regulation of genes associated with trehalose biosynthesis (tpsA, tpsC, and orlA) and the lack of trehalose biogenesis in spores. As a consequence, spores of ΔvosA strains are much less resistant to heat, UV, and other stress conditions and exhibit a strongly reduced survival rate after 10 d. Studies of spore viability of the ΔvelB mutant revealed that the interaction of VosA with VelB is required for proper expression of the trehalose biosynthesis genes in fungal spores [12],[13]. Similar to the ΔvosA strain, the ΔvelB strain produces spores that contain virtually no trehalose, rendering them much more susceptible to desiccation and other stresses.
The role of velvet proteins in other fungi has been extensively studied in the past few years. While external stimuli can be different, their regulatory function on secondary metabolism and development seems to be conserved. In the human pathogen Histoplasma capsulatum, the switch from filamentous growth to the pathogenic yeast form is triggered by a temperature increase and requires the VosA and VelB orthologues Ryp2 and Ryp3, respectively [14],[15]. In Fusarium fujikuroi, the deletion of the veA and velB homologues Ffvel1 and Ffvel2 affects the secondary metabolism and virulence on rice [16]. In several cases the veA null mutation could be rescued by complementation of cross-genus veA from other fungi [16]–[18]. Numerous recent studies support a role of velvet proteins in fungal virulence [19]–[22].
Here we report the molecular basis of the velvet-mediated gene regulation. Genome-wide and targeted DNA binding studies of VosA reveal that its velvet domain recognizes an 11-nucleotide sequence present in the promoter regions of many regulatory and structural genes. The crystal structure analysis of the velvet domains of VosA and the heterodimeric VosA-VelB complex demonstrate that the velvet domain is an RHD-like domain related to NF-κB. Besides the velvet domain, VosA contains a predicted C-terminal transcriptional activation domain, implying that it is likely a transcription factor [11]. Taken together, the existence of novel fungus-specific transcription factors possessing a mammalian NF-κB–like DNA-binding domain suggests a common functional origin for the coordination of fungal development with secondary metabolism and the immuno-inflammatory response control in humans.
Due to their regulatory roles and nuclear localization, a function as transcription factor was proposed for velvet proteins. However, based on their amino acid sequences, no known DNA-binding domain could be identified. To test for a potential DNA binding activity of velvet proteins, in vivo chromatin immuno-precipitation (ChIP) employing the VosA protein tagged with FLAG followed by A. nidulans tiling microarray analysis (ChIP-chip) was carried out. The results revealed that more than 1,500 genes' promoters were enriched by the VosA-FLAG-ChIP (Table S1). For verification of these results, we further carried out ChIP-PCR and demonstrated that VosA-FLAG-ChIP indeed specifically enriched the promoter regions of the genes associated with asexual development (brlA, wetA, and vosA) and trehalose biosynthesis (tpsA and treA) (Figure 1A; Figure S3). Using VosA-ChIP-chip result, we then performed the consensus motif analysis, which led to several motifs (Table S2). Independent of this computer-based analysis, we carried out a series of electrophoretic mobility shift assays (EMSAs) with full-length or truncated VosA proteins (VosA, VosA_N, VosA_C) using various regions of the brlA promoter as probes. Such a promoter-walking EMSA revealed that full-length and the N-terminal half of VosA (VosA_N, residues 1–216) containing the entire velvet homology region binds to a 35 bp fragment of the brlAβ promoter (−1,395 to −1,361, marked by the arrowhead in Figure 1B). To develop a preliminary motif recognized by VosA, we used 14 sequences from ChIP-chip and Northern blot results (Table S3) and one 35 bp brlAβ fragment from EMSA. These 15 sequences were subject to MEME (Multiple Em for Motif Elicitation) analysis [23] and an 11-nucleotide consensus sequence was found (Figure 1C). This motif (positions 5 through 10) is similar to motif 1 from ChIP-chip results (Table S2) and has a very good palindrome structure (CCGCGG). In addition, the TGG sequence (positions 2 to 4 in a preliminary motif sequence) is included in three motifs from ChIP-chip results. To test whether these sequences (CCGCGG and TGG) in the 35 bp brlAβ fragment are core sequences that are needed for DNA-binding, we designed three probes that specifically deleted these regions (Figure 1C). VosA-DNA binding was decreased when the mutated probes were used (Figure 1D). These results suggested that the VosA velvet domain represents a novel type of DNA-binding domain that recognizes an 11-nucleotide DNA sequence.
Previous studies proposed that the VelB-VosA heterodimer is a functional unit of trehalose biosynthesis in spores and of spore maturation [12],[13]. To test whether VelB-ChIP also enriches the promoter regions of the VosA target genes, a VelB-ChIP-PCR analysis was performed. VelB-FLAG-ChIP enriched the same promoter, but not ORF-regions of VosA target genes (Figures 2A and S3). To test the molecular consequences of the lack of VosA or VelB in spores, we then examined the mRNA levels of the high score genes in wild-type, ΔvosA, and ΔvelB strain conidia (Table S3). Deletion of velB, similar to deletion of vosA, caused reduced accumulation of AN8694, nsdD, AN5371, cteA, AN6508, and AN5709 mRNAs, but increased transcript levels of brlA, treA AN8741, and rfeG (Figure 2B). These results suggest that these two velvet regulators play a dual role in activating genes associated with spore maturation and repressing certain development-associated genes. As mentioned above, the velvet domain of VosA is a DNA-binding domain. To test the DNA binding ability of the velvet domains of other velvet proteins, we carried out further EMSAs using VeA, VelB, or a heterodimer composed of VelB and the minimal velvet domain of VosA encompassing amino acid residues 1–190 (VosA1–190), which was designed for crystallization (see below). The VelB protein alone failed to bind to the brlA probe, whereas VosA1–190-VelB and VeA bind to this probe readily (Figures 2C and S4). In addition, VosA1–190-VelB and VeA binding to DNA was decreased when the mutated probes of the brlA-promoter region were used (Figure S5). Overall, these results imply that the homodimers of VosA and VeA may recognize the same promoter regions, while VelB alone at least does not bind the brlA promoter. However, binding sequences and affinities might be different for heterodimers.
In order to gain insights into the structural basis of the specific DNA recognition and to increase the chances for crystallization, a minimal velvet domain of VosA encompassing residues 1–190 (VosA1–190) was cloned, expressed, and purified. The DNA binding properties—albeit weaker than for the VosA_N—indicated a properly folded and active entity (Figure 1B), which crystallized readily forming well-diffracting tetragonal crystals. The crystal structure of VosA1–190 was determined de novo by means of single-wavelength anomalous dispersion (SAD). The structure was refined at a resolution of 1.79 Å (Table 1) and comprises residues 8–185 belonging to one VosA monomer occupying the asymmetric unit. Crystal packing analysis revealed the existence of a homodimer defined by a crystallographic 2-fold symmetry axis, which is consistent with the results from size exclusion chromatography and multi-angle-light-scattering (MALS), demonstrating that VosA1–190 exists in solution as a homodimer (Figures S6 and S7).
The VosA velvet-domain folds into a highly twisted β-sandwich containing seven antiparallel β-strands. One side of the β-sandwich is involved in dimer formation, whereas the other one is flanked by several loops of which two fold into an α-helix. These α-helical fragments are located between β-strands 2 and 3 and at the C-terminus (Figure 3A). The 2-fold symmetry of a homodimer results in an antiparallel orientation of β-strands 2, 3, 5, and 7 to the same strands of the other subunit (Figure 3B). The surface area covered by the interaction of the subunits is 1,078 Å2 corresponding to 12.7% of the total molecule surface sufficient for a stable interaction required for dimer formation. Both subunits contribute to a positively charged patch on the homodimer's surface likely to be involved in binding and recognition of DNA (Figure 3B).
A search for proteins structurally homologous to VosA using DALI [24] identified the mammalian transcription factor NF-κB-p50 [25],[26] as the most similar protein structure with a root mean square deviation (r.m.s.d.) of 2.8 Å for 113 common Cα-atoms (Figure 3C). Given the low amino acid sequence identity of 13.7%, this structural similarity was quite unexpected (Figure S8). NF-κB itself belongs to the family of Rel-proteins containing the conserved Rel-homology-region, which encompasses 300 residues that fold into two immunoglobulin-like domains [25],. Both of these domains are involved in DNA-binding, but dimerization of Rel-proteins is exclusively mediated by the C-terminally localized, shorter domain (Rel-C). Opposing, the fold of the VosA velvet domain resembles that of the longer N-terminal domain of NF-κB-p50 (Rel-N). The major difference between p50-Rel-N and the velvet domain is an insertion of three additional α-helices between β-strands 7 and 8 of p50-Rel-N (Figures 3C and S8). A C-terminal helix that covers the β-sheet in the region formed by β1, β2, β5, and β4 of VosA is missing in p50-Rel-N. Instead p50-Rel-C interacts with a long loop connecting β1 and β2, extending the protein on that side.
The crystallized VosA fragment comprises the 190 N-terminal residues corresponding to the velvet domain. An amino acid sequence analysis of the missing C-terminal part of VosA performed with HHpred [27] revealed a homology to the Sec24 transport protein for a fragment comprising residues 240 to 434 of VosA (sequence identity and similarity of 23% and 35%, respectively). This C-terminally located domain is connected via a 50-residue-long region without any detectable structural homology, which nevertheless could correspond to the C-terminally located Rel-C domain of NF-κB-p50. Superposition of VosA and NF-κB-p50 structures revealed that the flexible loop connecting two domains in NF-κB-p50 could be structurally equivalent to a loop preceding the C-terminal helix (K160-M165) in VosA. The interpretation of the experimental electron density map of that loop was not unambiguous, as it is located close to crystallographic 2-fold axis. Hence the two possible conformations of that loop affect the positioning of the remaining C-terminal fragment that can either cover the β-sheet of the same protein molecule or the β-sheet of the adjacent protein molecule related by the 2-fold symmetry by employing a domain swapping (Figure S9).
Utilizing the structural similarity to NF-κB, the mode of DNA binding of the velvet domain may be deduced from the superposition of the VosA monomer to crystal structures of NF-κB-DNA complexes [25],[26]—for example, the NF-κB p50 homodimer bound to DNA (PDB ID code 1SVC). The superposition reveals that the loop connecting the first and second β-strand (loop A) as well as the loop located before the C-terminal helix of VosA (loop B) could be involved in DNA binding by interactions with the major groove (Figure 3C). Indeed, several positively charged residues (Lys, Arg) prone for DNA-binding activity are located within these loops (Figure 3D). To test whether these residues are critical for DNA-binding, K37, K39, R41, and K42 located in loop A and K160 in loop B were individually substituted with alanine. The DNA binding activity of the mutated VosA1–190 proteins was tested by an EMSA with the 35 bp fragment of the brlAβ promoter containing the VosA binding motifs (Figure 4) and additionally with the same brlAβ promoter fragment with deleted VosA binding motifs 1 and 2 (Figure S10), verifying the importance of both motifs in VosA-DNA interaction. Remarkably, severely reduced DNA-binding activity was observed for all mutants located in loop A (Figure 4). In contrast, the loop B K160A mutant retained its DNA-binding capability. This observation suggests a minor, if any, involvement of the second loop in DNA binding and supports its assignment as a flexible loop joining two individual VosA domains. Substituting all four positively charged residues in this loop—namely K37, K39, R41 and K42—simultaneously with alanine completely abolished DNA binding activity. Similar to this quadruple mutant, already the double mutant K37A/K39A cannot bind to DNA anymore (Figure 4). This strongly indicates that this loop of VosA is involved in protein–DNA interactions, which is consistent with structural superposition with the NF-κB-DNA complex (PDB ID code 1SVC) (Figure 3C,D). The function of the double mutated protein K37/39A was also analyzed in vivo in A. nidulans. The introduction of the mutated version of the vosA gene in a vosA deletion strain only partially complemented the vosA deletion phenotype, suggesting that the mutated protein is not working properly due to its reduced DNA binding activity (Figure S11).
Adding the second molecule of the VosA homodimer to the model of a VosA-DNA complex reveals that a significant bending of the DNA has to occur if both subunits bind DNA simultaneously. The interacting loop A of the second VosA molecule would be positioned about 10 bases distant from the binding site of the first VosA monomer (Figure S12A,B).
Known physiological functions of VosA depend on the concerted action with the velvet protein VelB, which was shown to form a heterodimer with VosA [12],[13]. In order to unveil the molecular basis of the VosA-VelB interaction, we also determined the crystal structure of the VosA1–190-VelB heterodimer at a resolution of 2.2 Å (Table 1, Figure 5).
In contrast to the other velvet-proteins in A. nidulans (VosA, VeA, and VelC), the velvet-domain of VelB is not continuous but is interrupted by an insertion of 99 amino acids (residues 132–231; Figure S2). This insertion is rich in proline, glutamine, glycine, tyrosine, and serine residues, and it is predicted to form an intrinsically disordered region. Even though full-length VelB (369 residues) was used for complex formation and crystallization, the insertion is not present in the crystal structure most likely due to a proteolytic removal (Figure S13). This proteolytic activity might also be the reason why residues 161–190 of VosA in this heterodimer structure are not defined in the electron density map, even though residues 161–185 are defined in the structure of the VosA homodimer. Overall VosA exhibits an almost identical fold as in the homodimer structure with an r.m.s.d. of 0.80 Å. The VelB velvet domain adopts a fold similar to that of VosA (r.m.s.d. of 1.01 Å for 136 Cα positions), however it contains one additional N-terminal β-strand.
In the heterodimer, VosA and VelB share the same interaction surface with respect to the secondary structure elements involved in the interaction as the monomers of the VosA homodimer (Figure 5). The surface covered by the interaction of both molecules (1,453 Å2) is slightly larger than that in the VosA homodimer (1,078 Å2). A major difference to the VosA homodimer is that the VelB protein in the VosA-VelB heterodimer is oriented differently. With respect to the second VosA-molecule, in the homodimer it is rotated by around 30° and shifted about 3 Å closer toward VosA (Figure S14). Importantly, the lack of the 99-residue-long insertion in the VelB velvet domain does not compromise the binding of VelB to VosA (Figure S5).
Superposition of VelB with VosA reveals that DNA-binding loops A and B in VelB differ in arrangement and/or sequence from VosA (Figure S12). Loop B, which is highly similar in the overall structure to VosA, contains two lysines instead of one, both pointing away from the protein molecule. In both VosA and VelB, loop A is made up of eight residues. While loop A of VelB has an irregular conformation, the corresponding loop A of VosA contains a short helical element resulting in an elongated loop. The lysines in loop A of VosA play an important role in DNA binding, however there is only one lysine in loop A of VelB present and K42 is exchanged into R81. Interestingly, R80 (corresponding to R41 in VosA) is not pointing inward as in the VosA homodimer, but is oriented outward and interacts with a sulfate ion and the neighbouring D77 side chain and D79 carbonyl group. Flexibility of the loop regions comprising the positively charged residues might be a prerequisite for these interactions to contribute to both DNA binding and subsequent stabilization of the loop conformation of VelB (Figure S12).
All together the differences in the DNA-binding loops are indicative for a different interaction surface, which in turn could cause different DNA sequence specificity of VosA and VelB, respectively.
Members of the velvet protein family have been defined by a conserved sequence comprising some 150 amino acids, denoted as velvet domain. Only in VelB the velvet domain contains an insertion of 99 residues of yet unknown function. The crystal structure analysis of VosA and VelB revealed that the velvet domain represents a structural entity. The velvet domain is involved in specific DNA binding as well as in the dimerization of the different velvet proteins, resulting in formation of homo- and heterodimers.
The common fold of the VosA and VelB velvet-domain comprises a highly twisted β-sandwich composed of seven antiparallel β-strands, an α-helix inserted in the loop connecting β-strands 2 and 3, and a second, C-terminally located α-helix (Figures 3 and 5). VosA forms a homodimer, as indicated by an interaction surface encompassing 12.7% of the total surface. This is supported by results from gel-filtration and MALS experiments. Head-to-head homodimer formation buries equivalent outside surfaces formed by strands β4, β3, β6, β7 of each VosA monomer resulting in formation of an intradimer eight-stranded β-sandwich. The newly formed β-sandwich buries several hydrophobic residues located on β3, β6, and β7, in particular three Phe (F72, F136, and F145), positioned in a row perpendicular to β-sandwich axis. Thus a cluster of six Phe forming a large hydrophobic patch could be the major driving force for VosA oligomerization. Interestingly, although using the identical region for interaction in the VosA-VelB heterodimer, the complexes significantly differ in relative orientation of individual monomers. This is mostly due to different length and curvature of the β6 and β7 and interconnecting loop found in VelB β-sandwich, which in order to pack against a β-sandwich of VosA needs to be rotated by 32° and shifted by 3 Å. This results in a better fit of the two subunits and increase of the interaction surface, which similarly to the VosA homodimer buries several hydrophobic side chains in the core of newly formed intradimer eight-stranded β-sandwich (Figure 5).
Both proteins reveal a structural similarity to members of the NF-κB-p50-family, especially to a conserved region of about 150 amino acids that resembles an RHD-like fold. In contrast to NF-κB, where the C-terminal domain is responsible for the dimerization and both domains are capable of binding to DNA, the velvet domain harbours both functions. Based on the obtained structural information of VosA and VelB and their specific DNA binding properties (discussed below), we propose that the fungal velvet proteins represent a new class of direct DNA-binding transcription factors sharing a common ancestor(s) with the NF-κB-p50 family.
Comparison of VosA and VelB overall structure with NF-κB allowed identification of critical residues for DNA-binding activity located in a loop region within a patch of positively charged surface. According to the superposition, binding would occur to the major groove of the DNA. Substitution of these residues with alanine in VosA clearly abolishes DNA-binding activity. The strongest effect on DNA-binding is observed by replacing K37/K39, suggesting that these two residues together with K42 provide the major interactions with the DNA. The introduction of a mutated vosA K37/39A in the genome of A. nidulans proves that these two residues are important for proper function of the protein. The deletion of vosA results in an up-regulation of the brlA gene and to a loss of viability after 10 d of growth due to the lack of trehalose. Additionally, the deletion influences the production of sexual spores and of pigments released to the agar (Figure S11B). The introduction of the mutated vosA K37/39A allele in a ΔvosA strain fails to fully complement these defects. Only the production of sexual spores was restored, whereas the spore viability was partially restored, but the defected control of pigmentation (Figure S11B) and brlA expression (Figure S11C) was not rescued.
This patch of positively charged residues defined by K37, K39, and K42 is well conserved among all velvet proteins, suggesting a common mode of protein-DNA interaction for VosA and VelB. Furthermore, the structural comparison with NF-κB suggests that the VosA homodimer recognizes about 11 base-pairs, which is in agreement with the predicted 11 nucleotides consensus sequence recognized by VosA in our ChIP-chip analysis.
However, the arrangement of the VosA homodimer and the superposition and modelling of the DNA reveals that the putative DNA-binding loops of the second molecule is at least ∼13 Å distant to the backbone of a dsDNA with ideal B-form conformation. A simultaneous binding to both VosA molecules would require a kink in the DNA. In contrast to the VosA homodimer, in the putative VosA-VelB-DNA complex model with VosA in close proximity to the DNA, the distance of the velvet domain of VelB to the DNA backbone is reduced and would require less bending of an ideal B-form DNA for tight interaction. Both DNA sequence motifs 1 and 2 play an important role in DNA binding of VosA, however with differing importance. Deletion of motif 1 has a higher influence on binding efficiency than deletion of motif 2. A recent study demonstrated that the DNA-binding sequence of the velvet proteins Ryp2 and Ryp3 in H. capsulatum is highly similar to the motif 1 derived from our ChIP-chip result (Table S2) [28].
The electron density obtained from the VosA1–190 crystals allows an interpretation of two alternate conformations of its C-terminal helix. The missing C-terminal part of VosA might lead to an overall conformation of VosA similar to the one observed for NF-κB and could increase the effect of loop B on DNA binding. The additional residues present in VosA-N might therefore explain its increased DNA binding in comparison to VosA1–190.
In summary, we suggest that the modulation of gene transcription is achieved by the use of varying homo- and heterodimers as seen for VosA and VelB, potentially allowing the velvet proteins to specifically recognize different DNA sequences causing differential regulatory outcomes. In A. nidulans there are four velvet proteins, three of which have been studied. VosA-VelB is essential for the regulation of asexual development and spore viability [11]–[13] and VelB-VeA for sexual development [11]. The trimeric VelB-VeA-LaeA complex coordinates differentiation and secondary metabolism in response to external signals [9]. Little is known about the function of the velvet proteins in other clades of fungi outside of the ascomycetes, where much remains to be discovered. Interestingly, the unicellular eukaryote Capsaspora owczarzaki, which is a symbiont in the haemolymph of the tropical freshwater snail Biomphalaria glabrata, carries a gene for both an NF-κB and a VosA-like protein, suggesting the coexistence of both in this organism [29].
Several studies indicate that the velvet proteins are global regulators controlling a diverse set of processes ranging from toxin production and cell wall formation to the development of resting or sexual fruiting structures [6]. It will be interesting to determine what additional common themes and features exist between fungal growth and developmental control by the velvet protein family and the immune, inflammation, and differentiation response of animals by the NF-κB protein family. One candidate for a common denominator of RHD and velvets' functions is the COP9 signalosome, a conserved multiprotein complex controlling the life span of proteins. The COP9 signalosome is required for the control of NF-κB activation [30] as well as the control of fungal development and secondary metabolism [31]. Recently it has been shown that the physical interactions between the COP9 signalosome and an additional developmental regulator protein are conserved between humans and fungi [32].
Similarly to the NF-κB transcription factors, the founding member of the velvet protein family, VeA, resides in the cytoplasm but is prevented from nuclear import in the light by a yet unknown factor or mechanism. It is tempting to speculate that the light signal leads to a posttranscriptional modification—e.g. phosphorylation—of VeA, hindering the interaction with the nuclear import factor KapA. The control of nucleocytoplasmic transport by phosphorylation is known for various proteins like Hxk2, LASP-1, IPMK, and hTERT [33]–[36]. Notably, the regulation on the level of nucleocytoplasmic transport applies also for NF-κB, as the NF-κB inhibitor Iκ-Bα binds to NF-κB, thereby masking the NLS of NF-κB [37]–[39]. However, the similarity of the velvet proteins to NF-κB does not extend to this regulatory mechanism, since no Iκ-B homolog or functional homolog could be identified in fungi yet. Hence, the precise understanding of differences and similarities in the molecular mechanisms of the velvet/Rel family might help to control fungi, which not only cause increasing problems for human health and crop yield/quality, but also play a crucial role as environmental recyclers, fermenters, industrial producers, and agricultural aids.
In order to express the truncated VosA (residues 1–190), here denoted as VosA1–190, vosA cDNA was amplified using the oligos OZG479/480 containing the NcoI site. The amplicon was digested with NcoI and inserted into the NcoI site of pETM13 (EMBL, Heidelberg), yielding the plasmid pETM13-VosA190 with a 3′ coding sequence for a Strep-tag. For construction of plasmid pME3815, velB was amplified from A. nidulans cDNA with primers JG45/46 and cloned into plasmid pETM-13 digested with NcoI and XhoI.
The VosA1–190 mutations K37A, K39A, K37/39A, R41A, K42A, K160A, and the dead mutation (K37A, K39A, R41A, K42A) were inserted by PCR with mutated primers. For the mutation K37A, the N- and C-terminal VosA fragments were amplified from pETM13-VosA190 with primers OZG479/JG365 and JG366/367. Then, the fragments were fused by PCR with primers OZG479/JG367. The mutations K39A, K37/39A, R41A, K42A, K160A, and the dead mutation were designed in the same way as K37A with the following primers: K39A (OZG479/JG368, JG366/367, OZG479/JG367), K37/39A (OZG479/JG642, JG366/367, OZG479/JG367), R41A (OZG479/JG369, JG366/367, OZG479/JG367), K42A (OZG479/JG370, JG366/367, OZG479/JG367), K160A (OZG479/JG372, JG373/367, OZG479/JG367), and dead (OZG479/JG371, JG366/367, OZG479/JG367). The fused fragments were cloned in NcoI-digested pETM13, resulting in plasmids pME3845 (K37A), pME3846 (K39A), pME3845 (K37/39A), pME3847 (R41A), pME3848 (K42A), pME3850 (K160A), and pME3849 (dead).
The plasmid pETM13-VosA190 and the mutant forms were transformed into E. coli Rosetta 2 (DE3). Expression was carried out in ZYM5052 media [40] at 16°C. Cells were harvested by centrifugation for 20 min at 5,300×g and resuspended in lysis buffer (30 mM HEPES pH 7.4, 400 mM NaCl, 30 mM Imidazol). Cell lysis was performed using a Fluidizer (Microfluidics) at 0.55 MPa. The resulting lysate was cleared by centrifugation at 30,000×g for 30 min at 4°C. The supernatant was applied to a 5 ml StrepTactinHP column (GE Healthcare) equilibrated with lysis buffer. After extensive washing, the protein was eluted with elution buffer S (lysis-buffer +2.5 mM des-thiobiotin). The eluate was applied to a Superdex 200 16/60 column (GE Healthcare) equilibrated in gel-filtration buffer (10 mM HEPES pH 7.4, 400 mM NaCl). The fractions containing VosA1–190 were pooled, concentrated to 10.4 mg/ml in centrifugal concentrators (Vivascience), and used for crystallization. VosA mutant forms were expressed and purified as the wild-type, and the gel-filtration step was omitted since the purity was more than 95% as judged by SDS-PAGE.
As a final test for integrity, MALS was performed for selected complexes (Figure S7) and the mutant forms of VosA1–190 were compared to the wild-type by means of CD spectroscopy (Figure S15).
His-tagged VelB was expressed as described above. Cells expressing VosA1–190 with a C-terminal Strep-tag and full-length VelB-His6 were harvested, mixed, and lysed in lysis-buffer as described before. The cleared supernatant was applied to a 10 ml NiNTA (GE Healthcare), washed, and eluted with elution buffer N (lysis-buffer +400 mM Imidazol). The resulting eluate was directly applied to a 5 ml StrepTactinHP, washed, and eluted with elution buffer S. The complex was further purified using a Superdex 200 16/600 column (GE Healthcare) equilibrated with gel filtration buffer. The fractions containing the monomeric VelB and VosA1–190 proteins were pooled, concentrated to 11.6 mg/ml, and used for crystallization.
To express the GST tagged VosA proteins used for EMSA and the GST-pull-down experiments, cDNA of the full-length vosA, vosA-N (N-terminal region, 1–216 aa), or vosA-C (C-terminal region, 217–430 aa) was cloned between EcoRI and SalI sites (for full-length vosA) in pGEX-5X-1 (Amersham) or cloned between BamHI and SalI sites (for vosA-N and vosA-C) in pGEX-4T-3 (Amersham) to make pNI47, 49, and 50, respectively. These plasmids were introduced into E. coli BL21 (DE3). The GST fusion protein expression and purification was performed following the manufacturer's (GE Healthcare) instructions. To concentrate and buffer exchange, Amicon Ultra Centrifilter Units (Milllipore) were used. BCA Protein Assay Kit (Pierce) was used to estimate the protein concentration.
To express the GST tagged VeA protein used for EMSA experiments, cDNA of the full-length veA was amplified with primers OHS723/724 and cloned between SalI and NotI sites in pGEX-5X-1 (Amersham). This plasmid (pHS51) was introduced into E. coli BL21 (DE3). The GST fusion protein was expressed as described for VosA1–190, and purification was performed following the manufacturer's (GE Healthcare) instructions. VeA1–224 was cloned into the NcoI site of pETM13 (EMBL, Heidelberg), yielding the plasmid pETM13-VeA1–224 with a 3′ coding sequence for a Strep-tag. The protein was expressed and purified as described for VosA1–190.
VosA1–190 and VosA1–190-VelB were crystallized by the sitting-drop vapour diffusion method. X-shaped crystals of VosA1–190 grew after 1 d in a condition containing 100 mM MES pH 6.5, 30% PEG 4000 at 20°C. Further optimization led to crystals with the same morphology in a condition containing 100 mM MES pH 6.5, 32% PEG 2000 MME, 150 mM KI, which were used for structure determination. Prior to data collection, crystals were cryo-protected by soaking in reservoir solution supplemented with 12% (v/v) 1,4-butane-diol. X-ray diffraction data were collected at 110 K on a Rigaku MicroMax 007 rotating Cu-anode equipped with a MAR345dtb image-plate detector (Mar Research). The VosA crystals belong to the space group P4122 and have cell dimensions of a = b = 45.40 Å and c = 189.43 Å.
The VosA1–190-VelB complex was crystallized in 100 mM MES pH 5.5, 150 mM ammonium sulfate, and 25% (w/v) PEG 4000. The plate-like crystals were cryo-protected in crystallization solution +10% (v/v) 1,4-butanediol. Diffraction data were collected at 100 K at the ESRF microfocus beamline ID23-2. The crystals belong to the space group P212121 and have cell dimensions of a = 52.03, b = 56.75 Å, and c = 138.17 Å.
X-ray diffraction data from the VosA1–190 crystal were integrated and scaled with the XDS package [41]. Phases were obtained by SAD using SHELXC/D/E [42] navigated through HKL2MAP [43], which found 10 iodine ions. However, for further processing only iodine ions with occupancy >0.2 were used, which resulted in three sites with occupancies of 1.0, 0.36, and 0.24. The initial electron density map was readily interpretable and the majority of residues were built automatically using ARP/wARP [44]. Manual model building of missing residues was done with COOT [45]. Refinement was carried out in PHENIX [46] and Refmac5 [47]. The final model contains one monomer in the asymmetric unit. Diffraction data obtained from the VosA1–190-VelB crystals were integrated and scaled with MOSFLM [48] and SCALA from the CCP4-package [49], respectively.
The calculated Matthews coefficient of 1.59 Å3 Da−1 (corresponding to a solvent content of 22.9%) excluded the presence of both proteins, VosA and VelB, used for crystallization trials in the asymmetric unit. Assuming that the asymmetric unit comprises two protein molecules with the size of VosA1–190, the calculated Matthews coefficient value is 2.27 Å3 Da−1 (45.6% solvent content). At this step of structure solution, the assumption was made to look for two molecules using the available model of VosA1–190 comprising 143 amino acids (Ser 17 to Asp 79 and Ala 86 to Met 165). As the two proteins (VosA and VelB) share about 42% amino acid identity, the VosA1–190 model could also be used to search for the fragments of VelB in case a proteolytic digest had occurred prior to crystal formation. The molecular replacement search was carried out with PHASER [50] using data between 30 and 2.2 Å resolution. The search yielded two prominent solutions with an overall likelihood gain (LLG) score of 587 (the first molecule RFZ = 10.7, TFZ = 2 0.4, LLG = 236; the second molecule RFZ = 4.4, TFZ = 16.5, LLG = 501) and R factor of 53.4%. The quality of initial electron density maps (overall mean FOM of 0.41) was not good enough to distinguish whether two VosA1–190 or VosA1–190 and truncated VelB molecules were occupying the asymmetric unit. The structure was manually rebuilt in COOT and verified against simulated annealing (SA) omit maps calculated with CNS [51], which was also used during initial refinement steps. Refinement was based on slow-cooling SA (both torsion angle dynamics and Cartesian dynamics) combined with standard minimization and individually restrained B-factor refinement. Careful inspection of the electron density maps indicated differences between the two molecules in the asymmetric unit that were modelled and finally let us identify the second molecule as a proteolytically truncated VelB. The final model contains two molecules in the asymmetric unit.
Probes for EMSA were generated by annealing two single-stranded reverse-complementary oligonucleotides. Binding reactions were performed in a 10 µl reaction volume containing 10 mM HEPES/NaOH (pH 7.4), 150 mM NaCl, ∼53 pmol DNA probe, and appropriate amount of each purified VosA protein. The reactions were incubated at RT for 15 min. The complexes were resolved on a 6% polyacrylamide gel (37.5∶1 crosslinking) with 0.5% TBE running buffer at 200 V at RT for 20 min. The gel was stained with ethidium bromide.
For sample preparation, 2-d-old conidia (∼5×109 conidia for three ChIP experiments) from the strain TNI10.34.1 (ΔvosA; vosA::FLAG) were crosslinked with fresh 1% formaldehyde at RT for 30 min. Then, 1/20 volume of 2.5 M glycine solution was added to stop the cross-linking reaction. The conidia were collected by centrifugation and the conidia were washed with cold PBS for three times. About 1.5 ml FA lysis buffer (50 mM HEPES-KOH [pH 7.5], 150 mM NaCl, 1 mM EDTA, 1% Triton X-100, 0.1% Na deoxycholate, 0.1% SDS, 1 protease inhibitor cocktail tablet (Roche) per 50 ml) was added before use. Silica beads (∼300 µl) were added into cross-linked cell lysates and the lysates were broken by a mini-bead beater for three cycles (1.5 min homogenization with 1.5 min sitting on ice). Subsequently the samples were sonicated for seven cycles (30 s on, 60 s off) with a sonifier equipped with a microtip at 70% amplitude and level 5 of output control. All steps were carried out on ice. The sonicated cell lysates were cleared of cellular debris by a 2,000×g spin for 3 min five times. The supernatant was collected, and its DNA concentration was checked using a biophotometer (Eppendorf). About 25 µl supernatant was kept as input chromatin control (glycerol was added to 10% final concentration if samples had to be frozen). The rest of supernatant was adjusted to 10 ml with FA lysis buffer, and 50 µl of Anti-Flag M2-agarose from mouse (SigmaAldrich) was added for ChIP with constant mixing overnight at 4°C. The agarose beads were collected by centrifugation and washed with FA lysis buffer three times. About 50 µl elution buffer (10 mM Tris, pH 8.0, 1 mM EDTA, 1% SDS) was added to the beads, and the sample incubated at 65°C for 10 min to elute chromatin samples from the beads. Both the recovered supernatant from the elution and the input chromatin supernatant (saved before ChIP) were adjusted to 170 µl by elution buffer, and incubated at 65°C overnight to reverse the crosslinking reaction. Then, the samples were treated by protease K for 2 h at 37°C, and protein extracted twice with phenol (equilibrated with TE, pH 8.0) and once with chloroform∶isoamyl alcohol (24∶1). DNA was precipitated by ethanol and resuspended in 30 µl RNase TE (to digest RNA). Finally, the DNA was purified by QIAquick PCR purification kit (Qiagen) and eluted in 30 µl of the elution buffer provided with the kit. To obtain sufficient amounts of DNA for further labelling and hybridization, DNA was amplified using the WGA kit (Sigma, WGA2). DNA labelling and hybridization were performed by Roche Nimblegen. The DNA bound by the VosA-FLAG protein was labelled with Cy5, while the control DNA was labelled with Cy3. We used the A. nidulans whole-genome tiling-oligonucleotide array containing 65,536 oligonucleotides (50∼65 nucleotides at a 75-bp interval, Roche Nimblegen). The array data from hybridization of two independent immune-precipitation experiments for the VosA∶FLAG strain were used for analysis. The data were processed using NimbleScan (Roche Nimblegen). Briefly, NimbleScan detects peaks by searching four or more probes whose signals are above the specified cutoff values using a 500 bp sliding window. The ratio data were then randomized 20 times to calculate the probability for being “false positive.” Every peak is assigned with a false discovery rate (FDR) score based on the randomization. The lower the FDR score, the higher the possibility that the peak corresponds to a real binding site. When finalizing the candidates, we used the cutoff value “1” (log 2 ratio of experiment to control) as peak score and cutoff value “0.05” for FDR score. Then we found the overlapping candidates from two chips.
To define the DNA motif recognized by VosA, we employed two separate approaches. First, using the VosA-FLAG ChIP-on-chip results, sequences of 400 bp on the midpoint of about 1,500 enriched peaks from the two VosA-ChIP biological replicates were used as target sequences. As a background, 6 kbp sequences on about 6,000 promoter regions of genes without the VosA peaks were used (BIOINFORX Inc., Madison, WI, USA). The data were then analysed using HOMER (Hypergeometric Optimization of Motif EnRichment; http://biowhat.ucsd.edu/homer/). The predicted VosA binding motifs are presented in Table S2. Second, the ChIP-on-chip data were imported into RINGO [52], an R/Bioconductor package, for the analysis of ChIP-chip readouts, including the quality assessment, normalization, smoothing, and peak calling. We normalized the probe intensities by variance-stabilizing normalization (VSN) [53]. We only included the peaks that were consistent in two independent VosA-ChIP biological replicates. Then several sequences were selected according to maxLevel, the highest smoothed probe level in the enriched region (Table S3). We then examined mRNA levels of these genes and found that 10 genes' mRNA levels are affected by VosA and/or VelB (Figure 2B). Finally, VosA-ChIP enriched sequences from these 10 genes, four sequences from the known target genes wetA, tpsA, orlA, and treA, and the 35 bp brlAβ promoter fragment shown to be bound by VosA in EMSA were subject to MEME (Multiple Em for Motif Elecitation) analysis, which led to the predicted VosA binding motif CTGGCCaaGGC (Figure 1C).
ChIP-PCR analysis was performed according to the manufacturer's instructions with a minor modification using MAGnify Chromatin Immunoprecipitation System (Invitrogen). Two-day-old conidia (1×109) of WT (FGSC4), ΔvelB (THS16.1), and ΔvosA (THS15.1) strains [13] were cross-linked with fresh 1% formaldehyde at RT for 15 min. Then, 1/20 volume of 2.5 M glycine solution was added to stop the cross-linking reaction. The conidia were washed and broken by a mini-bead beater for two cycles (1 min homogenization with 1 min sitting on ice). The cell lysates were sonicated for four cycles (30 s on, 60 s off) with a sonifier. The sonicated cell lysates were cleared of cellular debris by centrifugation at 13,000×g for 10 min. The diluted chromatin extracts were incubated with 2 µg of anti-FLAG antibody-Dynabeads complex for 2 h at 4°C and then washed three times with the IP buffer. The input control and chromatin sample were eluted from the beads at 55°C for 15 min with reverse crosslinking buffer with proteinase K. DNA was purified by DNA purification Magnetic Beads (Invitrogen). For amplification of precipitated DNA by PCR, the GO Taq DNA polymerase (Promega) was used. The primer sets used for PCR are shown in Table S4. As negative controls, the chromatin extract being incubated with bead only (without anti-FLAG antibody) and the samples of FGSC 4 lacking FLAG-tagged VosA or VelB (Figure S3) were used. Individual input DNA samples before immune-precipitation (IP) were used as positive controls. Two biological replicates have provided essentially identical ChIP-PCR results. Signal intensities of PCR results obtained from ChIP assays were analyzed by the ImageJ software available online (National Institutes of Health; http://rsbweb.nih.gov/ij/).
Total RNA isolation and Northern blot analyses were carried out as previously described [54],[55]. The DNA probes were prepared by PCR-amplification of the coding regions of individual genes with appropriate oligonucleotide pairs using FGSC4 genomic DNA as a template (Table S4).
Coordinates and structure factors have been deposited in the PDB, namely VosA as PDB ID code 4N6Q and VosA-VelB as PDB ID code 4N6R.
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10.1371/journal.pntd.0000922 | The Extinction of Dengue through Natural Vulnerability of Its Vectors | Dengue is the world's most important mosquito-borne viral illness. Successful future management of this disease requires an understanding of the population dynamics of the vector, especially in the context of changing climates. Our capacity to predict future dynamics is reflected in our ability to explain the significant historical changes in the distribution and abundance of the disease and its vector.
Here we combine daily weather records with simulation modelling techniques to explain vector (Aedes aegypti (L.)) persistence within its current and historic ranges in Australia. We show that, in regions where dengue presently occurs in Australia (the Wet Tropics region of Far North Queensland), conditions are persistently suitable for year-round adult Ae. aegypti activity and oviposition. In the historic range, however, the vector is vulnerable to periodic extinction due to the combined influence of adult activity constraints and stochastic loss of suitable oviposition sites.
These results, together with changes in water-storage behaviour by humans, can explain the observed historical range contraction of the disease vector. For these reasons, future eradication of dengue in wet tropical regions will be extremely difficult through classical mosquito control methods alone. However, control of Ae. aegypti in sub-tropical and temperate regions will be greatly facilitated by government policy regulating domestic water-storage. Exploitation of the natural vulnerabilities of dengue vectors (e.g., habitat specificity, climatic limitations) should be integrated with the emerging novel transgenic and symbiotic bacterial control techniques to develop future control and elimination strategies.
| Dengue transmission has not always been confined to tropical areas. In some cases, this has been due to a reduced geographic range of the mosquitoes that are able to carry dengue viruses. In Australia, Aedes aegypti mosquitoes once occurred throughout temperate, drier parts of the country but are now restricted to the wet tropics. We used a computer modelling approach to determine whether these mosquitoes could inhabit their former range. This was done by simulating dengue mosquito populations in virtual environments that experienced 10 years of actual daily weather conditions (1998–2007) obtained for 13 locations inside and outside the current tropical range. We discovered that in areas outside the Australian wet tropics, Ae. aegypti often becomes extinct, particularly when conditions are too cool for year-round egg-laying activity, and/or too dry for eggs to hatch. Thus, despite being a global pest and disease vector, Ae. aegypti mosquitoes are naturally vulnerable to extinction in certain conditions. Such vulnerability should be exploited in vector control programs.
| Dengue fever is a public health problem of global importance, producing a spectrum of disease spanning febrile arthralgia to hemorrhagic death. Dengue viruses are transmitted between human hosts almost exclusively by Aedes (Stegomyia) aegypti and Aedes (Stg.) albopictus mosquitoes, both of which are well adapted to using artificial containers for larval habitat. Many urban areas in the tropical world are subject to dengue transmission [1], the geographic range of which is limited by the distribution of the vectors. However, these ranges are not static, with numerous expansions and retractions recorded through time. Despite great progress in the development of novel control techniques for Ae. aegypti [2], [3], our understanding of how dengue and its vectors become extinct is poor.
The principal vector, Ae. aegypti, is thought to have originated in Africa and extended its range globally with the expansion of commercial shipping in the 17th and 18th centuries [4], [5]. While this range was significantly reduced by numerous eradication programs in the Americas from the 1930s to the 1970s [6], [7], Ae. aegypti soon regained much of its former range after these programs ceased [7].
An ultimate cause of such range plasticity is human activity. The production of suitable larval habitats (i.e. artificial containers) and human-facilitated transport has encouraged the dispersal and establishment of these mosquitoes. Increased urbanisation without properly planned waste management and water handling systems has also created ideal conditions for mosquito breeding [7]. Human activity is thus a key determinant of dengue vector populations.
In Australia, dengue transmission is currently restricted to tropical north Queensland (Qld) (Fig. 1). The vector there, Ae. aegypti, is most abundant and active year-round in the tropics, yet its distribution extends into sub-tropical coastal central Qld, and some arid inland areas [8]. Dengue has been recorded in Australia from as early as 1873 [9], and although outbreaks have been most common in the tropics, sporadic activity has also occurred in the subtropics and temperate regions [10]. This was due in part to the distribution of Ae. aegypti formerly extending well into temperate regions (up to 33°S in Western Australia (WA)). However, a range retraction occurred in the last half of the 20th century, with the last collections from New South Wales (NSW) in 1948, and WA in 1970. The last records from the Northern Territory (NT) were from 1956 [11], with established (incursant) Ae. aegypti populations not discovered again until 2004 and 2006 [12]. It also disappeared from southern Qld in the 1950s. The current range has been relatively stable for the last three decades at least.
The cause of Ae. aegypti range retraction in Australia has not been resolved, yet is probably related to a number of social improvement factors. In particular, a reduced prevalence of larval habitats (i.e. water-filled containers) due to improved reticulated water supplies and a concomitant decrease in domestic rainwater tanks, the decline of steam rail with its attendant water storage infrastructure and potential for dispersal, and, in rural areas in particular, the gradual replacement of domestic food storage cabinets (e.g. Coolgardie safes with their associated water containers) by kerosene and then electric refrigerators. Additionally, adult mosquito productivity and survival may have been reduced by greater yard sanitation with the advent of motor mowers limiting trash containers and adult resting sites and the development of residual insecticides (such as DDT, BHC and dieldrin) for domestic use. Furthermore, there was enhanced organization of vector control operations by local governments with the return in the late-1940s of well trained public and environmental health officers from military service who were rigorous in their destruction of breeding sites [10], and the relatively small human population sizes of Ae. aegypti infested areas in many parts of Australia may have facilitated extinction in some places. Finally, there may have been various biological factors that contributed, in some regions at least, to displacement or extinction, such as larval habitat competition from the indigenous ‘container mosquito’ Aedes (Finlaya) notoscriptus that was becoming domesticated and gradually spreading westwards in NSW from its native coastal habitats [10], [13].
However, many of these possible causes of Ae. aegypti range retraction remain speculative and not readily testable. We used computer-based simulation modelling to investigate why Ae. aegypti may have disappeared from much of its former range in Australia that appears still to be climatically favourable [14], [15] and to determine how well it may persist if reintroduced in the future.
Extinction processes have been previously studied through the use of mathematical models [16], [17]. In a recent application of computer-based modelling [18], a mechanistic approach was adopted to explain Ae. aegypti distribution in Australia, and described the historic range of the species in terms of its ability to survive in large breeding sites (rainwater tanks). This work demonstrated that large parts of coastal Australia could support survival of the species if such tanks were present, consistent with the historic range, but did not go as far as explaining range retraction. Furthermore, the model used by those authors made use of historic mean climate data, an approach that does not incorporate the stochasticity of daily weather variation that may contribute to extinction processes.
Here we describe the use of the Container Inhabiting Mosquito Simulation (CIMSiM) to determine the persistence of Ae. aegypti throughout Australia in its current and historic ranges. CIMSiM is a weather-driven depiction of the Ae. aegypti larval and adult habitat that describes the interaction between the mosquitoes and their environment [19], and has been validated for use in Australia [20]. In addition, we compared the performance of Ae. aegypti in terms of productivity throughout its range, examined the relative prevalence of life stages (i.e. eggs, larvae, adults) over time, and examined the relative prevalence of eggs in different habitats for selected localities. In doing this we hoped to explain its current range compared with its more extensive historic one in terms of climate suitability, and to comment on future risk of establishment in areas of Australia that are currently dengue free.
CIMSiM [19], which accurately models Ae. aegypti population dynamics in Qld [20], generates daily estimates of egg, larval, pupal and adult numbers per hectare by integrating daily meteorological observations with information about available breeding habitats. Thirteen study locations were selected from both the current and historic Ae. aegypti range [10], [21]. Simulations were performed for 10 years (1998–2007). Model parameters for larval habitats [20] are provided (Table S1). All other model settings for CIMSiM were default values [19] with the exception of egg survivorship parameters which were modified (Table S1). The following daily weather observations were used: maximum, minimum and average daily temperature, relative humidity, saturation deficit, and rainfall. These were obtained for each study location from the Australian Bureau of Meteorology (www.bom.gov.au).
Ten simulations of 10 years length were performed for each location (with the exception of Harvey (WA), for which only six years of meteorological data were available), realising a total of 1170 simulated years. For each study location, we aimed to characterise the following performance measures for Ae. aegypti:
The simulations described here have allowed a quantitative assessment of Ae. aegypti performance and persistence at localities inside and outside the current range in Australia. Coupled with information about the ecology of larval habitats derived here, explanations of the current and historic range of this disease vector in Australia are possible.
The continued presence of Ae. aegypti in its current range in Qld can be explained by its continuing year round adult and larval activity. This is facilitated by the continuous presence of suitable larval habitats, which remain wet enough (often due to constantly wet containers such as pot plant saucers in these simulations) and warm enough for year-round activity of all life stages (mean daily temperatures exceed 15°C year-round in the current north Qld range, www.bom.gov.au). An analysis of egg densities at Cairns throughout the dry season (May – Nov) (Fig. 2) revealed continued oviposition activity during this period.
Conversely, there are a number of factors which make the species vulnerable at localities in the historic range. In tropical Darwin where Ae. aegypti is now extinct, our modelling showed the species to be heavily reliant on manually-filled (i.e. continuously wet) containers for activity through the dry season. In our simulations, pot plant saucers (manually filled) were the major container type for eggs during the dry season. This represents vulnerability for Ae. aegypti in such locations, in that source reduction activities incorporating vector control and public education programs selectively targeting artificially flooded breeding sites could have a large negative impact on population growth. Kay and others [25] demonstrated that selective control of Ae. aegypti in continuously flooded subterranean wells in Qld reduced recolonization of surface containers during the wet season, reducing overall populations.
Such source reduction was evident in Darwin after World War II, when health officers returning from military service set about removal of rainwater tanks concomitant with the establishment of reticulated water supplies (Peter Whelan, NT Health Department, pers. comm. 22 Apr 2009). Rainwater tanks, while not manually filled, are preferentially filled from rainfall run-off and retain water for extended periods when naturally filled containers may have dried out. We were not able to simulate rainwater tanks here, which limits our ability to interpret their contribution to persistence. However, our inclusion of pot plant saucers (albeit much smaller but very productive sites) allow us to assess the importance of continuously wet larval habitats. Field productivity values for Ae. aegypti in rainwater tanks would be useful for future simulation modelling.
The importance of continuously wet containers for the persistence of Ae. aegypti in Darwin described here illustrates how vulnerable the species may have been when rainwater tanks were removed en masse post-war. Coupled with the small human population size in Darwin at the time (approx. 8016 people in the NT in 1948) [26], extinction by a combined action of habitat specificity and a lack of that habitat during the dry season due to source reduction is plausible. Insecticide application does not appear to have played a significant role in this process [11].
Thus, the local extinction of Ae. aegypti at Darwin was likely due to a synergistic combination of processes. This would have included a primary extinction driver (loss of habitat), combined with secondary drivers such as the specialisation of the species for a narrow range of habitat types and physiological vulnerability to dry conditions. Synergistic effects of extinction processes have been well studied for extinction dynamics in other species [27], [28]. Habitat and host specificity were both factors identified as being significant in the extinction of butterflies [16]. Such specificity is evident in Ae. aegypti, in its strong preference for artificial containers and blood-feeding on humans. While such associations promote the proliferation of the species in human habitats, they also render it vulnerable to changes in such habitat.
In modelling for Brisbane, the cool conditions through the austral winter were shown to preclude adult activity, making the species vulnerable in all habitats. Mean daily temperatures at this location are below the threshold for adult activity (15°C) [24] for June – Aug (www.bom.gov.au). Persistence is greatest in continuously wet containers. Considering the timing of its apparent extinction in Brisbane, during the 1950s, the decreasing prevalence of rainwater tanks concomitant with increased reticulated water supplies might explain the disappearance of Ae. aegypti from an area in which it was vulnerable to extinction.
Clearly, more than just strong seasonal productivity of Ae. aegypti is required for persistence at a location. This is evident in the very similar productivity values for the species in Brisbane (where extinction occurs in the model) and at Charters Towers (where it does not) (Table S2). In our modelling, the main difference between the locations is temperature, which is slightly higher at Charters Towers, permitting longer periods of adult activity and oviposition. This reduces extinction risk due to egg die-off as the egg-only periods are shorter (Fig. S1).
Here we confirm the ability of this species to survive in areas where it no longer exists (Fig. 1); a finding consistent with previous distribution reports [10]. However, by demonstrating extinction at some locations, our work challenges the idea that the historic range is climatically suitable for long-term Ae. aegypti survival as has been indicated [14], [15]. The former presence of Ae. aegypti in many parts of Australia is not questioned here; rather, the ability of CIMSiM to simulate strong periodic productivity in areas where the species was once considered seasonally common but is now extinct provides validation for our approach. The finding that Ae. aegypti passes several months of the year only as eggs (Fig. 1) is consistent with early field reports from southern temperate regions of NSW [29].
A number of possible factors for the retraction of the Ae. aegypti range in Australia have been suggested [10]. Each of these factors could have separately or in common with others plausibly reduced the size of local Ae. aegypti populations and the daily survivorship probability of adult mosquitoes, thereby contributing to local extinction. However, while the widespread introduction of town water reticulation in rural and regional areas has often been proposed as a crucial factor in the disappearance of Ae. aegypti from many southern localities/regions, many houses in many towns in these southern rural areas retained their water tanks throughout and following the period during which the mosquito disappeared, indicating there is no simple explanation that covers all situations.
The simulations performed here reveal that when adult daily survivorship probabilities are held high (0.91 in these simulations) and suitable breeding containers are available, Ae. aegypti is still vulnerable to extinction, particularly in southern Australia. In the first half of the 20th Century, Ae. aegypti populations were widely sustained in southern Australia, no doubt with the aid of increased numbers of larval habitats and high adult survivorship probability. When these two factors became less prominent, the natural vulnerability of the species (as demonstrated by simulation here) could plausibly have led to extinction. Our sensitivity analysis revealed the importance of egg survivorship. Changes in the construction/materials of breeding containers over time (e.g. a greater proportion of plastic containers with time) could also have reduced egg survivorship rates.
We acknowledge that in applying a CIMSiM model field validated for north Qld across an entire continent we assume that Ae. aegypti performance in relation to temperature, humidity and rainfall remains constant. Furthermore, we also assume a constant breeding site diversity and density throughout Australia, and identical amounts of organic material (i.e. larval food) falling into containers. Naturally, we do not anticipate that in the field such generalities will hold true; there will almost certainly be some site-specific variation in local container-breeding mosquito ecology. However, such local scale differences would be very difficult to define accurately, and our approach in applying a constant set of model parameters at different locations (only differing with local meteorological data) was the only plausible way for us to model such a range of localities.
From our sensitivity analyses, we now understand that changing container densities by up to 20% is unlikely to influence persistence (at least using the containers simulated here). However, persistence is sensitive to changes in egg survivorship rates. Therefore, understanding how such rates are influenced in the field is critical for determining how persistence at a location may vary.
Furthermore, the relationship between ambient weather conditions and water temperature in the various container situations that form Ae. aegypti larval habitat has only recently become the focus of study in Australia [18]. The conversion factors for ambient to water temperature built into the CIMSiM program [19] accord well with actual observed temperatures in field-deployed tyres and buckets, albeit with some overestimation of maximum temperature on some days (MRK unpubl. data). Thus, when the water temperature is less than the thermal optimum for Ae. aegypti development, Ae. aegypti productivity in CIMSiM will be overestimated, and when above this threshold, productivity could be underestimated.
In addition, our choice of criterion for determining extinction at a location; densities of eggs, larvae and adults <0.5 per ha., could be scrutinized for the absence of pupae. Pupal densities were not included in the criterion, given the relatively short duration of this life stage (typically 1–3 d). However, it is possible (albeit improbable) that the pupal stage alone could facilitate persistence at a location when other life stages are at their nadir.
In applying the CIMSiM model so widely, we have assumed that on balance, our predictions of Ae. aegypti performance and persistence are satisfactory mid-range estimates that are useful for the kind of population-level analysis presented here.
Previous examination of Ae. aegypti range by climate-driven modelling indicated that this species could persist at locations in the historic range (such as Brisbane Qld and Darwin NT) in rainwater tanks (which always retained at least 1 cm of water depth), but not in small buckets, which frequently became dry [18]. Modelling of Ae. aegypti distribution using a genetic algorithm [21] also showed suitability of the historic range in the current climate. Our findings, in which Ae. aegypti eggs were most common in Darwin in manually-filled pot plant saucers during the dry season, were consistent with those of previous studies [18] which found that continuously wet habitats were required for persistence at this location.
Domestic water storage in tanks is increasing in southern Australia [30], and in the simulations presented here for southern locations (Harvey WA, Horsham Vic, Gosford and Wagga Wagga NSW, and Brisbane Qld), Ae. aegypti was reduced to existing as eggs only in continuously wet containers. Thus, any increase in water storage behaviour could improve the probabilities of survival of dengue vectors outside of its current range [21]. For this reason, the regulation of water storage behaviour to minimise mosquito breeding is crucial.
Areas of northern Australia where Ae. aegypti has become extinct (e.g. Darwin NT) remain vulnerable to re-establishment of the species, as evidenced by recent infestations at Tennant Creek and Groote Eylandt (NT). The absence of Ae. aegypti from these areas can only be maintained by adequate surveillance and source reduction activities targeted at manually filled containers (such as pot plant saucers) and domestic water storage.
According to our modelling, the introduction of a single cohort of Ae. aegypti into southern parts of the historic range in Australia is unlikely to result in a persistent population based on current climate, with container densities similar to that in the current range. Conversely, introductions into northern regions of the historic range (e.g. Darwin) may readily lead to persistence of the species.
The failure of classical mosquito control methodologies (e.g. source reduction, insecticide application) for restricting dengue has stimulated the development of novel molecular strategies [2], [3]. While there is no doubt such strategies will be integral to the future of dengue control, the natural vulnerability of dengue vectors to extinction should not be forsaken. Incorporating extinction processes into integrated dengue control strategies in the future will ensure a greater probability of success. Furthermore, in subtropical and temperate regions where dengue is a problem, there may be no need for novel, biologically-engineered solutions.
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10.1371/journal.pgen.1006737 | A fungal transcription factor essential for starch degradation affects integration of carbon and nitrogen metabolism | In Neurospora crassa, the transcription factor COL-26 functions as a regulator of glucose signaling and metabolism. Its loss leads to resistance to carbon catabolite repression. Here, we report that COL-26 is necessary for the expression of amylolytic genes in N. crassa and is required for the utilization of maltose and starch. Additionally, the Δcol-26 mutant shows growth defects on preferred carbon sources, such as glucose, an effect that was alleviated if glutamine replaced ammonium as the primary nitrogen source. This rescue did not occur when maltose was used as a sole carbon source. Transcriptome and metabolic analyses of the Δcol-26 mutant relative to its wild type parental strain revealed that amino acid and nitrogen metabolism, the TCA cycle and GABA shunt were adversely affected. Phylogenetic analysis showed a single col-26 homolog in Sordariales, Ophilostomatales, and the Magnaporthales, but an expanded number of col-26 homologs in other filamentous fungal species. Deletion of the closest homolog of col-26 in Trichoderma reesei, bglR, resulted in a mutant with similar preferred carbon source growth deficiency, and which was alleviated if glutamine was the sole nitrogen source, suggesting conservation of COL-26 and BglR function. Our finding provides novel insight into the role of COL-26 for utilization of starch and in integrating carbon and nitrogen metabolism for balanced metabolic activities for optimal carbon and nitrogen distribution.
| In nature, filamentous fungi sense nutrient availability in the surrounding environment and adjust their metabolism for optimal utilization, growth and reproduction. Carbon and nitrogen are two of major elements required for life. Within cells, signals from carbon and nitrogen catabolism are integrated, resulting in balanced metabolic activities for optimal carbon and nitrogen distribution. However, coordination of carbon and nitrogen metabolism is often missed in studies that are based on comparisons between single carbon or nitrogen sources. In this study, we performed systematic transcriptional profiling of Neurospora crassa on different components of starch and identified the transcription factor COL-26 to be an essential regulator for starch utilization and needed for coordinating carbon and nitrogen regulation and metabolism. Proteins with sequence similar to COL-26 widely exist among ascomycete fungi. Here we provide experimental evidence for shared function of a col-26 ortholog in Trichoderma reesei. Our finding provides novel insight into how the regulation of carbon and nitrogen metabolism can be integrated in filamentous fungi by the function of COL-26 and which may aid in the rational design of fungal strains for industrial purposes.
| Filamentous fungi are one of the primary degraders of plant biomass because of their ability to produce enzymes that break down complex polysaccharides, including cellulose, hemicellulose, and pectin in the plant cell wall and starch, which is the major storage component in plants. Starch consists of two types of polysaccharides, amylose and amylopectin. Amylose is composed of linear chains of α-1,4-linked glucose units, while amylopectin is composed of α-1,4-linked glucose polymers, with branched α-1,4-glucan connected through α-1,6 glucosidic bonds at branch points. Our understanding of starch degradation by filamentous fungi mainly comes from work in Aspergillus spp. (reviewed in [1]), which are industrially important producers of starch-degrading enzymes. Three types of enzymes, α-amylases, glucoamylases, and α-glucosidases, hydrolyze starch synergistically to produce glucose. α-Amylases hydrolyze α-1,4-glucan chains endolytically to produce maltose, while α -glucosidases and glucoamylases hydrolyze maltose and α -1,4-linkage exolytically from non-reducing ends to form glucose. Glucoamylases also hydrolyze α -1,6 linkages at branch connections. Recently, a new family of lytic polysaccharide monooxygenases (LPMO) active on starch was identified in Neurospora crassa [2]. The starch-active LPMOs together with a biological redox partner oxidatively cleave amylose, amylopectin, and starch. The expression of genes encoding amylolytic enzymes can be induced by starch and its degradation products, maltose and glucose [3–5].
In Aspergillus spp., expression of genes encoding amylolytic enzymes requires the transcriptional activator AmyR, a zinc binuclear cluster (Zn(II)2Cys6) DNA-binding protein belonging to the Gal4p family of transcription factors [6]. Disruption of amyR in A. oryzae and A. nidulans leads to significantly decreased amylolytic enzyme activities and restricted growth on starch medium [7, 8]. A similar role in starch hydrolysis was demonstrated for amyR homologs in Penicillium decumbens [9], Fusarium verticillioides and F. graminearum [10]. Genome sequencing of two Trichoderma reesei mutant strains, RUT C30 and PC-3-7, with enhanced cellulase production and resistance to carbon catabolite repression (CCR) identified SNPs in the bglR gene, a homolog of amyR [11, 12]. Although a T. reesei strain bearing a deletion of bglR was reported having reduced growth on maltose and glucose, further investigation on the phenotype of the ΔbglR mutant was not reported. Instead, Nitta et al. (2012) suggested that BglR regulates genes encoding β-glucosidases and belongs to a new functional transcription factor group distinguishable from AmyR based on two observations [11]. First, when induced by cellobiose, expression of some β-glucosidase genes was lower in the ΔbglR mutant as compared to the parental PC-3-7 strain. Second, AmyR and BglR form two separate clusters in phylogenetic analyses. However, the AmyR homologs in F. graminearum and F. verticillioides (FgART and FvART, respectively) are in the same cluster as BglR and are essential for starch utilization [10].
COL-26 is the N. crassa ortholog of BglR and was named colonial-26 (col-26) for its colonial phenotype on medium containing sucrose, glucose or fructose as a sole carbon source [13, 14], suggesting COL-26 plays a role in regulating glucose metabolism. In N. crassa, COL-26 was shown to function synergistically with CRE-1, a transcription factor important for CCR and in regulating cellulase gene expression and enzyme production [14]. The Δcol-26 mutant is also resistant to 2-deoxyglucose, suggesting it has impaired CCR.
In this study, we tested growth phenotypes of the Δcol-26 mutant on a variety of carbon sources and determined that COL-26 is essential for maltose and starch utilization. We determined that the absence of col-26 led to a decrease in expression of amylolytic genes. Metabolic analyses of the Δcol-26 mutant in comparison to WT cells indicated that mis-regulation of the TCA cycle, GABA shunt, and amino acid biosynthesis occurs in the Δcol-26 mutant. Replacing ammonium as a nitrogen source on preferred carbon sources with glutamine alleviated the growth defects of Δcol-26 on glucose, but not on maltose medium. Our study indicates that COL-26 has an important and conserved role in the regulation of starch degradation as coordinating primary carbon and nitrogen metabolism in filamentous fungi, and provides insight for the rational design of strains for the food and biofuel industries.
The Δcol-26 mutant poorly utilizes simple sugars, including glucose, fructose, and sucrose, but grows well on complex polysaccharides such as cellulose [14]. To test whether COL-26 is important for the utilization of other carbon sources, we tested the growth of the Δcol-26 mutant on different mono-, di- or polysaccharides as a sole carbon source. As observed previously, the Δcol-26 mutant showed reduced growth in glucose, fructose and sucrose [14], but also showed reduced growth on xylose and cellobiose and essentially no growth on maltose or trehalose (Fig 1A). On complex polysaccharides, such as xylodextrins and albumin, the Δcol-26 mutant grew similarly to the WT parental strain. However, the Δcol-26 mutant showed a severe growth defect on amylopectin (Fig 1A). To verify that col-26 is causative for these growth phenotypes, we introduced a copy of the col-26 gene under regulation of the A. nidulans gpd promoter at the csr-1 locus in the Δcol-26 mutant (see Materials and methods). This Pgpd-col-26; Δcol-26 strain showed a similar growth phenotype as the WT strain on these different carbon sources (Fig 1A). Consistent with the hypothesis that COL-26 plays a role in regulating genes encoding enzymes required for utilization of starch, trehalose and maltose, the expression level of col-26 was induced 4 to 8 -fold by a 4-hr exposure to trehalose, maltose, amylopectin and amylose (Fig 1C).
A genetic interaction between cre-1 and col-26 was revealed in the regulation of cellulase production; increased expression levels of cre-1 was observed in the Δcol-26 mutant [14]. This observation suggested that mis-regulation of cre-1 (and thus inappropriate triggering of CCR) may play a role in the growth phenotype of the Δcol-26 mutant. To test this hypothesis, we examined the growth phenotype of the Δcre-1; Δcol-26 double mutant as compared to the WT strain and the Δcol-26 and Δcre-1 single mutants on a variety of carbon sources. The Δcre-1; Δcol-26 mutant grew similarly to the Δcol-26 mutant when glucose, xylose, sucrose, cellobiose, maltose, trehalose, or amylopectin was used as the sole carbon source (Fig 1B), indicating that the mis-regulation of cre-1 expression was not causative for the poor growth phenotype observed in the Δcol-26 mutant.
Neurospora has long been known to be a starch utilizer ever since its discovery over 170 years ago on contaminated bread in a French Bakery [15]. Although mutants deficient for the utilization of starch (sor-4, gla-1 and gla-2) have been identified [16], how N. crassa transcriptionally responds to starch in its environment has not been previously investigated. To provide systematic data on expression changes in response to defined polysaccharide constituents of starch, we performed transcriptional profiling of WT cells exposed to Vogel’s minimal medium (VMM) [17] containing amylose or amylopectin as the sole carbon source (1% w/v) and WT cells exposed to VMM containing maltose as the sole carbon source (2% w/v). RNA-seq data from N. crassa cultures exposed to VMM with no carbon (NC) or VMM with 2% (w/v) sucrose were included as controls.
The fifteen sets of RNA-seq data were first evaluated using principle component analysis (PCA). Biological replicate samples from the same carbon condition clustered tightly (Fig 2A). Expression patterns from cultures exposed to amylose and amylopectin also clustered closer to each other than to the NC, maltose and sucrose samples, suggesting a common transcriptional response in N. crassa upon exposure to polysaccharides of starch. Additionally, expression patterns from cultures exposed to maltose were distant from those exposed to sucrose in the PCA plot, suggesting substantial transcriptional changes specifically induced by maltose.
Pairwise comparison between the transcriptome of WT cells exposed to amylose or to amylopectin compared to that of VMM-NC revealed genes with differential expression levels (fold change greater than 2, and false discovery rate (FDR)-corrected p value below 0.01). After subtracting genes that were also differentially induced or repressed in sucrose as compared to VMM-NC, we identified 322 genes that increased in expression level in WT cells upon exposure to amylose/amylopectin and 108 genes that showed reduced expression levels in amylose/amylopectin (Fig 2B; S1 Table, Sheet 1). We name this 322-gene set the “starch regulon”. Indeed, the only overrepresented KEGG pathway in this set of 322 genes was starch and sucrose metabolism (adjusted p-value: 4.8e-3). No KEGG pathway was overrepresented in the 108 reduced expression gene set. Analyses of RNA-seq data from WT on maltose revealed a set of 1871 genes that increased in expression level and 1881 genes that decreased in expression level compared to data from WT on NC. After subtracting genes that were similarly regulated by sucrose, we identified 736 genes with increased expression level and 696 genes with decreased expression level in WT cells on maltose medium (Fig 2B; S1 Table, Sheet 2). The maltose-inducible gene set was enriched in genes from functional categories of biogenesis of cell wall, perception of nutrient and nutritional adaptation, and electron transport and membrane-associated energy conservation. Additionally, the maltose-inducible gene set overlapped the starch regulon by 111 genes (S1 Table, Sheet 3). A search in the Carbohydrate Active Enzymes (CAZyme) database (http://www.cazy.org/) [18] revealed that 7 of the 111 genes were predicted to act on carbohydrates. Three of them, NCU04674 (gh31-3), NCU01517 (gla-1), and NCU08746 have annotated functions in degrading starch. gh31-3 encodes a α-glucosidase, gla-1 encodes a glucoamylase and NCU08746 encodes a lytic polysaccharide monoxygenase that acts on starch [2] (Fig 2C). A BLASTP search of the transporter classification databases (TCDB) (http://www.tcdb.org/) with cut-off value less than 1e-20 identified 14 genes likely encoding transporters. Four of them, hgt-1 (NCU10021), NCU05627, NCU04963, and NCU04537 are annotated as sugar transporter genes (Fig 2C). hgt-1 shows high affinity glucose transport activity [19], while NCU05627 (xyt-1) has xylose transporting activity [20]. The transport substrates of NCU04963 and NCU04537 remain to be determined. There are also 5 TF genes induced by all three starch components, tah-3, vad-2, kal-1, hac-1, and NCU03975 (Fig 2C). tah-3 was found to be required for tolerance to a harsh plasma environment [21]. For VAD-2 and KAL-1, a role in nutrient metabolism or sensing has been proposed [13]. HAC-1 is involved in the unfolded protein response and is necessary for growth on cellulose, but not hemicellulose in N. crassa [22].
Genes in the starch-regulon, but that were not in the maltose-inducible gene set, included gh13-6, tre-1, and clr-2 (Fig 2D). gh13-6 encodes an α-amylase, tre-1 encodes a trehalase, and CLR-2 is the major transcriptional regulator of cellulase genes in N. crassa and is essential for the utilization of cellulose [23–25]. Among genes significantly induced by maltose, but not by starch polysaccharides were NCU00801 (cdt-1) and NCU12154 (Fig 2D). CDT-1 is a cellodextrin transporter and NCU12154 was annotated as maltose permease. The latter shows low homology to the yeast maltose permease (P53048; TCDB database). Interestingly, the α-amylase gene (gh13-6) in the starch regulon was not induced by maltose (Fig 2D). Instead, two other α-amylase genes (NCU09805 gh13-1 and NCU08131 gh13-2) were significantly induced (Fig 2D).
The Δcol-26 mutant failed to grow on maltose and amylopectin (Fig 1A). To investigate the functions of COL-26 required for utilization of these substrates, we evaluated transcriptional changes in the Δcol-26 mutant when switched to medium containing amylose or maltose under identical conditions as with the WT parental strain (see above). RNA-seq data from the WT and Δcol-26 biological replicates were subjected to PCA analysis and data from the same strain grown under the same growth conditions clustered together (Fig 3A). On the PCA plot, data from the Δcol-26 mutant exposed to amylose and data from the Δcol-26 mutant exposed to maltose did not cluster.
Under amylose conditions, the expression level of 1242 genes was significantly lower in the Δcol-26 mutant, while the expression level of 1124 genes increased (Fig 3B, S2 Table). Strikingly, 252 genes out of the 322-gene starch regulon gene set (78%) were down regulated in the Δcol-26 mutant (Fig 3B), including three of the four amylolytic genes, gla-1, the starch-active LPMO (NCU08746), gh13-6 and 19 transporter genes including hgt-1, xyt-1, NCU04963, and NCU04537 (Fig 3C). Seventeen TF genes in the starch regulon were also down regulated in the Δcol-26 mutant, including tah-1, tah-3, vad-2, ada-5, and kal-1 (S2 Table, sheet 1). The majority of the remaining 70 starch-regulon genes (41 genes) whose expression levels were not affected by the col-26 deletion were annotated as hypothetical. These data indicate that COL-26 is a major regulator of the starch regulon of N. crassa. The down regulation of expression of the starch regulon genes by deletion of col-26 is consistent with the growth defect observed in the Δcol-26 mutant on starch polysaccharides (Fig 1A).
Under maltose conditions, the expression levels of 1110 genes were significantly increased in Δcol-26 mutant, while the expression levels of 988 genes decreased (Fig 3B and S2 Table, sheet 2). The three amylolytic genes, i.e., gla-1, gh13-2, and the starch LPMO (NCU08746) and the four sugar transporter genes, hgt-1, xyt-1, NCU04963, and NCU04537 were members of the down-regulated gene set (Fig 3C). This down-regulated gene set also included additional 100 transporter genes, many from the Mitochondrial Protein Translocase (MPT) family, the Nuclear mRNA Exporter (mRNA-E) family, the Mitochondrial Carrier (MC) family, and the Major Facilitator Superfamily (MFS) (S2 Table). The down-regulated 988-gene set in the Δcol-26 mutant only overlapped the 736 maltose-inducible set in WT cells by 63 genes (Fig 3B). Directly comparing the amylose and maltose RNA-seq data between the WT and the Δcol-26 mutant showed that 363 genes were down regulated and 421 genes were up regulated in absence of col-26. We named the 363 genes as the “COL-26-dependent gene set” and the 421 genes as the “COL-26-reduced expression gene set” (S3 Table, Fig 3D). Only 33 genes in the COL-26-dependent gene set (less than 10%) were induced in WT cells by exposure to maltose, amylose, or amylopectin.
For the COL-26-dependent gene set, a functional enrichment analysis using FunCat [26] showed that transcription and protein synthesis were overrepresented, including rRNA processing, where 79 of 198 genes in this category were identified as being COL-26 dependent (p = 9e-52). Genes from categories such as RNA binding functions, ribosome biogenesis, rRNA modification, mRNA synthesis and mitochondrial transport were also enriched (p = 3e-17, 2e-13, 3e-9, 2e-7, and 1e-2 respectively). The COL-26-dependent gene set contained 6 TF genes besides col-26. Three were annotated to be hypothetical, and the other three were vib-1 (NCU03725), nit-2 (NCU09068), and cpc-1 (NCU04050) (Fig 3E). VIB-1 (vegetative incompatibility block-1) is required for extracellular protease secretion in response to both carbon and nitrogen starvation [27] and for the utilization of cellulose [14]. The cpc-1 gene (cross-pathway control-1) is the ortholog of S. cerevisiae GCN4, and is required in N. crassa for the expression of many amino acid biosynthetic genes in response to amino acid starvation [28–30]. Ten genes in CPC-1 regulon [30] were also found in the COL-26-dependent gene set (S3 Table). The nit-2 gene (nitrate nonutilizer-2) is the major regulatory transcription factor in N. crassa regulating nitrogen catabolism and is critical for utilization of nitrate, nitrite, purines, and most amino acids as a nitrogen source (reviewed in [31]). Also in this set were genes encoding catabolic enzymes in nitrogen metabolism and amino acid synthesis such as am, which encodes the NADP-glutamate dehydrogenase (NADP-GDH), gln-1 (NCU06724) and gln-2 (NCU04856), both of which encode glutamine synthases. Several transporters in this set are also predicted to be involved in nitrogen and amino acid assimilation, including uc-5 (NCU07334), mtr (NCU06619), pmb (NCU05168) and tam-1 (NCU03257). uc-5 encodes a uracil permease [32]. The mtr mutant is defective in transport of neutral aliphatic and aromatic amino acids. The pmb mutant is defective in basic L-amino acid transport and has reduced uptake of L-arginine, L-lysine, and L-histidine [16] and tam-1 encodes a predicted ammonium transporter.
We also compared the COL-26-dependent gene set to the set of genes that showed reduced expression levels in WT cells in carbon-free medium as compared to WT on maltose or on amylose to reflect the effects of carbon starvation under these two conditions. This comparison revealed that 291 of the 363 COL-26-dependent genes also showed reduced expression in WT cells when no carbon source was available (S3 Table), including vib-1, nit-2, uc-5, mtr, pmb, am, and 5 of the 10 CPC-1 regulon genes. However, cpc-1, gln-1 and gln-2 were not among these 291 genes.
The COL-26-reduced expression gene set was enriched with genes in the functional categories of non-vesicular cellular import (p = 7e-8), secondary metabolism (p = 4e-6), degradation or biosynthesis of phenylalanine (p = 2e-5), allantoin and allantoate transport (p = 2e-7), polysaccharide metabolism (p = 2e-6), and C-compound and carbohydrate transport (p = 9e-6). This gene set also included 39 transporter genes, 7 TF genes, and 26 CAZyme genes (S3 Table). All predicted TF genes in this set have no assigned function. The majority of the transporter genes (25 of 39) belong to the MFS family, but only two, cdt-2 and cbt-1 (NCU08114 and NCU05853) have been characterized (Fig 3E). CDT-2 transports cellodextrins and xylodextrins [33–35], while CBT-1 has transporting activity for cellobionic acid [36, 37]. The 26 CAZymes are from 21 CAZyme families, and two of them, a cellulose LPMO gene pmo-3 (NCU07898) and a cellobiose dehydrogenase gene cdh-2 (NCU05923) have been characterized in N. crassa [38, 39]. Of these 421 genes, 195 were also de-repressed in WT cells under carbon-free conditions relative to WT on maltose or amylose.
COL-26 was essential for the utilization of starch components and was essential for expression of a large fraction of genes associated with utilization of starch in WT cells (Figs 1 and 3B). However, its growth defect on preferred carbon sources was unique, as other mutants, such as Δclr-1 and Δclr-2 are unable to grow on cellulose, but have WT growth rates on preferred carbon sources [23]. Our observation of the down regulation of tam-1, am, gln-1 and gln-2 genes (Fig 3E) in the Δcol-26 mutant and the fact that loss-of-function of glutamine synthase renders N. crassa dependent upon glutamine for normal growth [40] prompted us to test whether the reduced growth of the Δcol-26 mutant in VMM-glucose was due to impaired nitrogen metabolism.
To test this hypothesis, we examined growth of the Δcol-26 mutant in VMM where ammonium nitrate was replaced by glutamine, VMM(Gln). Bird’s minimal medium (BMM) [41] was also used for assessing the effect of glutamine substitution, BMM(Gln); BMM(NH4Cl) has ammonium chloride as the sole nitrogen source. The carbon source for both VMM(NH4NO3) and BMM(NH4Cl) was glucose; the Δcol-26 mutant showed decreased growth on VMM(NH4NO3)-glucose (Fig 1A). Mycelial biomass from 24-hr cultures of the WT and Δcol-26 strains, as well as a glutamine synthetase mutant (Δgln-1) [16] were compared (Fig 4A). In VMM(NH4NO3) with 2% (w/v) glucose as the carbon source, both Δcol-26 and Δgln-1 grew poorly, reaching only 11~ 12% of WT biomass. Both mutants grew much better in VMM(Gln), with Δcol-26 and Δgln-1 reaching 47% and 72% of WT biomass, respectively (Fig 4A). Similar rescue effects of glutamine were observed in the Δcol-26 and Δgln-1 mutants in BMM(Gln) as compared to that in BMM(NH4Cl) (Fig 4A). Substitution of glutamine for ammonia also partially rescued the growth of the Δgln-1 mutant on maltose or amylopectin, but failed to rescue the growth of the Δcol-26 mutant under the same conditions (Fig 4B). The distinctive rescue effect of glutamine in the Δcol-26 mutant on medium with glucose versus medium with maltose argues for glutamine being used as a nitrogen source rather than a carbon source. To test this hypothesis, WT, and the Δcol-26, Pgpd-col-26 and Δgln-1 strains were grown on VMM with either glutamine, glutamate, arginine or proline as both the carbon and nitrogen source or as the carbon source with ammonium nitrate as the nitrogen source (S1 Fig); none of the strains efficiently utilized these amino acids as a carbon source, with only minimal mycelial biomass observed after 9 days of growth (S1 Fig).
The Δcol-26 and Δcre-1 mutants are resistant to 2-deoxyglucose (2-DG), a glucose analog that cannot be metabolized but is able to trigger CCR [14]. It is often used to select for, or evaluate, impairment of CCR or glucose repression in filamentous fungi [42, 43]. As Δcol-26 grows extremely slowly in VMM(NH4NO3) with glucose, we hypothesized that its insensitivity to 2-DG may be a result of its mis-regulation of carbon/nitrogen metabolism. Since the growth of the Δcol-26 mutant in VMM(Gln) was enhanced, we evaluated whether this restoration in growth rescued the sensitivity to 2-DG in the Δcol-26 mutant. We used cellobiose as the sole carbon source, as the Δcol-26 mutant grows better under these conditions (Fig 1A). The WT, Δcre-1, Δcol-26, and Δgln-1 strains were grown in VMM-2% (w/v) cellobiose with or without 0.2%(w/v) 2-DG, with either NH4NO3 or glutamine as the nitrogen source. The Δcol-26 mutant showed a clear resistance to 2-DG, independently of whether NH4NO3 or glutamine was used as the nitrogen source (Fig 4C). These data indicate that Δcol-26 resistance to 2-DG inhibition (and thus impaired COL-26-mediated CCR) remains independent of the nitrogen source.
To further understand the changes in primary carbon, nitrogen, and amino acid metabolism in the Δcol-26 mutant relative to the WT strain, we profiled 45 intracellular metabolites from WT, Δcol-26 and Δgln-1 strains grown on VMM (NH4NO3) or VMM(Gln) with glucose as the carbon source (S4 Table). Strains were first grown in VMM(NH4NO3)-cellobiose (2% w/v) to accumulate fungal biomass, then switched to VMM(NH4NO3)-NC for 18 hrs and subsequently grown in VMM(NH4NO3) or VMM(Gln) with glucose (2% w/v) for an additional 5.5 hrs. Intracellular metabolites were extracted and subjected to analyses using gas chromatography coupled to mass spectrometry (GC-MS) augmented with liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS); normalized abundances of metabolites were compared between WT and the mutants. Relative quantitative analysis showed that 17 metabolites were significantly different between WT and the mutants (p < 0.05) (Fig 5A). However, surprisingly little similarity in metabolite profile was observed when the Δgln-1 and Δcol-26 mutants were compared.
In the Δgln-1 mutant, glutamine levels were significantly lower than WT when grown in VMM(NH4NO3), but the intracellular glutamine deficiency was rescued by growth on VMM(Gln) media (Fig 5A). However, although the growth defect of the Δcol-26 mutant on glucose was partially alleviated when grown on VMM(Gln) media, the intracellular glutamine levels in the mutant were similar to that of WT grown on either VMM(NH4NO3) or VMM(Gln) (Fig 5A). Instead, the Δcol-26 mutant accumulated high levels of four metabolites under both conditions: 4-aminobutanoic acid (GABA), phenylalanine, cysteine and succinate and was deficient in three metabolites: valine, threonine and succinic semialdehyde. Only the high level of GABA and the low level of lysine were shared phenotypes between the Δcol-26 and the Δgln-1 mutant (Fig 5A). GABA and succinic semialdehyde are two intermediate metabolites in the GABA shunt, a metabolic pathway that bypasses two enzymatic steps of the TCA cycle to produce succinate from α-ketoglutarate via glutamate (Fig 5B). As the GABA shunt links primary nitrogen and carbon metabolism, the abnormal level of these intermediates suggests a mis-regulation of primary carbon and nitrogen metabolism occurs in the Δcol-26 mutant. The levels of several amino acids showed a difference between the Δcol-26 mutant and WT grown on VMM(NH4NO3), including homoserine, valine, lysine and threonine.
In filamentous ascomycete fungi, COL-26, ART, and AmyR are conserved in their functions in regulating starch degradation [7, 8, 10](this study). We further demonstrated critical functions of COL-26 in integrating nitrogen and carbon metabolism, a role not previously reported for AmyR/BglR/ART orthologs in other fungi. Although phylogenetic analyses have been performed to infer functional conservation of these homologs [10, 11], either a single homolog per fungal genome was chosen or homologs from very few model organisms were included in the analyses. Our search for col-26 homologs in 44 fungal species within the Ascomycota using BLASTP with cut-off E value of e-20 revealed that many fungi have more than one predicted col-26 homolog and that the number of col-26 homologs varies within each species (S5 Table). For example, some Fusarium species and Trichoderma species have 5 or 6 col-26 homologs, while other species such as Metarhizium spp., Verticillium spp., Myceliophthora thermophile, Thielavia terrestris, Chaetomium globosum, Cordyceps militaris, and Beauveria bassiana, each have only one homolog of col-26. Three Aspergillus spp. have 3 col-26 homologs, including amyR, but amyR from both A. oryzae and A. nidulans was not the best hit by col-26. In order to gain a broader view regarding functional conservation of the col-26 homologs, we constructed a phylogenetic tree of the 86 COL-26 protein sequences using a Maximum Likelihood algorithm. CLR-2 (NCU08114; also identified as Neucr2 6271 in Mycocosm) was used as outgroup to root the tree (Fig 6). Although two COL-26 homologs exist in T. reesei (BglR/Trire2 52368 and Trire2 55109), only BglR was within the same clade as COL-26. Similarly, although F. graminearum and F. verticillioides possess 5 and 6 homologs of COL-26 respectively, only FgART and FvART were in the same clade as COL-26 and BglR. The genome of Magnaporthe oryzae (also called Magnaporthe grisea) has a single COL-26 homolog, named MoCOD1 [44]. Interestingly, the ΔMocod1 mutant showed significant growth reduction on glucose and maltose-containing medium but not on starch-containing medium, while the ΔFgART1 mutant displayed a severe growth defect on glucose and starch-containing medium, but not on maltose-containing medium [10, 44]. AmyR from A. oryzae, A. niger, and A. nidulans together with two COL-26 homologs from A. flavus and A. terreus, respectively, form a clade distant from the COL-26 clade, while a MalR (AO90038000235) from A. oryzae and two homologs from A. flavus and A. nidulans, respectively, are in a clade more closely aligned to the COL-26 clade. Although AmyR is reported to be required for growth on both starch and maltose in A. nidulans [8], A. oryzae largely relies on MalR for growth on maltose [45].
Our phylogenetic tree indicated that BglR is the closest T. reesei homolog of COL-26. Reduced growth of the ΔbglR mutant on maltose has been reported [11]. To test if BglR functions similarly to COL-26, we replaced the endogenous bglR coding sequence in T. reesei with the pyr4 gene in a Δpyr4 auxotrophic mutant [46]. All PCR verified transformants grew slowly on MM with 2% glucose agar plates. Three independent ΔbglR mutants were selected for further assessment. We subsequently tested growth of the ΔbglR mutant in minimal medium with ammonium sulfate, MM((NH4)2SO4) as the sole nitrogen source with glucose, maltose, trehalose, amylose or amylopectin as the sole carbon source. In contrast to parental WT strain QM6a, almost no growth of the ΔbglR mutants in MM-glucose, MM-amylopectin or MM-trehalose was observed (Fig 7A). Surprisingly, neither the parental QM6a strain nor the ΔbglR mutant grew in MM when maltose or amylose was used as the sole carbon source.
To assess the influence of glutamine on glucose utilization in the ΔbglR mutant, we measured changes in glucose concentration in liquid cultures of QM6a and the ΔbglR mutant in MM((NH4)2SO4) or MM(Gln) with 2% glucose as the sole carbon source. This approach was chosen because T. reesei utilized glutamine as a carbon source more efficiently than N. crassa, which prevented an unambiguous conclusion about glucose consumption rate based on growth phenotypes (S2 Fig). In MM((NH4)2SO4), only 11% of the glucose in the medium was used after 2 days by the ΔbglR mutant versus a 90% reduction in glucose levels in the parental QM6a strain (Fig 7B). In MM(Gln), an increase of glucose consumption to 60% by the ΔbglR mutant was detected when glutamine was used as the nitrogen source, while QM6a showed similar glucose consumption on MM(Gln) as MM((NH4)2SO4) (Fig 7B). These data suggest that, like COL-26 in N. crassa, BglR also plays a critical role in regulating starch degradation and primary carbon and nitrogen metabolism in T. reesei. Based on the phylogenetic analyses, such a multi-regulatory role may also be conserved in col-26 orthologs in many other filamentous fungal species.
In nature, filamentous fungi must integrate data from available carbon sources to coordinate with nitrogen, phosphorus and sulfur assimilation for optimal growth. How this coordination is achieved in these organisms is currently not clear, as most studies evaluate physiological/transcriptional differences based on comparison between single carbon or nitrogen sources. In this study, we identified a conserved regulator, COL-26, that plays a role in coordinating the utilization of starch components with nitrogen regulation.
By comparing the amylose and maltose RNA-seq data between the WT and the Δcol-26 mutant, we identified a 363-COL-26-dependent gene set. This gene set contained many genes with functions in primary nitrogen and amino acid metabolism, including the transcription factors vib-1, nit-2, and cpc-1. A large percentage of these genes were also induced in WT when cells were exposed to maltose or amylose, indicating coordinate regulation of nitrogen metabolism with carbon metabolism. The down regulation of genes such as gln-1 and gln-2 in the Δcol-26 mutant and its unique phenotype of poor growth on preferred carbon sources led us to speculate that an inability to coordinate carbon and nitrogen metabolism may occur in the Δcol-26 mutant. This hypothesis was supported by the partial rescue of growth defects in the Δcol-26 mutant by the use of glutamine as the sole nitrogen source with glucose as the carbon source. As in N. crassa, growth defects on glucose medium were noted for a F. graminearum ΔFgART mutant [10], a M. oryzae ΔMoCOD1 mutant [44] and a T. reesei ΔbglR mutant [11]. Here we provided experimental evidence that in T. reesei, BglR was essential for amylopectin and trehalose utilization and for ammonium assimilation on preferred carbon sources. Our data also showed that T. reesei cannot grow on amylose or maltose. These data indicate that, unlike N. crassa, T. reesei may rely on α-1, 6 linkages for signaling for starch degradation. Whether these functions are all conserved by the COL-26 orthologs in other filamentous fungi awaits further verification. However, as many filamentous fungi are plant pathogens of starch crops, such as F. graminearum and M. oryzae, and deletion of the col-26 orthologs reduced pathogenicity in both these fungi, understanding the regulatory mechanisms by the COL-26 orthologs could shed light on the development of future anti-fungal strategies.
The data from metabolic analyses showed that several metabolites in the TCA cycle and GABA shunt pathway were either at a higher or lower level in the Δcol-26 mutant as compared to WT cells. In particular, high levels of succinate and GABA persisted and succinic semialdehyde remained below detectable levels in the Δcol-26 mutant even when glutamine was provided as the nitrogen source and growth was partially restored. The GABA shunt pathway is a metabolic route conserved among bacteria, fungi, plant and vertebrates. The role of the GABA shunt has been extensively investigated in animals and plants due to GABA being a key neurotransmitter in the central and peripheral nervous system of vertebrates and a signal molecule in response to many biotic and abiotic stresses in plants [47]. The GABA shunt in fungi has received less attention, but has been associated with nitrogen metabolism, spore germination, asexual sporulation, redox homeostasis, acidogenic growth, response to hypoxia, oxidative stress and virulence [48–53]. Besides the GABA shunt, an alternative pathway exists for GABA catabolism in many eukaryotes including S. cerevisiae, through which the intermediate metabolite, succinic semialdehyde (SSA), is reduced to γ-hydroxybutyric acid (GHB) [54]. In the Δcol-26 mutant, it is possible that mis-regulation of enzyme activities at either the transcriptional and (or) post-transcriptional level and/or defects in the transport of glutamate or GABA between cytoplasm and the mitochondria may occur. Whether the reduction of succinic semialdehyde or the other metabolites in the mutant is caused by increased activity of enzymes in one pathway versus a re-wiring the metabolite to other pathways warrants further investigation. Our metabolite data is consistent with a regulatory role of COL-26 in the GABA shunt and in coordinating primary carbon and nitrogen metabolism for optimal fungal growth.
In addition to a role in the coordination of primary carbon and nitrogen metabolism, COL-26 is essential for the utilization of starch. In A. niger, transcriptional analyses via microarrays of carbon-limited chemostat or batch cultures growing on maltose versus xylose revealed that only three amylolytic genes aamA (acid α-amylase), glaA (glucoamylase), agdA (α-glucosidase) were induced by maltose [55, 56]. In A. oryzae, ten genes annotated to encode glucoamylase, maltose permease, maltase, sugar transporters and maltose O-acetyltransferase were up regulated by maltose [57]. In this study, we performed systematic transcriptional profiling of N. crassa on different components of starch, including maltose, amylose, and amylopectin. From these analyses, we identified a starch regulon consisting of 322 genes; COL-26 is required for WT expression patterns of 252 of these 322 genes. Surprisingly, our data showed that expression changes in N. crassa in response to polysaccharides of starch differed substantially from those induced by maltose (Fig 2A), where only ~1/3 of the starch regulon genes were induced (Fig 2B). Such transcriptional differences may reflect changes in signaling or utilization strategies by Neurospora for optimal uptake of nutrients of different forms (disaccharides versus polysaccharides, for example).
The function of a COL-26 homolog in Aspergilli, AmyR, the transcriptional regulator associated with maltose and starch utilization in Aspergillus spp., shows some divergence in function even among Aspergillus species. In A. oryzae, where maltose-utilizing (MAL) clusters are found, AmyR is important for starch degradation, but MalR is required for maltose utilization and AmyR activation [45]. In A. nidulans and A. niger, which lack MAL clusters, AmyR is critical for both maltose and starch utilization [5, 8]. N. crassa does not have MAL clusters and no protein exhibits higher homology to MalR than COL-26. Here, we demonstrated that COL-26 is essential for the utilization of maltose, amylopectin and amylose, all components of starch. Consistent with this essential role, the expression of col-26 increased in presence of amylose, amylopectin, and under a low concentration of maltose (2 mM), while deletion of col-26 led to decrease in expression level of 78% of the starch-regulon genes.
Genes related to cellulose degradation were among the genes that increased in expression level in the Δcol-26 mutant. These included cellodextrin and cellobionic acid transporter genes, cdt-2 and cbt-1, respectively and cellulase genes pmo-3 and cdh-2. Substrates of CDT-2 and CBT-1 are in fact products from PMO-3 and CDH [38, 39]. A screen for N. crassa hypersecretors of cellulases also identified a modest increase of cellulase production in the Δcol-26 mutant [46]. These data support the hypothesis that cellulose degradation by N. crassa is negatively regulated by a COL-26-mediated glucose repression, consistent with the robust 2-DG resistance in Δcol-26 mutant. In support of a conserved function of COL-26, a Penicillin oxalicum ΔamyR mutant also showed decreased amylase activity and increased cellulase expression on cellulose [58]. These observations suggest an antagonizing effect between activation of amylolytic genes versus cellulase genes in filamentous fungi, which is mediated by COL-26/AmyR.
In this study, although we focused on elucidating the essential roles of COL-26 in regulating starch degradation and primary carbon and nitrogen metabolism, we also demonstrated that COL-26 and BglR were essential for trehalose utilization. Trehalose is the major internal carbohydrate reserve in N. crassa and other fungi and trehalose mobilization occurs during germination of fungal spores, a process that can be enhanced by glucose combined with a nitrogen source [59]. The tre-1 gene, encoding trehalase, was within the starch regulon, but was not differentially expressed in the Δcol-26 mutant on starch components. Whether the inability to utilize trehalose is a consequence of the inability of the Δcol-26 mutant to efficiently utilize glucose (cleavage of trehalose yields two glucose molecules) is unclear. In the insect pathogen Metarhizium acridum, enhancing fungal utilization of trehalose, the main carbon source in insect hemolymph, has been shown to improve virulence [60]. Single col-26 orthologs occur in the genomes of the insect-pathogenic fungi Metarhizium acridum and Metarhizium robertsii. Further study of functions of the COL-26 orthologs in trehalose utilization in these fungi may aid in developing more potent strains for insect biocontrol.
Finally, we identified a number of predicted transporter genes within the starch regulon, including hgt-1, xyt-1, NCU04963, and NCU04537, while cdt-1 and NCU12154 were significantly induced by maltose. NCU12154 has been annotated as maltose permease based on bioinformatics analyses, although biochemical evidence is lacking. It is possible that one of these uncharacterized transporters encode a maltooligosaccharide transporter that accompanies activity of intracellular α-amylase, which are part of the starch regulon. Testing transporting activity of the predicted transporters will aid in our understanding of diverse nutrient assimilation pathways by filamentous fungi.
N. crassa Δcol-26 (FGSC 11031) and Δgln-1 (FGSC 19959) were obtained from the Fungal Genetics Stock Center (http://www.fgsc.net/). The Pgpd-col-26; Δcol-26 strain was constructed by transforming the Δcol-26 mutant with a DNA fragment containing the A. nidulans gpdA promoter, the open reading frame and 3’ untranslated region (UTR) of col-26, and flanking regions homologous to the upstream and downstream genomic sequence of the csr-1 gene. Transformants were selected for resistance for cyclosporin [61] and tested for genotypes by diagnostic PCR. The transformants with positive results were backcrossed to FGSC 2489 to obtain a csr-1::PgpdA-col-26; Δcol-26 homokaryotic strain.
The T. reesei ΔbglR mutants were created by transforming protoplasts of an uridine auxotrophic strain made from QM6a (Δpyr-4) [46] with two split-marker DNA fragments using method described in [62]. One of the split-marker fragment contains a ~1 kbp sequence homologous to upstream genomic sequence of the bglR gene followed by the promoter and the first half of the pyr-4 coding sequence and the other contained the second half the pyr-4 coding sequence with ~400 bp of overlap sequence with the first half of the pyr-4 coding sequence and a ~1 kb sequence homologous to the downstream genomic sequence of the bglR gene. Transformants were first grown on the plates with minimal media and subsequently transferred to PDA plates for conidiation. Conidia were tested for correct integration of the pyr-4 gene at the bglR locus using diagnostic PCR. The strains with the bglR gene disrupted were subjected to single colony purification. Three verified ΔbglR homokaryotic strains were used for downstream analysis.
N. crassa cultures were grown on slants, each with 3 mL of Vogel’s minimal medium (VMM) with 2% sucrose [17] and 2% agar, at 30°C in dark for 24 hours, followed by 4–10 days in constant light at 25°C to stimulate conidia production. For growth phenotype testing in 24-well plates, conidia were inoculated at 106/ml into 3 mL of VMM with selected carbon and nitrogen sources in 24-well plates covered with breathable rayon film seal, and the culture were grown at 25°C in constant light with shaking at 200 rpm. The film was taken off before imaging. At least two replicates were included in each experiment and the same experiments were done at least twice.
For mycelial biomass measurement, conidia were inoculated at 106/ml into 100 mL of VMM with selected carbon and nitrogen sources and grown at 25°C in constant light with shaking at 200 rpm. For crosses, one parental strain was grown on plates with synthetic crossing medium [63] for 2 weeks at room temperature for protoperithecial development. Conidia of the other parental strain were added to the plates for fertilization. Plates were kept for 3 weeks at room temperature. Ascospores were collected and activated as described [64], plated on VMM with 1% sucrose, and incubated at room temperature for 18 hrs. Germinated ascospores were transferred to VMM slants supplemented with cyclosporin or hygromycin B and screened for desired genotypes by diagnostic PCR.
T. reesei cultures were grown in either minimal media [65] for selecting transformants or with PDA for conidiation. For growth phenotype testing, conidia were inoculated at 106/ml into 3 mL minimal media with a selected carbon source in 24-well plates, and the culture were grown at 28°C in dark with shaking at 200 rpm.
For RNA-seq experiments on VMM with 2% (w/v) maltose and metabolite analyses, conidia were inoculated at 106 conidia/mL into 3 mL VMM with 2% cellobiose and grown at 25°C in constant light and shaking at 200 rpm for 28 hrs. The mycelial biomass was washed twice with VMM-NC, followed by 18 hrs of incubation in VMM-NC. Mycelia were then transferred to VMM with maltose and grown 5.5 hrs for RNA-seq experiments, or transferred to VMM or VMM(Glu) with 2% (w/v) glucose and grown 5.5 hrs for metabolite profiling experiments.
For RNA-seq experiments on VMM with other carbon sources, conidia were inoculated at 106 conidia/mL into 3 mL VMM with 2% sucrose and grown at 25°C in constant light and shaking at 200 rpm for 16 hrs. The mycelial biomass was washed twice with VMM-NC and then transferred to VMM with the selected carbon sources for 4 hrs prior to RNA extraction. Concentrations of carbon sources were glycerol (2 mM), fructose (2 mM), mannose (2 mM), trehalose (2 mM), sorbose (2 mM), xylose (2 mM), sucrose (2% w/v), cellobiose (2 mM), maltose (2 mM), avicel (1% w/v), amylose (1% w/v), amylopectin (1% w/v), xyloglucan (1% w/v).
Mycelia of cultures were harvested by filtration and flash frozen in liquid nitrogen. RNA was extracted using the Trizol method (Invitrogen) and further purified using RNeasy kits (QIAGEN). RNA-seq libraries of WT and Δcol-26 from 2% (w/v) maltose were prepared at the Functional Genomics Lab, a QB3-Berkeley Core Research Facility at UC Berkeley and sequenced on an Illumina HiSeq2000 at the Vincent J. Coates Genomics Sequencing Lab. Other libraries were prepared and sequenced at JGI as part of the Neurospora ENCODE CSP project. Total RNA starting material was 1 μg per sample and 10 cycles of PCR was used for library amplification. The prepared libraries were then quantified using KAPA Biosystem’s next-generation sequencing library qPCR kit and run on a Roche LightCycler 480 real-time PCR instrument. The quantified libraries were then multiplexed into pools of 9 libraries, and the pool was then prepared for sequencing on the Illumina HiSeq sequencing platform utilizing a TruSeq paired-end cluster kit, v3, and Illumina’s cBot instrument to generate a clustered flowcell for sequencing. Sequencing of the flowcell was performed on the Illumina HiSeq2000 sequencer using a TruSeq SBS sequencing kit, v3, following a 1x100 indexed run recipe.
The sequencing reads that passed filtering from the CASAVA 1.8 FASTQ files were subjected to quality score checking using the FASTX-Toolkit (http://hannonlab.cshl.edu/fastx_toolkit/). Only reads with all bases scoring greater than 22 were used to map against predicted transcripts from the N. crassa OR74A genome v12 (Neurospora crassa Sequencing Project, Broad Institute of Harvard and MIT http://www.broadinstitute.org/) with Tophat v2.0.4 [66]. The output bam files were sorted and indexed using the SAMtools package [67] and the indexed files were visualized in Integrative Genomics Viewer [68]. Transcript abundance reflected in FPKM was estimated with Cufflinks v2.0.2 [66] mapping against reference isoforms. Profiling data are available at the GEO (http://www.ncbi.nlm.nih.gov/geo/; Series Record GSE GSE92848 and GSE95350). For differential gene expression analysis, the bam files were first processed using the HTSeq package v0.6.0 [69] to generate raw counts, and the raw counts are subjected to differential analysis using the DESeq2 package version 1.10.1 [70].
The FungiFun2 online resource tool was used in functional enrichment analysis (https://elbe.hki-jena.de/fungifun/fungifun.php) [71]. The gene to category associations was tested for over-representation using hypergeometric distribution and the probability for false discovery rate was controlled by the Benjamini-Hochberg procedure.
Mycelia from 3 mL cultures in 24-well plates were harvested by filtration followed by a quick wash in distilled water. Half of biological replicates were used for metabolite extraction and the other half were dried for biomass measurement. Washed mycelia for metabolite extraction were quickly put into a tube containing 200 μL zirconia beads (0.5 mm) and 500 μL extraction buffer (80% acetonitrile, 20% water, 0.1 M formic acid) and snap frozen in liquid nitrogen. Samples were stored at -80°C until extraction before mass spectrometry (MS) analysis. For metabolite extraction, the frozen samples were immediately put in a bead-beater (BioSpec) and homogenized for 1 min, and cooled on ice. The homogenate were centrifuged at 4°C at 14 000 rpm for 5 minutes, and the supernatants were subjected to either GC-MS or LC-MS analysis.
For GC-MS analysis, 20 μL of the supernatant was collected and transferred to 1.5 mL micro-tubes containing 50 μL internal standard solution (d27-Myristic acid in methanol, 250 μM). Samples were dried under reduced pressure using a speedvac (Savant). Samples were derivatized for GC-MS analysis according to the method of Kind et al [72]. Briefly, 10 μL of methoxyamine hydrochloride dissolved in pyridine (40 mg/mL) was added to each dried sample, and shaken at 30°C at maximum speed for 90 min using a thermomixer (Eppendorf). A mixture of retention time marker standards were prepared by dissolving fatty acid methyl esters (FAMEs) of different linear chain lengths in chloroform (C8, C9, C10, C12, C14, C16 FAMES at 0.8 mg/ml, and C18, C20, C22, C24, C26, C28, C30 at 0.4 mg/ml). The FAME mixture (20 μL) was added to 1 mL of N-methyl-N-trimethylsilytrifluoroacetamide (MSTFA) containing 1% trimethylchlorosilane (TMCS), and 90 μL of the FAMEs/MSTFA solution was added to each sample. Samples were shaken at 37°C at maximum speed in a thermomixer for 30 min, and then transferred to and sealed in amber GC-MS sample vials containing glass inserts (Agilent). Extraction blanks were prepared following the above procedure but starting with empty Eppendorf tubes.
For LC-MS analysis, supernatant (350 μL) was collected and filtered through 0.2 μm spin filters (Pall) by centrifugation for 1 min at 14000 rpm. Fifty μL of the filtrate was transferred to HPLC vials containing 50 μL of an internal standard mixture solution. Samples were kept at 4°C in the LC-MS autosampler chamber. Extraction blanks were prepared in triplicate by following the above sample preparation procedure with empty microtubes.
For GC-MS analysis, samples were analyzed using an Agilent 7890 gas chromatograph (Agilent Technologies, Santa Clara, CA) connected to an Agilent 5977 single quadrupole mass spectrometer, all controlled by Agilent GC-MS MassHunter Acquisition software. Samples were injected using a Gerstel automatic liner exchange MPS system (Gerstel, Muehlheim, Germany) controlled by Maestro software. Sample injection volume was 2 μL, and the injector was operated in splitless mode. Samples were injected into the 50°C injector port which was ramped to 270°C in a 12°C/s thermal gradient and held for 3 min. The gas chromatograph was fitted with a 30m long, 0.25mm ID Rtx5Sil-MS column (Restek, Bellefonte, PA), 0.25 mm 5% diphenyl film with a 10 m integrated guard column. Initial oven temperature was set at 50°C, and the over program was as follows: ramp at 5°C/min to 65°C, held for 0.2 min; ramp at 15°C/ min to 80°C, held for 0.2 min; ramp at 15°C/min to 310°C, hold for 12 min. The mass spectrometer transfer line and ion source temperature was 250°C and 230°C, respectively. Electron ionization was at 70 eV and mass spectra were acquired from 50 to 700 m/z at 8 spectra per second. Raw data was visually inspected using Agilent MassHunter Qualitative Analysis software (Agilent Technologies, Santa Clara, CA). Agilent MassHunter Unknowns Analysis software v. B.07.00 (Agilent Technologies, Santa Clara, CA) was used to perform peak deconvolution and library matching. A library match score was calculated for using FAME markers for retention time calibration, and matching mass fragmentation spectra to those in the Fiehn GC-MS Metabolomics RTL Library [72]. Metabolites of interest were only included in further analysis if their library match scores were greater than 75%. The identities of some metabolites with scores lower than 90% were confirmed by comparing mass spectra and retention times with that of authentic reference standards (S6 Table). Mass spectral and retention time data from identified target metabolites were used to make an analysis method in MassHunter Quantitative Analysis Software for GCMS (v.B.07.00). For each metabolite, a quantifier ion and two qualifier ions were defined to produce an extracted ion chromatogram in a specified retention time window. Integration of the extracted ion chromatogram peaks yielded peak areas that were further normalized by the mean of dry fungal biomass from biological replicates. The normalized peak areas were used for comparing the relative abundance of metabolites across samples.
Targeted LC-MS analysis was performed for select metabolites not detected by GC-MS (S7 Table). Samples were analyzed on an Agilent 6550 ESI-QTOF LCMS fitted with a Merck SeQuant Zic-HILIC column (150 x 1 mm, 3.5 mm, 100 Å) with a guard column. Mobile phase consisted of 5% ammonium acetate in water (solvent A), and 5% ammonium acetate in water-acetonitrile (10:90) (solvent B). The following LC solvent time-table was used: 0 min, 100% B; 1.5 min, 100% B; 25 min, 50% B; 26 min, 35% B; 32 min, 35% B; 33 min, 100% B; 40 min, 100% B. Flow rate: 0.25 ml/min; injection volume: 2 μL. Each sample was analyzed in positive and negative ionization mode. Raw data was analyzed using Agilent MassHunter Qualitative analysis. Extracted ion chromatograms were produced from raw scan data using calculated m/z values for target metabolites, corresponding to their molecular ion and potential adducts: (M+H)+, (M+Na)+, (M+K)+ for Positive mode; (M-H)-, (M+COOH)-, (M+CH3COOH)- for Negative mode. The identity of detected ions were confirmed by comparing retention time with reference standards, or checked by performing MS/MS analysis of the target ion, and comparing ion fragments with those in the METLIN online database. Integration of the extracted ion chromatogram peaks yielded peak areas that were further normalized by the mean of dry fungal biomass from biological replicates. Normalized peak areas were used for comparing the relative abundance of target metabolites across samples.
For differential metabolite analysis, the normalized peak areas were log transformed and then used in the independent t-test of hypothesis that there is no difference between WT and the mutant. P values of less than 0.05 were considered significantly different and values between 0.05 and 0.1 were interpreted as indicating a trend toward statistical significance. Four biological replicates measured by GC-MS and two biological replicates measured by LC-MS were used in differential analysis. All metabolites that were found to be either significantly different or with a trend toward statistical significance were subjected to hierarchical clustering analysis.
Hierarchical clustering analysis is performed with Cluster 3.0 [73] using log transformed mean of normalized peak areas from biological replicates. The values were centered to the mean across different growth conditions and normalized on a per metabolite basis. Average linkage clustering was performed with Euclidean distance as the similarity metric.
Protein sequences of selected ascomycetes were downloaded from JGI Mycosm [74] and used to construct a local protein database using the NCBI BLAST+ application (version 2.2.31) (S5 Table). The putative COL26 orthologs were searched in the database using BLASTP with a cut-off E value less than e-20. All hits were tested by reciprocally BLASTP against the N. crassa database and only ones that resulted in COL26 as the best hit were retained for protein sequence alignment. Protein sequences of the putative COL26 orthologs from selected species were aligned using three different programs: Clustal Omega [75], MAFFT [76], and MUSCLE [77], and the best alignment was chosen and further trimmed using trimAl [78]. The trimmed alignment file was used for phylogenetic tree construction by the RAxML program with 200 bootstraps [79]. The result were visualized and edited in iTOL (http://itol.embl.des/) [80].
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10.1371/journal.pgen.1005436 | Dominance of Deleterious Alleles Controls the Response to a Population Bottleneck | Population bottlenecks followed by re-expansions have been common throughout history of many populations. The response of alleles under selection to such demographic perturbations has been a subject of great interest in population genetics. On the basis of theoretical analysis and computer simulations, we suggest that this response qualitatively depends on dominance. The number of dominant or additive deleterious alleles per haploid genome is expected to be slightly increased following the bottleneck and re-expansion. In contrast, the number of completely or partially recessive alleles should be sharply reduced. Changes of population size expose differences between recessive and additive selection, potentially providing insight into the prevalence of dominance in natural populations. Specifically, we use a simple statistic, BR≡∑xipop1/∑xjpop2, where xi represents the derived allele frequency, to compare the number of mutations in different populations, and detail its functional dependence on the strength of selection and the intensity of the population bottleneck. We also provide empirical evidence showing that gene sets associated with autosomal recessive disease in humans may have a BR indicative of recessive selection. Together, these theoretical predictions and empirical observations show that complex demographic history may facilitate rather than impede inference of parameters of natural selection.
| Dominance has played a central role in classical genetics since its inception. However, the effect of dominance introduces substantial technical complications into theoretical models describing dynamics of alleles in populations. As a result, dominance is often ignored in population genetic models. Statistical tests for selection built on these models do not discriminate between recessive and additive alleles. We show that historical changes in population size can provide a way to differentiate between recessive and additive selection. Our analysis compares two sub-populations with different demographic histories. History of our own species provides plenty of examples of sub-populations that went through population bottlenecks followed by re-expansions. We show that demographic differences, which generally complicate the analysis, can instead aid in the inference of features of natural selection.
| In diploid organisms, the fitness effect of an allele, or a group of alleles, can be categorized as additive, dominant or recessive, or as part of a more general epistatic network. A large body of existing work is devoted to the development of statistical methods for the detection and quantification of selection using DNA sequencing data, including comparative genomics and the sequencing of population samples [1–3]. However, much less progress has been made toward developing methods to identify the mode of selection as additive, recessive or dominant. Substantial experimental work in the last 50 years has been devoted to identifying the average dominance coefficient in model organisms, often with disagreement between different studies and techniques [4, 5]. These studies, in an attempt to identify the relationship between dominance coefficients and selective effects, largely focus on mutation accumulation experiments and subsequent laboratory propagation, determining dominance coefficients from the viability of crosses [4, 6]. At least one study attempts to determine the relationship between dominance coefficient and selective effect from natural populations, propagating crosses directly from wild-type samples, however the methodology relies on the often inapplicable assumption of mutation-selection balance [7]. A particularly useful overview of various techniques and studies can be found in [8], with some more modern techniques described in [9]. Additionally, more recent work taking advantage of a large amount of yeast knockout data has made progress towards quantifying the distribution of dominance effects (restricted to the discussion of nonsense mutations), with emphasis on the variance and skew of this distribution [10, 11].
Despite these substantial steps forward, all of the methods employed rely on the ability to rapidly breed laboratory-friendly organisms, either for the purposes of mutation accumulation or production of homozygotes and heterozygotes through crosses. Unfortunately, such techniques are infeasible when dealing with long-lived macroscopic organisms, particularly in the case of humans. In the present work, we hope to provide steps towards the development of techniques applicable to natural populations of such organisms by making use of naturally occurring demographic events and describing the dynamic response of populations to such events.
The genetics of model organisms and of human disease provide plenty of anecdotal evidence in favor of the general importance of dominance [12]. Although genome-wide association studies suggest that alleles of small effects involved in human complex traits frequently act additively, estimation of genetic variance components from large pedigrees suggests a substantial role for dominance in a number of human quantitative traits; LDL cholesterol levels, for example, have a substantial dominance component, as shown in [13]. Alleles of large effects involved in human Mendelian diseases often behave similarly to large effect (and even lethal) spontaneous and induced mutations in model organisms, such as mouse, zebrafish, or flies, that are frequently recessive [4, 14]. In spite of these observations, the role of dominance in population genetic variation and evolution remains largely unexplored in the majority of diploid species and no formal statistical framework is currently available to identify dominance coefficients in natural populations deviating from mutation-selection balance.
A number of theoretical studies suggested that demographic processes associated with the increase in variance of allele frequency distribution result in a more efficient removal of recessive deleterious alleles [15–18]. Such demographic scenarios include population bottlenecks, population subdivision, range expansion, and inbreeding. Increase in the variance of allele frequency distribution during a bottleneck can be characterized by inbreeding coefficient (even in case of a panmictic population). For structured populations, the increase in variance is characterized by FST. Substantial theoretical work and associated experimental studies explored the removal of recessive variants due to increased inbreeding coefficient during sustained population bottlenecks [19–22]. Additionally, several studies note that bottlenecks have a strong effect on nonadditive variation, specifically loci with epistatic interactions [19, 23–30]. To complement these analyses, we focus on genetic variation in panmictic populations that experienced a population bottleneck and subsequent re-expansion, similar to the scenario recently analyzed in [30]. Using a combination of theoretical analysis and computer simulations, we demonstrate that recessive selection can be qualitatively distinguished from additive selection in populations that recently recovered from a temporary bottleneck, and detail the dynamics of the average number of mutations per haploid.
An important study by Kirkpatrick and Jarne [31] qualitatively described how, perhaps counterintuitively, the number of deleterious recessive alleles per haploid genome is transiently reduced after re-expansion following a population bottleneck, while the number of additively or dominantly acting alleles is increased. We focus on this insight and quantitatively extend the analysis of these dynamics to show that, in spite of a well-documented increase in the frequency of some recessively acting variants in founder populations, the average number of deleterious recessive alleles (with dominance coefficient h ≪ 0.5) carried by an individual is reduced as a consequence of the bottleneck. With the growing availability of DNA sequencing data in multiple populations, these results demonstrate the potential to directly evaluate the role of dominance, either on a whole genome level, or in specific categories of genes.
Population bottlenecks are a common feature in the history of many human populations. For example, the “Out of Africa” bottleneck involved the ancestors of many present-day human populations. Numerous recent bottlenecks affected, among others, the well studied populations of Finland and Iceland. More generally, bottlenecks followed by expansions are standard features in the recent evolution of most domesticated organisms, including an analogous “Out of Africa” event in Drosophila melanogaster [32], highlighting the ubiquity of these events in natural populations. We suggest that complex demographic history may assist rather than complicate statistical inference of selection in population genetics.
Here we focus on a comparison between two populations that recently split, after which their demographic histories diverged, one exhibiting a founder’s event (a population bottleneck followed by subsequent re-expansion), and the other maintaining a fixed population size. We analyze their accumulated differences to shed light on the type of selection dominating the dynamics of deleterious alleles, and show that the average number of mutations per individual, 〈x〉, is dependent on the mode of selection characterized by the average dominance coefficient, h. We introduce a measure BR (the “burden ratio” defined below) that is the ratio of per-haploid deleterious allele accumulation in the two populations. This potentially allows for the qualitative distinction between predominantly additive selection (h ≈ 0.5), where mutations accumulate due to relaxed selection during a bottleneck, resulting in BR < 1, and predominantly recessive selection (h ≪ 0.5), where homozygous deleterious mutations are purged from the population after re-expansion from the bottleneck, resulting in BR > 1, as shown in Fig 1.
For qualitative demonstration and development of intuition, the analysis assumes strictly additive and strictly recessive selection with a highly idealized demography. However, this behavior is not restricted to the simplified demographic model presented in this paper, but rather suggests a quite generic qualitative signature for the presence of recessive (or near-recessive) selection in comparison between two populations, one of which experienced a bottleneck event. Additionally, our simulations suggest the potential to distinguish between partially recessive and additive alleles, as the change in the qualitative behavior of BR occurs at intermediate values of the dominance coefficient, h. The temporal dependence of the “critical dominance coefficient”, hc, describing the boundary between BR > 1 and BR < 1, as well as the sensitivity to partial recessivity, is discussed in the S1 Text.
To ask whether the behavior of the BR statistic is consistent with the dynamics of recessive selection in natural populations, we perform a statistical analysis of genes annotated in the literature as causing autosomal recessive (AR) disease. We use the “Out of Africa” event to differentiate between variation in African and European populations, potentially allowing for the identification of recessive selection in natural human populations. We find that sets of AR disease genes show a statistically significant deviation from neutrality, with BR > 1. This suggests that at least some disease-associated genes with autosomal recessive mode of inheritance may be under recessive selection. Although this observation is not surprising, it is nontrivial, as disease genes could be neutral, highly pleiotropic, or contain variants with different modes of inheritance. This analysis demonstrates the potential to use our methodology to identify sets of genes under predominantly recessive selection.
We work with a simple demography described by an ancestral population of N0 individuals that splits into two subpopulations, one with population size N0 equal to the initial population size (“equilibrium”), and one with reduced bottleneck population size NB (“founded”). The latter population persists at this size for TB generations before instantaneously re-expanding to the initial population size N0, as shown in Fig 1. Time t is measured after the re-expansion from the bottleneck, as we are interested in the dynamics during this period. Quantities measured in the equilibrium population, and equivalently prior to the split, are denoted with a subscript “0”. We consider only deleterious mutations with average selective effect of magnitude s > 0, such that s represents the strength of deleterious selection. Extensions of this analysis to a full distribution of selective effects can be found in the S1 Text. The initial population is in a quasi-steady state with 2N0Ud deleterious alleles introduced into the population with a one-way mutation rate Ud per haploid individual per generation and rare fixation of deleterious alleles. In the absence of back-mutations, the population is not strictly in static equilibrium, however, this approximation is reasonable when the back-mutation rate and average derived allele frequencies are relatively low. In approximate equilibrium, the site frequency spectrum (SFS), denoted ϕ(x), for polymorphic alleles is given by Kimura [33].
ϕ e q ( x ) = 4 N U d e - 4 N s h x - 2 N s ( 1 - 2 h ) x 2 x ( 1 - x ) [ 1 - ∫ 0 x d y e 4 N s h y + 2 N s ( 1 - 2 h ) y 2 ∫ 0 1 d y e 4 N s h y + 2 N s ( 1 - 2 h ) y 2 ] (1)
Here h ≥ 0 is the dominance coefficient for deleterious mutations, where h = 1/2 corresponds to a purely additive set of alleles, and h = 0 corresponds to the purely recessive case. For the present analysis, we primarily focus on these two limits, contrasting their effects on the genetic diversity. An expanded discussion of the treatment of intermediate dominance coefficients can be found in the S1 Text. The solution represents a mutation-selection-drift balance in which new mutations are exactly compensated for by the purging of currently polymorphic alleles by both selection and extinction due to stochastic drift. In this way, an approximately static number of polymorphic alleles exists in the population at any given time.
As noted above, a qualitative insight on the effect of the bottleneck on recessive variation was previously obtained by noting that the expected change in frequency of recessive allele is accelerated due to the increased variance of allele frequencies (inbreeding coefficient). We offer a different approach and attempt to quantitatively describe the difference in dynamics between additive and recessive variation.
We follow the expected number of mutations per chromosome in the population, noting that it is simply the first moment of SFS.
〈 x 〉 = ∫ x ϕ ( x ) (2)
When multiplied by s, this is the effective “mutation load” of each individual in the additive case, but in the case of purely recessive selection this is not proportional to the fitness, as selection acts only on homozygotes. We refer to this statistic generally as the “mutation burden” to avoid assumption of any given mode of selection. As described below, comparison between the mutation burden in the equilibrium and founded populations in the form of the “burden ratio”, BR, may prove useful in the identification of sets of alleles under recessive selection.
B R ≡ 〈 x 〉 e q 〈 x 〉 f o u n d e d = { < 1 for additive selection > 1 for recessive selection (3)
To gain intuition for this qualitative difference, we work to quantitatively understand the population dynamics in a simple demography, first for purely additive selection, and then for purely recessive selection for comparison.
We checked our analytic results using a forward time population simulator, described in detail in the S1 Text. Given the ubiquity and analytic simplicity of the exponential decay in the additive scenario, we focus here on our predictions for recessive variation. We compare analytic expressions of BR(tmin) at the peak response given in Eq (21) for various selection coefficients. We simulated a wide range of bottleneck parameters to probe the limitations of our theoretical understanding. In Fig 4, we demonstrate the accuracy of our analytic results, by plotting the ratio of the simulated values of BR(tmax, s, IB) to our analytic predictions BR(tmax, s, IB) as presented in Eq (21). We arrange our simulated data by bottleneck intensity IB, as we expect the single-generation bottleneck approximation to break down as intensity is increased due to longer bottleneck duration TB ≫ 1. As plotted, complete agreement between simulated data and analytic predictions is represented by a flat line at B R s i m / B R a n a l y t i c = 1. As expected, we find deviations as we approach the limitations of our perturbative approximation, roughly around Tb ∼ 2NB/10 when IB ∼ 0.1. Below these higher intensities, we find quite good agreement for all parameter sets well below 10% error, even at IB = 0.05. Further comparison between simulation and analytic results is presented in S1 Text and illustrated in S2 Fig.
The BR statistic provides a qualitative indication of recessive selection (h ≪ 0.5), in that values over one theoretically correspond to recessivity. This corresponds to a reduction in the average number of deleterious alleles per haploid locus in a founder population relative to a non-bottlenecked population. To test whether the statistic is sensitive to recessive selection, we analyze human exome data from the Exome Sequencing Project (ESP) [37]. We compare European Americans (EA), known to have undergone a relatively intense bottleneck during the “Out of Africa” event, to African Americans (AA), who have substantial African ancestry that did not experience this founder’s event. We aggregate a set of genes and compute the per-haploid mutation burdens, 〈x〉AA and 〈x〉EA for each gene set by summing the frequencies of all variants occurring in those genes within the AA and EA populations separately, such that 〈x〉AA≡∑ixiAA and 〈x〉EA≡∑ixiEA. This provides a group burden ratio score BR≡∑ixiAA/∑ixiEA for the entire gene set ranging from predicted additive (or dominant) with BR < 1 to predicted recessive with BR > 1. While this strategy could in principle be applied directly to a single gene, substantial statistical fluctuations tend to make this measure unreliable on the individual gene level.
We assemble sets of genes associated with known autosomal recessive (AR) diseases, some of which are potentially under recessive selection, and compute a corresponding BR score. In the absence of pleiotropy and the presence of purifying selection against these disease phenotypes, we naively expect these genes to act under partial (h < 0.5) or total recessive selection (h ≈ 0). We check for significant deviation from BR = 1 in several gene sets: 44 genes associated with diseases with “autosomal recessive” in the name of the disease with at least 5 annotated variants in the Human Gene Mutation Database (HGMD), 37 genes associated with congenital hearing loss (HL) and found only with AR mode of inheritance in a clinical genetics lab, and 1348 genes with Clinical Genomic Database (CGD) AR annotations [38–40]. Additionally, we aggregate non-overlapping HGMD and HL genes into a larger combined list of 72 genes.
To compute BR gene scores, we assume that derived variants at a given locus are deleterious, and include derived alleles of all frequencies, including those fixed in one or both of the populations. We restrict our analysis to nonsense variants and non-synonymous variants predicted to be damaging using a human-free version of PolyPhen2 [36] developed to remove bias due the ancestry of the human reference. Derived alleles fixed in one of the two populations are included in the analysis of the burden, as they contribute to the weighted mean 〈x〉.
We estimate significance using bootstrapped standard errors, as described in detail in the S2 Text. First, we compute the burden ratio for all genes in the genome, and find no statistical deviation from one, replicating previously published results [35, 36]. Analysis of the CGD gene set again shows no statistically significant deviation from one. Given the whole genome result, this is not unexpected, as this set of over 1000 genes is plausibly large enough to representatively sample the set of all genes. It is likely that many of these genes have only one or a few variants under recessive selection, with the rest being neutral or even dominantly acting. In contrast, we find statistically significant BR > 1 values in the potentially more reliable HGMD and HL gene sets, despite their small size, as well as in the combined set. We partially replicate our results from ESP using an independent dataset, from the 1000 Genomes Project (1KG), again finding statistical significance in the HGMD disease gene set [41]. A detailed discussion of the data sets and statistical analyses used is provided in S2 Text and detailed in S1 Table (with full gene lists included in a supplemental spreadsheet).
We find statistical significance for two separately obtained disease gene sets, as well as in the combined set. The HGMD gene set is significant in both ESP and 1KG. Additionally, we find null results in nearly all controls presented in S2 Text and detailed in S2 Table. Together, the empirical analysis provides suggestive evidence that genes associated with autosomal recessive disease and thus potentially under recessive selection can show significant burden ratio values BR > 1. The resulting analysis is summarized in Table 1. In light of these findings, we believe we have demonstrated the potential usefulness of this method for identifying sets of genes under recessive selection.
Given the significant observed values of BR > 1 in these gene sets, one can gauge the degree of recessivity for a given set. Specifically, we can readily estimate the average dominance coefficient for damaging and nonsense mutations within a set of genes under the assumption that these mutations all act with a single average dominance coefficient h ‾ and an average selection strength s ‾. We caution that estimates using a single h and s pair of values for all derived mutations may be inappropriate if there is substantial variance in either or both of these parameters. In the absence of information about the variance in dominance coefficients, we believe this approximation may still be informative (if only as a rough guide) in gene sets that clearly deviate from neutrality. Given the details of the Out of Africa demography, the data for the HGMD gene set are consistent with an average dominance coefficient h ‾ H G M D ≲ 0 . 2 (with 95% confidence), however, this bound is conservative over all possible values of the average strength of selection in this gene set. For average selective strengths of s ‾ H G M D = { 0 . 001 , 0 . 01 , 0 . 1 } in damaging and nonsense variants, we find that the corresponding allowed average dominance coefficients are h ‾ H G M D ≲ { 0 . 15 , 0 . 2 , 0 . 05 } (with 95% confidence), respectively. Note that the non-monotonicity in these values is a consequence of the behavior shown for the Out of Africa demography in Fig 3. Additionally, all average dominance coefficients for HGMD are inconsistent with weak average selective strengths below roughly s ‾ H G M D ∼ 0 . 0003. Complementary population data from distinct founder’s events may provide stricter bounds on both the average dominance coefficients and average selective strengths for a given gene set.
The increase in prevalence of recessive phenotypes following population bottlenecks has attracted the interest of geneticists for a long time [19, 42]. Theoretical analysis of allele frequency dynamics in a population expanding after a bottleneck suggested that frequency of an individual allele may rise due to increased drift [42–44]. Here, we focus on a more general question of the collective dynamics of recessively acting genetic variation. In line with the qualitative description found in [31], our analysis suggests that the number of recessively acting variants per haploid genome is reduced in response to a bottleneck and subsequent re-expansion. Generally, we have demonstrated that features of the derived allele spectrum of recessive deleterious polymorphisms behave distinctly from additively acting variation following a population bottleneck and subsequent re-expansion. The response of additive variation depends crucially on the average number of deleterious alleles, and on the number of generations for which selection is relaxed during the bottleneck. In contrast, the dynamics of recessive variation crucially depend on the variance of the site frequency spectrum, rather than the average number of mutations per individual, such that the accumulation of deleterious mutations can respond strongly even to a single-generation bottleneck. Importantly, the temporal dynamics of the accumulation of deleterious alleles depends qualitatively on dominance coefficient and quantitatively on selection coefficient. The qualitative dependence on dominance coefficient suggests that one can learn about recessivity from analysis of the population dynamics in response to a founder‘s event. If the variation is additive, the number of deleterious variants per a haploid genome is larger in a bottlenecked population than in a corresponding equilibrium population. If the variation acts recessively, this number is smaller. The selection coefficient determines the timing of response to a bottleneck.
By explicitly analyzing the non-equilibrium response to a bottleneck, we suggest that naively confounding demographic features may actually shed light on underlying population genetic forces. In realistic populations, for example in modern humans, substantial work has been done to identify and understand the recent demographic history of geographically disparate populations [37, 45–54]. In a recent paper, Simons, et al. [35] use the BR statistic on the whole genome level to empirically compare the accumulation of mutations in European Americans and African Americans. The authors find no statistically significant differences in the whole genome mutation burden of these populations, a result that was extended to all two-point comparisons between a diverse set of humans by Do, et al. [36]. To explain this observation, Simons, et al. derive a complementary theoretical treatment of the dynamics of segregating alleles using branching process techniques and extensive simulations, providing results that are consistent with those presented here.
In the case of the “Out of Africa” event, a historically substantiated and believable demographic model can be used to understand the difference between African and European populations since their divergence. The comparison between populations that have and have not undergone a bottleneck can be used to elucidate plausible selection and dominance coefficients by making use of a simulated version of this demography. As shown in Fig 3 for the comparison between Africans and Europeans, a realistic demographic model can be used to bound the selection and dominance coefficients in modern populations based on a single observation, such as those detailed in [35, 36].
Although the net number of recessive deleterious mutations is reduced as a consequence of a founder‘s event and subsequent re-expansion, the fitness of individuals carrying these alleles is not necessarily increased, as the number of homozygotes is known to increase after a population bottleneck. However, the number of heterozygous deleterious sites, or the average carrier frequency for associated alleles, is suppressed, such that the mating of individuals from disparate bottlenecked populations may result in a decreased incidence of recessive phenotypes in such mixed lineages. In studies of model organisms, this may have applications when comparing laboratory populations founded from a few wild type individuals to their corresponding natural populations.
We have demonstrated that analysis of the BR statistic on the gene set level shows significant deviations above one in genes known to be responsible for autosomal recessive human disease. In principle, the results of this study can be extended to the analysis of any specific groups of genes beyond those with a known mode of inheritance. Sufficiently large subsets of alleles that are medically relevant may be analyzed in humans to identify the mode of selection for candidate variants of potentially recessive diseases.
In sum, the non-equilibrium dynamics induced by demographic events is an essential, and indeed insightful, feature of most realistic populations. Population bottlenecks, abundant in laboratory populations and in natural species, have the potential to provide a novel perspective on the role of dominance in genetic variation.
Simulation details. We performed analysis using a forward time population simulator, custom written in C, available at http://genetics.bwh.harvard.edu/wiki/sunyaevlab/dbalick. For computational speed, the simulator only keeps track of allele frequencies in a freely recombining diploid system, rather than containing full genome information. We use an infinite sites model with a mutation rate of 2 × 10−8 per generation per site. Allele counts in the current generation are sampled based on the frequencies in the previous generation xold, the selection coefficient s, and the dominance coefficient h. We calculate the expected frequency xcurrent in the current generation as:
x c u r r e n t = ( x o l d 2 ( 1 + s ) + x o l d ( 1 - x o l d ) ( 1 + s ) h ) ( x o l d 2 ( 1 + s ) + 2 x o l d ( 1 - x o l d ) ( 1 + s ) h + ( 1 - x o l d ) 2 ) . (25)
The simulator has arguments for per base mutation rate Ud, selection coefficient s, and dominance coefficient h, with a default burn-in of 300,000 generations where sampling occurs every 100 generations in sped-up mode before transitioning to sampling every 1 generation at 1000 generations before time t = 0.
The code was designed to allow for flexible demographic histories, in order to accurately represent events such as the “Out of Africa” migratory event in human population genetic history. For the purposes of comparison to our analytic results, we ran simulations for a simple, square bottleneck of varying population sizes for both the equilibrium population with size 2N0 = 2 × 104 and bottlenecked populations with temporarily reduced sizes of 2NB = {2000,1000,400,200,100} for a duration of TB = {200,100,50,20,10} generations. These simulations were performed under both purely additive (h = 0.5) and purely recessive (h = 0) selection, for a wide range of selection coefficients s = {1,0.1,0.02,0.01,0.001}. For simulations of a range of selective effects and dominance coefficients shown in Fig 3, we used a square bottleneck with parameter 2N0 = 20000, 2NB = 2000, TB = 100, and tobs = 1000 and a realistic Out of Africa demography detailed in Tennessen, et al. [48].
Human polymorphism data. We analyze exome data from the Exome Sequencing Project (ESP) and validate some of our findings using exome data from the 1000 Genomes Project (1KG)[37, 41]. We use available frequency information for polymorphic variants to compute an average per haploid mutation burden per gene for all genes in ESP in 1088 European Americans(EA) with largely European ancestry and 1351 African Americans (AA) with substantial African ancestry. In 1KG, we compare 85 Northern Europeans from Utah (CEU) to 88 Yorubans (YRI) by computing the same statistic. We sum these mutation burdens over genes of interest to compute an aggregate BR score for a given gene set.
Human-free Polyphen2. To compute mutation burden gene scores for putatively deleterious mutations, we restrict our analysis to non-synonymous nonsense variants and variants predicted to be damaging using a human-free version of PolyPhen2 [36]. This software was developed to remove bias due to the mixed ancestry of the human reference sequence, and annotates derived alleles based on chimpanzee orthologs.
Disease gene sets. We use several lists of genes associated with AR diseases that we naively expect to act under partial or total recessive selection. First we compile a set of genes from the Human Gene Mutation Database (HGMD) only associated with diseases with “autosomal recessive” in the disease name [38]. We restrict this set to genes with at least 5 disease-associated variants to guarantee sufficient polymorphism and reduce noise in the BR statistic. This set contains 38 genes that appear in the list of ESP scored genes (44 in 1KG) and is referred to as “HGMD”. We use Congenital Hearing Loss as an example of a polygenic, largely recessive disease. We obtained an annotated gene list of AR genes associated with hearing loss from the Laboratory for Molecular Medicine (LMM) [39]. This list contains 30 genes in ESP (37 in 1KG) and is referred to as “Hearing Loss”. Notably, this list excludes connexin 26 (GJB2), among other genes, which has additional association with AD hearing loss. Additionally, we assemble a combined list of all genes from HGMD and Hearing Loss, with a total of 60 genes in ESP (72 in 1KG) after removing overlap, referred to as “Combined”. To assemble a larger, though noisier gene set, we use all annotated AR genes in the Clinical Genomic Database, referred to as “CGD”, which contains 1268 genes in ESP and 1348 genes in 1KG [40].
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10.1371/journal.pbio.1000073 | Two Modes of Transcriptional Activation at Native Promoters by NF-κB p65 | The NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival, proliferation, differentiation, and inflammation. The molecular basis by which NF-κB activates endogenous promoters is largely unknown, but it seems likely that it should include the means to tailor transcriptional output to match the wide functional range of its target genes. To dissect NF-κB–driven transcription at native promoters, we disrupted the interaction between NF-κB p65 and the Mediator complex. We found that expression of many endogenous NF-κB target genes depends on direct contact between p65 and Mediator, and that this occurs through the Trap-80 subunit and the TA1 and TA2 regions of p65. Unexpectedly, however, a subset of p65-dependent genes are transcribed normally even when the interaction of p65 with Mediator is abolished. Moreover, a mutant form of p65 lacking all transcription activation domains previously identified in vitro can still activate such promoters in vivo. We found that without p65, native NF-κB target promoters cannot be bound by secondary transcription factors. Artificial recruitment of a secondary transcription factor was able to restore transcription of an otherwise NF-κB–dependent target gene in the absence of p65, showing that the control of promoter occupancy constitutes a second, independent mode of transcriptional activation by p65. This mode enables a subset of promoters to utilize a wide choice of transcription factors, with the potential to regulate their expression accordingly, whilst remaining dependent for their activation on NF-κB.
| Transcriptional activation by the NF-κB family of transcription factors is crucial for the expression of multiple genes involved in cell survival, proliferation, differentiation, and inflammation. The activation domain of the p65 subunit of NF-κB has been extensively studied in vitro and on artificial reporter plasmids, but the molecular basis by which it drives expression of natural target genes in vivo is still not well understood. Moreover, it is unclear how any single activation mechanism could allow different target genes to fine tune their timing and expression according to their biological requirements. To address this, we experimentally blocked the interaction of p65 with the Mediator complex—a key factor for transcription by most, if not all, activators. While this prevented expression of many NF-κB–dependent genes, others were unaffected, revealing that p65 is able to drive their expression by an independent mode, which does not depend on direct contact with Mediator. Further experiments indicated that p65 accomplishes this by controlling the recruitment of other, secondary transcription factors to its target promoters. This may enable NF-κB to retain overall control over activation of its target genes, but at the same time allow secondary transcription factors to specify appropriate expression levels according to the cell-type and stimulus.
| The goal of understanding transcriptional activation encompasses the description of an unbroken chain of events leading from the binding of a transcription factor to its natural target promoters in an intact cell, until the initiation of mRNA synthesis by RNA polymerase II (pol-II). In the case of the NF-κB family of transcription factors, this is a challenging task, since the tremendous functional diversity of its target genes makes it difficult to imagine a single activation mechanism able to satisfy the needs of all of them.
Transcription factors belonging to the NF-κB family are found in metazoan organisms ranging from insects to mammals, and are essential in regulating the activation of hundreds of genes in response to various extracellular stimuli and developmental cues [1]. In most vertebrate cell types, NF-κB exists as a combination of five related proteins: p65, c-Rel, RelB, p50, and p52. They share a structurally conserved Rel homology region at their amino terminus, which is responsible for dimerization, interaction with inhibitory IκB proteins, nuclear entry, and binding to their specific DNA target sequences (known as κB sites). In unstimulated cells, dimers of NF-κB are held in the cytoplasm through the binding of inhibitory proteins (IκBs or p100), but upon stimulation they are released to enter the nucleus. There they are capable of binding with high affinity to their target sequences, found both in gene promoters and in enhancer regions [2]. In contrast to our detailed understanding of the signalling events that control the level of NF-κB present in the nucleus, little is known about the mechanisms of transcriptional activation by the various dimer species whilst bound to endogenous target genes. It is particularly unclear whether promoter binding by a given NF-κB dimer always triggers the same fixed response, leading to an identical transcriptional output at all genes, or, as seems more reasonable, different genes should somehow be able to fine tune their transcription levels after binding and activation by NF-κB. However, the transcriptional activation domain of p65 has been extensively studied in vitro and on artificial reporter plasmids, and the data from these systems provide a foundation on which one can try to build an understanding of its function on natural promoters.
During the last two decades, experiments using reconstituted cell-free systems have succeeded in defining the minimal apparatus needed to drive activated transcription. An essential set of general transcription factors (GTFs) is sufficient to direct the binding of, and initiation of basal transcription by pol-II at the core regions of most promoters [3]. In order to respond to transcriptional activators such as NF-κB, though, additional elements are required, foremost amongst which is the Mediator complex. This is a large, multi-subunit complex, which was independently identified by several laboratories through its ability to bind to various transcriptional activation domains (including that of p65), or by its necessity as a co-factor for transcriptional activation in vitro by other transcription factors (reviewed by Malik and Roeder [4]). Using highly purified components in vitro, the combination of pol-II, GTFs, and the Mediator complex is sufficient to drive transcriptional activation by NF-κB [5]. Conversely, depletion of Mediator from total nuclear extracts using antibodies abolishes all in vitro transcription by pol-II, including the response to activators such as p65 [6,7].
The capacity of p65 to activate transcription has also been the subject of numerous studies using synthetic reporter plasmids in transfected cell lines. In this context, the carboxy terminus of p65 (like those of c-Rel and RelB) is able to drive transcription in isolation when fused to a heterologous DNA-binding domain, leading to its definition as a transcriptional activation domain (TAD [8,9]). Since the Mediator complex has been shown to interact with the TAD of p65 [10], a straightforward model would be that direct binding to Mediator constitutes the initial, essential step in p65-driven transcription; however, to our knowledge this has never been tested in vivo at native promoters. Part of the reason for this may be that the Mediator complex is essential for viability, and thus it is not readily amenable to loss-of-function-based experiments in intact cells. In order to test the requirement for this interaction at native NF-κB target promoters, we sought to disrupt the contact between p65 and Mediator by eliminating a single Mediator subunit in vivo.
We found that contact with Mediator is indeed essential for p65 to drive the expression of many NF-κB target genes. Unexpectedly, though, many others were still expressed normally even when this contact was disrupted. Further experiments revealed that p65 has a second, independent mode of transcriptional activation, which acts by regulating promoter occupancy by secondary transcription factors.
We wanted to identify a subunit of the Mediator complex that directly contacts p65, and whose removal would abolish the interaction of p65 with the remaining complex. As a cue, we noted that the Drosophila NF-κB homologue Dif has been shown to interact with Med17 (amongst other Mediator subunits [11]). Although the TAD of p65 shows no obvious sequence homology with that of Dif, we speculated that it might nonetheless contact the corresponding mammalian Mediator subunit, Trap-80. Using the yeast two-hybrid system, we were able to detect an interaction between the amino-terminus of Trap-80 and the far carboxy-terminus of p65 (Figure S1A). Since none of the known components of the Mediator complex are well conserved between yeast and mammals at the primary amino acid sequence level [12], this strongly suggests that the interaction of p65 with Trap-80 is direct; however, at this point we could not exclude that it may be bridged or stabilized by interaction with some endogenous yeast protein(s).
An over-expressed, tagged form of Trap-80 could be co-immunoprecipitated with p65 from nuclear extracts of transfected HEK-293 cells (Figure S1B), confirming that the two proteins can associate into a complex together. To establish whether they occupy adjacent positions within the complex, we used the bimolecular fluorescence complementation (BiFC) approach [13]. We co-expressed fusion proteins of p65 joined, via a short peptide linker, to an amino-terminal fragment of the fluorescent protein Venus, and of Trap-80 similarly joined to a complementary fragment from the Venus carboxy-terminus. Neither of these fragments is itself fluorescent, but if brought sufficiently close by an interaction between their respective fusion partners, they form a bimolecular fluorescent entity (the maximum permissible distance separating the tethered ends is limited by the peptide linkers (16 amino acids, or around 60 Å each), and has been empirically estimated at around 100 Å [13]—roughly comparable to the diameter of the Rel homology region of p65 [14]). Fusions of Venus fragments to the carboxy-terminus of p65, or to the amino-terminus of Trap-80 were nonfluorescent when expressed alone, but, in close agreement with the yeast two-hybrid experiments, cells co-expressing both together were fluorescent, indicating that the two proteins are juxtaposed in vivo (Figure S1C).
Together, these data suggested that Trap-80 forms part of the contact surface of the Mediator complex through which it interacts with p65. Although this interaction may include regions of contact with other Mediator subunits, we considered that Trap-80 was a good candidate as a subunit whose removal might destabilize binding by p65. Therefore, we attempted to disrupt the p65-Mediator interaction by generating cell lines in which Trap-80 expression was stably knocked-down by RNA interference. At the outset, this seemed a risky approach, since in other systems Trap-80 has been shown to be essential for cell viability. Yeast with a null mutation of the homologous Srb4 gene are nonviable, and in cells carrying a temperature-sensitive allele, most mRNA synthesis ceases at the restrictive temperature [15,16]. Likewise, dTrap-80 is needed for both basal and activated transcription in Drosophila SL2 cells [11], and Boube et al. [17] have shown in the Drosophila epidermis that mutation of dTrap-80 is lethal for cells.
Strikingly, then, we were able to generate clonal lines of mouse 3T3 fibroblasts in which Trap-80 mRNA expression was reduced by >90% compared to wild-type levels (Figure 1A), and Trap-80 protein levels were no longer detectable by western blotting (Figure 1B). These Trap-80–deficient fibroblasts proliferated equivalently to control cells and appeared morphologically normal (Figure S2), could be grown in culture for at least 12 wk, and expanded by at least 1020-fold (∼30 passages; unpublished data). Moreover, microarray analysis indicated that the expression levels of >96% of transcripts were changed by less than 1.5-fold in Trap-80–deficient cells (see Figure 2B later).
We used the Trap-80–deficient cells to determine whether Trap-80 is indeed essential for binding of p65 to the Mediator complex. To this end, we tested whether p65 could be co-precipitated with an alternative Mediator subunit, Trap-95, from nuclear extracts of Trap-80–deficient fibroblasts. We used streptavidin beads to pull-down the Mediator complex from cells expressing a biotin-tagged allele of Trap-95. In cells containing Trap-80, p65 was pulled-down with the Mediator complex, reconfirming their in vivo interaction (Figure 1C). However, no p65 was pulled-down from Trap-80 knock-down cells, indicating that the Trap-80 subunit is required for the interaction between p65 and Mediator in vivo.
Therefore, Trap-80–deficient cells represent an experimental system with which we could test the importance of the interaction with the Mediator complex for transcriptional activation in vivo by p65.
We examined the expression of endogenous NF-κB target genes in Trap-80–deficient cells, in response to stimulation with the cytokine tumour necrosis factor-α (TNF-α). 3T3 fibroblasts are a particularly useful model system in which to study activation by p65, since in these cells most NF-κB–driven transcription relies on this subunit [18,19]. We predicted that if transcriptional activation of endogenous genes by p65 depends on its interaction with Mediator, as implied by in vitro studies [6,10], then they should not be expressed in Trap-80–deficient cells. In agreement with this, we found that expression of the Ip-10 and Il-6 genes was abolished in cells lacking Trap-80 (Figure 2A). Two independent small hairpin RNAs (shRNAs) targeting Trap-80 gave the same result, and expression could be restored by reconstitution of Trap-80 knock-down cells with an shRNA-resistant form of Trap-80, ruling out the possibility that the block in expression was caused by an off-target effect of the shRNAs (Figure S4).
Unexpectedly, however, two other NF-κB target genes, Mip-2 and Nfkbia, were unaffected by the absence of Trap-80, and were expressed in cells containing either shRNA at the same level as in control cells (Figure 2A). To verify that transcription of these genes was indeed dependent on p65, we analysed their expression in fibroblasts derived from p65-knockout mice. In agreement with earlier published results [18,19], production of Mip-2, Nfkbia, and Ip-10 mRNA was completely abolished, and Il-6 mRNA levels were strongly reduced (Figure S5). Thus, in 3T3 fibroblasts, p65-dependent genes can be subdivided, depending on whether they require the interaction of p65 with the Mediator complex for their expression (Trap-80–dependent; exemplified by Ip-10 and Il-6), or instead can be expressed even when this interaction is disrupted (Trap-80–independent; exemplified by Mip-2 and Nfkbia).
To examine the generality of this grouping, we performed a microarray analysis of the levels of 29,000 transcripts in wild-type and Trap-80 knock-down cells, before and after stimulation of NF-κB activity using TNF-α. Genes induced by TNF-α are dominated by known NF-κB targets, and their promoters are significantly enriched for NF-κB binding motifs (Tables S1 and S2). Amongst these TNF-α–induced genes, Trap-80–dependent genes are strongly enriched (22%, compared with <3% of non-TNF-α–induced genes; Figure 2B)—supporting the importance of the interaction with Mediator for p65-driven transcription. On the other hand, when considering all Trap-80–dependent genes, although TNF-α–induced genes are significantly over-represented (10%, compared with <0.2% of Trap-80–independent genes; Figure S6), the majority are unaffected by TNF-α treatment, indicating that NF-κB is not alone in its functional requirement for Trap-80. A subset of both Trap-80–dependent and Trap-80–independent NF-κB target genes were validated by quantitative reverse transcription (RT)-PCR (Figure S7), and the results closely correlated with those of the microarray (r = 0.85). We chose to focus on Ip-10, Il-6, Mip-2, and Nfkbia for further study, since these displayed clear-cut dependencies on Trap-80.
We initially considered the mechanism of activation of Trap-80–dependent genes. First, we performed chromatin immunoprecipitation (ChIP) using antibodies against p65, to establish whether disrupting its interaction with Mediator could somehow inhibit p65 from binding to some of its target promoters. We found that p65 was efficiently recruited to the promoters of the Trap-80–dependent genes Ip-10 and Il-6 upon TNF-α stimulation, and its level of binding was only slightly reduced in Trap-80–deficient compared to wild-type cells (Figure 3A). Moreover, the level of p65 binding to the Trap-80–independent Nfkbia promoter was also slightly reduced to a similar extent, arguing that this is not sufficient to explain the failure in Ip-10 and Il-6 transcription. Binding to the Mip-2 promoter was completely unaffected.
Next, we did ChIP with antibodies against pol-II to investigate whether its recruitment to promoters was a consequence of the p65-Mediator interaction. Indeed, association of pol-II with the promoters of Ip-10 and Il-6 was completely prevented in Trap-80–deficient cells (Figure 3B). In contrast, it was strongly recruited to the Mip-2 promoter both with and without Trap-80. Pol-II was also recruited to the Nfkbia promoter in the absence of Trap-80, but at a reduced level, mirroring the lower level of p65 binding noted earlier.
We also examined the recruitment of the general transcription factor IIB (TFIIB) to promoters, in the presence and absence of Trap-80. TFIIB is an essential component of the pre-initiation complex, shown in vitro to be required for the recruitment of pol-II [20]. Consistent with this, TFIIB appeared at the Ip-10 and Mip-2 promoters concomitantly with pol-II in wild-type fibroblasts (Figure 3C), although at later time points the relative levels of promoter-associated TFIIB declined. In Trap-80–deficient cells, the recruitment of TFIIB to the Mip-2 promoter was unimpaired (and even slightly augmented; Figure 3C). However, TFIIB levels at the Trap-80–dependent Ip-10 promoter were severely reduced, foretelling the failure of this promoter to recruit pol-II.
Since Trap-80 seemed not to be required for the recruitment of pol-II or TFIIB to Trap-80–independent promoters, we wondered whether a Mediator complex containing Trap-80 associates with these promoters at all. To check this, we used antibodies against Trap-80 to examine its presence at promoters by ChIP. As expected, we detected Trap-80 at the promoters for the Trap-80–dependent Ip-10 and Il-6 genes (Figure 3D). We also found Trap-80, though, at the Mip-2 and Nfkbia promoters, despite the fact that these genes can still be expressed normally in cells where Trap-80 levels have been knocked-down. This suggests that a Mediator complex which ordinarily contains Trap-80 is involved in transcriptional activation at all of these promoters, but that the Trap-80 subunit is functionally essential only at some of them. However, one caveat to this interpretation is that the apparent difference in Trap-80 dependency between the two classes of promoters might be only quantitative, and the seemingly Trap-80–independent Mip-2 and Nfkbia promoters might actually manage to bind to the low level of residual Trap-80 remaining in the knock-down cells. To deal with this concern, we also checked for the presence of Trap-80 at these promoters in Trap-80–deficient cells. Trap-80 was undetectable at any promoters in Trap-80 knock-down cells, including those of Mip-2 and Nfkbia—confirming that it is truly dispensable for the expression of these genes.
In Trap-80–deficient cells, the Mediator complex is “invisible” when using antibodies against Trap-80. We therefore used antibodies against another component, Med-26, to assess the involvement of the Mediator complex when Trap-80 is missing. After stimulation of Trap-80–deficient cells we could still find roughly normal levels of Med-26 at the Mip-2 promoter, confirming that its transcription involves the Trap-80–independent participation of Mediator, and also serving as a control that in these cells the Mediator complex is not drastically disrupted (Figure 3E). In these cells, however, Med-26 was undetectable at the Trap-80–dependent Ip-10 promoter. This supports the notion that the inability of p65 to interact with the Mediator complex in Trap-80–deficient cells underlies their failure to transcribe Trap-80–dependent genes.
Interestingly, we noticed that Trap-80 was present on promoters at above-background levels in resting wild-type cells, preceding the stimulus-induced promoter-binding by NF-κB (Figure 3D; compare with Figure 3A). This was particularly apparent at the promoters for Nfkbia and Ip-10, and in parallel experiments in which HA-Trap-80 was stably over-expressed by around 10–100×, we could also detect HA-Trap-80 at the Mip-2 and Il-6 promoters before stimulation (Figure S8). In contrast, we detected Med-26 at the Ip-10 and Mip-2 promoters only after transcription was induced by stimulating wild-type cells with TNF-α (Figure 3E). The Med-26 subunit is associated with an active subcomplex of Mediator that is able to bind pol-II, and which accounts for its transcriptional cofactor activity in vitro [21–24]. Our results indicate that while some Mediator seems to be preloaded on promoters in vivo, as has recently been described in yeast [25], contact with p65 is required for the establishment of an active, Med-26-containing complex at target promoters upon stimulation.
Taken together, our data indicate that one mechanism of transcriptional activation by p65 depends on its direct interaction with Mediator, and that this is essential for expression of a subset of its target genes in vivo. Without Trap-80, p65 binding to the promoters of these genes is not prevented, but once bound it is unable to interact with the Mediator complex, and thereby drive the recruitment of pol-II and the initiation of transcription.
Three predictions arise from this model: first, the binding sites for p65 should be situated close to the transcriptional start sites of Trap-80–dependent promoters. We analysed the TNF-α–induced genes revealed by the microarray, and could identify conserved (between mouse and human) NF-κB binding motifs with high confidence in 85% of Trap-80–dependent promoters. At >92% of these, the promoter-proximal site lies within 800 bp of the transcriptional start site (see later), consistent with a direct role for p65 in interacting with Mediator to recruit pol-II.
Second, one should be able to bypass the need for Trap-80 by artificially recruiting an alternative transcriptional activation domain capable of interacting with a different Mediator subunit, to Trap-80–dependent promoters (schematically depicted in Figure 4). To attempt this, we chose to use the well-studied transcriptional activation domain of the herpes simplex virus VP16 protein. Transcriptional activation by the VP16 TAD depends on its direct interaction with the Med25 subunit of the Mediator complex, which can occur through either of two subregions (H1 and H2 [26]). Also, the only proteins it can pull-down from total nuclear extracts are Mediator components [10], implying that additional, unwanted interactions with other nuclear constituents are weak or nonexistent. To effect recruitment to NF-κB target promoters, we used the Rel homology region of p65 (p65 DBD, encompassing both its DNA-binding domain and also the region required for regulation by IκBα). When over-expressed in wild-type cells, the p65 DBD is able to out-compete full-length p65 for binding to κB sites in promoters, and acts as a dominant-negative allele (Figure S9). We generated retroviruses encoding fusion proteins between the p65 DBD and the H1 region of the VP16 TAD, since the H2 region has been shown to make nonessential contacts with Trap-80 [4,26]. After stimulation with TNF-α, Trap-80–deficient fibroblasts transduced with a control virus encoding full-length p65 still showed severely impaired Ip-10 expression compared to wild-type fibroblasts (although the over-expression of p65 did slightly increase Ip-10 levels above those seen in untransduced cells; Figure 4). Expression of the p65 DBD fused to the H1 region of VP16, however, fully restored Ip-10 expression in the absence of Trap-80, to levels that even exceeded those seen in wild-type fibroblasts (Figure 4). Thus, when contact between p65 and Mediator is prevented by the absence of Trap-80, artificial contact with a different Mediator subunit is sufficient to rescue expression of a Trap-80–dependent NF-κB target gene.
The third prediction is that it should be possible to mimic the absence of Trap-80 at NF-κB–dependent promoters by introducing mutations into p65 that disrupt its interaction with Trap-80. Two transcriptional activation regions have previously been identified within the carboxy-terminus of p65 (TA1 and TA2 [8,9]). We generated mutant forms of p65 in which either or both of these regions were deleted, and assayed their in vivo interaction with Trap-80 using BiFC. As a negative control we used the p65 DBD, which lacks the entire carboxy-terminus. All mutants were expressed at comparable levels, as detected by western blotting (unpublished data), and interacted to similar extents with full length p65 (Figure 5B). However, deletion of either TA1 or TA2 alone each diminished the interaction with Trap-80, and deletion of both together (p65ΔTA1&2) completely reduced it to background levels (Figure 5A). We next tested the ability of each mutant to rescue NF-κB target gene expression in TNF-α–stimulated p65-knockout fibroblasts. Transduction with viruses encoding full-length p65, or p65 with deletions of either TA1 or TA2 alone, restored transcription of both the Ip-10 and Mip-2 genes to levels that equalled or even exceeded those in wild-type cells (Figure S10). Notably though, the p65 mutant lacking both TA1 and TA2 was completely unable to drive transcription of the Trap-80–dependent Ip-10 gene, but it could still activate expression of the Trap-80–independent Mip-2 gene to wild-type levels (Figure 5C). Thus, p65 can activate transcription of Trap-80–dependent and –independent genes using separable regions within its carboxy-terminus. These findings can be explained by an inability of the p65ΔTA1&2 mutant to interact with Mediator. However, since it could also be argued that deletion of a substantial domain from p65 may have other, additional consequences for the protein's function, we sought to identify more subtle mutations in which interaction with Trap-80 was still disrupted. We used the p65 mutant lacking TA2 as a template, since this protein drives transcription of Ip-10 and Mip-2 normally, but depends on TA1 for its interaction with Trap-80 (Figures 5A and S10). By initially substituting blocks of seven amino acids within TA1 (e.g., TA1 mut528–534 and TA1 mut535–541), and subsequently by mutating adjacent pairs of amino acids, we were able to identify a p65 mutant in which only two amino acid changes result in the abolition of the interaction with Trap-80 (TA1 DF539AA; Figure 5D). This mutant can still activate transcription of the Trap-80–independent Mip-2 gene, but is inactive at the Trap-80–dependent Ip-10 promoter (Figure 5F).
Thus, using two independent approaches—knock-down of Trap-80 and targeted mutation of the p65 carboxy-terminus—we find that contact with the Mediator complex through the Trap-80 subunit is responsible for transcriptional activation by p65 at a subset of its target genes in vivo.
The observation that expression of many endogenous target genes (including Mip-2 and Nfkbia) is unimpaired in Trap-80–deficient fibroblasts, though, indicates that p65 can utilize a second mode of transcriptional activation at these promoters, which does not depend on either of the transcriptional activation domains identified in earlier in vitro studies. To try to uncover features that might explain their different requirements for activation by p65, we compared the promoters of Trap-80–dependent and –independent TNF-α–induced genes identified by our microarray analysis. We could identify conserved κB motifs in a similar fraction of Trap-80–dependent and –independent TNF-α–induced genes (85% versus 92%, respectively), and the consensus sequence did not obviously differ between the two (Figure S11). However, for a substantial fraction of Trap-80–independent promoters, the most proximal predicted κB site was >1 kb from the transcriptional start site (29%; Figure 6A). This is significantly different from the Trap-80–dependent genes, and suggests p65 may not be directly involved in assembly of the pre-initiation complex at these promoters. With this in mind, we investigated whether the two classes of promoters could be distinguished by the presence or absence of binding motifs for other transcription factors. Although we were unable to find any clear-cut motifs that could unambiguously discriminate between Trap-80–dependent and –independent promoters, there were clear differences in the “signatures” of transcription factor binding sites associated with the two promoter classes (Figure S12 and Table S2). Trap-80–independent promoters were highly enriched for the presence of GC-box motifs (the binding site for Sp1 and related transcription factors) compared with total mouse promoters, although this enrichment did not reach statistical significance when compared with Trap-80–dependent promoters. On the other hand, Trap-80–dependent promoters were themselves strongly enriched for the presence of a TATA-box, and for binding sites for the Ap-1 and HSF families of transcription factors. All promoters induced by TNF-α contained statistically elevated levels of NF-κB-binding and E-box motifs when compared with total mouse promoters.
These findings prompted us to investigate the co-occupancy of endogenous promoters by other transcription factors alongside p65. The promoters for Mip-2 and Nfkbia, as well as that of Ip-10, contain putative binding sites for Ap-1, ATF/CREB, and Sp1, in addition to NF-κB. We performed ChIP using antibodies against c-Jun and Jun-D (which form part of Ap-1), ATF-3, and Sp1. All of these transcription factors were recruited to both the Trap-80–independent Mip-2 and Nfkbia promoters, and the Trap-80–dependent Ip-10 promoter, upon stimulation of fibroblasts with TNF-α (Figure 6B). Remarkably, in every case, binding to these promoters was totally abolished in p65-knockout fibroblasts. This effect was specific for NF-κB–dependent genes, since Sp1 remained bound to the promoters of control, housekeeping genes in both wild-type and p65-knockout cells (Figure S13 [27]). Thus, at native NF-κB target promoters, the initial recruitment of p65 is required for the subsequent binding of other, secondary transcription factors.
To explore whether this phenomenon also occurs in another cell type, we used lipopolysaccharide (LPS)-stimulated primary dendritic cells (DCs), derived in vitro using cells from wild-type and p65-knockout mice. Unlike the situation in fibroblasts, many NF-κB target genes are expressed in DCs in the absence of p65; however, the Vcam-1 and Ip-10 genes are still largely p65-dependent (Figure S14A). The promoters for both of these genes contain binding sites for Ap-1, and in wild-type DCs both are able to recruit c-Jun upon LPS stimulation (Figure S14B). As we had observed in fibroblasts, though, binding to both promoters was prevented in p65-knockout DCs.
The above results indicate that one mechanism by which p65 could drive transcriptional activation at Trap-80–independent promoters would be by controlling the recruitment of secondary transcription factors whose activities do not require Trap-80 (as illustrated in Figure 7). If this explanation is correct, we should be able to rescue expression of Trap-80–independent genes in p65-knockout fibroblasts by bringing one of the relevant transcription factors to their promoters. We decided to attempt this using the transcriptional activation domain from Sp1. Sp1 is recruited in a p65-dependent fashion to NF-κB target promoters (Figure 6B). The binding site for Sp1 is frequently found in Trap-80–independent promoters (GC-box; Figure S12), and it has been implicated in the expression of several NF-κB–regulated genes (e.g., Mcp-1 [28]). Moreover, while the Sp1 TAD requires Mediator for its activity [29], it does not directly interact with the Mediator complex, so we reasoned that it was unlikely to show a particular dependency on the Trap-80 subunit.
Using a similar strategy to that used earlier (Figure 4), we generated retroviruses encoding a fusion protein between the p65 DBD and the Sp1 TAD, and used these to infect p65-knockout fibroblasts. Expression of the Trap-80–independent Mip-2 gene was completely restored to wild-type levels in infected cells (Figure 7). This demonstrates that recruitment of Sp1, an event that is normally controlled by p65, is sufficient to drive transcription even in an experimental setting in which p65 itself is absent. Therefore, the ability of p65 to control the binding of secondary transcription factors such as Sp1 to target gene promoters constitutes a second, indirect, mode of transcriptional activation, independent from its direct interaction with Mediator via Trap-80.
Although Sp1 binding sites are enriched at the promoters of Trap-80–independent genes, there also exist instances at those of Trap-80–dependent genes (e.g., Ip-10, Figure 6B). In such cases, binding of Sp1 to the promoter (along with other transcription factors) is not sufficient to drive transcription in Trap-80–deficient cells. In line with this, artificial recruitment of the Sp1 TAD to the Trap-80–dependent Ip-10 promoter failed to restore its expression in p65-knockout cells (Figures 7 and S15). A difference between the Trap-80–independent and Trap-80–dependent genes, then, corresponds to the ability of secondary transcription factors (exemplified here by Sp1) to drive their transcription following p65-dependent recruitment.
This raises the question of why promoter-bound Sp1 cannot drive transcription of Trap-80–dependent genes, such as Ip-10. Transcriptional activation by Sp1 in vitro depends on its direct interaction, through TAFII110, with a TFIID complex containing TAFII250 [30]. However, not all transcriptionally active genes in human cells are found in association with TAFII250, nor in yeast cells with its homologue TAFII145 [31–33]. We therefore examined TAFII250 occupancy at the Ip-10 and Mip-2 promoters. We found that TAFII250 is recruited to the endogenous Mip-2 promoter upon stimulation with TNF-α, but no such recruitment was seen at the promoter for Ip-10 (Figure S16). Thus, the differential responsiveness of these two promoters to bound Sp1 can be explained by their respective abilities to recruit a TFIID complex containing TAFII250; this, in turn, accounts for the ability of p65 to activate transcription of Mip-2, but not Ip-10, in the absence of Trap-80. Differential TAF usage by promoters may represent a widely used additional level of control over the activity of bound transcription factors. It has been shown in both yeast and mammals that promoters differ in their requirement for a TFIID complex containing TAFII250/145 [15,34,35]. Although the correlation is not absolute, one predictive factor for TAF-independence is the presence of a TATA-box, and it is worth noting that this motif is enriched in Trap-80–dependent promoters (Figure S12 and Table S2). However, just as there does not appear to be any single transcription factor binding motif that unequivocally separates the two classes of promoter, the association of Trap-80–independent promoters with TAFII250 presence is not perfect, and there exist some Trap-80–dependent genes whose human counterparts are bound by TAFII250 (e.g., Adm and Cebpb [31]), and some Trap-80–independent promoters which contain a TATA-box (e.g., Ccl7). Thus, while Sp1 serves as a successful example in the case of the Mip-2 promoter, we certainly do not suggest that all Trap-80–independent transcription is mediated by the same secondary transcription factor.
Rather, our data indicate that each NF-κB–dependent promoter contains a combination of sites for the binding of various transcription factors, any of which could drive transcription if present and active in that promoter context. Importantly, though, in fibroblasts and DCs this binding is subject to overall upstream control by p65, and it is likely that this reflects a general mechanism used by NF-κB–dependent promoters in other cell types.
When the studies described here were initiated, numerous in vitro data were known about transcription by NF-κB, but the actual mechanism of transcription downstream of p65 binding to endogenous genes in vivo was unclear. Since the Mediator complex was known to play an important role in most, if not all, transcription by pol-II, we set out to disrupt its interaction with p65, as a means to dissect p65-driven transcription. We found that expression of some NF-κB target genes depends on direct contact between p65 and Mediator, which occurs through the Trap-80 subunit and the TA1 and TA2 regions of p65. This contact is needed for the establishment of an active, Med-26–containing Mediator complex at promoters, recruitment of TFIIB and pol-II, and thereby the initiation of transcription. While this result is not surprising, it does provide important confirmation that events hitherto only described in minimalist in vitro experiments are necessary, and sufficient, for the expression of some genes in their natural context in vivo.
The finding that 3T3 fibroblasts remain viable even after depletion of Trap-80 to <10% of normal levels is remarkable, considering that its yeast homologue Srb4 is required for most, if not all, transcription by pol-II [15]. Srb4 is essential for the integrity of the yeast Mediator complex, which, without it, dissociates at the boundary between the structurally conserved head and middle modules [36,37]. In mammalian cells, like in yeast, the Mediator complex is critical for pol-II–driven transcription: its addition is required for the in vitro activity of various transcriptional activators [10,29], and its depletion from mammalian nuclear extracts abolishes all transcription by pol-II [7,38]. Hence, the survival of fibroblasts after Trap-80 has been knocked-down implies that some Mediator activity must still remain. One possibility is that the amount of cellular Mediator is not normally limiting for the expression of essential genes, and that the residual 5%–10% level of Trap-80 in knock-down cells suffices for these. However, we find that the expression of >96% of transcripts changes by less than 1.5-fold in Trap-80–deficient cells (Figure 2B). This finding argues that, instead, Trap-80–deficient cells contain Trap-80–less, but otherwise functional, Mediator complexes. Proteomic analyses have so far identified Trap-80 to be part of a common core of subunits, shared by all Mediator species [23,39]. It seems likely, then, that Trap-80 does not play the same essential structural role as yeast Srb4, and that Mediator complexes that normally incorporate Trap-80 are still able to at least partially assemble when this subunit is missing. This interpretation is supported by our finding that the Mediator complex, revealed by the Med-26 subunit, is recruited to the Mip-2 promoter even in Trap-80–deficient cells (Figure 3E).
We have shown that the interaction of p65 with Mediator through Trap-80 is sufficient to drive transcription. However, the discovery that a subset of p65-dependent genes are transcribed normally even when the interaction of p65 with Mediator is abolished was completely unanticipated. Moreover, a mutant form of p65 that not only cannot interact with Trap-80, but that also lacks both previously identified transcriptional activation domains, can still activate the Trap-80–independent Mip-2 gene in vivo (p65ΔTA1&2, Figure 5C). This finding prompted us to examine more closely the events that occur at promoters upon engagement of NF-κB. Remarkably, we found that without the binding of p65, NF-κB target promoters cannot be bound by many other transcription factors. Thus, it appears that a p65-containing NF-κB dimer binds to target promoters as a lone, “pioneer” transcription factor, and controls their subsequent co-occupancy by secondary transcription factors (illustrated in Figure 7).
One model for this, which we do not favour, could be that secondary transcription factors bind to promoters via direct, co-operative interactions with p65. Such a scenario has been previously shown in the context of particular promoters containing juxtaposed binding sites (e.g., HIV1-LTR [40], Ifnb1 [41]), but this arrangement is not a general feature of NF-κB target promoters. Moreover, it seems unlikely that pairwise interactions with p65 could account for the binding of multiple transcription factors to each of many different promoters (and at non-NF-κB target promoters, co-operative binding with p65 is clearly not required; see Figure S13).
A more plausible possibility is that p65 controls promoter accessibility by inducing local alterations to chromatin. In macrophages, NF-κB–driven activation is accompanied by nucleosome remodeling at target gene promoters [42]. However, we could detect no differences in promoter accessibility to micrococcal nuclease digestion after stimulation of wild-type and p65-knockout fibroblasts (unpublished data). Alternatively, p65 binding may bring about changes to histone modifications, several of which have been described to be associated with the expression of NF-κB target genes in different systems (e.g., lysine acetylation [43,44] and methylation [45], serine phosphorylation [46,47]). Further experiments are required to determine whether these could account for the control over secondary transcription-factor binding.
In p65-knockout cells, artificial recruitment of a secondary transcription factor is sufficient to restore gene expression (Figure 7), indicating that regardless of the mechanism, the regulation of promoter occupancy constitutes a second, independent mode of transcriptional activation by p65.
What, though, could be the benefit of having a second mode of transcriptional activation? After all, in real, nonexperimentally manipulated cells, an intact Mediator complex containing Trap-80 is always present. We can envisage two situations in which the ability of p65 to control recruitment of secondary transcription factors to a promoter could be important. First, if the only means by which p65 could activate transcription was through its direct binding to Mediator, then the transcriptional output at every NF-κB−dependent promoter should be the same, and upon its release from promoters, transcription would necessarily halt. There are numerous mechanisms that control the longevity of promoter-bound p65, including nuclear export by resynthesized IκB molecules [48], ubiquitination and proteasomal degradation [49], and replacement by other NF-κB dimer species [50]. Considering the tremendous diversity of NF-κB target genes, though, it seems inconceivable that the optimal biological window and level of expression for all of them can be identical (and experimentally this is not the case; compare, for example, expression of Mip-2 and Il-6 in Figure 2A). By endowing p65 with the ability to license promoters for the binding of secondary transcription factors, there is a means to customize expression levels, and prolong transcription after the departure of p65. From the point of view of a promoter, this would be an attractive solution, since NF-κB–dependence can be retained at the same time as tailoring the expression pattern by selecting binding sites for appropriate secondary transcription factors.
Second, κB sites are not always located in promoters close to the transcriptional start sites, and in some cases can be several kilobases away (Figure 6A; examples include Mcp-1 and JunB). At such a distance, looping of the intervening DNA would be required to bring bound p65 into the proximity of core promoter elements, and this may not allow a sufficiently stable interaction with Mediator to enable nucleation of the pre-initiation complex. However, the local presence of p65 is nevertheless adequate to regulate the recruitment of secondary transcription factors. In turn, these newly arrived transcription factors can stably bind to the promoter, and themselves interact with components of the pre-initiation complex to drive transcription. In this model, the Trap-80–independent mode of activation by p65 is critical to permit it to operate at enhancers.
A corollary of activation by p65 in this way is that the activity of a given target promoter, although entirely NF-κB–dependent, will depend on the availability of suitable secondary transcription factors. Since this is determined by both cell-type and stimulus, this mode of activation is likely to be essential to allow genes controlled by NF-κB to attain an appropriate pattern of expression in different biological contexts.
For expression in yeast, fragments from the N- or C termini of p65 (NT: amino acids [aa] 1–305; CT1: aa 306–549, CT2: 431–549) were cloned into pAct2. Full-length Trap-80, and amino or carboxy terminal fragments (NT: aa 1–335, CT: aa 336–649) were cloned into pGBT9. For expression in HEK-293 cells, the coding sequences for full-length mouse p65 and Trap-80 were cloned in pCDNA3. Trap-80 was tagged at the N terminus with the HA epitope MYPYDVPDYA. For BiFC, p65 and mutants thereof were fused to Venus fragment 1 (V1: aa 1–158) or fragment 2 (V2: aa 159–239) using the linker sequence SRGSGGGGSGGGGSSG, and Trap-80 was fused to V2. p65 mutations are as follows: ΔTA1 is truncated at aa 519; ΔTA2 lacks aa 441–474; mut528–534 and mut535–541 each substitute 7 aa for AAASAAA; DF539AA substitutes aa 539 and 540 for AA (numbers refer to aa positions in full-length p65). Trap-80 was knocked-down using hairpins directed against the sequences AGAGATGGTCGGGTAATCA or GACATTGGTGATCTTGGCA (in the Trap-80 CDS), cloned into pSuper-Retro-Puro. The shRNA-resistant Trap-80 contains two silent point mutations (underlined): AGAGACGGTCGGGTCATCA, and was cloned in pMY-IRES-GFP. For generation of biotin-tagged Trap-95, the Escherichia coli BirA coding sequence was cloned into pMY-IRES-Bsd (conferring resistance to blasticidin), and full-length Trap-95 was tagged at the C terminus with the peptide GLNDIFEAQKIEWH, and cloned in pMY-IRES-Tomato (expressing red fluorescent Tomato protein). For expression in fibroblasts, HA-Trap-80, full-length p65 and mutants thereof, and the p65 DBD (aa 1–305), either alone or fused to VP16-H1 (aa 411–456) or Sp1 TAD (aa 92–551), were all cloned in pMY-IRES-GFP.
Polyclonal antibodies against HA, p65, pol-II Rbp1 subunit, Trap-80, Med-26, TFIIB, ATF3, c-Jun, Jun-D, Sp1, and c-Rel were from Santa Cruz, that against TAFII250 was from Abcam. Monoclonal anti-p65 (N terminus) was from Santa Cruz. Monoclonal anti-HA is produced by the hybridoma 12CA5.
Y153 yeast were grown at 30 °C in YAPD medium and transformed using lithium acetate. Transformants were selected by growth on YNB plates without tryptophan or leucine, and additionally lacking histidine and containing 25 mM 3-amino triazole to select for interactions between hybrid proteins. Expression of LacZ was screened by transfer of colonies to nitrocellulose, lysis, and incubation at 37 °C with X-Gal.
HEK-293 and Ecotropic-Phoenix cells were were transfected using CaPO4. BiFC fluorescence intensities in transfected cells were measured by flow cytometry. 3T3 cells were infected with retroviral supernatants from Ecotropic-Phoenix packaging cells. Retroviral gene expression was monitored using flow cytometry to measure co-expressed fluorescent proteins. Where necessary, cells were sorted to obtain equivalent expression levels. Primary DCs were generated from foetal liver progenitor cells by culture for 8–10 d with GM-CSF (4% supernatant from transfected X63 cells). Cells were stimulated with 5 ng ml−1 mouse TNF-α or with 100 ng ml−1 LPS.
Nuclei were isolated by cell lysis in L1 buffer (50 mM Tris, 2 mM EDTA, 0.1% NP-40, 10% Glycerol [pH8]) and nuclear proteins were extracted using L1 +250 mM NaCl for 10 min. After centrifugation, the salt in the supernatant was diluted to 100 mM. For immunoprecipitations, extracts were incubated overnight with 2 μg antibody followed by 30 min with 5 μl protein A- or protein G-sepharose, per 100 μg total protein. For pull-down of in vivo biotinylated Trap-95, extracts were incubated with 2 μl streptavidin-M280 magnetic beads (Dynal) per 100 μg total protein. Bound material was washed in L1 +150 mM KCl and analysed by western blotting.
Total RNA was prepared with RNeasy (Qiagen) from three independent samples per group, and used to prepare labelled ss-cDNA for hybridization on Affymetrix Mouse Gene 1.0 ST microarrays. The data have been deposited at the National Center for Biotechnology Information (NCBI) Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo), with accession number GSE12697. Only transcripts whose microarray Δ log signals were reproducible between groups for all samples (σ/
< 0.5 and p < 0.1) were considered for analysis. A set of 36 transcripts were measured by RT-PCR from the same samples, and used to generate a standard curve (r = 0.84) to quantify the microarray probe signals. NF-κB binding motifs conserved between mouse and human were identified in the region −9,000 to +1,000 bp relative to the mouse transcriptional start site (TSS) using Consite (matrices MA0061, 0101, and 0107, conservation >70%, TF score >85% [51]). The fraction of promoters for which the most proximal κB site was >1 kb (kilobase pair) from the TSS were compared using the Fisher's exact test. Over-represented motifs in sets of promoters were identified using MotifSampler [52].
ChIP was performed as described [53], using primers which amplify promoter regions within 300 bp upstream of the TSS (binding sites for the transcription factors studied all lie within ±500 bp of the TSS at the promoters analysed). All PCR was performed using quantitative real-time analysis with gene-specific fluorescent probes. Primer sequences are available on request.
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10.1371/journal.pbio.1001527 | Transcriptional Corepressors HIPK1 and HIPK2 Control Angiogenesis Via TGF-β–TAK1–Dependent Mechanism | Several critical events dictate the successful establishment of nascent vasculature in yolk sac and in the developing embryos. These include aggregation of angioblasts to form the primitive vascular plexus, followed by the proliferation, differentiation, migration, and coalescence of endothelial cells. Although transforming growth factor–β (TGF-β) is known to regulate various aspects of vascular development, the signaling mechanism of TGF-β remains unclear. Here we show that homeodomain interacting protein kinases, HIPK1 and HIPK2, are transcriptional corepressors that regulate TGF-β–dependent angiogenesis during embryonic development. Loss of HIPK1 and HIPK2 leads to marked up-regulations of several potent angiogenic genes, including Mmp10 and Vegf, which result in excessive endothelial proliferation and poor adherens junction formation. This robust phenotype can be recapitulated by siRNA knockdown of Hipk1 and Hipk2 in human umbilical vein endothelial cells, as well as in endothelial cell-specific TGF-β type II receptor (TβRII) conditional mutants. The effects of HIPK proteins are mediated through its interaction with MEF2C, and this interaction can be further enhanced by TGF-β in a TAK1-dependent manner. Remarkably, TGF-β-TAK1 signaling activates HIPK2 by phosphorylating a highly conserved tyrosine residue Y-361 within the kinase domain. Point mutation in this tyrosine completely eliminates the effect of HIPK2 as a transcriptional corepressor in luciferase assays. Our results reveal a previously unrecognized role of HIPK proteins in connecting TGF-β signaling pathway with the transcriptional programs critical for angiogenesis in early embryonic development.
| An essential step during early embryonic development is to establish elaborate vascular networks that provide nutrients to ensure the proper growth of the embryos. This process, known as angiogenesis, requires coordinated regulation of cell proliferation, migration, and differentiation in endothelial cells, which provide the inner-most linings of blood vessels. It is well accepted that transforming growth factor–β (TGF-β) and its downstream signal pathways are required to regulate endothelial cell growth, but the exact mechanisms remain poorly characterized. Using mouse genetics and in vitro angiogenesis assays, we show that transcriptional cofactors in the homeodomain interacting protein kinase (HIPK) family are activated by TGF-β to control the expression of target genes that regulate proliferation and adherent junction formation in endothelial cells. Our study also identifies a highly conserved tyrosine residue in HIPK proteins that is required to transduce TGF-β signal. These results provide new insights into the mechanism of TGF-β signaling in angiogenesis, and how this process may be exploited to develop therapeutic targets that control angiogenesis during development and in disease conditions.
| Vascular morphogenesis is controlled by an intricate interplay of extrinsic factors and their downstream signaling mechanisms [1],[2]. At the early stage of vascular development, several critical events dictate the successful establishment of nascent vasculature in yolk sac and in the developing embryos. These include aggregation of angioblasts to form the primitive vascular plexus, followed by the proliferation, differentiation, migration, and coalescence of endothelial cells [2],[3]. Subsequently, branching morphogenesis and arteriovenous specification further facilitate the maturation of an interconnecting and fully functional network of blood vessels to provide nutrients to the entire organism [4]. Many of the mechanisms that govern the normal vascular development can also be recapitulated in angiogenesis that occurs during disease conditions, including tumorigenesis, metastasis, stroke, and tissue repair after injury [1],[5].
Transforming growth factor–β (TGF-β) represents a family of highly conserved cytokines that have profound effects in regulating epithelial–mesenchymal transition (EMT), vascular morphogenesis, and cellular and organismal functions during development and in disease conditions [6]–[8]. Indeed, genetic analyses in mouse and human have shown that mutations involving components of the TGF-β signaling pathway affect many aspects of vascular morphogenesis during development and in adult life [9]. For instance, loss-of-function analyses of TGF-β1, TGF-β type I receptor ALK1 or ALK5, or TGF-β type II receptor (TβRII) in mouse reveal a distinct role of each of these signaling components in regulating the proliferation, differentiation, and survival of endothelial cells and smooth muscle cells. These analyses further indicate that the outcome of the deletion involving different components of the TGF-β signaling pathway can be cell context-dependent. Furthermore, the timing of targeted deletion and the presence of genetic modifiers can also affect the phenotypic manifestations [7]. With respect to the roles of TGF-β signaling in endothelial functions, TGF-β type I receptors ALK1 and ALK5 have been shown to have opposite effects, with ALK1 contributing to the proliferation and migration of endothelial cells and ALK5 inducing the maturation of blood vessels [10],[11]. While the underlying mechanisms for distinct effects of ALK1 and ALK5 are still unclear, it is possible that the signaling downstream of the TGF-β type I receptors may diverge due to the involvement of Smad and non-Smad-dependent mechanisms that regulate the transcription of angiogenesis-related genes [12].
Homeodomain interacting protein kinase 2 (HIPK2) is a transcriptional cofactor in the downstream of TGF-β/BMP signaling pathway [13]–[17]. Interestingly, loss of HIPK2 reduces cellular responses to TGF-β during neuronal development and in mouse models of renal fibrosis [13],[17]. While mice lacking HIPK1 show no detectable defects [18], simultaneous loss of HIPK1 and HIPK2 leads to severe growth retardation and early embryonic lethality [19],[20]. Although the study by Aikawa and colleagues has implicated vascular defects in Hipk1−/−;Hipk2−/− double mutants [20], the detailed mechanism responsible for the phenotypes remains unclear. It is also unclear if HIPK1 and HIPK2 can cooperatively regulate TGF-β signaling and thereby contribute to the angiogenesis during early embryonic development.
Here, we show that HIPK1 and HIPK2 cooperatively suppress the expression of angiogenic genes that are critical for endothelial proliferation and adherens junction formation. Loss of HIPK1 and HIPK2 leads to a marked up-regulation of VEGF and MMP10, and early embryonic lethality due to excessive proliferation and poor adherens junction formation in the endothelial cells. Consistent with these results, siRNA knockdown of Hipk1 and Hipk2 results in similar phenotype in human umbilical vein endothelial cells (HUVECs). Furthermore, endothelial cell-specific deletion of TβRII results in phenotypes similar to those in Hipk1−/−;Hipk2−/− mutants. The mechanism of HIPK1 and HIPK2 involves their interaction with HDAC7 to suppress MEF2C-mediated transcriptional activation of Mmp10 and Vegf. Importantly, the activity of HIPK critically depends on the TGF-β-TAK1 mechanism, which promotes the phosphorylation of HIPK2 on a highly conserved tyrosine residue in the kinase domain. Together, these results provide novel insights into the role of HIPK1 and HIPK2 in the signal transduction mechanism downstream of TGF-β and the transcriptional control of angiogenic gene expression during the critical stages of vascular morphogenesis.
To determine if HIPK1 and HIPK2 cooperatively regulate gene expression, we analyzed vascular development in Hipk1−/−;Hipk2−/− mutants. In contrast to the previous report [20], CD31 (PECAM-1) staining in the yolk sacs of E9.5 Hipk1−/−;Hipk2−/− mutants showed an excessive growth of endothelial cells, with reduced avascular areas, reduced vascular branch points, increased fragment length, and a significant increase in BrdU incorporation (Figure 1A–E,H). Similar vascular phenotypes, including increase in endothelial cell proliferation and vascular density, were also detected in the endothelial cells in the head and trunk regions of E9.5 Hipk1−/−;Hipk2−/− (Figure 1F–H). Electron microscopy further revealed that the adherens junctions in the endothelial cells of Hipk1−/−;Hipk2−/− mutants were significantly smaller and showed reduced density per unit area compared to those in control (Hipk1+/−;Hipk2+/+) (Figure 1I–K). Despite these defects, the endothelial cells in Hipk1−/−;Hipk2−/− mutants showed no evidence of disruption or disorganization, and blood cells remained confined within the vessels with no evidence of vascular leakiness (Figure 1I–I'). Another prominent phenotype in Hipk1−/−;Hipk2−/− mutants was the absence of blood vessel growing into the neural tubes (Figure 1G–G'), which may have contributed to the increase in cell death and reduced proliferation in the neural progenitors in Hipk1−/−;Hipk2−/− mutants [19].
To investigate the molecular bases of the Hipk1−/−;Hipk2−/− mutant phenotype, we used the CodeLink Mouse Whole Genome Bioarrays to characterize gene expression profiles in E9.5 control (Hipk1+/−;Hipk2+/+), Hipk1−/−;Hipk2+/+, Hipk1+/−;Hipk2−/−, and Hipk1−/−;Hipk2−/− embryos. Unsupervised hierarchical clustering analyses of all genes showed that the transcriptomes of Hipk1−/−;Hipk2+/+ embryos were more similar to that of control (Hipk1+/−;Hipk2+/+), whereas the profiles of Hipk1+/−;Hipk2−/− were more similar to Hipk1−/−;Hipk2−/− embryos (Figure S1A). Consistent with this, Gene Ontogeny and KEGG pathway analyses indicated that only a very small number of genes in Hipk1−/−;Hipk2+/+ embryos showed altered expression patterns. In contrast, the number of affected genes in each pathway showed a progressive increase from Hipk1−/−;Hipk2+/+, Hipk1+/−;Hipk2−/−, to Hipk1−/−;Hipk2−/− mutants (Figure S1B). Together, these results supported the idea that HIPK1 and HIPK2 regulated target genes expression in a cooperative and interdependent manner.
Given the role of HIPK2 in the TGF-β-BMP signaling pathways [13],[14], we next asked if the concomitant loss of HIPK1 and HIPK2 could affect the expression of TGF-β-BMP downstream targets. Consistent with this idea, a number of TGF-β target genes were either up- or down-regulated in Hipk1−/−;Hipk2−/− embryos (Table S1). These included genes related to vascular development (e.g., Pai-1) [21] or cell cycle regulation (e.g., Cdkn2c, Cyclin E2, Pcna) (Figure 2A and Figure S1C) [22]–[24]. Remarkably, further analyses of the HIPK1/2 targets revealed several additional potent angiogenic genes, including Mmp10, Vegfa, Angiogenin 2, Nkx2.5, Gata-6, and PECAM-1 (CD31), that were drastically up-regulated in Hipk1−/−;Hipk2−/− mutants (Figure 2A). Indeed, immunohistochemistry using antibodies specific for VEGF-A, MMP10, or PAI-1 confirmed that these proteins were up-regulated in the endothelial cells of E9.5 Hipk1−/−;Hipk2−/− embryos (Figure 2B). In support of these results, qRT-PCR on Vegf, Pai-1, and Mmp10 showed that the up-regulation of these genes was much more drastic in Hipk1−/−;Hipk2−/− mutant, but modest in Hipk1−/−;Hipk2+/+ or Hipk1+/−;Hipk2−/− single mutants (Figure S1D), further supporting the cooperative role of HIPK1 and HIPK2 in the transcription of these targets.
To further investigate the mechanisms of HIPK1/2, we focused on the transcription of Mmp10 and Vegf because of their well-established functions in angiogenesis [2],[25]. Previous studies indicate that MEF2C promotes the transcription of Mmp10 by binding to the upstream promoter. Interestingly, transcriptional corepressor HDAC7 suppresses MEF2C-dependent activation of Mmp10 and that loss of HDAC7 leads to severe vascular phenotype and embryonic lethality similar to those in Hipk1−/−;Hipk2−/− mutants [25]. Since HIPK proteins have been implicated as transcriptional corepressors, we reasoned that HIPK1 and HIPK2 might suppress the transcription of Mmp10 through its participation in the transcriptional complex involving HDAC7-MEF2C. Due to the role of HIPK2 in the TGF-β signaling pathway [13],[15], it is possible that HIPK1/2 may regulate Mmp10 gene expression through Smad-dependent mechanisms. Alternatively, HIPK1/2 may function downstream of TGF-β downstream kinase, TAK1, which regulates vascular development during early embryogenesis [26]. Within the 1 kb upstream regulatory sequences of the Mmp10 gene, we identified one Smad-binding element (SBE) site in position −221 to −215, close to the previously reported MEF2 recognition motif (TAAAATA) (position −80 to −73) (Figure 2C). Interestingly, however, unlike MEF2C, Smad2/3/4 by itself did not activate the transcriptional activity of Mmp10-Luc reporter (Figure S2). Rather, Smad2/3/4 modestly suppressed both wild-type Mmp10-Luc reporter and Mmp10-Luc mutating the SBE site (Mmp10-mSBE-Luc) (Figure S2), suggesting that the inhibitory effects of Smad2/3/4 on Mmp10-Luc reporter were most likely nonspecific. Furthermore, the presence of TGF-β did not change these results (Figure S2). In contrast to Smad2/3/4, MEF2C showed similar effects in promoting the transcriptional activity of wild-type Mmp10-Luc and Mmp10-mSBE-Luc, whereas mutating the MEF2-binding elements in Mmp10-luciferase reporter completely abolished the effects of MEF2C on this reporter (Figure 2D) [25]. These results supported the idea that the SBE site in the promoter of Mmp10 was dispensable for MEF2C-mediated regulation of Mmp10 gene expression, and that HIPK2 may regulate Mmp10 transcription via MEF2C-dependent mechanism.
Consistent with its role as a transcriptional corepressor, HIPK2 showed a dose-dependent suppression of MEF2C-mediated activation of the Mmp10-Luc reporter (Figure 2E). The corepressor effects of HIPK2 required its kinase activity since the kinase inactive mutant HIPK2-K221A failed to suppress MEF2C-dependent activation of Mmp10-Luc reporter. Furthermore, the corepressor activity of HIPK2 required the protein–protein interaction domain (amino acids 582–898) because HIPK2 mutant protein lacking the C-terminal sequence from amino acid 898 to 1189 (HIPK2-Δ898) could still suppress Mmp10-Luc reporter, whereas further deletion from amino acid 582 to 1189 (HIPK2-Δ582) completely abolished the corepressor effects of HIPK2 (Figure 2E). Similar to HIPK2, HIPK1 could also suppress the MEF2C-dependent activation of Mmp10-Luc reporter. Although HIPK1 by itself was less effective compared to HIPK2 (unpublished data), HIPK1 and HIPK2 showed additive effects in suppressing the Mmp10-Luc activity (Figure 2F). To further characterize the transcriptional corepressor effects of HIPK2, we used siRNA to knock down the endogenous Hipk2 expression in HEK293T cells and showed that lowering HIPK2 levels resulted in further up-regulation of MEF2C-mediated activation of Mmp10-Luc activity without affecting the levels of MEF2C (Figure 2G and Figure S3). Together, these results supported the novel role of HIPK1 and HIPK2 as transcriptional corepressors in MEF2C-mediated activation of Mmp10 expression.
To determine if MEF2C and HIPK2 can also regulate the transcription of Vegf, we identified a potential MEF2 binding site in the Vegf locus (position −2679 to −2672) and generated a luciferase reporter that contained 4.5 Kb promoter sequence of Vegf gene (Vegf-Luc) (Figure 2H and Figure S4). Using similar approaches, we showed that MEF2C could indeed activate Vegf-Luc activity. Interestingly, MEF2C-mediated activation of Vegf-Luc could be suppressed by HIPK2 in a dose-dependent manner. Similar to the results from Mmp10-Luc, mutating the MEF2 binding element in Vegf-Luc reporter almost completely abolished the effects of MEF2C and HIPK2 (mVegf-Luc, Figure 2H). Furthermore, HIPK1 and HIPK2 also showed additive effects in suppressing the Vegf-Luc activity (Figure 2I). Although the effect of HIPK2 on Vegf-Luc reporter was not as robust as in Mmp10-Luc, these results were consistent with the previous results that the transcriptional controls of Vegf expression are a tightly regulated process such that loss of one Vegf allele or a slight increase in Vegf expression could result in marked abnormalities in angiogenesis during early embryonic development [27],[28].
To further characterize the role of HIPK2 in the transcriptional control of Mmp10 expression, we expressed MEF2C and HIPK2 in HEK293T cells and used co-immunoprecipitation (co-IP) to show that HIPK2 could indeed be detected in a complex with MEF2C (Figure 3A, upper panels). In addition, similar co-IP experiments using protein lysates from wild-type mouse embryonic fibroblasts (MEF) also showed that the endogenous HIPK2 proteins could be detected in a complex with MEF2C (Figure 3A, bottom panel). Consistent with the requirement of HIPK2 kinase activity in the transcriptional control of Mmp10 (Figure 2E), the protein complex formation between kinase-inactive HIPK2-K221A and MEF2C was significantly reduced compared to wild-type HIPK2 (Figure 3A), whereas the MEF2C protein levels were comparable in cells expressing wild-type HIPK2 and kinase inactive HIPK2-K221A. The trace amount of MEF2C detected in the complex with HIPK2-K221A showed smaller molecular mass, suggesting that HIPK2 may affect the posttranslational modifications of MEF2C (Figure 3A). Indeed, treatment of alkaline phosphatase abolished the upward shift of MEF2C by HIPK2 (Figure S5), supporting the idea that the stable complex formation between HIPK2 and MEF2C required phosphorylation of MEF2C. To further characterize the involvement of HIPK2 and MEF2C in the regulation of Mmp10 gene expression, we performed chromatin immunoprecipitation (ChIP) assays using native chromatin extracts from HUVEC and showed that endogenous MEF2C, HIPK1, and HIPK2 proteins were bound to the MEF2 site on the Mmp10 promoter (Figure 3B). Similar results could also be detected in mouse brain microvascular endothelial (bEnd.3) cells (unpublished data).
Given that HDAC7 suppresses MEF2-mediated expression of Mmp10 [25], we reasoned that HIPK2 might interact with the HDAC7-MEF2 transcriptional corepressor complex. Indeed, co-IP results using protein lysates from HEK293T cells overexpressing HIPK2, HDAC7, and MEF2C showed that HIPK2 and HDAC7 could each be detected in protein complexes with MEF2C (Figure 3C). Interestingly, however, the interaction between HIPK2 and MEF2C appeared to be reduced, but not completely eliminated, by the increasing amount of HDAC7. Conversely, the interaction between HDAC7 and MEF2 could also be reduced by the progressive increase in HIPK2 (Figure 3C). These results suggested that the recruitment of transcriptional corepressor complex to MEF2C might depend on the equilibrium between HIPK2 and HDAC7 [29]. Indeed, increasing the level of HIPK2 led to a progressive suppression of MEF2C-mediated activation of Mmp10-Luc reporter activity in the presence of HDAC7 (Figure 3D). To further determine if the corepressor activity of HIPK2 was dependent on HDAC7, we used a HDAC7 mutant that lacked the MEF2 interacting domain (HDAC7-ΔMEF) and therefore could not suppress MEF2-mediated transcription [25]. Interestingly, HIPK2 could suppress Mmp10-Luc activity in the presence of HDAC7-ΔMEF, suggesting that the transcriptional corepressor activity of HIPK2 could be independent of HDAC7 (Figure 3D). Consistent with these results, HIPK2 continued to suppress MEF2-mediated Mmp10-Luc activity in HEK293T cells in which the endogenous HDAC7 expression was reduced by siRNA (Figure S6). Similarly, HDAC7 could still suppress the Mmp10-Luc reporter activity in HEK293T cells treated with Hipk2 siRNA (Figure 3E).
Several previous studies have indicated that HIPK2 and TAK1 cooperatively regulate the transcriptional activity of c-Myb through phosphorylation and proteasome-dependent degradation in the Wnt-1 signaling pathway [30],[31]. Since both TAK1 and HIPK2 have been implicated in the downstream of TGF-β [13],[32]–[34], we postulated that the transcriptional corepressor activity of HIPK2 might be further regulated by TAK1 in response to TGF-β. Consistent with this idea, co-IP assays showed that the presence of TAK1 and TGF-β enhanced the interaction between MEF2C and HIPK2 (Figure 3F). Moreover, the presence of TAK1 and TGF-β enhanced the corepressor effects of HIPK2 on MEF2C-mediated activation of the Mmp10 luciferase reporter (Figure 3G). Consistent with these results, co-IP assays in HUVEC cells detected protein complex formation among endogenous HIPK2, TAK1, and MEF2C under normal growth conditions. Such interactions can be further promoted by treatment with TGF-β in HUVEC cells (Figure 3H).
The observation that mice lacking TAK1 exhibit severe vascular phenotype similar to Hipk1−/−;Hipk2−/− mutants [26] supports the idea that the protein complex involving HIPK2 and TAK1 may regulate TGF-β–dependent control of angiogenesis. To further characterize the role of HIPK2 in TGF-β signaling pathway, we performed immunoprecipitation–in vitro kinase (IP-IVK) assays and found that, under normal growth condition, HIPK2 showed a basal level of γ-32P-ATP incorporation. The addition of TGF-β further promoted the γ-32P-ATP incorporation in HIPK2 by 2- to 3-fold within 30′ to 1 h after treatment and remained higher than basal level for 24 h (Figure 4A). This effect was completely abolished in kinase-inactive HIPK2-K221A mutants or by TGF-β type I receptor ALK5 inhibitor SB431542 (Figure 4A,B). Since TAK1 has been shown to directly interact with TGF-β receptors [32],[34], we reasoned that the signal transduction from TGF-β to HIPK2 could induce a sequential activation of TAK1 and HIPK2 kinase activity through protein complex formation. Indeed, co-IP assays showed that TAK1 and HIPK2 formed a protein complex, and that the TAK1-HIPK2 complex formation could be further enhanced by TGF-β treatment (Figure 3F,H). These results were further supported by immunofluorescent confocal microscopy showing that TGF-β treatment promoted co-localization of HIPK2 and phospho-TAK1 in the nucleus of HUVEC cells (Figure S7). However, co-IP using TGF-β receptor antibodies showed protein complex formation between TGF-β receptors and TAK1, but not between TGF-β receptors and HIPK2 (unpublished data).
In addition to the interaction between TAK1 and HIPK2, our results showed that TAK1 could also activate the kinase activity of HIPK2. This effect was further enhanced by the treatment with TGF-β (Figure 4C). Surprisingly, expression of the dominant negative TAK1 (TAK1-DN), which carried a point mutation in the highly conserved lysine residue (K63W) in the kinase domain and therefore lacked kinase activity [35], led to a marked reduction in the HIPK2 protein level and HIPK2 kinase activity, even in the presence of TGF-β (Figure 4C). The effect of TAK1-DN on HIPK2 protein level appeared to be mediated by proteasome-dependent degradation since treatment with proteasome inhibitor MG-132 restored the level of HIPK2 protein in cells expressing TAK1-DN and further increased HIPK2 protein in cells expressing wild-type TAK1 (Figure 4D).
The robust effects of TGF-β-TAK1 on HIPK2 phosphorylation raised the possibility that TGF-β could induce phosphorylation on specific amino acids in HIPK2 and thereby influence its transcriptional corepressor effects. Examinations of the amino acid sequence in the activation loop of the kinase domain of HIPK2 revealed a region from positions 346 to 371 that were highly conserved in HIPK1, HIPK2, and HIPK3 and among other species (Figure 5A,B). Since phosphorylation in the tripartite Ser-Thr-Tyr residues in positions 359, 360, and 361 of HIPK2 are similar to those identified in the activation loop of other MAP kinases [36],[37], we reasoned that TGF-β or TAK1 might promote phosphorylation on these amino acids in HIPK2. To address this, we mutagenized each of these amino acids and found that replacing S359 or T360 with a neutral amino acid did not affect the ability of HIPK2 to incorporate γ-32P-ATP (Figure 5C). In contrast, replacing Y361 with phenylalanine drastically reduced the ability of mutant HIPK2 (HIPK2-Y361F) to incorporate γ-32P-ATP upon activation by TGF-β or TAK1 (Figure 5C,D).
To further confirm that TGF-β-TAK1 promotes the phosphorylation of HIPK2 on Y361, we used a phospho-Y361–specific antibody (HIPK2-P-Y361) in Western blot analyses with cell lysates from HIPK2-TAK1–expressing HEK293T cells treated with or without TGF-β (Figure 6A). Similar to the results in Figure 5, we showed that, under normal growth conditions, HEK293T cells exhibited a steady-state level of HIPK2 phosphorylation on Y361, which could be further promoted by TGF-β (Figure 6A). In contrast, cells expressing HIPK2-Y361F mutant proteins showed no evidence of phosphorylated proteins that could be recognized by this antibody (Figure 6A). Interestingly, treatment with TGF-β inhibitor SB431542 completely abolished the effects of TGF-β, but did not affect the basal phosphorylation level of HIPK2-P-Y361 in HUVEC cells. These results suggested that additional TGF-β–independent mechanism(s) might regulate the basal phosphorylation of HIPK2-P-Y361 (Figure 6B).
To characterize the functional consequence of TGF-β–induced phosphorylation of HIPK2 on Y361, we performed Mmp10-Luc assays using wild-type HIPK2 and mutant HIPK2 with specific point mutation in the tripartite S359, T360, or Y361. Whereas HIPK2-S359A and HIPK2-T360A dose-dependently suppressed MEF2C-dependent activation of Mmp10-Luc just like wild-type HIPK2, this suppressor effect was completely abolished in HIPK2-Y361F (Figure 6C). These results were also confirmed in the HUVEC cells (Figure 6D). Together, these results indicated that TGF-β and TAK1 control the expression of angiogenic genes (e.g., Mmp10) by activating transcriptional corepressor HIPK2 via phosphorylation on a highly conserved tyrosine residue in the kinase domain.
The results that HIPK2 can be activated by TAK1 in the TGF-β signaling pathway raised the possibility that endothelial cell-specific deletion of TGF-β signaling may result in phenotypes and perturbations in gene expression similar to those in Hipk1−/−;Hipk2−/− mutants. To test this, we generated conditional mutants that lacked TβRII in the endothelial cells by crossing the TβRIIfl allele with the Tie2-Cre, which targets recombination in the endothelial cells as early as E7.5–8.5 in the developing embryos and yolk sacs [38]. Similar to Hipk1−/−;Hipk2−/− mutants, the Tie2-Cre;TβRIIfl/fl mutants showed severe vascular defects and were lethal by E11.5–12.5. Analyses of the E9.5 Tie2-Cre;TβRIIfl/fl mutant embryos showed a significant increase in the number of CD31+ endothelial cells in the trunk vasculature and in the developing endocardium (Figure 7A,B). The endothelial cells in Tie2-Cre;TβRIIfl/fl mutants exhibited increases in BrdU incorporation (Figure 7C). Remarkably, qRT-PCR analyses of the mRNA from the E9.5 Tie2-Cre;TβRIIfl/fl mutant embryos showed misregulations of TGF-β targets and angiogenesis genes similar to those seen in the Hipk1−/−;Hipk2−/− mutants (Figure 7D).
To further determine if loss of HIPK1 and HIPK2 or perturbations in TGF-β signaling recapitulates the vascular phenotype in Hipk1−/−;Hipk2−/− and Tie2-Cre;TβRIIfl/fl mutants, we established in vitro angiogenesis assays using HUVEC cells cultured in growth-factor–reduced Matrigel to determine if siRNA knockdown of HIPK1 and HIPK2 (siHipk1/2) or TGF-β type I receptor ALK5 (siTβRI) could affect vascular development in vitro. Our results indicated that HUVEC cells treated with control siRNA formed an intricate network of capillary-like structures (Figure 7E). In contrast, those treated with siRNA for Hipk1/2 or TβRI showed poorly developed capillary-like structures and an increased propensity to form clusters of cells (Figure 7F–H), with a significant increase in BrdU incorporation (Figure 7I–L).
In addition to the Matrigel in vitro angiogenesis assays, we also examined the effects of TGF-β and HIPK1/2 in regulating the expression of Mmp10 and Vegf genes and cellular proliferation in HUVEC cells. Using qRT-PCR, we showed that siRNA knockdown of Hipk1/2 or TβRI led to up-regulations of Mmp10 and Vegf mRNA levels in HUVEC cells (Figure 7M). In contrast, treatment of TGF-β suppressed the Mmp10 and Vegf mRNA levels in HUVEC cells (Figure 7N). Interestingly, reducing HIPK1 and HIPK2 using siRNA blocked the ability of TGF-β to suppress the expression of Mmp10 and Vegf (Figure 7N). Similar to these results, TGF-β–induced suppression of cellular proliferation in HUVEC cells, measured by BrdU incorporation, could also be blocked by siRNA knockdown of Hipk1/2 (Figure 7O). Thus, the results from Hipk1−/−;Hipk2−/− mutants, Tie2-Cre;TβRII conditional mutants, the in vitro angiogenesis, and qRT-PCR assays in HUVEC cells supported the idea that the TGF-β–HIPK1/2 signaling pathway regulates a common set of target genes that are critical for angiogenesis during early embryonic development (Figure 8).
Perturbations to the TGF-β signaling mechanisms are known to have serious impacts on cardiovascular development in mice and in human diseases [7],[9]. The manifestations of mouse mutants with targeted deletion in TGF-β signaling components, however, are quite complex and, in some instances, seemingly conflicting. One possible contributing factor to such complexity is that different TGF-β receptors can trigger multiple, divergent downstream signaling via Smad and non-Smad-dependent mechanisms [12],[39]. In addition, the temporal and context-dependent effects of TGF-β on different cell types in the vasculature can further contribute to the final phenotypic outcomes [7]. TGF-β is known to either promote or antagonize endothelial proliferation and migration during vasculogenesis. Although the disparate outcomes of TGF-β are likely due to the differences in how TGF-β type I receptors ALK1 and ALK5 transduce its downstream signals, the exact mechanisms downstream of these receptors are not entirely clear [10],[11].
Our results reveal a previously unrecognized mechanism involving the cooperative role of HIPK1 and HIPK2 in the downstream of TGF-β–TAK1 signaling pathway that regulates the expression of a number of potent angiogenic genes during early embryonic development (Figure 8). First, based on the morphological analyses and gene expression profiling in Hipk1−/−;Hipk2−/− mutants, and the results from siRNA knockdown of Hipk1 and Hipk2 in Matrigel angiogenesis assays using HUVEC cells (Figures 1, 2, and 7), our data indicate that HIPK1 and HIPK2 act cooperatively to regulate a set of angiogenic genes, including Mmp10 and Vegf, that are critical for the early stage of vascular development. This is further supported by a series of in vitro biochemical assays that validate HIPK2 and HDAC7 as important transcriptional corepressors that regulate the expression of Mmp10 and Vegf (Figures 2 and 3). Consistent with these results, EM analyses also show that the endothelial cells in Hipk1−/−;Hipk2−/− mutants exhibit defects in the adherens junction formation similar to those described in Hdac7−/− mutants (Figure 1I–K) [25]. While HIPK2 and HDAC7 have synergistic effects in suppressing the transcription of Mmp10, each can work independently to suppress MEF2C-mediated gene expression. Surprisingly, the effect of HIPK2 and HDAC7 in MEF2C-mediated transcriptional control of Mmp10 expression seems to depend on a delicate balance of protein–protein interaction in the transcriptional complex because increasing abundance of HIPK2 can reduce the presence of HDAC7 in complex with MEF2C and vice versa. One possible explanation for the antagonistic effect of HIPK2 and HDAC7 is that both may compete for the same or similar binding site in MEF2C, which can reach equilibrium as more HIPK2 or HDAC7 are recruited to the complex. This is particularly appealing because the transcriptional machinery involves dynamic assembly of large protein complexes that include transcriptional corepressors, such as HIPK2 and HDAC7 [29]. Alternatively, and not mutually exclusive, it is possible that HIPK2 and HDAC7 may cross-regulate each other through posttranslational modifications, such as phosphorylation or acetylation, which are likely to change the equilibrium of transcriptional complex formation.
The role of HIPK2 as a transcriptional corepressor of MEF2C proteins is further supported by the protein complex formation between MEF2C and HIPK2 in HEK293T cells. Such protein complex formation between endogenous HIPK2 and MEF2C can also be detected in wild-type MEF and HUVEC cells (Figure 3). Interestingly, the interaction between HIPK2 and MEF2C seems to require the kinase activity of HIPK2 because significantly fewer MEF2C proteins are detected in a complex with kinase inactive HIPK2-K221A. Furthermore, the MEF2C proteins that do interact with HIPK2-K221A have lower molecular mass compared with those in complex with wild-type HIPK2, suggesting that HIPK2 may posttranslationally modify MEF2C and thereby inhibits the transcriptional activity of MEF2C. In support of this idea, alkaline phosphatase treatment reduces the HIPK2-induced high molecular mass migration of MEF2C in SDS-PAGE (Figure S5). Although there is no evidence that MEF2C is a direct phosphorylation substrate for HIPK2, it is possible that HIPK2 may activate other protein kinases, such as Cdk5 and GSK3β [40],[41], to phosphorylate MEF2 and thereby promote the pro-differentiation function of MEF2 in endothelial cells.
One remarkable finding from this study is the identification of TGF-β and TGF-β–activating kinase 1 (TAK1) as upstream mechanisms that regulate the interaction between HIPK2, HDAC7, and MEF2C (Figures 3 and 4). These results indicate that TAK1 have two distinct roles in regulating HIPK2 functions. First, using immunoprecipitation–in vitro kinase (IP-IVK) assays, we show that both TGF-β and TAK1 can activate HIPK2 by phosphorylating the tyrosine on position 361 (Y361), a highly conserved residue among all HIPK members in the activation loop of the kinase domain (Figure 5). These results are further verified using a phospho-HIPK2 specific antibody, HIPK2-P-Y361 (Figure 6). Strikingly, HIPK2 with a tyrosine-to-phenylalanine mutation (HIPK2-Y361F) on this amino acid completely loses its ability to suppress MEF2C-dependent transcriptional activity (Figure 6). Second, and quite unexpectedly, we discover that kinase inactive TAK1 blocks HIPK2 function by promoting the degradation of HIPK2 through proteasome-dependent mechanisms (Figure 4). Consistent with these results, treatment with TAK1 inhibitor 5Z-7-Oxozeaenol also promotes HIPK2 degradation in HEK293T cells (Y.S., unpublished observations). These results suggest that, in the absence of signal from TGF-β–TAK1, dephosphorylated HIPK2 proteins may undergo rapid turnover via proteasome pathway (Figure 8). Alternatively, kinase inactive TAK1 may alter intracellular transport of HIPK2 and promote proteasome-mediated degradation of HIPK2. Given the closely interconnected functions between TAK1 and HIPK2, it is perhaps not surprising that loss of TAK1 results in early embryonic lethality due to defects in vascular morphogenesis similar to those in Hipk1−/−;Hipk2−/− mutants [26].
While our results highlight the robust effects of HIPK1 and HIPK2 as corepressors in the MEF2C-dependent transcriptional activation of angiogenic genes, there are several indications that HIPK proteins may have broader functions in regulating the outcome of TGF-β signaling. For instance, HIPK2 has been shown to serve as a transcriptional coactivator in the Smad2/3/4-SBE reporter assays and in JNK-mediated functions, which critically regulate the decision of survival and apoptosis in dopaminergic neurons and in tumor cells, respectively [13],[16]. In addition, HIPK2 can also function as a corepressor in Ski-dependent suppression of BMP-Smad1/4-induced transcriptional activation [15]. Given the complexity of TGF-β signaling mechanisms, it is possible that the final outcomes of HIPK2 functions will likely be context-dependent. In support of this view, loss of HIPK1 and HIPK2 leads to down-regulation of several genes critical for the control of cell cycle progression (Figures 2 and 8). Although the magnitudes of reduction in these genes are not as drastic as the up-regulation of angiogenic genes, many of these genes have been well-documented to be the transcriptional targets in the canonical TGF-β–Smad pathway (Figure 8 and Table S1) [42]. It will be interesting to determine if HIPK1 and HIPK2 may regulate the transcriptional control of these target genes, thus establishing these kinases as novel mediators connecting the Smad and non-Smad signaling pathways downstream of TGF-β. Finally, the gene expression data in Hipk1−/−;Hipk2−/− mutants also reveal a significant, albeit modest, down-regulation of Alk1, Alk5, and Hdac7 transcripts. While it is unclear if HIPK1 and HIPK2 can also directly regulate the transcription of these genes, based on the well-characterized functions of these genes, their down-regulation could certainly amplify the vascular defects in Hipk1−/−;Hipk2−/− mutants.
The Hipk1−/− and Hipk2−/− mutant mice have been described previously [18],[43]. The Tie2-Cre and the floxed TGF-β type II receptor (TβRIIfl) mice were generously provided by Dr. Rong Wang and Dr. Harold Moses, respectively [38],[44],[45]. Animal care was approved by the Institutional of Animal Care and Use Committee and followed the NIH guidelines.
Embryonic day (E) 9.5 and E10.5 embryos and yolk sacs were fixed at 1% PFA in PBS for 2 h, cryoprotected in 15% sucrose for 30 min, and then in 30% sucrose for 30 min. Tissue sections were incubated with primary antibodies overnight and with secondary antibodies for 1 h. To label the cells in S-phase of cell cycle, pregnant mice were injected intraperitoneally with BrdU (50 mg/kg body weight, BD Bioscience) and sacrificed 2 h later. To detect BrdU+ endothelial cells, tissue sections were incubated with the CD31 antibody. Afterward, the tissue sections were fixed in 4% PFA for 30 min and then treated with 2N HCl at 37°C for 30 min. After three washes with Borax solution, the tissue sections were incubated with primary antibody against BrdU overnight, and then incubated with Alexa Fluor-conjugated secondary antibody for 1 h. For whole-mount immunofluoescent staining, E9.5 embryos and yolk sacs were fixed in 4% PFA in PBS overnight at 4°C, washed four times in PBS at 4°C, and blocked overnight at 4°C in 5% goat serum, 0.1% Triton X-100 in PBS. They were then incubated in rat anti-CD31 antibody (1∶500; Mec13.3; BD Biosciences) overnight at 4°C, washed in PBT overnight at 4°C, and incubated in Alexa Fluor 488 Goat Anti-Rat IgG (1∶1,000) for embryos and Alexa Fluor 555 Goat Anti-Rat IgG (1∶2,000) for yolk sacs. To determine the number of endothelial cells in S-phase of the cell cycle, tissue sections were double labeled with anti-CD31 (1∶20; Cat No. 550274; BD Biosciences) and anti-BrdU (1∶500; MAB3222; Millipore). Immunohistochemistry using PAI1 antibodies required antigen retrieval, in which the tissue sections were incubated in 10 mM sodium citrate buffer at 100°C for 30 min. Sample preparations and image capture for electron microscopy were described previously [14].
Neurolucida was used to determine the avascular area, fragment length (length of a vessel before it branches), and branch points in the yolk sacs. Individual avascular areas were manually traced and then added up to get the total avascular area per frame using “contour mapping” option in Neurolucida (MicroBrightField). Individual fragment lengths were measured with each fragment length separated by a different colored line. Fragment lengths were then averaged to get the average fragment length per frame [46].
Total RNA was extracted from embryos using PicoPure RNA Isolation Kit (Arcturus) and used as a template for reverse transcriptase with MessageAmp II-Biotin enhanced Kit (Ambion). Microarray analysis was performed using CodeLink Mouse Whole Genome Bioarray (Applied Microarrays). The microarray data have been deposited in Gene Expression Omnibus (http://www.ncbi.nlm.nih.gov/geo/), accession number GSE39253. The RNA from HEK293T cells, HUVEC cells, or MEF was isolated by Trizol reagent (Invitrogen) and used as a template for reverse transcriptase with random hexamer primers (Invitrogen). Primer sequences for specific genes are available in Table S2.
HEK293T cells were purchased from ATCC and MEFs was reported previously [47]. Both cell lines were cultured in DMEM growth medium with 10% fetal bovine serum (Hyclone). HUVEC cells were maintained in EGM-2 medium (Lonza Walkerville Inc.). For immunostaining, cells were plated on gelatin-coated glass coverslips, fixed in 4% PFA, and stained with appropriate primary antibodies as described previously [43],[47].
siRNA oligonucleotides for Hipk1 (Cat No. sc39048), Hipk2 (Cat No. sc39050), Hdac7 (Cat No. sc35546), or TGF-β type I receptor (TβRI) (Cat No. sc40222, specific for ALK5) were purchased from Santa Cruz Biotechnology, Inc. and used at a concentration of 30 pM to transfect HEK293T or HUVEC cells using Lipofectamine 2000 (Invitrogen). Two days after transfection, cells were harvested either for RNA isolation or for luciferase activity measurement. RT-PCR and Western blots were performed multiple times with comparable results. Primer sequences for PCR were provided in Table S2. Luciferase assays were performed using the dual-luciferase assay system (Promega) [13],[43],[47]. The luciferase reporter activity was measured using the dual-luciferase system on a luminometer (Turner Designs). Relative luciferase activity was reported as a ratio of firefly over Renilla luciferase readouts. The Mmp10-luciferase reporters, HDAC7 constructs, and myc-tagged MEF2C construct were gifts from Dr. E. Olson [25]. The Vegfa-luciferase reporter contained 4,512 bp to 1 bp of the mouse Vegfa gene, subcloned into pGL4.10[Luc2] vector (Promega). The Vegfa-luciferase construct that contained mutations in the MEF2 binding site (mVegfa-luc) was generated using the QuikChange II Site-Directed Mutagenesis kit (Stratagene).
Whole-cell lysates were collected from HEK293T cells 24 h after transfection in lysis buffer containing 50 mM HEPES (pH 7.4), 50 mM NaCl, 0.1% Tween 20, 20% glycerol, and 1× protease inhibitor cocktail (Roche Molecular Systems) with brief sonication. The same amount of supernatants was incubated overnight at 4°C with different primary antibody and then incubated with Protein A/G Plus Agarose beads for 3 h at 4°C. Immune complexes were washed in buffers containing 50 mM HEPES (pH 7.4), 300 mM NaCl, 0.2 mM EDTA, and 1% NP-40 and analyzed on SDS/PAGE. For in vitro kinase assays, cells were treated with DMSO or 10 ng/ml TGF-β 24 h after transfection, and then whole-cell lysates were collected in lysis buffer. Immune complexes were washed with kinase buffer (25 mM Tris-HCl, pH 8.0, 10 mM MgCl2), and then incubated with 1 mM ATP and 5 µCi of γ-32P-ATP (Perkin Elmer) for 3 h at room temperature. The resin beads were then washed with 10 nM Tris-HCl (pH 7.5) and the proteins eluted with 25 µl SDS loading buffer. Phosphorylation of HIPK2 on Y361 was confirmed by HIPK2-P-Y361 specific antibody (Thermo Scientific, Cat No. PA5-13045, 1∶500 dilution) in Western blots using HEK293T cell lysates.
ChIP assays were performed as described [47]. Briefly, HUVEC or bEnd.3 cells were fixed with 4% PFA and treated with SDS lysis buffer. After shearing with a sonicator and contrifugation, the supernatant of cell lysates were used for immunoprecipitation with different antibodies. The DNA–protein–antibody complexes were isolated using antibodies for HIPK1 (p-16, sc-10289), MEF2C (e-17, sc-13266) (Santa Cruz Biotechnology), or HIPK2 (ab28507, Abcam). The complexes were washed with buffers, and the DNA were eluted and purified. Primer sequences were available in Table S2.
HUVEC cells were cultured in EBM-2 medium containing serum and endothelial cell supplements (EGM2) according to the manufacturer's instructions (BD Biosciences). The siRNA-mediated knockdown was performed when the cells reached 80% confluence. For in vitro angiogenesis assays, HUVEC cells were trypsinized 48 h after transfection, and reseeded onto Matrigel-coated plate in the presence of EGM2 medium. After 18 h, vascular formation was assessed and photographed under a Nikon TE2000-U microscope with 4× objective. For BrdU incorporation assays, HUVECs were seeded onto gelatin-coated coverslips in 24-well plates, and incubated with BrdU (10 µM) for 2.5 h.
Data were analyzed by two-tailed Student's t test. Values were expressed as mean ± S.E.M. Changes were identified as significant if the p value was less than 0.05.
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10.1371/journal.pntd.0005751 | Urbanization is a main driver for the larval ecology of Aedes mosquitoes in arbovirus-endemic settings in south-eastern Côte d'Ivoire | Failure in detecting naturally occurring breeding sites of Aedes mosquitoes can bias the conclusions drawn from field studies, and hence, negatively affect intervention outcomes. We characterized the habitats of immature Aedes mosquitoes and explored species dynamics along a rural-to-urban gradient in a West Africa setting where yellow fever and dengue co-exist.
Between January 2013 and October 2014, we collected immature Aedes mosquitoes in water containers in rural, suburban, and urban areas of south-eastern Côte d’Ivoire, using standardized sampling procedures. Immature mosquitoes were reared in the laboratory and adult specimens identified at species level.
We collected 6,159, 14,347, and 22,974 Aedes mosquitoes belonging to 17, 8, and 3 different species in rural, suburban, and urban environments, respectively. Ae. aegypti was the predominant species throughout, with a particularly high abundance in urban areas (99.374%). Eleven Aedes larval species not previously sampled in similar settings of Côte d’Ivoire were identified: Ae. albopictus, Ae. angustus, Ae. apicoargenteus, Ae. argenteopunctatus, Ae. haworthi, Ae. lilii, Ae. longipalpis, Ae. opok, Ae. palpalis, Ae. stokesi, and Ae. unilineatus. Aedes breeding site positivity was associated with study area, container type, shade, detritus, water turbidity, geographic location, season, and the presence of predators. We found proportionally more positive breeding sites in urban (2,136/3,374, 63.3%), compared to suburban (1,428/3,069, 46.5%) and rural areas (738/2,423, 30.5%). In the urban setting, the predominant breeding sites were industrial containers (e.g., tires and discarded containers). In suburban areas, containers made of traditional materials (e.g., clay pots) were most frequently encountered. In rural areas, natural containers (e.g., tree holes and bamboos) were common and represented 22.1% (163/738) of all Aedes-positive containers, hosting 18.7% of the Aedes fauna. The predatory mosquito species Culex tigripes was commonly sampled, while Toxorhynchites and Eretmapodites were mostly collected in rural areas.
In Côte d’Ivoire, urbanization is associated with high abundance of Aedes larvae and a predominance of artificial containers as breeding sites, mostly colonized by Ae. aegypti in urban areas. Natural containers are still common in rural areas harboring several Aedes species and, therefore, limiting the impact of systematic removal of discarded containers on the control of arbovirus diseases.
| Outbreaks of yellow fever and dengue caused by Aedes mosquitoes have been repeatedly reported in rural and urban areas in humid tropical Africa, including Côte d’Ivoire. Although controlling immature stages of Aedes mosquitoes in their aquatic habitats before they become adult vectors remains the best method to fight arboviral diseases, failure to identify the larval habitats can compromise intervention success. We studied the larval ecology of Aedes mosquitoes in different settings (rural, suburban, and urban) in Côte d’Ivoire. We found that the degree of urbanization was significantly associated with Aedes breeding sites. Compared with rural areas, urban and suburban areas were characterized by high numbers of Aedes mosquito breeding sites; mostly artificial containers (e.g., tires and discarded containers) that were inhabited by the larvae of Ae. aegypti. In rural areas, natural containers (e.g., tree holes and bamboos) harbored several other Aedes species not found elsewhere. Our results suggest that removal of discarded containers–a common practice in arbovirus control programs–in urban areas does not suffice for controlling arboviral diseases because urban areas remain exposed to (re)infestation due to natural containers that host several Aedes species in rural areas. Additional vector control strategies are required.
| Several Aedes species act as vectors of arboviral diseases, such as yellow fever, dengue, chikungunya, Rift Valley fever, and Zika virus infections that are of considerable public health relevance [1]. The transmission patterns of these arboviruses and their geographic expansion are expected to change due to environmental transformation, including urbanization [2, 3]. Besides yellow fever, other arboviruses are likely underestimated and underreported in Africa because of low awareness by health care providers, other prevalent non-malarial febrile illnesses, lack of diagnostic tests, and absence of systematic surveillance [4]. Nevertheless, yellow fever, dengue (DENV1-4), chikungunya, and Zika viruses are currently circulating in West Africa through the sylvatic, rural, and epidemic cycles maintained by wild and urban vectors [5, 6]. Côte d’Ivoire has been repeatedly facing yellow fever and dengue outbreaks involving several vectors such as Aedes africanus, Ae. furcifer, Ae. luteocephalus, Ae. opok, and Ae. vittatus in rural, and Ae. aegypti in urban areas [7, 8]. These outbreaks have often occurred in foci characterized by high rate of urbanization due to economic development supported by palm oil and rubber farming, trade, and traffic [7].
Arboviral disease transmission is influenced by community-level effects of container-dwelling Aedes mosquito larvae by regulating the production and fitness of adult vectors [9]. Aedes mosquito larvae are highly sensitive to environmental changes, including urbanization [10]. Some Aedes species (e.g., Ae. aegypti) inhabit a wide variety of containers ranging from natural containers (e.g., tree holes) to artificial containers (e.g., tires, discarded items, and other water containers) due to their ecologic plasticity [11], while others are restricted to specific breeding sites because of the higher sensibility of their offspring to environmental changes [12]. The ecologic plasticity allows Ae. aegypti and Ae. albopictus to spread worldwide by sea, air, and land transportation networks, and to adapt to new and changing environments [10].
The choice of breeding sites is governed by competition and predation among immature stages of Aedes and other mosquitoes that co-exist in the same breeding site [11, 12]. For example, intra- and interspecific competition between Ae. aegypti and Ae. albopictus [13] and among several Aedes species [12] has been reported. Moreover, mosquito species such as Toxorhynchites spp., Eretmapodites spp., and Culex tigripes predate on the larvae of Aedes [12, 13]. The biotic factors may also interact with abiotic factors, such as the climate [13]. As larvae directly depend on water, precipitation is the most important physical factor. The complex patterns of flooding and drying of larval breeding sites govern arboviral transmission [14].
In Côte d’Ivoire, yellow fever has been a key factor that forced the transfer of the colonial capital from Grand-Bassam to Bingerville near Abidjan in 1899 [15]. However, more than a century later, yellow fever and dengue outbreaks still remain an unresolved public health issue [7, 8, 15]. During arbovirus epidemics, vector controls are mostly based on the systematic removal of artificial Aedes breeding sites in urban areas.
The most effective vector control strategy is the control of immature stages in their aquatic habitats [12]. Hence, effective larval control requires a deep understanding of larval ecology. Our study aimed to characterize the dynamics of Aedes larval breeding sites, species composition, and biological associations in terms of geographic and seasonal variations along a rural-to-urban gradient in south-eastern Côte d’Ivoire. As Aedes mosquito larvae are highly sensitive to environmental changes [10], we hypothesized that larval breeding sites differ in species composition between urban and rural areas.
The study protocol received approval from the local health and other administrative authorities. All entomologic surveys and sample collections carried out on private lands or private residential areas were done with the permission and written informed consent of the residents. This study did not involve endangered or protected species.
The study was conducted in three areas located within a traditional arbovirus focus in south-eastern Côte d’Ivoire: Ehania-V1 (geographic coordinates 5° 18’ N latitude, 3° 4’ W longitude), Blockhauss (5° 19 N, 4° 0’ W), and Treichville (5° 18 N, 4° 0’ W), representing an increasing urbanization gradient (Fig 1). The degree of urbanization is characterized by land use, vegetation coverage, human population density, state of roads, and public services, as described in Zahouli et al. [16].
Natural and artificial containers such as tree holes, bamboo, fruit husks, tires, discarded items, and water storage receptacles that may serve as potential breeding sites for Aedes mosquitoes vary according to human habitation and activities. The rural area is surrounded by farms of palm oil trees (Eleasis guineensis) covering 11,444 ha and a preserved rainforest of 100 ha, while the suburban area is located about 2 km away from the Banco National Park with over 3,750 ha of rainforest. The rainforest is inhabited by a diverse fauna (e.g., primates and birds) that serve as hosts for Aedes mosquitoes.
The climate is characterized by high temperature and precipitation with two rainy seasons. The seasons are distinguished by rainfall rather than temperature. The main rainy season extends from May to July, while the shorter rainy season occurs from October to November, with distinct dry seasons in between. The average annual precipitation ranges from 1,200 to 2,400 mm. The annual average temperature and relative humidity are around 26.5°C and 80–90%, respectively.
Aedes larval breeding sites were sampled quarterly in domestic (space inhabited by humans) and peri-domestic (surrounding vegetated environment within a 600 m radius from the domestic areas) sites in rural, suburban, and urban areas from January 2013 to October 2014. While water-holding containers, tree holes, and bamboo were repeatedly sampled, other potential breeding sites were sampled for the presence of immature stages of Aedes mosquitoes. All accessible properties were surveyed simultaneously in the three settings. Some properties could not be sampled because the residents refused to provide access or because there were physical barriers of access.
Potential larval breeding sites of Aedes mosquitoes were sampled in all three study sites by teams consisting of four trained mosquito collectors in each study area. Each mosquito collector team was composed of the same persons during all surveys. The number of experienced mosquito collectors was constant on any one day in each study area, whereas the teams made rotations from one study area to another in order to ensure similar sampling efforts and efficiency in the three study areas, and minimize potential biases. The collectors worked from 08:00 to 16:00 hours, and spent proportionally equal time periods searching for potential mosquito breeding sites in the study areas.
Readily visible and accessible containers in the selected households and surrounding premises were examined for the presence of water and mosquito larvae. In a preliminary survey, existing larval breeding sites, such as natural and artificial cavities or containers with a potential to contain water were kept in an inventory and assigned a unique label. Based on this preliminary survey, potential breeding sites were classified into two categories, three sub-categories, and 16 types, depending on their location, origin, material, and container type (Table 1 and S1 Fig). The breeding sites were assessed for abiotic and biotic characteristics, including geographic location (domestic and peri-domestic sites), color, exposure to sunlight (full shade, no exposure to sunlight; partial shade, partial exposure to sunlight; no shade, permanent exposure to sunlight), turbidity (transparent/clear, colored, opaque), substrate type (no substrate, foliage, moss, soil), surface of water, depth, presence of mosquito larvae, and predators (larvae of Cx. tigripes, Eretmapodites spp., and Toxorhynchites spp. mosquitoes, toad tadpoles, and arachnids).
Larvae and pupae of Aedes mosquitoes were sampled using the World Health Organization (WHO) standard equipment adapted to the aperture and the depth of larval habitats. A flexible collection tube connected to a manual suction pump was used to sample water from bromeliads and bamboo holes. Scoops of 350 ml capacity were used to collect immature mosquitoes from larger breeding sites (e.g., tree holes, discarded containers, tires, and puddles). The collected Aedes mosquito were counted using a pipette and classified as young larvae (1–2 instar), old larvae (3–4 instar), and pupae. Non-Aedes mosquito larvae such as Anopheles spp., Coquelitidia spp., Culex spp., Eretmapodites spp., Filcabia spp., Toxorhynchites spp., and Uranotenia spp. were also recorded. The predacious larvae of mosquitoes, such as Cx. tigripes, Eretmapodites spp., and Toxorhynchites spp. were removed from the samples to avoid predation on the other species and preserved separately. All mosquito samples were stored separately in plastic boxes and transported in a coolbox to a field laboratory.
In the laboratory, mosquito larvae were reared until they reached the adult stage. In order to minimize mortality, a maximum of 20 larvae were placed in 200 ml plastic cups, filled with 150 ml distilled water and covered with netting. Larvae of Aedes and other mosquitoes were fed each morning between 07:00 and 08:00 hours with Tetramin Baby Fish Food. Predacious larvae of Toxorhynchites spp. and Cx. tigripes were fed with larvae from colonies sampled from the study areas. Emerging adult mosquitoes were identified to species level using a morphological key [17]. As larval mortalities were low, the proportion of mosquito species was estimated on the basis of emerging adults. Adult specimens were stored by species and recorded in an entomology collection database.
The frequency of Aedes-positive breeding sites (FP) was calculated as the percentage of water holding containers with at least one larva or pupa (numerator) among the wet containers (denominator). The proportion of Aedes-positive breeding site types among the Aedes-positive breeding sites (PP) was expressed as the percentage of each Aedes-positive container type (numerator) among the total Aedes-positive containers (denominator) in each study area. To test whether there was a difference in the number of positive breeding sites and the number of available wet containers in each category, we used Fisher’s exact test and χ2, as appropriate, to test for differences in the frequency of Aedes-positive breeding sites across the three study areas, between the domestic and peri-domestic sites, and between dry and rainy seasons.
Aedes species proportions were calculated as the percentage of specimens belonging to the genus Aedes for each study area and then compared between breeding sites as above. Larval abundances of Aedes mosquitoes were standardized as the mean numbers of larvae per liter of water, expressed as the geometric mean, known as Williams’ mean (i.e., log[number of mosquito larvae + 1]) [18], and compared using the Kruskal-Wallis test, followed by Mann-Whitney. The Mann-Whitney U test was also performed to compare pairs of study areas when the Kruskal-Wallis H test showed a significant difference or only two habitats. Aedes species richness was defined as the number of collected species in each study area and compared using a one-way analysis of variance (ANOVA), followed by the Tukey post-hoc test for post-hoc pairwise comparisons [19]. Aedes species diversity and dominance were estimated using the Shannon-Weaver index [20] and Simpson index [21] and analyzed using a Kruskal-Wallis test. Kruskal-Wallis test was performed because a test for normality showed a significant difference in the variances after log-transforming the data. A significance level of 5% was set for statistical testing. All statistical analyses were conducted using Stata version 14.0 (Stata Corporation; College Station, TX, United States of America).
Table 2 shows the species composition of adult mosquitoes that emerged from the larvae and pupae sampled from the breeding sites along the rural-to-urban gradient in south-eastern Côte d’Ivoire and reared after transfer to the laboratory. In total, 7,661, 16,931, and 26,968 adult mosquitoes emerged from the collected larvae in rural, suburban, and urban areas, respectively. The rural setting had the highest mosquito species diversity (eight genera and 37 species), followed by the suburban setting (four genera and 14 species), and the urban setting (three genera and nine species). The genus Aedes predominated throughout, with proportions of 80.40% (n = 7,661) in rural, 84.75% (n = 16,931) in suburban, and 85.19% (n = 26,968) in urban settings. The rural setting had the largest number of Aedes species (17 species), followed by the suburban (eight species) and urban settings (three species).
The predacious mosquito species Cx. tigripes was sampled in each of the three study settings, while the predators Eretmapodites chrysogaster, Er. inornatus, and Toxorhynchites brevipalpis were primarily collected in rural settings. Moreover, several other vector competent mosquito species, namely Anopheles coustani, An. gambiae, Coquelettidia fuscopennata, Cx. quinquefasciatus, and Cx. poicilipes were sampled.
Table 3 summarizes the species composition of Aedes mosquitoes collected as larvae among different types of breeding sites in the rural, suburban, and urban areas. Ae. aegypti and Ae. vittatus were commonly encountered in the three settings. Ae. aegypti was the most prevalent species in the all study areas, and exhibited rising abundance from rural (n = 6,159; 75.12%) to suburban (n = 14,347, 93.94%), and urban (n = 22,974, 99.37%) areas. The highest prevalence of Ae. vittatus (5.18%) was found in suburban areas. In rural areas, Ae. furcifer (4.53%), Ae. palpalis (3.96%), Ae. dendrophilus (3.83%), Ae. vittatus (2.83%), Ae. africanus (2.31%), Ae. luteocephalus (1.49%), Ae. metallicus (1.28%), Ae. lilii (1.22%), and Ae. unilineatus (1.20%) were collected at frequencies above 1%. We also found two specimens of Ae. albopictus (0.01%) in the urban settings.
The presence of Aedes mosquito larvae in breeding sites significantly varied by species (Table 3). For example, Ae. aegypti were found in all types of Aedes-positive breeding sites sampled in all the three study areas. Moreover, Ae. dendrophilus, Ae. furcifer, and Ae. luteocephalus were found in all container types in the rural areas, while Ae. vittatus and Ae. metallicus were collected from both natural and artificial containers in the suburban areas. Ae. africanus, Ae. lilii, Ae. unilineatus, and Ae. usambara were mostly present in natural containers such as tree holes, bamboo, and fruit husks in rural settings.
Several species were found together in the same breeding sites. For example, Ae. aegypti, Ae. dendrophilus, Ae. furcifer, and Ae. africanus shared the same breeding sites in the rural areas, whereas Ae. aegypti co-existed with Ae. vittatus in suburban settings (n = 1,295, 12.8%). These two species co-occurred, albeit at low frequency (n = 57, 0.3%) in urban breeding sites. Additionally, Cx. quinquefasciatus and An. gambiae were often collected together with Ae. aegypti in tires and discarded containers in peri-domestic environments in the three study areas.
Mosquito predators, such as Cx. tigripes, Er. chrysogaster, and Tx. brevipalpis were found in the same breeding sites as Ae. aegypti, Ae. dendrophilus, Ae. furcifer, and Ae. africanus in rural settings. These ecologic associations were most present in tree holes, discarded containers, and tires in the rural areas and in peri-domestic breeding sites during the rainy season.
Among 3,569, 4,882, and 5,783 containers inspected in rural, suburban, and urban settings, 2,423, 3,069, and 3,374 were wet, respectively. The urban setting had a significantly higher Aedes-positive breeding site rate (2,136/3,374, FP = 63.3%), as compared to suburban (1,428/3,069, FP = 46.5%) and rural settings (738/2,423, FP = 30.5%) (χ2 = 478.9, df = 2, p < 0.05) (S1 Table). The Mann-Whitney U test indicated that the abundance of immature Aedes mosquitoes in one study area was significantly different compared to another. A significantly higher abundance of immature Aedes mosquitoes was found in urban areas with larval densities of 1.26 ± 0.01 larvae/l, followed by the suburban areas with 0.77 ± 0.01 larvae/l and rural areas with 0.42 ± 0.01 larvae/l (χ2 = 663.3, df = 2, p < 0.001) (Table 4). Urban settings showed significantly higher proportions of pupae (n = 23,126, 14.9%) and 3–4 instar larvae compared to rural setting with 9.6% (n = 6,212) of pupae and 47.8% of 3–4 instar larvae (p < 0.05). The presence of immature Aedes mosquitoes was significantly associated with the sites, seasons, breeding site types and categories, substrates, color, vegetal detritus, shade, water turbidity, and predators (p < 0.05).
Fig 2 shows that the Aedes-positive microhabitat rate varied widely from one breeding site type to another in all three areas. The rural area showed the largest variability in Aedes breeding sites grouped into 16 types, followed by the suburban and urban areas presenting 15 and 12 microhabitat types, respectively. S1 Table indicates that immature Aedes mosquitoes were found in both natural (163/738, PP = 22.1%) and artificial (575/738, PP = 77.9%) breeding sites in the rural, and mostly in artificial breeding sites in the suburban (1,405/1,428, PP = 98.4%) and urban (2,129/2,136, PP = 99.7%) areas, including higher proportions of industrial containers in the urban areas (2,066/2,136, PP = 96.7%). In the rural areas, the main Aedes-positive breeding sites represented natural types, such as three holes (62/69, FP = 89.9%), bamboo (17/45, FP = 37.8%), and fruit husks (59/195, FP = 30.3%), traditional containers such as metallic (27/44, FP = 61.4%) and clay pots (44/101, FP = 43.6%) and wood-containers (24/69, FP = 34.8%); and industrial containers such as tarps (41/66, FP = 62.1%), tires (183/324, FP = 56.5%), vehicle tanks (41/84, FP = 48.8%), discarded containers (104/254, FP = 40.9%), and vehicle carcasses (68/171, FP = 52.0%). In the urban setting, the most common Aedes breeding sites comprised of industrial containers such as tires (1,087/1,236, FP = 87.9%), discarded containers (601/767, FP = 78.4%), vehicle tanks (77/94, FP = 81.9%), vehicle carcasses (91/131, FP = 69.5%), and water storage containers (141/896, FP = 15.7%). Water storage containers were found to be more frequently infested with immature stages of Aedes mosquitoes in the urban than in the suburban (χ2 = 17.3, df = 1, p < 0.001) or rural settings (χ2 = 57.3, df = 1, p < 0.001). Furthermore, there was a statistically significant difference in Aedes mosquito positivity rate in water storage container between the suburban and rural settings (χ2 = 15.8, df = 1, p < 0.001). Besides the variations in the frequency in the colonization of Aedes breeding sites, the most abundant Aedes breeding sites were tires and discarded containers in all the study areas (all p < 0.05) (Fig 3). Also frequently positive were natural breeding sites such as tree holes (62/738, PP = 8.4%), fruit husks (59/738, PP = 8.0%), industrial containers such as tarps (41/738, PP = 5.6%), vehicle tanks (41/738, PP = 5.6%), and vehicle carcasses (68/738, PP = 9.2%) in the rural area, and water storage containers (141/2,136, PP = 6.6%) in the urban area (Fig 3).
Table 4 summarizes the abundance, richness, diversity, and dominance of Aedes mosquito species according to the breeding site types among sites and study areas. The Shannon’s diversity and Simpson’s dominance indices highly varied between the study areas and breeding sites, showing higher overall values in peri-domestic environments. The highest larval abundances of Aedes mosquitoes were recorded in tires in all study areas (p < 0.05). In addition, tree holes and metallic pots in the rural, vehicle tanks and building tools in the suburban, and discarded containers, vehicle tanks, and vehicle carcasses in the urban areas were also highly productive breeding sites for Aedes mosquito (S2 Fig). Aedes species richness was significantly different among the microhabitats in the rural (F = 4.3, df = 16, p < 0.001), suburban (F = 9.2, df = 7, p < 0.001), and urban settings (F = 11.1, df = 2, p < 0.001). Significantly higher numbers of species (13 species) were found in tree holes in the rural areas. The rural areas showed the highest species diversity, as demonstrated by a Shannon’s diversity index of 1.64, followed by 0.38 for the suburban and 0.06 for the urban areas. Among the breeding sites, the highest Shannon’s diversity index was found in the rural areas for the tree holes with a value of 3.13. Conversely, Simpson’s dominance index of Aedes species significantly decreased from the urban (0.99) to suburban (0.89) and rural (0.57) areas (F = 16.2, df = 3, p < 0.001).
Table 5 shows that the proportion of breeding sites positive for Aedes larvae significantly varied across the peri-domestic and domestic sites in all study areas. Overall, compared to domestic environment, peri-domestic sites showed a higher proportion of significantly Aedes-positive breeding sites, with FP of 84.8% (1,753/2,066) in urban (χ2 = 1,100, df = 1, p < 0.001), 70.2% (1,176/1,676) in suburban (χ2 = 829.2, df = 1, p < 0.001), and 42.6% (636/1,492) in rural (χ2 = 271.5, df = 1, p < 0.001) areas. In rural areas, 87.7% (143/163) of the natural breeding sites that hosted Aedes larvae were located in the peri-domestic sites. High numbers of tires were found infested in the domestic site, with FP of 66.5% (151/227) Aedes-positive breeding sites in the urban, and 35.8% (63/176) in the suburban area.
In all study areas, the proportion of Aedes-positive breeding sites and the number of larvae varied significantly over time with more breeding sites being positive during the rainy season (Fig 4 and S3 Fig). During the rainy season, proportionally more breeding sites were positive. The frequencies of Aedes-positive breeding sites were 69.6% (1,650/2,369) in the urban (χ2 = 137.7, df = 1, p < 0.001), 52.9% (1,196/2,263) in the suburban (χ2 = 138.4, df = 1, p < 0.001), and 34.6% (642/1,857) in the rural (χ2 = 63.5, df = 1, p < 0.001) areas (S2 Table). Significantly more Aedes-positive breeding sites were observed during the rainy season in the rural, urban, and suburban areas, with FP of 40.0% (187/468) and 72.0% (521/724) in July 2013, and 56.6% (327/578) in October 2013, respectively (S3 Fig). Moreover, higher densities of immature Aedes mosquitoes were recorded in July 2013 with 0.62 ± 0.03 and 1.70 ± 0.03 larvae/l in the rural, urban and suburban areas, respectively, and in October 2013 with 1.02 ± 0.02 larvae/l (Fig 5). There were significant differences in the highest Aedes microhabitat rates (χ2 = 121.2, df = 2, p < 0.001) and the highest abundance (χ2 = 156.5, df = 2, p < 0.001) between the three study areas. The highest frequency (i.e., 352/393, FP = 89.6%) of Aedes-positive breeding sites was observed in the peri-domestic sites in the urban areas during the rainy season in October 2013.
When designing strategies to monitor and control Aedes arbovirus vectors in their breeding sites, failure to identify the broad spectrum of potentially available breeding sites will bias the results from field sampling and will thus negatively affect the impact of larval control interventions. Our study pertaining to larval habitats of Aedes mosquitoes alongside a rural-to-urban gradient within yellow fever and dengue co-endemic areas in the south-eastern part of Côte d’Ivoire provided strong evidence for influence on species structure, breeding sites, and biological interactions among the immature forms (Fig 6).
Compared to a previous study conducted in the same area of Côte d’Ivoire [16], the current study identified 11 additional Aedes species (i.e., Ae. albopictus, Ae. angustus, Ae. apicoargentus, Ae. argenteopunctatus, Ae. haworthi, Ae. lilii, Ae. longipalpis, Ae. opok, Ae. palpalis, Ae. stokesi, and Ae. unilineatus) and 16 additional non-Aedes species that may influence arbovirus transmission patterns. To our knowledge, Aedes mosquito species such as Ae. lilii, Ae. stokesi, and Ae. unilineatus, and others such as Cq. fuscopennata and Tx. brevipalpis appear to be reported for the first time in Côte d’Ivoire. Ae. albopictus is not native to Côte d’Ivoire, but has previously been reported [22]. Presumably this species has been introduced through the seaport bordering the urban municipality of Treichville. The higher numbers of Aedes species is likely due to abundant presence of natural and artificial breeding sites, and their potentials to provide suitable microenvironments. Gravid Aedes females select oviposition sites according to their physical, chemical, and biological characteristics [11, 12] and these may change in space and time over the year [16].
The public health relevance of Aedes mosquitoes results from their invasiveness and ecologic plasticity, competence for multiple pathogens, potential as bridge vectors due to their opportunistic feeding behavior and adaptation to urban, rural, and forest areas [23]. Almost all of the container-specialist Aedes mosquitoes collected as larvae such as Stegomyia subgenus, including Ae. aegypti, Ae. africanus, Ae. albopictus, Ae. angustus, Ae. apicoargenteus, Ae. luteocephalus, Ae. metallicus, Ae. opok, Ae. vittatus, Ae. unilineatus, and Ae. usambara species, and Diceromyia and Aedimorphus subgenera comprising respectively Ae. furcifer and Ae. stokesi species have been shown to carry and/or to transmit in nature over 24 viruses, including yellow fever, dengue, Zika, chikungunya, and Rift Valley in tropical regions [5, 6]. In addition, Ae. (Aedimorphus) argenteopunctatus in South Africa [24] and Ae. (Neomelaniconion) palpalis [25] which show vector competence for Rift Valley fever virus in vitro and the other Aedes species like Ae. (Stegomyia) dendrophilus, Ae. (Stegomyia) lilii and Ae. (Aedimorphus) haworthi which belong to the same subgenera of species involved in the transmission of the arboviruses thus could be suspected as potential vectors of diseases. Still, Ae. (Finlaya) longipalpis belonging to the same Finlaya subgenus with Ae. niveus which has been the principal vector of dengue virus in Malaysia [26] may potentially transmit arboviruses in Côte d’Ivoire. Among non-Aedes mosquitoes, Er. chrysogaster, Er. inornatus and Cq. fuscopennata have been found to have natural infection, while Er. quinquevittatus has exhibited laboratory competence with yellow fever virus in Africa [6]. Moreover, An. coustani has been found to be infested with Zika virus [27], while O’nyong-nyong and chikungunya viruses have been isolated from An. gambiae [28]. Cx. quinquefasciatus [25] and Cx. poicilipes [26] have been shown susceptible to transmit Rift Valley fever virus. In conclusion, as in Senegal [12], the collections of immature stages of non-anthropophagic, unexpected and new potential vectors in rural areas suggest the co-existence of several still unidentified arbovirus cycles in south-eastern Côte d’Ivoire.
Our results also revealed that, urban areas showed higher capacity to support Aedes breeding sites and larvae than suburban and rural areas. The higher numbers of positive breeding sites and higher abundance of Aedes mosquito larvae may be due to the destruction of natural vegetation coverage for infrastructure buildings in the urbanized areas that may affect biological factors (e.g., fauna and flora), and increase the radiation budget thus modifying the microenvironments within and around the microhabitats [29]. Increased exposure to sunlight probably accelerates Aedes mosquito larval development and thus increases the size of adult vectors that possibly find more opportunities of blood feeding sources from larger human populations in urban areas [16, 29]. Still, urban Aedes populations are probably less exposed to the pressures from agricultural insecticide and predators (e.g., Eretmapodites spp. and Toxorhynchites spp.) compared to rural communities. We also found that less than two-thirds of breeding sites were infested with Aedes larvae thus suggesting that not all available containers filled with water were occupied by at least one larva or pupa of Aedes mosquitoes and the immature Aedes mosquitoes were not randomly distributed [12]. The presence of empty containers might imply that the gravid females of Aedes mosquitoes select their egg-laying sites carefully according to their physical characteristics (e.g., depth, color, clearance, surface, location, height, shade, sun exposure, and food sources) [12, 29], and biological interactions (e.g., competition and predation) [10, 11, 30] at play within the water-holding container systems.
In our larval surveys, we documented distinct geographic and seasonal variations in terms of the proportions of positive breeding sites and abundance of Aedes mosquitoes in all areas. Indeed, the highest proportions and relative abundance of Aedes mosquitoes were observed among vegetated peri-domestic breeding sites and during the rainy seasons in all areas. The shade of the vegetation reduces the water temperature [12], thus protecting breeding sites from drying out. Moreover, leaves supply organic detritus and associated microorganisms that may serve as food sources for the mosquito larvae [10]. The geographic and seasonal patterns in Aedes breeding sites are important from an epidemiologic perspective and suggest that the rainy season is the best period of time to identify breeding sites, while during the dry season it would be an ideal period of time to control immature Aedes mosquitoes, with particular attention for peri-domestic environments.
Our data revealed that the pattern of Aedes mosquito breeding sites changes substantially from natural containers to artificial containers along a rural-to-urban gradient. Although artificial breeding sites dominate in all areas, there is a higher proportion of natural containers (e.g., rock holes, animal detritus, leaf axils, fruit husks, bamboo, and tree holes) in rural areas, traditional containers (e.g., clay pots, wood-containers, and metallic pots) in suburban areas. However, in the urban areas, the most productive breeding sites for Aedes mosquito were industrial containers (e.g., tarps, discarded tires, vehicle tanks, carcasses, building tools, and water storage containers). The availability of, and the segregation among, Aedes breeding sites probably result from the strong impacts of human activities on the environment, while the natural breeding sites are provided by the natural landscape and agriculture [12]. We observed that tree holes, tires, and water storage containers showed higher Aedes species richness in rural, higher Aedes abundances in all areas, and high Ae. aegypti infestation rates in urban areas, respectively. Tree holes, found in the preserved rainforest, seem to provide ideal larval habitats for several species due to their greater stability, various trophic inputs, and retention of rainwater for longer periods of time [12]. Used tires are mostly associated with the palm oil industry in rural areas, production of the local dish “Attiéké” in suburban areas, and selling of tires and car repairs in urban areas. Tree holes and tires have bigger volumes and are expected to better protect the immature forms of Aedes mosquitoes against flushing during heavy rains [12, 14]. Moreover, tires are black-colored containers that are highly attractive to the gravid Aedes females searching for oviposition sites [11, 31]. The high number of water barrels infested with Aedes larvae might be due to the water being held for longer periods in uncovered receptacles [32].
Taken together, Aedes species diversity, richness, abundance, and dominance significantly changed from rural to urban settings. The variations in Aedes mosquito species may be explained by the sensitivity of their larvae to environmental changes induced by urbanization [10, 12]. Native species such as Ae. africanus, Ae. argenteopunctatus, Ae. longipalpis, Ae. stokesi and Ae. usambara were restricted to natural breeding sites in the rural areas. However, other wild species, such as Ae. furcifer, Ae. dendrophilus, Ae. palpalis, Ae. vittatus, Ae. luteocephalus, and Ae. metallicus were also surprisingly frequent in artificial containers. In contrast, our surveillance failed to sample Ae. fraseri that have been collected by ovitraps in the rural areas previously [16], probably due to its possible cryptic breeding sites or potential height-dependent oviposition behavior. The existence of multiple types of behavior in the same Aedes mosquito species may indicate the existence of generalist species or sibling strains of individuals from various origins [6, 11] that have experienced different selective urbanization pressures.
Lastly, our study showed that urbanization acts as a series of ecological filters for Aedes mosquitoes by advantaging Ae. aegypti, the primary vector of yellow fever, dengue, chikunguya, and Zika viruses [1–3]. Ae. aegypti was the most prevalent species in all study areas, exhibiting an increasing abundance along rural-to-urban gradient towards an higher abundance in urban areas where larvae mostly inhabit in anthropogenic containers (e.g., tires, discarded containers). Ae. aegypti displayed behavioral plasticity in that the females lay eggs in a vast array of containers ranging from natural containers such as rock holes, tree holes, and bamboo to a wide range of man-made containers [11], including water storage containers in urban areas [32]. The ecologic variations in oviposition behavior of Ae. aegypti and other Aedes mosquitoes may be discussed in ecologic, evolutionary, and epidemiologic approaches [11], and suggest possible overlaps of sylvan and urban vector distributions thus linking several potential mixed arbovirus transmission cycles [5, 6, 12, 16]. In addition, if highly infested microhabitats are targeted for removal, Aedes mosquito females may possibly adapt to changes in breeding habitats and alternatively oviposit in other containers previously unoccupied [33]. The ability of Ae. aegypti to adapt ovipositional behaviors to changing environments possibly enabling to overcome ecological constraints (e.g., instability and disturbance of the breeding sites) imposed by urbanization [10, 11]. Ae. aegypti-transmitted yellow fever outbreaks are historically well known in Côte d’Ivoire to have forced the transfer of the capital from Grand-Bassam to Abidjan in 1899 [15]. Since then, several unpredictable resurgences of yellow fever and dengue have been occurring in rural and urban areas causing many suspected, confirmed and fatal cases, and remain presently an unresolved major public health concern [7, 15, 34], with the current outbreak of dengue DENV-3 resulting in one confirmed and 17 suspected cases recorded in Abidjan in May 2017. Our study suggests that the unique removal of artificial containers that is a common practice in arbovirus control programs in Côte d’Ivoire might not effectively control diseases in the south-eastern part of the country. Vector control measures should combine removals of artificial containers [6] and autocidal gravid ovitrap-based on mass trapping [35], and insecticide auto-dissemination approaches [36].
In south-eastern Côte d’Ivoire, urbanization is associated with larval habitats of Aedes species at a finer scale by driving their breeding sites from natural to artificial containers, and at the larger scale by transforming rural to urban areas. Ae. aegypti is most prevalent in urban areas, suggesting that urbanization is a driver for producing suitable breeding sites for this mosquito species, and hence related disease outbreaks. However, rural settings still support irremovable containers such as natural breeding sites (e.g., tree holes) that host several wild Aedes species and Ae. aegypti. Therefore, even effective removal of discarded containers in urban areas (a common practice in arbovirus control programs) might not be sufficient to control arboviral diseases. Instead, vector control strategies should embrace a more holistic approach, combining different tools and methods of proven efficacy [6, 35, 36].
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10.1371/journal.pgen.1005183 | Auxin Influx Carriers Control Vascular Patterning and Xylem Differentiation in Arabidopsis thaliana | Auxin is an essential hormone for plant growth and development. Auxin influx carriers AUX1/LAX transport auxin into the cell, while auxin efflux carriers PIN pump it out of the cell. It is well established that efflux carriers play an important role in the shoot vascular patterning, yet the contribution of influx carriers to the shoot vasculature remains unknown. Here, we combined theoretical and experimental approaches to decipher the role of auxin influx carriers in the patterning and differentiation of vascular tissues in the Arabidopsis inflorescence stem. Our theoretical analysis predicts that influx carriers facilitate periodic patterning and modulate the periodicity of auxin maxima. In agreement, we observed fewer and more spaced vascular bundles in quadruple mutants plants of the auxin influx carriers aux1lax1lax2lax3. Furthermore, we show AUX1/LAX carriers promote xylem differentiation in both the shoot and the root tissues. Influx carriers increase cytoplasmic auxin signaling, and thereby differentiation. In addition to this cytoplasmic role of auxin, our computational simulations propose a role for extracellular auxin as an inhibitor of xylem differentiation. Altogether, our study shows that auxin influx carriers AUX1/LAX regulate vascular patterning and differentiation in plants.
| The vascular tissues in the shoot of Arabidopsis thaliana (Arabidopsis) plants are organized in vascular bundles, disposed in a conserved periodic radial pattern. It is known that this pattern emerges due to the accumulation of the phytohormone auxin, which is actively transported by the so-called efflux and the influx carriers. Efflux carriers facilitate polar transport of auxin from inside the cell to the extracellular space, while influx carriers pump auxin from outside the cell to its interior in a non-polar manner. Although a role for auxin efflux carriers in the emergence of this pattern has been recognized, the role of auxin influx carriers has remained hitherto neglected. In this study, we combine theoretical and experimental approaches to unravel the role of the auxin influx carriers in the formation of plant vasculature. Our analysis uncovers primary roles for the auxin influx carriers in vascular patterning, revealing that auxin influx carriers modulate both patterning and the differentiation of the water transporting vascular cells, known as xylem cells.
| Auxin is an essential phytohormone for the control of plant growth and development. Its transport and distribution throughout the plant create numerous organized patterns in plant tissues, such as leaf venation [1], the wide variety of phyllotactic patterns [2–5], and the periodic shoot vascular patterning [6,7]. Auxin is also involved in the emergence of new organ primordia [4,8], root apical meristem maintenance [9,10], root gravitropism [11–13], lateral root development [8,14], and xylem differentiation [15,16] amongst other developmental processes.
A proportion of auxin is synthesized in the shoot apex and polarly transported in a cell-to-cell manner to the root and to other plant tissues [17]. The chemiosmotic model explains how auxin is polarly transported throughout the plant [18,19]. According to this model, once auxin enters the cell where the pH is less acidic (cytosol pH≈7) than in the apoplast (pH≈5.5), it becomes deprotonated; this hydrophilic form remains then trapped inside the cell. In order to exit the cell, auxin needs active protein transporters that can pump it out. The asymmetric localization within the cell membrane of a subset of these transporters or auxin efflux carriers named PIN (PIN-FORMED) [6] results into one of the main characteristics of auxin transport: its polarity. Depending on the positioning of the PINs, directional fluxes and auxin gradients are created, driving the accumulation of auxin maxima in specific groups of cells [3–5]. Disruption of auxin polar transport significantly alters auxin maxima distribution, resulting in aberrant development [2,6–8,12]. In addition to the PIN efflux carriers, the PGP (P-glycoprotein) ABC-like transporters also export auxin from the cytoplasm to the apoplast [20]. PGP transporters do not localize asymmetrically, but they have been proposed to interact with PINs, subsequently affecting auxin polar transport [21–23].
Unlike auxin efflux from cells, auxin enters the cells either by passive diffusion or by the action of auxin influx carriers. These comprise a multi-gene family in Arabidopsis containing four highly conserved genes: AUX1 and the AUX1-like genes LAX1, LAX2, and LAX3 [24–26]. In contrast with efflux carriers, influx carriers do not show a polar distribution within most cells with exception of the root protophloem cells [27,28]. The absence of strong phenotypes in influx mutants, especially under long day conditions, as well as their non-polar distribution has prevented extended studies on the role of influx carrier mutants on patterning in the past. So far, all the AUX1/LAX family members have been associated with changes in vascular transport [29], leaf positioning [30,31] and root stem cell patterning [32]. Despite the reported redundancy, AUX1/LAX family members not only have distinct tissue-specific expression patterns, but also exert different functions, suggesting that these genes underwent sub-functionalization and likely provided additional mechanisms of regulation to the plant [25,31]. For instance, within Arabidopsis primary root, AUX1 has been reported to localize in the columella, the lateral root cap, the epidermis and the stele [25,33,34], LAX1 is localized within the mature vascular tissue, with weak expression in the root tip immature vasculature [25], LAX2 is localized within the root stem cell niche, the provascular cells and the stele [25] and LAX3 is localized in the columella and the stele cells [25,35]. In addition, AUX1, LAX1 and LAX2 are differently localized at multiple developmental stages in the lateral root primordia whereas LAX3 is localized in the outer tissues in front of the primordia [25,35,36]. AUX1, the most studied influx carrier, has been attributed roles in root gravitropism, petal initiation and lateral root development [36–38]. Furthermore, LAX2 influx carrier has been recently reported to confer continuity to the vascular strands in cotyledons [25]. In addition, LAX3 has been shown to promote lateral root emergence and apical hook development [35,39]. Theoretical studies have proposed a stabilizing role for influx carriers on periodic patterning rather than major roles in pattern emergence [40–42]. Experimentally, the analysis of aux1lax mutants confirmed the stabilizing role in shoot phyllotactic patterning [31]. Phenotypes were visible under short day conditions, suggesting that AUX1/LAX transporters may be particularly relevant under certain environmental conditions. Nevertheless, the functional relevance of AUX1/LAX proteins in the periodic vascular patterning of the shoot remains unknown.
Here we provide theoretical and experimental evidence for a yet uncharacterized role of auxin influx carriers controlling periodic vascular patterning and the differentiation of xylem cells in plants. The vascular tissues in the shoot of Arabidopsis plants are organized in vascular bundles (VB), disposed in periodic repetitions along a circular vascular ring. Each VB is composed of meristematic procambial cells and of differentiated vascular cells termed xylem and phloem (S1 Fig, shown as grey, blue and green cells, respectively), which arise each from centripetal and centrifugal divisions of the procambial cells [43,44]. In between the VBs, interfascicular fibers (IF) differentiate, supporting the inflorescence stem [43,45] (S1 Fig, light blue cells). The analysis of quadruple knockout mutants of AUX1/LAX transporters disclosed fewer and more spaced VBs in the shoot of Arabidopsis. This phenotype is in agreement with our mathematical and computational modeling predictions, which show that auxin influx carriers facilitate periodic patterning and increase its periodicity. Furthermore, a reduced differentiation of the vascular cells is observed in both shoot and root tissues. Our data support that influx carriers promote cytoplasmic auxin signaling, which has been previously shown to drive xylem differentiation [15,16,46,47]. In addition, our modeling analysis predicts a novel role for apoplastic auxin as inhibitor of xylem differentiation.
We have previously shown that periodic auxin distribution is relevant for VB pattern formation [7]. In order to investigate the role of influx carriers on periodic distributions of auxin, we first performed a theoretical and computational analysis (Materials and Methods). A minimal modeling approach was selected by assuming that auxin polar transport sets auxin maxima along a ring of provascular cells, which ultimately drive VB emergence [7]. Albeit this approach with its simplified geometry cannot drive quantitative predictions, it is expected to provide key features underlying the role of influx carriers for periodic auxin patterns (S2 Fig). A previous model on auxin polar transport known to drive periodic auxin maxima [3,41] was considered and further elaborated by including auxin apoplastic diffusion [7] (Materials and Methods, S2 Fig). In the model, auxin uptake into the cells occurs actively through influx carriers as well as passively, while auxin exits the cells through polarly localized efflux carriers as described in [3,41]. The model also takes into account that the synthesis of both types of carriers, as well as the polar localization of efflux carriers, depends on auxin concentration [3,14,35,41,48,49]. Parameter values for auxin polar transport were chosen according to the literature (S1 Table) [34,50–54]. Both theoretical and computational analyses were performed through linear stability analysis of the homogeneous state and numerical integration of the dynamics, respectively (Material and Methods and S1 Text). The model drives periodic maxima of auxin concentration as expected [3,4,55] with influx and efflux carriers being more abundant in those cells harboring auxin maxima (S3 Fig). This localization of carriers arises from the auxin-induced synthesis of influx and efflux carriers, which was set in the model to embrace experimental evidences on the auxin-induced expression of carriers [14,35,48,49]. Yet, according to the modeling of auxin dynamics, the auxin-induced synthesis of carriers is not essential for pattern formation [3,4,55] (S4 Fig). We defined I as intensity of the active influx transport (S1 Text and S1 Table), which is proportional to the maximal amount of influx carriers a cell in the ring array can have. For simplicity, hereafter we use the term 'amount of influx carriers' for I.
Our analysis predicts that the amount of influx carriers can control the periodicity of the pattern, driving changes in the number of auxin maxima (Fig 1A and S1 Video). When the amount of influx carriers is decreased, less auxin maxima arise in a ring with a fixed number of cells (Fig 1A right panel). Hence, influx carriers promote auxin maxima to be closer together in terms of number of cells, up to a limit (Fig 1B and 1C). While pattern periodicity modulation was previously associated only to efflux carriers [3,4], our modeling results unveil a novel role for influx carriers in this process. Auxin entrance into the cells is essential for periodic pattern formation, by enabling the polar transport of auxin to take place. We confirmed that passive entrance into the cells, independently from influx carriers, can be enough to sustain periodic patterning, as expected (Fig 1C). Yet, we found that influx carriers become essential for patterning in high apoplastic diffusion conditions, in which passive entrance of auxin into the cell is not enough to enable the periodic patterning (Fig 1C and S1 Text). Therefore, our results show that influx carriers promote pattern formation as well.
To study the role of auxin influx carriers in the shoot vascular patterning, we first evaluated the expression pattern of the influx carriers’ proteins in the shoot. Radial sections at the basal region of the shoot inflorescence stem revealed that AUX1, LAX1, LAX2 and LAX3 fluorescent protein fusions show expression in the shoot vascular tissues (S5 Fig). This expression is localized at the VB (S5 Fig), where both auxin response and efflux carriers expression are known to occur [6,7,56] in agreement with the distribution predicted by modeling (S3 Fig).
We then analyzed radial sections at the basal region of the shoot inflorescence stem of mutants for auxin influx carriers [31] grown in short days. The depletion of all auxin influx carriers resulted in a significant reduction in the number of VBs as compared to the WT (Fig 2A–2D). Single aux1 mutant showed no VB number phenotype whereas aux1lax1lax2 triple mutant exhibited a similar phenotype than the quadruple (S6 and S7 Figs). These results are consistent with all AUX1/LAX family members being expressed in the shoot vasculature where they could play redundant functions. As previously reported in the context of phyllotaxis [31], the quadruple mutant exhibited higher phenotypic variability than the wild type (S8 Fig), supporting a stabilizing role for auxin influx carriers.
We next evaluated the number of cells involved in this pattern. To this end, we decomposed the vascular pattern of shoot cross-sections into vascular units (S1 Fig), each one of them constituted by the cells along a cell file in a VB and by the cells within the immediately adjacent interfascicular fibers [7]. The spacing of VBs was defined as the number of cells in the vascular unit, i.e. the vascular unit size. Our results show a significant decrease in the number of VBs and the total cell number accompanied by a larger spacing of VBs in terms of number of cells (Fig 2E and 2F). This enlargement of the spacing is in agreement with the role of influx carriers predicted by the mathematical model (Fig 1).
Since the reduction of VB number could be influenced by both the increase in VB spacing and the reduction of provascular cell number, we quantified the expected contribution of each of these two elements to this phenotype (Materials and Methods). Our results show that the increase in VB spacing in aux1lax1lax2lax3 mutants can account for 57% of the change in VB number, while the decrease in total cell number explains the remaining 43% of the change in VB number (Fig 2G). In the triple aux1lax1lax2 mutant, a similar trend is found (S7 Fig).
Besides the phenotype in the periodic patterning, the vascular differentiation was also impaired in the influx mutants. Compared to the WT, aux1lax1lax2lax3 and aux1lax1lax2 mutants clearly show a reduced differentiation of both the interfascicular fiber cells and of the xylem cells within the shoot VB (Fig 3A–3F). This impairment was accompanied by a significant increase and a higher variability in the number of undifferentiated cell layers within the VB of aux1lax1lax2lax3 mutants, when compared to the WT (Fig 3A–3G). Together, these results uncover a role for auxin influx carriers in promoting xylem differentiation in the plant shoot.
To address whether the roles of influx carriers in periodic patterning and differentiation are independent, we investigated the vascular differentiation phenotype in tissues where vascular patterning is not periodic, such as the primary root. Histological analysis on the primary roots of aux1lax1lax2lax3 mutants showed impaired xylem vessel differentiation (S9 Fig), and AUX1/LAX-VENUS lines revealed root cambium/xylem-specific expression (see Materials and Methods and S10 Fig), confirming that AUX1/LAX promote xylem differentiation independently of modulating periodic patterning.
Next, the vascular phenotype in the shoot stem of influx mutants grown in long day was analyzed, since in these light conditions no apparent vascular bundle number phenotype is seen in aux1lax1lax2lax3 mutants (Figs 4A, 4B, 4D and 4E and S11), in agreement with the phyllotactic phenotypes described previously for these mutants in these conditions [31]. We found that the vascular differentiation was impaired in the quadruple mutants, albeit the phenotype was milder than in short day conditions (Figs 4G, 4H, 4J and 4K and S11). These results support that the role of AUX1/LAX in vascular differentiation is more prevalent than their role on modulating the vascular bundle number.
The role uncovered herein for the auxin influx carriers on xylem differentiation is opposed to that already described for efflux carriers [6] (Fig 4G–4L). In agreement, the efflux carrier mutant pin1pin2 grown in short days shows increased xylem and interfascicular fiber differentiation similarly to what was shown in long days [7] (Fig 4C, 4F, 4I and 4L). Therefore, our results disclose that influx and efflux carriers have opposite roles in xylem cell differentiation.
Since auxin signaling mediates vascular differentiation processes [16,46,57–59], we reasoned that alterations of auxin concentration mediated by influx carriers could result into impairment of auxin signaling, which in turn would drive defects in xylem differentiation and vascular patterning. To evaluate whether auxin signaling is impaired in influx mutants we analyzed the expression of the auxin response reporter DR5:GFP [60] in the aux1lax1lax2 mutant backgrounds. The triple influx carrier mutant DR5:GFP aux1lax1lax2 exhibited diminished auxin response, specifically at the VB, when compared to the DR5:GFP WT (S7 Fig). Since DR5:GFP reports the level of TIR1/AFB-mediated auxin signaling [61], these results point at a reduction of cytoplasmic auxin in the absence of influx carriers. We then turned into our modeling approach to unveil which of the multiple effects of influx carriers on auxin transport and distribution could underlie the control of xylem differentiation. To this end, we searched for those effects of influx carriers on auxin distribution that satisfy the restrictions we find in the xylem differentiation phenotype: the effect of influx carriers on xylem differentiation is (i) independent of the role on modulating the periodic pattern, (ii) is more pervasive than the modulation of the periodic pattern and (iii) is opposed to that of efflux carriers. Note that our mathematical analysis revealed that influx and efflux carriers do not necessarily drive antagonistic effects. For instance, both influx and efflux carriers promote periodic pattern formation (Figs 1 and S12) [2,3], faster patterning processes (S1 Video) [7], and stabilization of the pattern [40–42]. Therefore, the third condition (iii) also imposes a restriction on which effect of influx carriers controls xylem differentiation.
The analysis showed that influx carriers increase cytosolic auxin in those cells harboring the auxin maxima (Figs 1A and S13), in agreement with the reduced response exhibited by DR5:GFP in the auxin maxima of the triple aux1lax1lax2 mutant background (S7 Fig). In addition, influx carriers tend to reduce spatial differences in the concentration of apoplastic auxin (what we call pattern amplitude of the apoplastic auxin) as well as to diminish, as expected [51], the apoplastic auxin concentration (S13 Fig). Importantly, this role influx carriers have on apoplastic auxin concentration satisfies the three conditions described above (i-iii). This role is more pervasive than that on modulating the pattern periodicity (Figs 5 and S14). Moreover, it is independent of the modulation of the periodicity, since changes of the periodicity of the pattern do not require changes in the concentration of extracellular auxin (S15 Fig). In addition, it is opposed to the effect driven by efflux carriers, which tend to increase the differences of apoplastic auxin across the ring of cells and to increase the apoplastic auxin concentration (Figs 5 and S13 and S14, Materials and Methods). While extracellular auxin is dependent on influx carriers such that the three conditions (i-iii) above hold, cytosolic auxin is not. Cytosolic auxin is promoted both by influx and efflux carriers at the cells harboring auxin maxima (Figs 5 and S13 and S14). Taken together, our computational analysis supports that influx carriers promote cytoplasmic auxin signaling and thereby xylem differentiation and proposes that auxin signaling may respond as well to changes in extracellular apoplastic auxin concentration driven by influx carriers to control vascular differentiation (Fig 6).
Mathematical modeling has recently emerged as an effective discipline to characterize auxin patterns and the processes driven by them, like vascular patterning in the root [1–4,9,10,40,42,55,62]. From these studies it is known that auxin-dependent polarization of efflux carriers can drive periodic patterning [3,4,55]. Our mathematical model predicts that auxin influx carriers, despite not being polarized in our model, can modulate the periodicity of the auxin pattern. The observed changes in the vascular pattern periodicity in the shoots of influx mutants are in agreement with the model prediction. Moreover, our model shows that influx carriers can facilitate periodic patterning. Intuitively, the roles of auxin influx carriers in patterning disclosed in this study can be understood from the competition between polar transport and apoplastic diffusion. Polar transport is at its most effective mode when the auxin expelled from the cytosol is able to reach only the adjacent cells and not cells located further away [51]. Efficient uptake of auxin by influx carriers facilitates that this happens.
Early mathematical models proposed that influx carriers stabilize phyllotactic patterning [40–42]. Recently, it has been shown that influx carriers can have additional roles in patterning, such as setting which root cells have high levels of auxin [63]. The role of influx carriers on promoting auxin maxima and facilitating periodic patterning may have previously remained unnoticed in theoretical analyses because either apoplastic diffusion and/or auxin-induced synthesis of carriers were not taken into account. These novel roles are uncovered through modeling only when either one or both elements are included (S1 Text). The inclusion of apoplastic diffusion in the model revealed another interesting aspect of auxin periodic patterning: efflux carriers do not modulate the periodicity of patterns as influx carriers do (S12 Fig). Therefore, this suggests that the distorted vascular bundle phenotypes in efflux mutants are not because of modulation of the periodicity, and may arise from the strong slowing down of the auxin transport dynamics as previously proposed [7]. In addition, future 3D modeling approaches that take into account the connectivity of the vascular strands with the phyllotactic pattern [64] will help in a better understanding of the phyllotactic and vascular pattern formation.
Based on our modeling results, it is tempting to speculate what in auxin transport is distinct between short day and long day conditions that can explain the differences we found in phenotypes (Fig 4), namely, that quadruple influx carrier mutants in long day only show reduced xylem differentiation and not a vascular bundle phenotype. Assuming the xylem differentiation scheme of Fig 6, the model indicates that long day conditions could be mimicked by (1) the absence of auxin-induced synthesis of carriers together with lower auxin apoplastic diffusion coefficients than in short day (S14 Fig), or by (2) an increase of the ratio of passive influx transport across the cell membrane over the active one when compared to short day conditions (Fig 5). We found that carriers are localized at auxin maxima in long day conditions (S16 Fig), supporting auxin-induced synthesis for this photoperiodic condition and discarding the first scenario. Regarding the second scenario, the model shows that the concentration of apoplastic auxin is lower when the passive influx transport across cell membranes increases (Figs 5 and S13 and S1 Text). This predicts that the influx carriers mutant plants should display a milder differentiation phenotype in long days than in short days. This prediction is in agreement with the phenotypes exhibited by aux1lax1lax2lax3 mutant shoots inflorescence stems (Figs 3 and 4 and S11). It is worth stressing that predicting these differences between the differentiation phenotypes in long day and short day conditions of influx carriers mutants is restrictive. For instance, no difference of differentiation phenotype is expected if long day conditions corresponded just to lower active influx transport than in short days, while the opposite difference is predicted by a photoperiod-dependent change of apoplastic auxin diffusion (S14 Fig). Based on this analysis, we may hypothesize that the photoperiod could change the balance between passive and active influx transport across the cell membrane, being passive influx more relevant at long day conditions than at short days. Yet, in both conditions, active influx transport is expected to be more important than passive entrance into the cells. Passive auxin entrance into the cells could increase in long days by increasing the cell membrane permeability, for instance. In addition, the amount of active influx carriers may decrease in long days as well. Potentially, active influx transport could be modified by the photoperiod through light-modulation of intracellular trafficking [65,66].
In summary, by combining experimental and theoretical approaches, we propose novel roles for auxin influx carriers in vascular patterning and differentiation during plant development. By assuming that auxin maxima position VBs [7], we evaluated the role of auxin influx carriers in the periodic patterning of auxin maxima in the Arabidopsis shoot inflorescence stem. auxlax1lax2lax3 mutants showed a reduction in VB number in the shoot stem involving both an increase in the spacing of the pattern and a reduction in the total number of cells along the provascular ring. This increase in the spacing can be explained by the role of influx carriers predicted by our modeling approach. Moreover, the quadruple auxlax1lax2lax3 mutants also depicted decreased xylem differentiation. Analysis of shoot and root phenotypes indicates a pervasive role of auxin influx carriers in promoting differentiation of xylem cells that is independent on their role on periodic vascular patterning. Our data support the established idea that TIR1/AFB-mediated auxin signaling, operating in cytoplasm and nucleus, is required for xylem differentiation [15,16,46,47]. In addition, our computational analysis predicts that extracellular auxin can be sensed from the apoplastic space and inhibit xylem cell differentiation (Fig 6). While no direct empirical evidence for apoplastic auxin signalling controlling xylem differentiation is described, it has been reported that apoplastic auxin can be sensed by the cell surface receptor ABP1-TMK and drive the downstream auxin signaling [67]. Furthermore, the potential connection between apoplastic and cytosolic auxin signaling pathways has been also shown [67]. However, very recent evidence has challenged the role of ABP1 as auxin signalling component [68]. Further work will contribute to unravel the molecular mechanism that drives these phenotypes as well as to understand the impact of environmental conditions on auxin-driven patterning.
We used the mathematical model of polar auxin transport by [3] in the form it is presented in [41] with the inclusion of apoplastic auxin transport as in [7] (S2 Fig). Given that auxin diffuses fast in the cytosol [69], we considered for simplification auxin concentration homogeneously distributed inside the cell, what would be consistent with instantaneous diffusion of auxin across the cytosol. In the model, auxin is pumped into the cell through the influx carriers, which are homogeneously distributed in the cell membrane. Moreover, auxin is pumped out of the cell to the apoplast through the efflux carriers, which can be polarly distributed in the cells. We considered efflux carriers’ localization to depend on the concentration of auxin in neighboring cells and, for simplicity, to be at equilibrium, as done in [3,40,41]. The model takes into account that a constant fraction of the auxin (set by the pH condition) is protonated and is passively transported into the cells as in [3]. Auxin production and degradation is set to occur inside cells. In the model, we refer to cytosolic and apoplastic auxin as the auxin inside and outside the cell, respectively.
The dimensional model equations for cytosolic auxin concentration in cell i and apoplastic auxin concentration in the apoplastic compartment i (see scheme in S2 Fig) read
dAidτ=−∑j∈n(i)WijJij−νcAi+σcdaidτ=∑j∈N(i)WjiVcellVapJji+Dw∇i2ai,
(1)
with τ being time, Dw the apoplastic diffusion coefficient, σc and vc the auxin production and degradation rates, Vcell the cell volume, Vap the apoplast volume, Wij the ratio between the contact area of cell i with apoplast j and the cell volume. ▽i2 is the discrete Laplacian. Jij stands for the auxin flux from the cell i to the apoplast j and contains the protonated passive auxin transport, and both active transports due to the influx and efflux carriers as described in [41] (see S1 Text for details). Active influx transport was simplified to drive only entrance of anionic auxin into the cell. Analogously, active efflux transport was set to mediate only the exit of anionic auxin from the cell. For simplicity and with the aim of focusing in the linear regime of the dynamics, we considered linear auxin fluxes for both passive and active transports (S1 Text).
For convenience, we analyzed the model in nondimensionalized time units (S1 Text). The resulting model parameters can be related to physico-chemical magnitudes that have been measured or can be estimated, even though we expect having robust behaviors that are not very dependent on parameter values. In our study, we chose to mainly vary the following effective dimensional parameters: the influx parameter I, the apoplastic diffusion coefficient D and the efflux parameter E. Our simplified geometry corresponds to a line of cells with periodic boundary conditions and no cell division was included.
We integrated the dynamical model for auxin transport (Eqs S7-S8) through a Runge-Kutta method of 4th order [70] with time step dt = 0.0001 being t the non-dimensional time. Most of the parameter values were set according to already published work, some of it based on experimental data (S1 Table). Unless otherwise stated, we set as initial conditions the homogeneous solution with small variability. We integrated the dynamics until a fixed time point (t = 17.5). This time point was chosen such that a periodic pattern of auxin maxima was established for the parameter values of the normal conditions (Fig 1A) but was still incipient at efflux mutant conditions, yielding distorted patterns for this mutant (S12 Fig and S1 Text).
For each pattern at time t = 17.5, we quantified the ratio of the number of cytosolic auxin maxima over the number N of cells in the simulated provascular ring. For a periodic pattern, this provides a measurement of the characteristic wavenumber. Note that the inverse of this quantity is the average number of cells between two auxin maxima (i.e. the characteristic wavelength or spacing). In the simulations, we observed very incipient maxima much smaller than the rest. We omitted such incipient maxima for the quantification of maxima spacing, the cytosolic and apoplastic auxin average maxima and pattern amplitudes. We determined that a cytosolic (or apoplastic) auxin maxima was incipient at the end of any given simulation when the difference of its cytosolic (or apoplastic) auxin value with the average cytosolic (or apoplastic) minima in such single simulation was less than 0.15 times the cytosolic (or apoplastic) auxin pattern amplitude in such simulation. By omitting these maxima, which arise especially at high influx levels (see incipient maxima near cell 60 in left panel of Fig 1A), we have seen that the numerical simulations are in better agreement with the theoretical prediction for the periodicity of the pattern, especially at higher influx levels.
Simulations were performed with custom-made programs written in Fortran77 and in C++. We provide a code written in Mathematica [version 9.0, Wolfram Research; code provided in pdf and Mathematical notebook format (.nb)] that can be used by the reader to test how influx carriers and other model parameters affect to the pattern formation process (see S1 Code).
In pattern formation studies, linear stability analysis enables the prediction of the characteristic wavelength of the emerging patterns [71], and it has already been used for mathematical auxin transport models (see for instance [3,40,41]). We performed linear stability analysis over the stationary homogeneous state [71] (details are found in S1 Text). This analysis provides theoretical predictions on which parameter values can drive periodic pattern formation and on how the periodic pattern that starts to be formed depends on the parameter values. Accordingly, through this analysis we extracted predictions on how the characteristic wavenumber (κ) (i.e. the number of periodic maxima over the total number of cells) depends on the model parameters (see S1 Text for details).
All the mutant plants analyzed here were in Arabidopsis thaliana Columbia-0 (Col-0) ecotype background. Seeds for DR5:GFP in aux1lax1lax2 and Col-0 WT backgrounds and auxlax1lax2lax3 were described elsewhere [31]. Seeds were surface-sterilized in 35% sodium hypochlorite, vernalized at 4°C for 48h, and germinated on plates containing 1x Murashige and Skoog (MS) medium. Seedlings were grown for 10 days on plates under short day photoperiod (8h light / 16h dark; 8470 lux; 20–23°C) and then transplanted to soil and grown under the same conditions for 14 weeks. The main shoot inflorescence stem was cut at approximately 1 cm above the rosette for sectioning and further histological analysis. Short day conditions can occasionally drive the emergence of aerial rosettes in both WT and aux1lax1lax2lax3 adult plants. Plants that showed these aerial rosettes development were not considered for our analysis.
The VENUS fluorescent protein [72] fusions of AUX1/LAX proteins were generated by a recombineering approach [73] and have been described before for AUX1 [63]; LAX1 and LAX2 [25]. In brief, VENUS was fused in frame after the codon 116 for AUX1; 122 for LAX1; 110 for LAX2 and 114 for LAX3 to create respective AUX1/LAX fluorescent protein constructs (ProAUX1:AUX1-VENUS; ProLAX1:LAX1-VENUS; ProLAX2:LAX2-VENUS and ProLAX3:LAX3-VENUS). Transformation of Agrobacterium (C58) and Arabidopsis was done as described before [74]. Transgene-specific cDNA sequences of these lines were PCR-amplified and sequenced to ensure against rearrangements of the transgenes.
Inflorescence stem sections from both WT and mutant Arabidopsis plants were fixed at 4°C overnight in 1.25% glutaraldehyde in 0.1 M sodium cacodylate buffer (pH 7.4). Samples were dehydrated through a graded series of ethanol (30%, 50%, 70%, 90% and 100%; 45 min each one) and then infiltrated in 1:1 Historesin-I (Technkovit):ethanol for 30 min at room temperature, followed by 100% Historesin-I 100% at 4°C overnight. Blocks were prepared by placing samples into plastic molds, which were filled with 100% Historesin-II (Technkovit). Each mold was covered with parafilm and kept overnight at 4°C to accelerate their solidification. Historesin-I and II were prepared following the manufacturer’s instructions. Transverse stem sections (3 μm) were obtained with a Leica Microtome (Microtome RM2265, Leica). Sections were stained with 0.1% Toluidine blue in 0.1M NaPO4 pH7.0, rinsed and mounted in water for microscopical visualization in an Axiophot Microscope (Zeiss). GFP-fluorescence was observed in hand-made sections from the same part of the stem in a stereomicroscope (SZX16, Olympus). VENUS fluorescence lines (same part of the stem as described above) grown in short day conditions were incubated for 1–2 hours in 4% para-formaldehyde in PBS under vacuum conditions. After three washes with PBS, the samples were mounted in a hand-made block of 4% agarose and 0.01% Triton X-100 in PBS (pH 7.2–7.4). 150 μm sections were cut in a Micron HM650V vibratome and analyzed in a Leica TCS SP5II HCS A confocal microscope (Leica). Kr/Ar 488 laser was used with an excitation wavelength of 514 nm and detected an emission window of 525–569 nm for the VENUS/YFP. An excitation wavelength of 405 nm and an emission window of 434–483 nm were used for the blue autofluorescence of the xylem.
For the root histological studies, seedlings were grown on soil for 5 weeks on long day photoperiods (16h light/ 8h dark; 23°C). The root samples were embedded in Historesin (Leica) as described in [75], and 5–10 μm sections were cut approximately 5 mm below the hypocotyl. The sections were stained with toluidine blue and only the vessel elements in the primary phase of secondary xylem development were quantified with ImageJ. Vessels formed during the secondary phase were not quantified, since fibers with thick cell walls are formed then [76], thus making it difficult to distinguish the vessel elements from fibers.
Quantification of all the vascular parameters (stem diameters, number of cells, interfascicular fiber length and number of undifferentiated cell layers) was performed manually or using ImageJ software (http://rsb.info.nih.gov/ij/). Vascular bundles and cells were manually counted from microscope images. To determine whether the WT samples were statistically significant with respect to mutant samples, we performed the Wilcoxon rank sum test with Matlab. When we performed the test on the vascular unit sizes of WT samples against those of mutant samples, we chose the average vascular unit size per plant as the tested variable, but we have confirmed that the test results were very similar if we took the median of the vascular unit size per plants. Plots were performed with Python 2.7 by means of the Matplotlib package and with Excel. Quantification of the contribution of VB spacing (characteristic wavelength, λ) and total cell number (N) to the change in VB number (V) was computed through
ΔVVWT=ΔλλWT+ΔNNWT,
(2)
where ΔX = Xmutan t−XWT with X being the median value found for each variable. This relation stems from V = N / λ. The percentage of contribution of VB spacing is then 100ΔλVWT / (ΔV λWT).
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10.1371/journal.pcbi.1002580 | The Generation of Phase Differences and Frequency Changes in a Network Model of Inferior Olive Subthreshold Oscillations | It is commonly accepted that the Inferior Olive (IO) provides a timing signal to the cerebellum. Stable subthreshold oscillations in the IO can facilitate accurate timing by phase-locking spikes to the peaks of the oscillation. Several theoretical models accounting for the synchronized subthreshold oscillations have been proposed, however, two experimental observations remain an enigma. The first is the observation of frequent alterations in the frequency of the oscillations. The second is the observation of constant phase differences between simultaneously recorded neurons. In order to account for these two observations we constructed a canonical network model based on anatomical and physiological data from the IO. The constructed network is characterized by clustering of neurons with similar conductance densities, and by electrical coupling between neurons. Neurons inside a cluster are densely connected with weak strengths, while neurons belonging to different clusters are sparsely connected with stronger connections. We found that this type of network can robustly display stable subthreshold oscillations. The overall frequency of the network changes with the strength of the inter-cluster connections, and phase differences occur between neurons of different clusters. Moreover, the phase differences provide a mechanistic explanation for the experimentally observed propagating waves of activity in the IO. We conclude that the architecture of the network of electrically coupled neurons in combination with modulation of the inter-cluster coupling strengths can account for the experimentally observed frequency changes and the phase differences.
| There is a profound interest in the dynamics of neuronal networks and the simulation of network models is a prevalent approach to study these dynamics. Generally, network models contain neurons that are connected mostly through chemical synapses to form either a completely regular topology (such as nearest neighbor connections), a completely random topology, small-world networks or scale-free networks. We investigate the dynamics of an atypical network, inspired by the Inferior Olive (IO) network, a brain structure located at the end of the brainstem that is responsible for timely execution of motor commands. This network is atypical in the sense that it has neurons in a clustered topology, which are connected solely by electrical synapses. The dynamics in the IO are enigmatic as the membrane voltage of some neurons can oscillate at the same frequency while maintaining phase difference with other neurons. It has also been demonstrated that propagating waves of activity occur spontaneously in this network. Using computer simulations we unraveled the mechanism underlying these previously enigmatic experimental observations. In so doing, we stress the importance of investigating more realistic network topologies to explore complex brain dynamics.
| There is a profound interest in the dynamics of neuronal networks and the simulation of network models is a prevalent approach to study these dynamics. One aspect of network dynamics is the generation of oscillatory activity. It has been hypothesized that oscillations subserve brain-wide communications. For instance, “binding” to connect distinct sensory streams in the brain [1], [2], or entrainment of brain regions [3], [4] to facilitate communication and filtering of information [5], [6]. Computational models provide mechanistic explanations for these phenomena and explore their functional consequences. As such, electrical oscillations in the brain have been studied by using network models containing only chemical synapses [7], [8], or a mixture of chemical and electrical synapses [9]. Network oscillations (and associated experimental findings) are generally not addressed in networks connected solely by electrical synapses despite the fact that such brain regions, such as the Inferior Olive, exist and are known to produce oscillations. Also, most models of oscillatory neuronal activity focus on oscillatory behavior in the suprathreshold, spiking regime of neurons. In contrast, subthreshold oscillations are rarely considered outside the realm of intrinsic neuronal properties. Here we report on a network model of the subthreshold oscillations and their dynamic behavior in the Inferior Olive.
The Inferior Olive (IO) nucleus is the exclusive provider of cerebellar climbing fibers. Neurons in the IO form a network solely through electrical connections (gap junctions) between them. This electrically coupled network of neurons generates subthreshold voltage oscillations, which were observed both in-vitro [10]–[13] and in-vivo [14], [15]. Spiking activity is generally strictly phase-locked to the peaks of the oscillations. As a result of this peculiar anatomy and electrophysiological dynamics, the IO has been implicated as a timekeeper for the cerebellum and has been suggested to play an important role in the timely execution of motor commands [16]–[18] and in the generation of well-timed signals used in learning [19]–[21].
There are two observations in relation to the function of the IO as a timekeeper. The first observation is that the frequency of the subthreshold oscillation shifts from time to time [14], [22]. The base frequency of the IO subthreshold oscillation is normally well below 10 Hz and shifts of 1 to 6 Hz around the base frequency are reported [15],[22],[23]. The second observation is that while different neurons oscillate at the same frequency, phase differences among neurons are observed. Stable phase differences up to 90° between IO neurons were recorded in in-vitro preparations [22]. In-vivo, Purkinje cells complex spikes, which are considered to be the manifestation of olivary activity, displayed phase differences up to 180° [24]. The observation of phase differences in a network consisting only of neurons with direct electrical coupling is in itself problematic: how can phase differences in the subthreshold regime persist over time between two electrically coupled neurons that oscillate at the same frequency? While several theoretical models have been proposed to account for the subthreshold oscillations in the IO [10], [25]–[29], none of these works provided an explanation for the controllable modulation of frequencies or for the generation of persistent phase differences.
In this work we address both frequency modulation and the generation of phase differences in the IO network. To this end we built a network model of the IO consisting of basic conductance-based model neurons [30] in an architecture based on anatomical and physiological data. The model neurons contain leak (gl) and low-threshold Ca2+-conductances (gCa, see Methods). At particular densities of these two conductances, the neuron model exhibits spontaneous oscillations [30]. Anatomically, it is known that somata of IO neurons cluster together in small groups of 8–12 neurons [10], [31]. This causes considerable overlap between the dendrites of neurons from the same cluster. In turn, this overlap gives rise to many dendro-dendritic gap junctions between neurons of the same cluster. Because of the limited space in which neurons are situated, there is, arguably, less overlap between dendrites of neurons belonging to different clusters. Hence, gap junctions are less frequent between neurons of different clusters. Additional details about the connectivity come from physiological experiments in which pairs of IO neurons are recorded simultaneously. It is known that each neuron connects to 1–38 other neurons [1], [2] and that the coupling coefficient (CC1 = V2/V1, CC2 = V2/V1, and see Methods) ranges from 2–20%. Although nearby neurons are more likely to be connected, the strength of individual connections is only weakly correlated with distance from the soma. There is also physiological support for nearby neurons having similar biophysical features, such as the density of low-threshold calcium conductances. The experimental support is indirect and stems from two different lines of evidence. First, in vitro preparations show that nearby neurons oscillate with the same phase and frequency [32]. Since the coupling strength between neurons is notoriously low, such similar oscillations can only occur when the neurons share the same conductance densities that drive the oscillation. Second, the coupling coefficient between nearby neurons is symmetrical [33] – a feature that only results from neurons with equal input resistances. As the input resistance at rest is mainly determined by the leak and low-threshold calcium conductances (in combination with the h-type conductance), the densities of these conductances must be very similar.
These data constrain the model's architecture to a topology in which similar neurons (in terms of conductance densities) are clustered together and are densely connected via gap junctions. The anatomical clustering of dendrites leads to sparse connectivity between a given cluster and all other clusters, i.e., neurons from one cluster are connected to neurons in one or a few other clusters but not necessarily to all other clusters. Thus, major constraints on the network architecture are imposed by the connectivity scheme, the limited number of connections per neuron, and the weak coupling coefficient between cell pairs.
We demonstrate that network models which obey these experimental constraints, and in which electrical-coupling strength is subject to modulation, are sufficient to account for frequency changes and for the generation of phase differences across frequencies. The robustness of the results is discussed and the key mechanisms that support the observed network dynamics are highlighted. We also discuss a prediction based on our theoretical study.
The aforementioned constraints still leave several free parameters. The exact number of neurons in a cluster is bounded by biological data (8 to 12 neurons per cluster [31]), but not uniquely defined. Also, the number of clusters is variable and might be dynamic as there is evidence for dynamic control of the effective coupling strengths between clusters [15]. Since there is a hard limit on the maximal number of connections per neuron (38, from [2]), the actual number of connections per neuron varies with the cluster size and the number of clusters. In this work we devised a reference network of 4 clusters, each containing 12 neurons. The structure of this network within the gl-gCa space is shown in Figure 1A. Only the oscillating area is marked and the frequency of the oscillations is color-coded (for further details see Supporting Text S1). Cells are marked as red squares and clusters are delineate by ellipses. We limited ourselves to four clusters for the sake of clarity. To satisfy the connectivity constraints, we connected each neuron inside a cluster with 4 peers. To simulate a connection between two clusters, we connected 80% of the neurons in one cluster with a matching number of randomly selected neurons in the other cluster. The conductance of the gap junctions was chosen so as to result in a coupling coefficient of 2–20% (Figure 1B). In Figure 1B the coupling coefficient of each intra-cluster connection (red) and each inter-cluster connection (blue) in the network is illustrated. Note that we provide two CCs per connection because the inter-cluster CC is asymmetrical due to differences in the input resistances of connected neurons. Clustering is organized in such a way that neurons inside each cluster share similar conductance densities. For the sake of demonstration, we picked the clusters in such a way that they were on the boundary in parameter space where neurons can either display spontaneous oscillations or not. We picked neurons on this boundary because the robustness of the oscillatory behavior suggests that at least some of the neurons behave as spontaneous oscillators. On the other hand, stable, non-oscillating, neurons are also encountered [1], [23].
In our reference network, the conductance densities of twenty-six out of forty-eight model neurons are such that they oscillate spontaneously (Figure 2A, left panel). After adding intra-cluster gap junctions in accordance with the connectivity scheme described above, all neurons in clusters C0, C1 and C3 started oscillating, whereas the oscillations in cluster C2 diminished within 1 second (Figure 2A, center panel). With further addition of the inter-cluster gap junctions, all neurons in the network started oscillating and the network exhibited stable oscillations (defined as non-dampening over 5 s) at a frequency of 9.2 Hz (Figure 2A right panel and Figure 2B). Close examination of these oscillations revealed that neurons within a cluster oscillate at precisely the same frequency and phase (Figure 2C), whereas phase differences were evident when neurons from different clusters were compared (Figure 2B). The amplitude of the subthreshold oscillations is less constrained in the experiments and varies on a cell-to-cell basis. However, as indicated by its name, the peak of the oscillations should remain in the subthreshold regime and not provoke suprathreshold events. The simulated voltages observed in our simulations fit nicely with the experimentally observed range of 0.5–25 mV [1], [12], [22]. We use the term “synchronized oscillations” to describe the network state in which all neurons oscillate at the same frequency (but not necessarily with the same phase).
It is important to stress that the network dynamics are robust with respect to the free network parameters (i.e., the exact number of clusters and the cluster size), as long as the resulting connectivity pattern meets the anatomical and physiological constraints outlined before. Namely, we can obtain different networks composed of various numbers of clusters and cluster sizes that exhibit synchronized oscillations. To support this claim, we simulated two sets of pseudo-random network. In the first set, we simulated networks consisting of 10 neurons per cluster and varied the number of clusters from 4 to 8. The inter-cluster connectivity scheme was also sampled randomly, with each cluster connecting to 1–3 other clusters. In the second set of simulations, we varied the number of neurons inside each cluster between 8 and 16, while keeping the number of clusters constant, and using a fixed inter-cluster connectivity scheme as in the reference “4 clusters×12 neurons” network. The resulting frequencies at which these networks exhibited spontaneous oscillations are shown in Figures 2 D & E, respectively. In both sets of simulations, the actual conductance densities of each neuron were sampled from within the experimentally observed range, and the actual gap junction conductances were sampled so as not to violate the strict constraints on coupling coefficients between neurons. We found that the generated networks displayed stable, synchronized oscillations in a wide variety of frequencies. Note the difference in the results between the two “4 clusters×10 neurons” simulations shown in Figures 2 D & E. This difference stems from the distinct inter-cluster connectivity schemes.
We also want to stress that roughly 50% of neurons in our “4 cluster×12 neurons” reference network oscillate spontaneously. Evidently, the mechanism we presented for generating synchronized oscillations also holds in networks with a higher proportion of spontaneously oscillating neurons (e.g., 85%, as in [15]). We thus show that our network model is able to mimic the experimentally observed subthreshold oscillations, and that the “4 clusters×12 neurons” reference network is a good representative of a larger set of networks that satisfies the experimental constraints. (Also see Supporting Text S1)
Two model IO neurons are known to be able to oscillate synchronously when they are connected with a suitable coupling strength [30]. Moreover, it was previously found that such a pair would behave as a single neuron that contains the average density of the conductances of both neurons. The same mechanism also works for networks of IO model neurons. Indeed, we show that the reference network can exhibit oscillations between 6–12 Hz upon modification of the electrical coupling strength. Figure 3A shows the voltage in four neurons: one from each cluster. At the beginning of the simulation (t<5 s) the network oscillates at 6.3 Hz, and after modulating the connection strength (at t = 5 s), the network oscillates at 10.9 Hz. We changed the coupling strength in a biologically plausible way. Although the exact conductance change of each connection was randomized, the changes of all the connections between two clusters followed the same trend and either decreased or increased. This way, heterogeneity was maintained. The changes were always limited to a sevenfold decrease/increase of the present conductance. In the reference network, the modulation consisted of strengthening the connections from groups C3 and C4 to group C1 up to sevenfold, while moderately decreasing their connection strength with C0 by a factor of up to four. Intuitively, the frequency at which the network synchronously oscillates is the frequency of the “center of mass” of the connected neurons, i.e., the frequency of the weighted average (in terms of the conductances) of all connected neurons in the network. The reported shift in network frequency can then be interpreted as a shift of the “average neuron” on the gl-gCa plane (Figure 1A) from bottom left to top right. The frequency change can be verified by a short-time Fourier transformation (Figure 3B) and the standard Fourier transformation (Figure 3C). As a second step we assessed the robustness of the mechanism that modulates the network frequency by repeatedly changing the inter-cluster strength. For this purpose we simulated a large number of instances of the same “4 cluster×12 neurons” network but with different inter-cluster connection strengths. Additionally, we also changed the coupling coefficients randomly by 20% to 400% during simulation of the network (while still staying within the limit of CC<20%). By doing so we found networks displaying synchronized oscillations in the 6–11.5 Hz frequency range both before and after changing the connection strengths (Figure 3D).
Thus, we identified a robust mechanism to change the frequency of the synchronized oscillations by means of (small) changes of the inter-cluster strengths that in turn change the weighted-average neuron that dictates the frequency of the synchronized oscillation.
An emergent feature of the proposed clustered network architecture is that such networks display a phase difference between neurons (Figure 4A). This phase difference is a consequence of the difference in the ion channel density in each cluster. The voltage build-up in neurons with a higher density of Ca2+-conductance is faster. As a result, these high Ca2+-conductance neurons oscillate at a higher frequency when uncoupled. In the coupled case, the faster voltage build-up leads to their advance in phase over neurons with less Ca2+-conductance. During the period directly after the peak, the current flowing between both neurons reverses and causes both neurons to remain in pace with each other. When the coupling strength is sufficient, it is this mechanism that binds the two connected neurons to the same frequency. The same principle holds for networks with clusters of similar neurons: the cluster with highest concentration of Ca2+-conductance is advanced in phase over clusters with less Ca2+-conductance. Figure 4A shows the membrane potential of a representative neuron for each cluster, illustrating that while the network oscillates in synchrony, the temporal succession of the voltage peaks corresponds to the decrease in Ca2+-density (the colors of the traces match the colors of the clusters in Figure 1A.) The observed phase differences in the reference network are summarized in Figure 4B. The respective phase of each neuron is color-coded with respect to that of the reference neuron. It can be verified that within a cluster, the neurons oscillate at roughly the same phase, whereas a larger phase difference exists between different clusters. In the 9.2 Hz regime, the maximum phase difference between any pair of neurons was 72° (Figure 4B). The aforementioned phase difference is stable inasmuch as the phase relations between neurons are maintained over a period of time. This stability over time is illustrated by the cross-correlation between the peak-times (as done with spike times) of the different clusters (measured between one neuron from each cluster and over the 4 seconds of simulated time, Figure 4C). We assessed the robustness of this phenomenon by analyzing the data from the previously generated variants of the reference network (from Figure 3D) and found that the maximal phase difference observed was 140°. Most inter-cluster phase differences were between 20° and 130° (data not shown).
The implication that neurons advanced in their phase also have higher voltage amplitude (because of the larger gCa) can be verified using Figure 4D. In this figure, the peak voltage of all neurons is plotted against their gCa-density. The size of the data points indicates the phase difference relative to the reference (0° phase difference). Hence, larger data-points in Figure 4D indicate a greater offset of phase with respect to the reference neuron. The number of gap junctions and the connectivity between neurons also play a role in the generation of phase differences: the gap junction in itself changes the input resistance (which in our model neurons is a manifestation of the leak conductance). This different connectivity results in a different number of gap junctions, which can account for the difference between clusters 2 and 3 in Figure 4C.
The observed phase difference also provides an explanation for the “propagating waves of activity” found experimentally [22]. In the event that there is spatial correlation between the clusters, different clusters will be activated sequentially, in descending order of gCa. This sequential activation can be observed as a propagating wave (see Supporting Text S1 and Supporting Video S1).
Thus, our model also successfully reproduces the experimental observation of phase differences, and provides a mechanistic explanation for this phenomenon.
In this work we proposed a plausible model of the IO network that provides an explanation for timing and timekeeping within the IO. The activity in the IO is crucial for the proper function of the olivo-cerebellar circuit, and as such it is at the focus of many studies. Different models of IO neurons have been proposed to explain single-cell subthreshold oscillations [30], complex firing dynamics [29], the influence of dendritic spines on synchrony [25] and rhythmogenesis [26], [28]. The dynamic formation of clusters and transient phase differences were demonstrated to emerge from chaotic dynamics [34]. To our knowledge, our IO network model is the first model to reproduce previously unexplained experimental findings such as the non-chaotic, controllable frequency changes and the generation of phase differences, and to provide a mechanistic explanation for these findings.
We purposely used minimalistic model neurons, as the focus of this work was the dynamics of the subthreshold oscillations in the IO network. The model neuron contains only a leak and a Ca2+-current because these currents are most prominent in the subthreshold voltage oscillation regime ([−65 mV,−50 mV]) [29], [30]. Clearly, there are many other voltage-gated ion-channels expressed in IO cells that were not included in this study [29], [35]. However, these channels mostly affect action potentials (especially, the characteristic high-threshold Ca2+ spikes). These currents could be added in the future in large-scale models of the olivo-cerebellar circuit. Despite its limitations, our model is elegant in its minimalistic, yet biologically rooted approach.
In this work we re-evaluate a finding from an earlier work in which it was shown that two IO model neurons that are not necessarily oscillatory in isolation can be connected in such a way that they oscillate synchronously [30], and we interpret this result in a network context. Previously, it was shown that, in the limit of strong coupling, a pair of IO model neurons could be considered as a single neuron containing the average conductance of both individual neurons. Consequently, the frequency of the synchronous oscillation in a pair of such neurons is determined by the frequency of the hypothetical average neuron [30]. Manor et al. proposed as a rule of thumb that an electrically coupled pair of IO model neurons will oscillate only when the “average neuron” lies in the region of the gl-gCa plane where a single neuron would oscillate spontaneously [30] (i.e., inside the colored region in Figure 1A). We continued to show that the same mechanism holds for a network of IO model neurons. In that sense, and as we demonstrated, the inter-cluster connection strength dictates the frequency of the synchronized oscillations because it weighs the contributions of each cluster to the average neuron. In the Supporting Text S1 we provide analytical and empirical support for the demonstrated effects of coupling strength on the frequency of synchronized oscillations.
Having shown that the inter-cluster coupling strength determines the frequency of oscillation, it is straightforward to see that changes in the inter-cluster coupling strength change the oscillatory frequency in the network. We note that the intra-cluster coupling strength does not contribute to the network frequency because inside a cluster all neurons are electrically similar and hence the average neuron that represents a single cluster is very stable; only the inter-cluster connections can change the frequency. We also note that in the clustered network as we propose it, the synchronized oscillations can cease in two ways. First, the virtual, weighted average (neuron) can be moved to a region in the gl-gCa space were no oscillations occur (i.e., the white space in Figure 1A). In this case the whole network is stable and no oscillations occur in any of the neurons. Second, the coupling coefficient between particular clusters can be decreased to a point that their mutual influence is too low to sustain synchronized oscillations. In this case the network breaks down into smaller functional units in which oscillations may persist, albeit with different frequencies. The second mechanism allows for resizing and reassembling the functional network in which synchronized oscillations occur.
Changes in the functional coupling strength can be induced by the GABAergic inputs coming from the deep cerebellar nuclei (DCN). DCN inputs to the IO are co-located at the sites of the gap junction [17], [36] and can shunt the current between two neurons [32], [37]–[39]. Increased input from the DCN can thus serve to decrease the coupling strength, while a release from (tonic) inhibition can increase the coupling strength [39], [40]. Thus, a whole range of coupling strengths can be achieved between clusters, which can result in a continuum of frequencies at which the network can oscillate in synchrony. Our proposed mechanism contrasts with the mechanisms proposed in [15], in which discrete network frequencies result from coupling and decoupling of individual neurons.
Blocking of GABAergic inputs has been reported to have the effect of increasing the size of the group of synchronously oscillating neurons [12], [32], [41]. Thus, apart from the effect of modulating the frequency, GABA could also modulate the size of the group of synchronously oscillating neurons, which in turn has an effect on the coherence in Purkinje cell activity. Our model also captures the re-arrangement of the group of synchronously oscillation neurons. In Figure 2A (center panel), the network activity is shown when only intra-cluster connections are present, which effectively mimics a situation in which clusters are uncoupled by GABA. Then, when we add the inter-cluster connections (effectively mimicking blocking of GABAergic inputs), the complete network goes into a state of synchronized oscillations (Figure 2, right panel). Thus, our network model also captures the effect of blocking GABA, which increases the number of coherently oscillating neurons.
We found that basic neuron models including one active component (Ca2+ T-type current) in combination with a clustered network with differential inter-cluster electrical connections can account for synchronized network oscillations, the modulation of the frequency and the emergence of phase differences, which in turn lead to propagating waves of activity. There is a great deal of theoretical literature related to synchrony in neural network [42]–[45]. Synchrony of suprathreshold dynamics (spikes) is often explained in terms of the coupling functions between neurons [43]–[46]. On the other hand, synchrony between the subthreshold dynamics in neurons has received less attention and is rarely considered in isolation from its suprathreshold counterpart, despite the fact that this is exactly what happens in the IO, in which the firing rate is an order of magnitude slower than the subthreshold oscillations. Theoretical studies are well suited to find transitions in dynamics (bifurcations) and allow researchers to pinpoint the necessary conditions for particular experimental observations [47]. To our knowledge, there is no study illustrating the conditions required for a network to maintain non-zero phase lags between purely subthreshold oscillations. We presented a network in which such non-zero phase lags are exhibited and explained their existence in terms of the biophysics of voltage-gated Ca2+ current. However, it remains unclear what the minimal conditions are for realistic, synchronized subthreshold oscillations in our network. The minimal conditions depend on what is functionally relevant for the network. For instance, shifts between 1 and 4 Hz have been observed experimentally [22]. Clearly, as demonstrated in our network model, the difference between the intrinsic frequencies of any cluster in the network will place an upper bound on the size of the shift achievable in that network. As a rule of thumb, the maximum shift in a network is limited by the difference between the intrinsic frequencies (uncoupled) of the clusters (Figure S3 in Text S1). Thus, to create a shift of 2 Hz in the network, the intrinsic frequencies of the contributing clusters should be at least 2 Hz apart. However, there is a trade-off between the magnitude of the shift and the ability of the network to synchronize: the more dissimilar the intrinsic frequencies of the clusters, the harder it becomes to create coherent oscillations across the entire network (Figure S2B in Text S1). A second rule of thumb is that to synchronize two highly dissimilar neurons or clusters (say, F1–F2>2 Hz), synchrony can be obtained more easily by introducing an intermediate neuron or cluster. Consequently, the minimal conditions for a network to synchronize depend on the exact requirements, e.g. the frequency of the synchronized oscillations and the size of the frequency shift. For now we offer the aforementioned rules of thumb, but finding the precise minimal conditions required for synchrony will be addressed in future work.
Many network models are devised to address a particular question dealing with a part of the natural, experimentally observed dynamics. To model different dynamics in the same system, a new model is constructed in the present study that can accommodate diverse sets of dynamics. We have shown that our network model, which successfully reproduces subthreshold oscillations, also accounts for the experimentally observed frequency changes and phase differences. Moreover, based on current data from the DCN [40], it is a plausible that the actual connectivity between the DCN and the IO could implement the proposed mechanism of IO frequency modulation. No structural changes (such as a different connectivity statistics) are required in our model in order to generate oscillations, to change the frequency and to maintain stable phase differences between different IO cells. The fact that our model can reproduce a variety of experimentally observed behaviors increases our confidence that we have captured in our model the key mechanisms underlying the observed behavior.
The results presented in this study also give rise to a testable prediction about the IO. Our prediction addresses the possibility of modulating IO oscillation frequencies by changing the inter-cluster coupling strength. This prediction could be tested in an in-vitro preparation in which a single intracellular recording is made from an IO neuron while GABAergic input is emulated by GABA application. We predict that when GABA is released in small areas close to the dendrites of the recorded cell, a reversible change in the frequency should be detected. The aim would be to apply GABAergic input only to the dendrites to shunt some of the gap-junctional current while maintaining the rest, thus leaving the intrinsic dynamics of the cell largely unaffected. Consequently, the neuron would not be uncoupled completely from the network, but the influence from the network would change. This corresponds to changing the inter-cluster coupling strength and should affect the oscillatory behavior of that neuron.
In conclusion, we present the first anatomically and physiologically plausible (albeit reduced) network model of the IO that provides a biophysical explanation for previously unexplained experimental observations. As such, we believe that our model is suitable to test future hypotheses about the origin of the subthreshold oscillations and their role in timing.
We use conductance-based model neurons based on the model presented in [30]. These conductance-based model neurons contain only a leak current and a low-threshold (T-type) Ca2+ current. Formally, the dynamics of the model neurons are described by:in which Cm is the membrane capacitance, El and ECa are the reversal potentials for the leak and low-threshold Ca2+ current, respectively. gl and gCa are the maximum conductances of these currents. m and h are the gating variables for the time and voltage dependent T-type current and follow
In all presented simulations, EL = −63 mV while gl and gCa vary between [0.15,0.4] mS/cm2 [0.2,1.4] mS/cm2 [23], respectively. Neurons containing specific amounts of gl and gCa can exhibit spontaneous oscillations over a range of frequencies as illustrated in Figure S2 in Text S1. A model neuron can be equipped with different densities of the associated leak (gl) and calcium (gCa) conductance. Depending on the exact density of gl and gCa the neuron can be i) a spontaneous oscillator and oscillate at different frequencies (Figure 1), ii) a conditional oscillator, iii) bistable or, iv) stable [30].
We create the network model by connecting selected neurons through electrical coupling (gap-junctions). The effect of a gap-junction on a single neuron can be represented by an additional current that mimics the current flowing between two connected cells proportionally to the difference in membrane potential in both cells: and , which is added to the right-hand side of the appropriate equation (1). The precise values of Rc1 and Rc2 are of little importance as they depend on the actual input resistance of a neuron. A more useful measurement of coupling through gap-junction is the coupling coefficient: CC1 = V2/V1 = R2/(R2+Rc1) and CC1 = V2/V1 = R1/(R1+Rc2) as it directly assesses the electrical impact of one neuron on the other. Note that the voltages V1 and V2 are not the same in the calculation of CC1 and CC2 because they are measured from two separated experiments; one in which the current is injected in the first neurons and another experiment in which the current is injected in the second neuron. Due to the dependence on the input resistances, CC1 and CC2 also do not need to be the same.
Based on anatomical and physiological data the network architecture has to satisfy three interconnected constraints. First, neurons similar in terms of their conductances densities are clustered together and connected more densely to neurons inside the same cluster than to neurons belonging to different clusters. Second, the number of connections per neurons is between 1 and 38 [2]. Third, the connection strength is limited to a coupling coefficient between 2 and 20%. However, the majority of connections have a reported strength of CC<10% [1].
We generated pseudo-random networks in which we manually set the meta-parameters of the network, namely the number of neurons per cluster (12), the number of clusters (4), the number of connected neighbors inside a cluster (4), the overall connectivity scheme between clusters (Figure 1B), and, the number of connections between 2 connecting clusters (1 per neuron). In the networks generated for Figure 2 D&E, we sampled one cluster center for each cluster. We then sampled according to a normal distribution around this center (μ = 0.005 mS/cm2 and μ = 0.01 mS/cm2 for gl and gCa, respectively) to get set the actual values for the conductances of the model neurons inside that cluster. The networks in Figure 2D have a randomized connectivity scheme in which each cluster was connected to one to three other clusters. The networks in Figure 2E had a fixed connectivity scheme, namely the scheme from Figure 1 (left). The networks in Figure 3D were the same as the reference network and only differed in their inter-cluster strengths.
We implemented all simulations in PyNEURON [48]; the code is available on ModelDB (accession number: 144502). Analysis of the network dynamics was done with custom routines in Python/SciPy/Matplotlib (Python: http://python.org, SciPy: http://www.scipy.org/, Matplotlib: http://matplotlib.sourceforge.net/). The “phase-map” in Figure 4B is generated by computing the phase difference between each pair and setting the first neuron in the first cluster as the reference (i.e., 0° phase-difference). For the visualization, the clusters were ordered from bottom-to-top in order of larger phase-difference to the reference. The cross-correlation in Figure 3C is computed from the peak times (as is generally done with spike times) and not from the full membrane potential trace.
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10.1371/journal.ppat.1004480 | Candida albicans Ethanol Stimulates Pseudomonas aeruginosa WspR-Controlled Biofilm Formation as Part of a Cyclic Relationship Involving Phenazines | In chronic infections, pathogens are often in the presence of other microbial species. For example, Pseudomonas aeruginosa is a common and detrimental lung pathogen in individuals with cystic fibrosis (CF) and co-infections with Candida albicans are common. Here, we show that P. aeruginosa biofilm formation and phenazine production were strongly influenced by ethanol produced by the fungus C. albicans. Ethanol stimulated phenotypes that are indicative of increased levels of cyclic-di-GMP (c-di-GMP), and levels of c-di-GMP were 2-fold higher in the presence of ethanol. Through a genetic screen, we found that the diguanylate cyclase WspR was required for ethanol stimulation of c-di-GMP. Multiple lines of evidence indicate that ethanol stimulates WspR signaling through its cognate sensor WspA, and promotes WspR-dependent activation of Pel exopolysaccharide production, which contributes to biofilm maturation. We also found that ethanol stimulation of WspR promoted P. aeruginosa colonization of CF airway epithelial cells. P. aeruginosa production of phenazines occurs both in the CF lung and in culture, and phenazines enhance ethanol production by C. albicans. Using a C. albicans adh1/adh1 mutant with decreased ethanol production, we found that fungal ethanol strongly altered the spectrum of P. aeruginosa phenazines in favor of those that are most effective against fungi. Thus, a feedback cycle comprised of ethanol and phenazines drives this polymicrobial interaction, and these relationships may provide insight into why co-infection with both P. aeruginosa and C. albicans has been associated with worse outcomes in cystic fibrosis.
| In many human infections, several species of microbes are often present. This is typically the case with the disease cystic fibrosis, characterized by thick mucus in the lungs that is colonized by bacteria and fungi. Here, we show evidence that interactions between the bacterium Pseudomonas aeruginosa and the fungus Candida albicans result in attributes of infection that are worse for the human host. We found that ethanol, such as that produced by C. albicans, causes increased levels of a signaling molecule in P. aeruginosa that promotes biofilm formation. Biofilm formation by P. aeruginosa is associated with infections that are more difficult to treat. Ethanol stimulated P. aeruginosa colonization of plastic surfaces and airway cells, and we identified components of this mechanism. Fungally-produced ethanol also changes the spectrum of phenazine toxins produced by P. aeruginosa, and phenazines are associated with worse lung function in people with cystic fibrosis. In light of the fact that phenazines interact with C. albicans to promote ethanol production, we propose a positive feedback loop between C. albicans and P. aeruginosa that contributes to worse disease. Our findings could have implications for the study and treatment of multi-species infections.
| Pseudomonas aeruginosa is an opportunistic pathogen capable of causing severe nosocomial infections and infections in immunocompromised patients. P. aeruginosa is a common pathogen of individuals with cystic fibrosis (CF), a genetic disease that is caused by a mutation in the gene coding for the CFTR ion transporter and strongly associated with chronic, recalcitrant lung infections. Altered CFTR function leads to a fluid imbalance that results in thick, sticky mucus in the lungs that is difficult to clear, thus creating a hospitable environment for microbial growth, biofilm formation, and persistence. While P. aeruginosa is a common microbe in the CF lung, it is rarely the only microbe present [1]–[5]. Co-infections of P. aeruginosa with other bacterial and fungal species are common, and there is a need to understand how these complex multi-species infections impact disease course and treatability. For example, the presence of the fungus Candida albicans correlates with more frequent exacerbations and a more rapid loss of lung function in CF patients [6], [7]. Additional studies are needed to determine if the presence of the fungus contributes to more severe disease.
Published reports strongly suggest that in the CF lung, P. aeruginosa forms biofilms [8], described as hearty aggregations of cells in a sessile group lifestyle that includes extracellular matrix comprised of proteins, membrane vesicles, DNA, and exopolysaccharides. A biofilm existence provides many advantages to P. aeruginosa including increased antibiotic tolerance [9], [10]. As with many Gram-negative species, P. aeruginosa biofilm formation is positively regulated by the secondary signaling molecule cyclic-di-GMP (c-di-GMP) [11]. C-di-GMP is formed from two molecules of GTP by diguanylate cyclases (DGCs) and its levels inversely correlate to motility. High levels of c-di-GMP promote biofilm formation in a number of ways including via increased matrix production and decreased flagellar motility [12]–[14].
P. aeruginosa also produces a class of redox-active virulence factors called phenazines. In CF sputum, the phenazines pyocyanin (PYO) and phenazine-1-carboxylate (PCA) are found in micromolar (5–80 µM) concentrations, and their levels are inversely correlated with lung function [15]. Phenazines play a role in the relationships between P. aeruginosa and eukaryotic cells. Several studies have shown how phenazines can negatively affect mammalian physiology [16], [17]. In addition, phenazines impact different fungi, including C. albicans. At high concentrations, phenazines are toxic to C. albicans, and lower concentrations of phenazines reduce fungal respiration and impair growth as hyphae [18]. Phenazines figure prominently in shaping the chemical ecology within mixed-species communities. For example, when exposed to low concentrations of phenazines, C. albicans increases the production of fermentation products such as ethanol by 3 to 5 fold [18]. Furthermore, P. aeruginosa-C. albicans co-cultures form red derivatives of 5-methyl-phenazine-1-carboxylic acid (5MPCA) that accumulate within fungal cells [19].
In the present study, we show that ethanol produced by C. albicans stimulated P. aeruginosa biofilm formation and altered phenazine production. Ethanol caused a decrease in surface motility in both strains PA14 and PAO1 concomitant with a stimulation in levels of c-di-GMP, a second messenger nucleotide that promotes biofilm formation. Through a genetic screen, we found that the diguanylate cyclase WspR, a response regulator of the Wsp chemosensory system, was required for this response. Elements upstream and downstream of WspR signaling were required for the ethanol response. Ethanol no longer stimulated biofilm formation in a mutant lacking WspA, the membrane-localized sensor methyl-accepting chemotaxis protein (MCP) that is involved in the activation of WspR [20]. In addition, an intact Pel exopolysaccharide biosynthesis pathway, known to be stimulated by c-di-GMP derived from the Wsp pathway [21], [22], was also required for ethanol stimulation of biofilm formation. The effects were observed on both abiotic surfaces and a cell culture model for P. aeruginosa and P. aeruginosa-C. albicans airway colonization. We found that both exogenous and fungally-produced ethanol enhanced the production of two phenazine derivatives known for their antifungal activity [19], [23], 5MPCA and phenazine-1-carboxamide (PCN), through a Wsp-independent pathway and independent of ethanol catabolism. Because phenazines stimulate fungal ethanol production [18], we present evidence for a signaling cycle that helps drive this polymicrobial interaction.
Our previously reported findings show that P. aeruginosa produces higher levels of two phenazines, PYO and 5MPCA [24], [25], when cultured with C. albicans and that phenazines stimulate C. albicans ethanol production [18]. Thus, we sought to determine how fungally-derived ethanol affects P. aeruginosa. A concentration of 1% ethanol (v/v) was chosen for these studies based on the detection of comparable levels of ethanol in C. albicans supernatants from cultures grown with phenazines [18]. The presence of 1% ethanol in the culture medium did not affect P. aeruginosa growth in minimal M63 medium with glucose (Fig. S1) or LB (doubling time of 36±2 min in LB versus 39±2 min in LB with ethanol), or on solid LB medium (Fig. S1 inset) except that the final culture yield in M63 was slightly higher in cultures amended with ethanol (Fig. S1).
When we performed a microscopic analysis of the effects of ethanol on P. aeruginosa strain PAO1, we observed a significant increase in attachment of cells to the bottom of a titer dish well within 1 h (15±5 cells per field in vehicle treated compared to 31±6 cells per field in cultures with ethanol, p<0.01) and development of microcolonies was strongly enhanced (Fig. 1A). Ethanol also promoted an increase in the number of attached cells and microcolonies in cultures of another P. aeruginosa strain, PA14 (Fig. 1B).
Using two assays that assess biofilm-related phenotypes (swarming motility and twitching motility), we sought to gain additional insight into how ethanol impacted biofilm formation. Our initial studies focused on strain PA14. We found that ethanol repressed swarming motility, a behavior that is inversely correlated with biofilm formation (Fig. 1C). Ethanol did not affect type IV-pili-dependent twitching motility, a form of movement that is required for microcolony formation in biofilms on plastic (Fig. S2B) [26].
Because P. aeruginosa can catabolize ethanol [27], we sought to determine if ethanol consumption contributed to the repression of swarming motility. P. aeruginosa first oxidizes ethanol to acetaldehyde by an ethanol dehydrogenase, ExaA, which requires the cofactor PQQ (pyrroloquinoline quinone) [27]. Acetaldehyde is further oxidized to acetate by an NAD+ dependent acetaldehyde dehydrogenase (ExaC), and the acetate is subsequently oxidized to acetyl-CoA by AcsA [28]. We retrieved the exaA::TnM, pqqB::TnM, and acsA::TnM mutants predicted to be defective in ethanol catabolism from the P. aeruginosa strain PA14 NR transposon library [29] and confirmed the transposon insertion sites by PCR (see Materials and Methods for more detail). As predicted, none of these mutants grew with ethanol as the sole carbon source, and growth on glucose was unaffected (Fig. S3A).
When we used these mutants in the swarm assay, we found no difference in the effects of ethanol on these three ethanol catabolism mutants in comparison to the wild-type parental strain (Fig. S3B) indicating that ethanol catabolism was not required for the ethanol response. Furthermore, other carbon sources such as glycerol, another fungal fermentation product, or choline, another two-carbon alcohol degraded by a PQQ-dependent enzyme, did not inhibit swarming motility (Fig. S4).
Ethanol stimulated attachment and biofilm formation on plastic and inhibited swarming motility (Fig. 1). These two phenotypes are positively and negatively regulated by levels of the second messenger molecule c-di-GMP [30]. Thus, we measured intracellular levels of this dinucleotide in P. aeruginosa strain PA14 cells grown on swarm plates with or without 1% ethanol for 16.5 h as described previously. We found a 2.4-fold increase in c-di-GMP levels in cells exposed to ethanol (Fig. 2).
To identify the enzyme(s) responsible for this increase, we screened a collection of 31 P. aeruginosa strain PA14 mutants [31] defective in different genes predicted to encode proteins that may modulate c-di-GMP levels based on the detection of a DGC and/or an EAL domain [22], [31]. We found that one mutant, ΔwspR, was strikingly resistant to the repression of swarming by ethanol (Fig. 3). As expected, this mutant also had a slight hyperswarming phenotype when compared to the wild type in control conditions [31], and both phenotypes were complemented by the wild-type wspR allele on an arabinose-inducible plasmid when grown in the presence of 0.02% arabinose (Fig. 3). The empty vector (EV) control exhibited a swarming pattern comparable to that of the ΔwspR mutant.
WspR is a response regulator with a GGDEF domain [32], which is associated with diguanylate cyclase activity [20]. Consistent with the observation that ΔwspR continued to swarm on medium with ethanol, c-di-GMP levels were not different between cultures with and without ethanol in the ΔwspR background (Fig. 2). These data suggest that WspR activity, and thus c-di-GMP levels, are enhanced by ethanol.
WspR is known to regulate the production of the Pel polysaccharide [21], [22], and production of Pel is associated with colony wrinkling and biofilm formation [33]. After 72 hours on swarm plates, we also observed that ethanol strongly promoted colony wrinkling while the addition of equivalent amounts of other carbon sources, such as glycerol or choline, did not have this effect. Furthermore, the colony wrinkling induced by ethanol was less apparent in a ΔwspR strain (not shown) and completely absent in a strain lacking pelA, an enzyme required for Pel biosynthesis (Fig. 4). The ΔpelA mutant, like the ΔwspR mutant, continued to swarm in the presence of ethanol (Fig. 4) suggesting that the repression of swarming in the presence of ethanol was, at least in part, due to increased Pel production.
As we found that ethanol stimulated biofilm formation in P. aeruginosa wild-type strains PA14 and PAO1 (Fig. 1) and that WspR mediated the ethanol effect in strain PA14, we also examined the role of WspR in the ethanol response in strain PAO1. As shown above, PAO1 wild-type cells had increased early attachment and subsequent microcolony formation on plastic when ethanol was added to the medium (Fig. 5). Consistent with our model that ethanol is acting through WspR, ethanol did not stimulate surface colonization in the PAO1 ΔwspR mutant (Fig. 5). We also examined the ethanol-responsive phenotype for P. aeruginosa strain PAO1 ΔwspA, which lacks the membrane bound receptor that is the most upstream element described in the Wsp system [20]. Like ΔwspR, ΔwspA did not show increased attachment to plastic upon the addition of ethanol (Fig. 5) suggesting that both the MCP sensor and the WspR response regulator were required for the response to ethanol. Ethanol also promoted colony wrinkling in strain PAO1, as was observed in strain PA14, consistent with the prediction that increased WspR activity would lead to increased matrix production. Enhanced wrinkling with ethanol was shown most clearly for both strains in non-motile (flgK) mutants which formed colonies of similar size regardless of the presence of ethanol (Fig. S5). Because strain PAO1 WT does not swarm robustly in control conditions, the effects of ethanol on swarming in strain PAO1 were not quantified.
Previous studies have shown that the fluorescently-tagged WspR protein forms intracellular clusters when in its active phosphorylated form upon incubation of cells on an agar surface, and cluster formation is positively correlated with WspR activity [20]. To complement the mutant analyses, we determined if ethanol also promoted WspR-YFP clustering, and if known components of the WspR activation system were required for WspR stimulation by ethanol. To facilitate these analyses, we used the WspR variant WspRE253A-YFP, which forms larger clusters that are more easily visualized [34]. In these studies, we observed a two fold increase in WspR clustering in the presence of ethanol (Fig. 5C). To determine if WspF, a methylesterase that negatively regulates WspR activity [21], was involved in the regulation of WspR in response to ethanol, we also assessed WspR clustering in a ΔwspF background where WspR is constitutively active. In ΔwspF, WspR clustering was higher than in the wspF+ reference strain, and WspR clustering was not further stimulated by ethanol, lending support for the model that ethanol was acting through the Wsp system and not through an independent pathway for WspR activation.
To understand the effects of ethanol on P. aeruginosa in a well-established CF-relevant disease model, we studied the effects of ethanol on P. aeruginosa strain PAO1 in the context of bronchial epithelial cells with the most common CF genotype (homozygous CFTRΔF508) [35], [36]. We cultured P. aeruginosa strain PAO1 with the epithelial cells in medium without and with 1% ethanol, and observed an obvious enhancement in the size of biofilm microcolonies (Fig. 6A) and a 2.2-fold increase in colony forming units (CFUs) on the airway cells with ethanol (Fig. 6B). When the same experiment was performed with the ΔwspR or ΔwspA mutants, no stimulation by ethanol was observed. Ethanol alone did not impact epithelial cell viability as measured by an LDH release assay (9.44%±0.98 LDH release for control and 10.47%±1.2 LDH release with ethanol, N = 3) and other studies have also found these concentrations of ethanol to be well below those that cause overt toxicity to epithelial cells or disruption of epithelial barrier integrity [37], [38].
When P. aeruginosa PAO1 and C. albicans were co-inoculated into epithelial cell co-cultures, 4.7-fold more P. aeruginosa CFUs were found to be associated with the monolayer after 6 h (Fig. 7). To determine if C. albicans-derived ethanol contributed to the enhanced colonization by P. aeruginosa in the presence of C. albicans, we used a C. albicans adh1/adh1 mutant that produced lower levels of ethanol. We constructed the adh1 null strain and its complemented derivative, and confirmed that the absence of ADH1 caused a reduction in ethanol by HPLC analysis of culture supernatants, a finding consistent with previously published work [39]. When P. aeruginosa was co-cultured with the C. albicans adh1/adh1 strain, there was a significant decrease in P. aeruginosa CFUs recovered, and this defect was corrected upon complementation with the ADH1 gene in trans. Furthermore, there was no significant difference in the stimulation of colonization by wild-type or adh1/adh1 mutant C. albicans in the ΔwspR or ΔwspA backgrounds (Fig. S6). Together, these data strongly suggest that C. albicans-produced ethanol promotes P. aeruginosa colonization of both abiotic and biotic surfaces through activation of the Wsp system, which likely exerts these effects through promoting Pel production.
In part, these studies were instigated by the finding that P. aeruginosa phenazines strongly stimulate C. albicans ethanol production [18]. Thus, we were intrigued by the observation that colonies on ethanol-containing swarm plates, but not control plates, contained abundant emerald green crystals, similar to those formed by reduced phenazine-1-carboxamide (PCN) [40] (Fig. 8A, Fig. 4 and Fig. S7A), which could indicate a reciprocal relationship between ethanol and phenazines. Phenazine concentrations were measured using HPLC in either extracts from P. aeruginosa strain PA14 colonies or extracts from the underlying agar. In extracts from wild type colonies, PCN and PCA concentrations were 24.2- and 5.8-fold higher, respectively, when ethanol was in the medium (Fig. S7B); much smaller differences in PCN and PCA concentrations were found in extracts of the underlying agar (Fig. S7C). Because PCA is the precursor for all other phenazine derivatives, including PCN (Fig. S7A), we further explored the effect of ethanol on PCA production. For this, we measured levels of PCA in a strain lacking all of the PCA modifying enzymes (PhzH, PhzM, and PhzS; see Fig. S7A for pathways) [41]. We found that ΔphzHMS colonies contained 1.7-fold more PCA (Fig. S7D) and released 1.3-fold more PCA into the agar (Fig. S7E) when grown in the presence of ethanol compared to control conditions. These data suggest that ethanol may cause a minor increase in PCA, and that it has greater effects on which species of phenazines are formed. The differences in phenazine levels or profiles did not appear to be responsible for ethanol effects on swarming as the Δphz mutant [42], which lacks phzA1-G1 and phzA2-G2, was like the wild type in that its swarming was repressed in the presence of ethanol, but it swarmed robustly in its absence (Fig. S7F).
To determine if there was a connection between ethanol effects on Wsp signaling and ethanol stimulation of PCN levels, we assessed PCN accumulation in mutants lacking wspR or pelA. We found that both strains responded like the wild type in terms of PCN crystal formation upon growth with ethanol (Fig. S8A and Fig. 4). Similarly, ethanol catabolic mutants still showed enhanced levels of PCN crystals upon ethanol exposure (Fig. S8A).
Having observed alterations in the phenazine profile induced by ethanol, we examined the impact of ethanol in the production of a fourth phenazine derivative, 5MPCA, which we have previously shown to be released by P. aeruginosa when in the presence of C. albicans [19], [24]. Because P. aeruginosa-produced 5MPCA is converted into a red pigment within C. albicans cells, 5MPCA accumulation can be followed by observing the formation of a red color where P. aeruginosa and C. albicans are cultured together [19], [24]. To examine the effects of ethanol production on the accumulation of red 5MCPA derivatives, we again used the C. albicans adh1/adh1 mutant and its complemented derivative. Strikingly, when P. aeruginosa was cultured on lawns of the C. albicans adh1/adh1 strain, a strong decrease in red pigmentation was observed (Fig. 8B). When ADH1 was provided in trans to the adh1/adh1 mutant, accumulation of the red pigment was restored (Fig. 8B). Neither ethanol catabolism nor WspR activity was required for the stimulation of levels of 5MPCA derivatives by P. aeruginosa on fungal lawns (Fig. S8B).
Together, our data suggest that ethanol only slightly increases total phenazine production (Fig. S7D and E) but more strongly affects the derivatization of phenazines in P. aeruginosa colonies (Fig. S7B and C). Furthermore, C. albicans-produced ethanol stimulated P. aeruginosa 5MPCA production, and in turn, phenazines, including 5MPCA analogs, promote ethanol production [18]. Thus, it appears that P. aeruginosa-C. albicans interactions include a positive feedback loop that promotes fungal ethanol production and P. aeruginosa Wsp-dependent biofilm formation when the two species are cultured together.
This paper reports new effects of ethanol on P. aeruginosa virulence-related traits, and illustrates that these effects occur through multiple pathways (Fig. 9). We found that ethanol: i) promoted attachment to and colonization of plastic and airway epithelial cells, ii) decreased swarming, but not twitching motility, iii) increased Pel-dependent colony wrinkling, and iv) increased c-di-GMP levels. All of these responses to ethanol required the diguanylate cyclase WspR. WspR is part of the Wsp chemosensory system, which is a member of the “alternative cellular function” (ACF) chemotaxis family [20], [21], [43]. The Wsp chemosensory system is different from the chemotaxis systems in P. aeruginosa in terms of its localization and response to environmental signals [44]. The membrane-bound receptor WspA and the CheA homologue WspE are necessary for the Wsp system to function, and WspE activates WspR via phosphorylation [44]. Consistent with our hypothesis that the entire Wsp system is required for the response to ethanol, we found that a wspA mutant was also insensitive to the effects of ethanol on biofilm formation (Fig. 5). The activation of WspR was independent of ethanol catabolism and independent of phenazine production. Ethanol and other alcohols can increase the rigidity of cell membranes by promoting an altered composition of fatty acids [45], and future studies will determine if the Wsp system, particularly the membrane localized WspA, can be activated by changes in the lipid composition or changes in the physical properties of P. aeruginosa membranes. Because the Wsp system is also activated upon contact with a surface [20], it is intriguing to consider how these stimuli might be similar. Ethanol had mild, if any, effects, on biofilm formation at the air-liquid interface in a commonly used 96-well microtiter dish assay in either strain (Fig. S9) suggesting that in this environment, different Wsp activating cues were not additive.
C. albicans and other Candida spp. are commonly detected in the sputum of CF patients, and clinical studies suggest that the presence of both P. aeruginosa and C. albicans results in a worse prognosis for CF patients [6], [46]. In vivo ethanol production by other fungi has been documented [47], [48], but a link between Candida spp. and ethanol production in the lung has not yet been made. It is important to note, however, that ethanol was one of two metabolites in exhaled breath condensate that differentiated CF from non-CF individuals [49]. Thus, regardless of the source of ethanol, be it fungal or bacterial, the effect of ethanol on pathogens such as P. aeruginosa is likely of biological and clinical relevance. We tested this interaction in the context of CF, but this polymicrobial interaction likely occurs in other contexts as well.
As shown above, ethanol promoted biofilm formation and likely concomitant increases in drug tolerance. In the airway epithelial cell system, P. aeruginosa CFU recovery was increased 3-fold by addition of ethanol (Fig. 6B) and 4.7-fold by co-culture with C. albicans (Fig. 7). A two-fold difference is comparable to the differences in colonization between wild-type P. aeruginosa strains and mutants lacking genes known to play a role in virulence in animal models. For example, a ΔplcHR mutant lacking hemolytic phospholipase C or a Δanr strain defective in a global regulator have 1.3- to 2.6-fold fewer CFUs recovered from airway cells compared to wild type, and notable differences in animal models [50], [51]. Hence the presence of ethanol may result in increased virulence of P. aeruginosa in the host. Ethanol has also been shown to promote P. aeruginosa conversion to a mucoid state [52], in which the exopolysaccharide alginate is overproduced; mucoidy is common in CF isolates and is correlated with a decline in lung function [53], [54]. Ethanol has been shown to enhance virulence and biofilm formation by other lung pathogens such as Staphylococcus aureus [55] and Acinetobacter baumanii [56]–[59] via mechanisms that have not yet been described. Like in P. aeruginosa (Fig. S1A), ethanol caused a slight stimulation of growth in A. baumanii [58].
In addition to the effects of ethanol on P. aeruginosa, ethanol is an immunosuppressant that negatively influences the lung immune response [60]–[64]. In a mouse model, ethanol inhibits lung clearance of P. aeruginosa by inhibiting macrophage recruitment [65]. Together, these observations suggest that in mixed infections, P. aeruginosa may promote the production of ethanol by fungi, and that fungally-produced ethanol may in turn enhance the virulence and persistence of co-existing pathogens, and thus may directly impact the host.
It is not yet known how ethanol influences the spectrum of P. aeruginosa phenazines produced. In a previous study, we found evidence for increased production and release of 5MPCA when P. aeruginosa is grown in co-culture with C. albicans, and that live C. albicans is required for this effect [19]. More recent studies show that C. albicans ethanol production increased in the presence of even very low concentrations of the 5MPCA analog phenazine methosulfate [18], that the 5MPCA-like compounds were even more effective inhibitors of fungi than PCA and PYO, the two phenazines normally produced when P. aeruginosa is grown in mono-culture. Here, our findings suggest a feedback loop in which C. albicans-produced ethanol promoted the release of phenazines (Fig. 7) that may promote further ethanol production [18]. It is also important to consider that some studies have reported that 5MPCA and PCN have enhanced antifungal activity when compared to PCA and PYO [19], [23], [24]. The ethanol-induced changes in PCA were not as dramatic when compared to the ethanol-induced changes in PCN and 5MPCA, suggesting that ethanol mainly affected the biosynthetic steps after the formation of PCA leading to its conversion to PCN, 5MPCA and PYO. In different settings, such as liquid cultures or in clinical isolates lacking activity of LasR, a transcriptional regulator for quorum sensing that controls phenazine production, the presence of C. albicans enhanced the production of 5MPCA and PYO [24], [25]. Taken together, all these observations indicate that fungally-produced ethanol may enhance the conversion of PCA to end products such as PCN, 5MPCA and PYO.
These studies indicate how microbial species can alter the behavior of one another and suggest that the nature of these dynamic interactions can change depending on the context. In the rhizosphere, where pseudomonad antagonism of fungi includes the colonization of fungal hyphae and phenazine production, the enhancement of fungally-produced ethanol by phenazines and stimulation of biofilm formation and phenazine production by ethanol may create a cycle that is relevant to biocontrol [23], [66], [67]. In chronic infections where these two species are found together, such as in chronic CF-associated lung disease, this molecular interplay may be synergistic and promote long-term colonization of both species in the host. These findings indicate that the treatment of colonizing fungi may be beneficial due to their effects on other pathogens even if the fungi themselves are not acting as overt agents of host damage.
Bacterial and fungal strains and plasmids used in this study are listed in Table S1. Bacteria and fungi were maintained on LB [68] and YPD (2% peptone, 1% yeast extract, and 2% glucose) media, respectively. When stated, ethanol (200-proof), choline chloride or glycerol was added to the medium (liquid or molten agar) to a final concentration of 1%. Control cultures received an equivalent volume of water. When ethanol was supplied as a sole carbon source, glucose and amino acids were omitted. Mutants from the PA14 Non-Redundant (NR) Library were grown on LB with 30 µg/mL gentamicin [29]. When strains for the NR library were used, the location of the transposon insertion was confirmed using sets of site-specific primers followed by sequencing of the amplicon. The primers are listed in Table S2.
For growth curves, overnight cultures were diluted into 5 ml fresh medium (LB or M63 with 0.2% glucose [69] with or without ethanol) to an OD600 nm of ∼0.05 and incubated at 37°C on a roller drum. Culture densities below 1.5 were measured directly in the culture tubes using a Spectronic 20 spectrophotometer. At higher cell densities, diluted culture aliquots were measured using a Genesys 6 spectrophotometer.
To measure the attachment of cells to the plastic surface in 6-well or 12-well untreated polystyrene plates, wells were inoculated with a suspension of cells at an initial OD600 nm of 0.002 from overnight cultures. Every 90 minutes, the culture medium was removed and fresh medium was supplied. Pictures were taken using an inverted Zeiss Axiovert 200 microscope with a long distance 63× DIC objective at specified intervals. To quantify the number of cells or microcolonies in control cultures compared to cultures with ethanol, images were captured, randomized, and analyzed by a researcher who was blind to the identity of the sample at the time of analysis. In each experiment, more than 10 fields were counted for each strain. Microcolonies were defined as clusters of more than 5 cells in physical contact with one another. Biofilm formation on plastic microtiter dishes were performed and analyzed using the crystal violet assay as described in [55] and biofilm values were measured by quantification of dye as measured absorbance at 650 nm.
The analysis of P. aeruginosa colonization of airway epithelial cells was performed using CFBE human bronchial epithelial cells (CFBE410−) with the CFTRΔF508/ΔF508 genotype [70] as described previously [35], [36]. For imaging, cells were grown in 6-well glass bottom dishes (MatTek). For quantification of attached cells, CFBEs were grown in 6 or 12 well plates. P. aeruginosa strain PAO1 cells were added at an MOI of 30∶1, and the medium was exchanged every 1.5 hours. For experiments with C. albicans, PAO1 cells and C.albicans were added together to CFBE monolayers, where C.albicans was at an MOI of 10∶1 with respect to the epithelial cells. Pictures were taken using a Zeiss Axiovert 200 microscope with a 63× DIC objective at specified intervals. We performed multiple experiments with technical replicates (between three and six) on different days and analyzed the data with a one-way analysis of variance and Tukey's post hoc t-test using Graph Pad Prism 6. We observed that cells from different passages had differences in the mean attachment across all samples from that day. Thus, we normalized values to the mean across all samples from each experiment. LDH release was measured after six hours using the Promega CytoTox96 Non-Radioactive Cytotoxicity kit as described in the manufacturer's instructions.
Swarming motility was tested by inoculating 2.5 µL of overnight cultures on fresh M8 (M8 salts without trace elements supplemented with 0.2% glucose, 0.5% casamino acids, and 1 µM MgSO4) containing 0.5% agar as described previously [71]. Plates were incubated face up at 37°C with 70–80% humidity in stacks of no more than 4 for 16.5 hrs. To quantify the degree of swarming, percent coverage of the plate was measured using ImageJ software [72]. Twitching motility was analyzed as described previously [26].
Cells were collected from swarm plates after incubation at 37°C for 16.5 h and placed in pre-weighed 1.5 mL Eppendorf tubes. Tubes were centrifuged at 5,000 rpm for 4 minutes. The pellets were then resuspended in 250 µL of extraction buffer by vigorous vortexing (extraction buffer: MeOH/acetonitrile/dH2O 40∶40∶20+0.1 N formic acid stored at −20°C). The extractions were incubated at −20°C for 30 minutes in an upright position. The tubes were then centrifuged at 13,000 rpm for 5 minutes at 4°C. 200 µL of the extraction were recovered into new Eppendorf tubes and neutralized with 4 µL of 15% NH4HCO3 per 100 µL of sample. The tubes with cell debris were left to dry and reweighed for normalization of cell numbers from swarm plates. 150 µL of samples were sent to the RTSF Mass Spectrometry and Metabolomics Core at Michigan State University for LC-MS analysis.
Sample preparation and microscopy were performed as previously described [20], [34]. To analyze liquid-grown cells, cultures were grown at 37°C while shaking to an optical density at 600 nm (OD600) of 0.3 in M9 medium (1× M9 salts pH 7.4, 2 mM MgSO4, 0.1 mM CaCl2, 0.2% glycerol, 0.2% casamino acids and 10 µg/ml thiamine HCl). 1% arabinose was included for induction of wspR, and 1% ethanol was added when comparing its effect on WspR clustering. From each culture, 3 µl were spotted onto a 0.8% agarose PBS pad on a microscope slide and then covered with a coverslip.
More than 100 cells were counted for each condition.
Preformed lawns of C. albicans CAF2 and adh1/adh1 were prepared by spreading 700 µL of a YPD-grown overnight culture onto a YPD 1.5% agar plate followed by incubation at 30°C for 48 hr. Exponential phase P. aeruginosa liquid cultures were spotted (5–10 µL) onto the C. albicans lawns, then incubated at 30°C for an additional 24 to 72 hours.
Overnight cultures of P. aeruginosa PA14 wild-type and ΔphzHΔphzMΔphzS strains were grown in LB at 37°C (shaken at 250 rpm). Ten microliters of each culture were spotted onto a track-etched membrane (Whatman 110606; pore size 0.2 µm; diameter 2.5 cm) that was placed on a 1.5% agar M8 medium supplemented with either vehicle (water) or 1% v/v ethanol. Plates contained 3 ml of medium in a 35×10 mm agar plate (Falcon). The colonies were incubated at 37°C for 24 hours and then at room temperature for 72 hours, after which phenazines were extracted from the colonies and agar separately. Each track-etched membrane with a colony was lifted off the agar plates and nutated in 5 mL of 100% methanol overnight at room temperature. Similarly, the agar was nutated overnight in 5 mL of 100% methanol. Colony and agar extracts were filtered (0.2 µm pore) and phenazines in the extraction volume (5 mL) were quantified by high-performance liquid chromatography as previously described [41] at a flow rate of 0.4 mL/min.
All data were analyzed using Graph Pad Prism 6. The data represent the mean standard deviation of at least three independent experiments with multiple replicates unless stated otherwise. For normally distributed data, comparisons were tested with Student's t-test.
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10.1371/journal.pgen.1005000 | Transcriptome Wide Annotation of Eukaryotic RNase III Reactivity and Degradation Signals | Detection and validation of the RNA degradation signals controlling transcriptome stability are essential steps for understanding how cells regulate gene expression. Here we present complete genomic and biochemical annotations of the signals required for RNA degradation by the dsRNA specific ribonuclease III (Rnt1p) and examine its impact on transcriptome expression. Rnt1p cleavage signals are randomly distributed in the yeast genome, and encompass a wide variety of sequences, indicating that transcriptome stability is not determined by the recurrence of a fixed cleavage motif. Instead, RNA reactivity is defined by the sequence and structural context in which the cleavage sites are located. Reactive signals are often associated with transiently expressed genes, and their impact on RNA expression is linked to growth conditions. Together, the data suggest that Rnt1p reactivity is triggered by malleable RNA degradation signals that permit dynamic response to changes in growth conditions.
| RNA degradation is essential for gene regulation. The amount and timing of protein synthesis is determined, at least in part, by messenger RNA stability. Although RNA stability is determined by specific structural and sequence motif, the distribution of the degradation signals in eukaryotic genomes remains unclear. In this study, we describe the genomic distribution of the RNA degradation signals required for selective nuclear degradation in yeast. The results indicate that most RNAs in the yeast transcriptome are predisposed for degradation, but only few are catalytically active. The catalytic reactivity of messenger RNAs were mostly determined by the overall structural context of the degradation signals. Strikingly, most active RNA degradation signals are found in genes associated with respiration and fermentation. Overall, the findings reported here demonstrate how certain RNA are selected for cleavage and illustrated the importance of this selective RNA degradation for fine tuning gene expression in response to changes in growth condition.
| RNA stability is a critical determinant of gene expression required for the adjustment of RNA abundance in response to changes in growth conditions [1]. Alterations of mRNA stability are associated with many gene expression programs like T cell activation [2], response to osmotic shock [3] and change in carbon source [4]. In addition, selective RNA degradation was shown to play a central role in both cellular and organismal development underlining the importance of this process to the gene expression program [5]. However, despite these profound effects on cell function and growth, the mechanisms by which specific transcripts are selected for degradation remain unclear. RNAs with similar degradation or processing signals often display distinct decay profiles and respond to different cellular cues [6]. Attempts to define the features required for selective RNA degradation are seriously hindered by the limited understanding of the ribonucleases involved in those processes.
In general, RNA turnover and quality control are achieved by exoribonucleases which are mostly controlled by the accessibility of the substrate’s 5’ and 3’ ends [7]. On the other hand, conditional degradation of RNA molecules is often triggered by endoribonucleases that accurately identify specific sequences or structures at a particular time or growth condition [8]. The most studied of these selective endoribonucleases are members of the dsRNA specific ribonuclease III (RNase III) family, which was first discovered in bacteria [9]. These ubiquitous enzymes are defined by their homology to structural elements, which include a nuclease domain (RIIID) that exhibits a conserved divalent metal binding motif, and a double-stranded RNA binding domain (dsRBD) [10]. In bacteria, RNase III regulates the expression of many conditionally expressed genes like those implicated in metal transport [11] and fermentative growth [12]. Similarly, baker’s yeast RNase III (Rnt1p) directly cleaves the mRNA of genes implicated in glucose sensing [13,14], cell cycle and cell wall stress response [15]. In metazoans, the RNase III enzymes Drosha and Dicer are required for the processing of the short non-coding RNA needed for sequence specific RNA degradation [16,17].
The sequence and structural features of natural substrates are hard to identify for most RNase IIIs. Studies of E. coli RNase III suggest that substrate selection is influenced by antideterminant nucleotides (nucleotides that deter cleavage) [18]. On the other hand, eukaryotic RNase IIIs possess more specific mechanisms of substrate selectivity. For example, human Dicer recognizes terminal loops and RNA ends and its substrate specificity is modified in vivo by protein factors like TRBP and PACT [19,20]. Similarly, substrate recognition by Drosha requires a combination of RNA structure and chaperon proteins [8,21]. The most selective enzyme among the members of the RNase III family is found in yeast Saccharomyces cerevisiae. Rnt1p prefers short stem loop structures, capped with either NGNN tetraloop (G2-loop) [10,22,23] or AAGU (A1-loop) structures to long RNA duplexes [22]. Deletion or mutation of these loops block cleavage and reduce binding under physiological conditions [10,24]. This apparently strict substrate specificity suggests that Rnt1p has fewer and more homogeneous targets than other RNase III. However, our knowledge of Rnt1p substrates was deduced from a relatively small number of related substrates (e.g. snoRNAs) [25] that may not reflect the entire spectrum of the enzyme reactivity. Indeed, the broad impact of RNT1 deletion on yeast phenotypic behavior and transcriptome suggests that Rnt1p reactivity is not restricted to non-coding RNA processing [13,26]. This is consistent with the fact that Rnt1p is the only homologue of RNase III proteins in S. cerevisiae.
In this study, we used a combination of genome-wide analysis techniques to outline the overall contribution of Rnt1p to the regulation of gene expression in S. cerevisiae and define the nature of its cleavage signals. Direct cleavage assay of the entire transcriptome permitted unbiased characterization of Rnt1p reactivity in vitro and defined the predisposition of all transcripts to selective RNA degradation. The results indicate that although Rnt1p cleavage signals are randomly distributed across the yeast genome, only 10% of the genes are upregulated in vivo in the absence of RNT1 and 5% are directly cleaved by the recombinant enzyme in vitro. Many of the newly identified cleavage sites were found in mRNAs associated with nutritional sensing, carbohydrate metabolism and energy production indicating that yeast RNase III is a key regulator of the cell response to growth conditions. Surprisingly, Rnt1p cleavage sites were not restricted to fixed loop sequence and size but extended to different types of structures that include stems terminating with tri- and penta- loops with varying sequences. The variety and frequency of the cleavage signal suggest that Rnt1p has developed a flexible substrate recognition mechanism capable of discriminating between a wide-range of structured RNAs, while avoiding the cleavage of duplex RNA, which is the classical target of other members of the RNase III family. This unusual substrate specificity explains how a single RNase III may regulate the expression of single RNA under specific condition [14] with high precision, while retaining the flexibility needed for transcriptome surveillance [27].
There are 55 known substrates of Rnt1p (Fig. 1A), the majority of which exhibit a well-defined stem loop structure that features an AGNN tetraloop (G2-loop) (Fig. 1B). Therefore, we took advantage of the distinct sequence and structural features of the G2-loop to create an algorithm capable of identifying potential Rnt1p cleavage signals across the entire yeast genome in silico (S1A Fig.). This algorithm assigns a score (ranging between 0 and 1) to each predicted structure based on sequence conservation, structural stability and similarity to known Rnt1p targets. As shown in Fig. 1C, 80% of the known substrates exhibited scores higher than 0.85. On the other hand, substrates not folding into a G2-loop like snR48 or MATa1 intron and those not forming at least three stable base-pairs downstream of the tetraloop (e.g. snR46 and ARN2-1) were given no score. Substrates generated via long-range interaction or based on non-canonical stems (e.g. ADI1, snR59 and snR190) were scored between 0.68–0.81 (S1 Table). Accordingly, we chose 0.85 as a cutoff score to retain the majority of known substrates and reduce the number of false positives. Decreasing the cutoff to 0.8 increased the number of detected known substrates by one, while adding 7071 weak hits. On the other hand, increasing the score to 0.9 resulted in the loss of two known substrates and the removal of 4036 putative hits.
Overall, the algorithm identified 254349 possible loops of which 6321 exhibited a score equal to or higher than 0.85 (Fig. 1D and S2 Table). To validate the reactivity of the predicted cleavage signals and directly evaluate the validity of the cutoff threshold, we synthesized 24 randomly selected stem-loop structures spanning the score range between 0.85 and 1 and tested them for cleavage in vitro. The majority of the synthesized loops exhibited scores ranging from 0.85–0.90, which reflect the overall score distribution. To ensure the efficient transcription and structural stability of the different loops we added three G-C base-pairs at the ends of the stems. The added nucleotides are located outside the known binding and cleavage regions and thus, should not affect cleavage efficiency [23,28]. As indicated in S1B Fig. and S3 Table, the enzyme cleaved all but four of the tested stem-loop structures. The 4 non-reactive stem-loops did not share similar scores but instead featured wobble base pairing (G-U) in the first 2 positions downstream of the loop (S1 Fig. and S4 Table). Based on this result we expect that 83% (with a 95% confidence range of 61 to 95%) of the in silico predicted cleavage sites with scores between 0.85 and 1 are cleaved by Rnt1p in vitro. Therefore, while the algorithm may falsely recognize a group of non-reactive stem-loops due to the inclusion of inhibitory features, like non-canonical base pairing, the majority of the predicted loops appears to be cleavable by Rnt1p in vitro.
Analysis of the genomic distribution of the newly identified G2-loops indicated that 46% reside in protein coding genes (PCGs), 44% opposite to an annotated gene (antisense) and 8.2% in intergenic regions, while only 1.1% were detected in non-coding RNA (Fig. 1E and S4 Table). This distribution reflects the normal repartition of the yeast genome without preferences to transcript type. On average, potential Rnt1p substrates were distributed in the yeast genome every 4 to 6 kb and displayed similar distribution patterns in other genomes and randomized sequence (S1C Fig.). Remarkably, the majority of the predicted cleavage motifs were found in untranscribed regions (e.g. antisense, intergenic regions) confirming that the genomic distribution of the cleavage motifs is not driven by RNA expression (Fig. 1E and S2 Table). Therefore, the high substrate frequency does not indicate a particularly high demand for RNA degradation in yeast but instead reflects the loose features of Rnt1p substrates.
As expected, examination of the 30 sequences showing the highest score revealed strong enrichment in cleavage signals associated with the processing of pre-snoRNAs (Fig. 1F), which constitute the majority of transcripts in the algorithm’s training set. Interestingly, 30% of the top scoring stem-loops were found in mRNAs and 77% of these were located in previously unidentified substrates (S5 Table). Cleavage of three of the highest scoring mRNAs was tested in vitro (Fig. 1G). All three mRNAs (POM33, SYG1 and HSP60) had a loop score > 0.98 and their expression levels varied between 1.3–8.7 copies per cell [29]. Despite the similarity between cleavage motifs, only SYG1 and HSP60 mRNAs were cleaved by Rnt1p suggesting that sequence outside the stem-loop structure may influence substrate reactivity. Consistently, a short RNA transcript corresponding to the POM33 stem-loop was accurately cleaved by Rnt1p in vitro when expressed outside its natural mRNA context (S1D Fig.). This confirms the accurate prediction of the stem-loop structure and suggests that the lack of cleavage is due to context dependent changes in the stem-loop structure, stability or accessibility. We conclude that RNA degradation in the yeast transcriptome is not limited by the recurrence of Rnt1p cleavage motifs but depends on the surrounding sequence context that influences its formation and reactivity.
Ribonuclease dependent changes in RNA expression were previously used to identify RNA degradation targets [30,31]. Therefore, we compared the transcriptome of RNT1 and rnt1∆ cells using tiling arrays (Fig. 2 and S2). The assay was performed once and the data was normalized using unaffected genes, auxotrophic markers or intergenic region as reference or using robust variance stabilization (S3 Fig.). The different methods resulted in similar overall data distribution and exhibited comparable statistical confidence when validated using quantitative RT-PCR (S2C Fig., S3 Fig. and S6 Table). However, normalization using auxotrophic markers resulted in a slightly better Spearman correlation coefficient with the quantitative RT-PCR data and was thus used for further data analyses. Indeed, the auxotrophic markers based normalization method resulted in a correlation factor of 0.798 (p < 2.2E-16, n = 202, which meets the previously established standard for expression array [32]. Moreover, 94.2% of the genes displaying more than two folds increase in the expression array also showed more than 2 folds increase by quantitative RT-PCR (n = 52). Accordingly, we used the 2 folds change in RNA levels as a robust indicator of Rnt1p dependent modification of gene expression.
As expected, the expression of most known Rnt1p substrates increased in rnt1∆ cells by > 2 folds (Fig. 2A upper panel and S1 Table). A minority of known substrates was overexpressed between 1.2 and 2 folds and only one (snR66) [33], which is also processed by other ribonucleases, was not upregulated. Overall, 498 segments (overlapping 721 genes) were upregulated by more than 2 folds in rnt1∆ cells (S9 Table). In comparison, only 36 genes (mostly snoRNA genes) were found to be upregulated in the absence of the nuclear exoribonuclease RRP6 (Fig. 2A and S8 Table), The majority of Rnt1p dependent transcripts associated with protein coding genes (Fig. 2B), while the majority of the 30 most upregulated sequences in rnt1∆ cells associated with snoRNA genes (Fig. 2C and S10 Table). The big difference in the expression of snoRNAs resulted mostly from the retention of the externally transcribed spacers (ETS) that are normally processed by Rnt1p (S2D Fig. and [25]). In contrast, none of the 7 most overexpressed mRNAs were previously identified as Rnt1p targets. Northern blot and quantitative RT-PCR analysis of three of those mRNAs confirmed the array predicted overexpression, but only RTC3 was cleaved by Rnt1p (Fig. 2D). We conclude that mRNA overexpression in rnt1∆ cells does not necessarily predict the enzyme biochemical reactivity but instead mostly identifies genes that are indirectly affected by the deletion of RNT1.
Detection of catalytic activity is the best and most direct way to uncover the substrates of any enzyme. Therefore, we have developed a new method termed “Cut and Chip” that permits direct detection of all the RNAs cleaved by Rnt1p in the yeast transcriptome (Fig. 3A and S4 Fig.). In this new method, the 3’ end cleavage products generated by Rnt1p in vitro are degraded by the 5’-3’ exoribonuclease Xrn1p [34] and the decrease in the RNA level is detected using tiling array (S4A Fig.). As shown in Fig. 3B, 50% cleavage of Rnt1p substrate (MIG2) [13] was easily detected by the decrease in the array signals downstream of the cleavage site in both Rnt1p and Xrn1p dependent manner. Overall, this approach detected cleavage in 237 RNA transcripts (S11 Table), which represent 4% of the yeast genes. Cleavage motifs were detected in 79% of the cleaved RNA (S4C Fig.) suggesting that the majority of Rnt1p substrates uses G2-loops for cleavage. The majority (71%) of the 55 known substrates were positively identified in this assay. However, we found that detection of cleavage events was dependent on the length of the transcribed sequence downstream of the cleavage site (i.e. length of the 3’ end product) and strength of cleavage (S4D Fig.). Therefore, weak cleavage events and those producing 3’ end fragments smaller than 50 nucleotides may not be detected by this technique. Interestingly, cleavage of native RNA by Rnt1p in whole cell extracts produced similar results to that detected by the cleavage in vitro (S11 Table). We conclude that the reactivity of the majority of Rnt1p targets (~80%) in vitro is not significantly modified through the RNA extraction process or concealed by other protein factors. However, it remains possible that the reactivity of certain RNA is affected by cellular compartmentalization or requires other in vivo events such as active transcription. Nonetheless, the results suggest that, at least in vitro, Rnt1p cleaves at least four times more transcripts than previously demonstrated.
The majority (83%) of the cleaved segments were found in coding sequence (Fig. 3C and S11 Table). Interestingly, 9 out of the 12 genes longer than 7.5 kb in the yeast genome were efficiently cleaved by Rnt1p (S11 Table). Also, only 8 cleavage events were found in introns and the majority of these (7/8) degrades the intron of mRNAs coding for RNA binding proteins. These introns did not encode for snoRNAs suggesting that cleavage in these pre-mRNAs is not part of a processing pathway. The top 30 genes cleaved by Rnt1p included 18 known substrates, 5 new non-coding RNA substrates (e.g. snR85, snR60, snR81, U3b and TLC1) and 7 new mRNAs, 3 of which code for genes associated with ribosome biogenesis (HAS1, MDN1 and BFR2) (Fig. 3D and S12 Table). Primer extension analysis of three newly identified substrates confirmed the capacity of “Cut and Chip” to accurately detect Rnt1p reactivity in vitro (Fig. 3E). However, primer extension also indicated that “Cut and Chip” does not accurately identify the precise site of cleavage. In most cases, the cleavage segment boundary was associated with several G2-loops and did not always coincide with the position of the cleavage site (Fig. 3E). This observation was further validated by primer extension of 3 additional “Cut and Chip” predicted substrates (S4E Fig.). Therefore, while “Cut and Chip” is a strong predictor of Rnt1p RNA targets in vitro, it does not directly identify the sequence of the cleavage site.
To directly detect Rnt1p cleavage site, we developed a Sequencing Assisted Loop Identification (SALI) technique that permits direct identification of Rnt1p cleavage product (Fig. 4 and S5 Fig.). In this method, the internal cleavage fragment released by Rnt1p is directly sequenced permitting the identification of reactive cleavage signal (Fig. 4A). An average of 4.2 million sequencing reads were obtained from both control and cleaved RNA and the 32–38 nucleotides-long reads enriched in the cleaved samples were retained (S5B Fig.). As expected, the cleaved RNA sample exhibited a net enrichment in reads ranging between 32 and 38 nucleotides, while most of the reads detected in the control RNA were mapped to abundant small RNAs shorter than 150 nucleotides like tRNAs and snoRNAs. Overall, this technique identified 34 out of 55 known Rnt1p cleavage signals (S13 Table). Notably, the boundaries of the enriched reads clusters matched almost perfectly with the position of the cleavage sites reported in the literature (S5C Fig.). The missing substrates were either poorly cleaved (e.g. HSL1) [26], produced products longer than 38 nucleotides (e.g. snR51) or were expressed at low levels (e.g. RGT1) [14]. The false positive rate of this technique is estimated to be < 7% based on a list of 30 mRNAs, which showed no cleavage by Northern blot (S14 Table). Therefore, SALI is a robust tool for the direct detection of highly reactive Rnt1p cleavage sites.
Overall, SALI detected 243 enriched read clusters corresponding to 203 unique targets (S5D Fig. and Tables S13 and S15). The cleavage sites were associated with 131 protein-coding genes, 69 non-coding RNA genes and 3 intergenic regions (Fig. 4B). The 30 most enriched cleavage products were found associated with 22 non-coding RNAs and 8 mRNAs (Fig. 4C and S16 Table). The most enriched sequence mapped to the 5’ETS of a previously uncharacterized cleavage site near the H/ACA snoRNA snR85. Northern blot analysis confirmed the cleavage of the snR85 precursor, which accumulates in rnt1∆ cells (Fig. 4D left panel). The 8 highest cleaved mRNAs included only 1 known substrate (MIG2) [13] and 7 new targets (HSP60, TUB1, AXL2, MAP2, HXK1, YTA6 and NAR1) (S16 Table). Northern blot analysis of three of these RNAs (HSP60, AXL2 and YTA6) confirmed the in vitro cleavage predicted by SALI. In addition, both AXL2 and YTA6 mRNAs accumulated in rnt1∆ cells indicating that these substrates require Rnt1p for normal expression.
Surprisingly, only 44% of the newly identified cleavage products formed the NGNN tetraloop structures deemed essential for Rnt1p reactivity (Fig. 4E and S13 Table). The rest of the cleavage fragments were either unfolded or formed non-canonical stem loop structures. The newly identified structures included stems capped with either AHNN and BHNN tetraloops or loops with sizes varying between 3 and 6 nucleotides (Fig. 4E). To verify the reactivity of these new structures, we generated T7 RNA polymerase transcripts representing the different loop structures and tested them for cleavage in vitro. Structures exhibiting AHNN tetraloops, pentaloops, or triloops were successfully cleaved by Rnt1p while those exhibiting BHNN and hexaloops displayed poor or no reactivity (Fig. 4F and S5E Fig.). Mutations of the AHNN, pentaloops and triloops indicate that both the structure and sequence of the new loops are required for optimal cleavage (Fig. 4F and S5E Fig.). Notably, replacement of the established U5 snoRNA G2-tetraloop with the newly identified pentaloop structure of OSH6 did not affect Rnt1p cleavage (S5F Fig.). This indicates that pentaloop and G2-loop have comparable reactivity and confirm the newly identified structure as robust Rnt1p substrate. Moreover, Rnt1p cleavage was also observed in the host transcripts of SEC26 and OSH6, further confirming their capacity to be cleaved by Rnt1p (S5G Fig.). We conclude that Rnt1p substrate selectivity in vitro is not limited to NGNN tetraloop, but extends to a broad range of structured RNAs, which can be distinguished from generic RNA duplexes that are not cleaved by Rnt1p [22].
The candidate substrates generated by the computational analysis, expression array, Cut and Chip and SALI were compared to evaluate the merit of each method. As indicated in Fig. 5A, all four methods were able to detect 65–80% of all known substrates and, in general, were better at detecting non-coding RNAs. The computational approach identified the highest number (90%) of the known non-coding RNA targets, which were used as training set for the algorithm, while Cut and Chip identified the highest number (67%) of the known mRNA targets. SALI performed worst at identifying known mRNA targets, presumably because SALI is more dependent on the expression level of the transcripts. Indeed, among 9 known mRNA substrates which were not detected by SALI, 7 (78%) are expressed at less than 1 copy / cell (Table S1).
Comparison between the results of the expression array, the Cut and Chip and SALI revealed little overlap between the newly identified RNA targets (Fig. 5B). Only 1 mRNA and 29 (31%) non-coding RNA targets were detected by all three methods. The lowest overlap was found between the in vitro cleavage (Cut and Chip and SALI) and the expression-based assays (Fig. 5B). Analysis of the stem-loop scores associated with RNA identified by each of the three detection methods indicated that, in general, the RNA identified by the expression array have low loop scores while the highest loops scores were found in RNA identified by the in vitro cleavage assays (Fig. 5C). This suggests that the RNA identified by the expression array have less potential to carry a reactive cleavages signal than those found with the in vitro cleavage assays. Indeed, out of the 653 RNAs detected by the expression array, only 36 were cleaved in vitro (Fig. 5B), suggesting that the majority of these RNAs are indirectly affected by the deletion of RNT1. On the other hand, a large proportion of the in vitro cleavage targets were not identified by the expression array, likely due to the limited sensitivity and the growth conditions. Indeed, several studies show that Rnt1p can affect the expression of its targets in a condition dependent manner [14,26]. Thus, the newly found targets may not accumulate in absence of RNT1 when cells are grown in optimal conditions. To directly evaluate this hypothesis, we tested the expression of 109 randomly selected in vitro cleavage targets that were not identified by the expression array using quantitative RT-PCR under three different growth conditions (S17 Table). As indicated in Fig. 5D, 74% of the tested RNAs were upregulated (>1.2 folds and p-value <0.01) by the deletion of RNT1, suggesting that the majority of the cleavage targets are inhibited by Rnt1p in vivo. However, the upregulation of many targets was detected only under specific growth conditions, suggesting the Rnt1p expression is required for condition dependent repression of gene expression. Notably, the cleavage product of 57% of the highly expressed in vitro cleavage sites could be detected in vivo upon the deletion of XRN1, which normally degrades Rnt1p cleavage products (Fig. 5E) [27,35]. Together these data supports the in vivo reactivity of the newly identified cleavage signals.
The large number of new substrates identified during this study permits better definition of Rnt1p substrates. Sequence comparison of the G2-loop substrates indicated that the ideal consensus sequence of the G2-loop is AGDU (Fig. 6A left panel), confirming the high conservation of the first two nucleotides and suggests that the 3rd and 4th nucleotides of the loop might be also important for the enzyme reactivity. This finding is supported by earlier work indicating that the enzyme interacts and forms hydrogen bonds with these two nucleotides [28,36]. In addition, comparison of the stem sequence revealed preference in the nucleotide adjacent to the loop, which was previously shown to affect cleavage [10,22,36]. The new model of G2-loop also indicated preference for base pairing in the first 7 nucleotides downstream of the tetraloop consistent with previous biochemical studies indicating the requirement of the first 5 base pairs for cleavage by Rnt1p [37]. Unlike the G2-loops, only a slight tendency to base pairing was detected near the A1- and 5nt-loops (Fig. 6A middle and right panels). The small number of candidates and high variability in sequence and structures of these new classes of substrates limited our ability to detect statistically enriched features. However, in general all classes of Rnt1p substrates displayed a tendency to form stable structures with an apparent Gibbs energy below -10.0 Kcal/mol.
Comparison between the Rnt1p G2-loops required for the processing of non-coding RNAs (NCG2-loop) to those required for mRNA (MG2-loop) degradation revealed few differences in sequence and structure. Uracil is preferred in the third position of the NCG2-loops, while guanine is predominant at this position of MG2-loops (Fig. 6B). Surprisingly, the most marked difference between the two groups of G2-loops was the stem base pairing preferences (Fig. 6C). The nucleotides near the cleavage sites (positions 9, 10, 43 and 44 in Fig. 6C) and those in the middle stem (positions 15 and 16) were preferentially unpaired in NCG2-loops. The increased mispairing in the more reactive NCG2-loop suggests that unpaired cleavage sites increase reactivity. Indeed, forced pairing of these nucleotides within the cleavage efficiency box decreased the catalytic rate without affecting the substrate affinity [10].
The function of genes either upregulated or cleaved in vitro by Rnt1p was examined using gene ontology [38], MIPS database [39] and literature search (S18 and S19 Table). The results indicated that several genes are associated with mitochondrial respiration and carbohydrate metabolism (Fig. 7A). Accordingly, we monitored the effects of variation in carbon sources and oxygen levels on the expression of Rnt1p substrates. As shown in Fig. 7B and S6A Fig., 8 substrates were repressed by the enzyme in dextrose, while 3 were repressed in galactose (CDC19, PSK2 and TYE7). Interestingly, three genes which were not affected or downregulated in absence of RNT1 in aerobic condition (CDC19, FBA1 and GPM1), showed clear differences in their expression pattern in response to the nitrogen shift (S6B Fig.). Together, these data indicate that the switch from respiration to fermentation modifies the expression of a subset of conditionally expressed genes in a Rnt1p dependent manner.
To evaluate the effect of Rnt1p on respiration, we monitored the levels of mitochondrial membrane electrical gradient (∆Ψm) using the Rhodamine 123 stain [40]. As shown in Fig. 7C, deletion of RNT1 reduced staining suggesting that the enzyme is required for normal respiration. The change in respiration was not due to changes in the number of mitochondria as indicated by the MitoTracker stain (Fig. 7D). Transformation of rnt1∆ cells with a plasmid expressing a wild-type allele of RNT1 (pRNT1) completely restored the Rhodamine staining to its normal levels confirming the requirement of RNT1 expression for normal respiration (Fig. 7C-E). Consistently, epifluorescence imaging indicated that the morphology of mitochondria was altered in rnt1∆ cells (Fig. 7F). Interestingly, despite the perturbation of both fermentation and respiration genes (S18 and S19 Table), RNT1 deletion did not block growth in either state, but reduced growth in all carbon sources (S6C and S6D Fig.). Therefore, Rnt1p repression of gene expression is not essential for either respiration or fermentation but instead appears to be needed fine-tuning gene expression between different metabolic states. To evaluate this possibility, we examined the effect of RNT1 deletion on autonomous oscillation. Autonomous oscillations in the concentrations of glycolytic intermediates like NADH reflect the dynamics of control and regulation of this major metabolic pathway required for both respiration and fermentation [41]. Oscillations of both RNT1 and rnt1∆ strains was induced by the addition of glucose and potassium cyanide and recorded as time traces of NADH fluorescence (Fig. 7G). As expected, oscillations were clearly observed in RNT1 cells and lasted for about 15 minutes. In contrast, rnt1∆ cells showed weaker response to glucose induction and oscillations ceased just a few seconds after induction. These results indicate that Rnt1p expression is required for glycolytic oscillations and the efficient coordination of metabolic flux in yeast.
In this study, we used in silico (Fig. 1), genetics (Fig. 2) and biochemical methods (Fig. 3 and 4) to define the characteristics and genomic locations of RNA degradation signals. The results indicate that while potential Rnt1p cleavage motifs are evenly distributed across the yeast genome, only few induce the degradation of the host transcript (Fig. 1 and S2 Table). The abundance of Rnt1p recognition motifs suggests that RNA degradation is not limited by the de novo evolution of the cleavage motif but instead controlled by the overall structure of RNA transcripts. This is supported by the fact that non-reactive cleavage signals may become reactive in different sequence and structural contexts (S1 Fig.). Therefore, while Rnt1p cannot cleave generic RNA duplexes like other members of the RNase III family [22], it retains broad substrate specificity by recognizing simple and widely distributed motifs. This flexible substrate specificity may explain how S. cerevisiae maintained its symbiotic relationship with the dsRNA killer virus, which confers selective advantage by producing a toxin that kills uninfected strains [42], without limiting the transcriptome surveillance functions of RNase III.
Expression profiling techniques are extensively used to probe the effects of different ribonucleases on RNA stability and gene expression. These techniques permitted the identification of new classes of unstable transcripts like the Rpr6p-dependant cryptic unstable transcripts (CUTs) [43] and Xrn1p-sensitive unstable transcripts (XUTs) [44]. However, the difficulty in distinguishing between the direct and indirect effects of ribonuclease deletions prevented positive identification of catalytically reactive substrates. Indeed, in this study, the results indicate that most transcripts upregulated after RNT1 deletion resisted cleavage in vitro (S20 Table). Biochemical assays like PARE (parallel analysis of RNA ends) were developed to identify the degradation targets of miRNAs [45]. In this study, we used analogous approaches that depend on microarray detection (Cut and Chip) and direct sequencing (SALI) for the identification of Rnt1p cleavage products. Both methods accurately identified the majority of the known Rnt1p substrates (Fig. 5A) and the newly identified cleavage events were confirmed using standard cleavage assays. These two methods identified distinct sets of Rnt1p substrates (S20 Table and Fig. 5B). Cut and Chip mostly detected long 3’ end cleavage products while SALI detected the cleavage of well-defined and highly expressed cleavage signals (see Figs. 5C and S7). For example, SALI detected the highly expressed snR62 cleavage signal, while Cut and Chip detected the long internal structure of snR51. In short, Cut and Chip is more effective in detecting poorly cleaved RNA with long 3’ end cleavage fragment, while SALI is more efficient in directly detecting the site of cleavage of highly expressed and highly reactive substrates. It should also be noted that both methods might fail to detect substrates expressed at very low levels or requiring special factors not present in vitro. Therefore, identification of RNA degradation signals may not be achieved by a single technique but requires a number of complementary approaches that together may provide a true portrait of enzymatic reactivity.
Comparison between the in vitro cleavage assay and expression profiling data indicates that the number of RNA that are both cleaved by Rnt1p and overexpressed after the deletion of RNT1 is very small. Indeed, only 36 out of the 296 mRNA cleaved by Rnt1p in vitro are upregulated upon the deletion of Rnt1p in vivo as predicted by the tiling array. This discrepancy reflects the effect of the growth condition tested and the limitation of the expression profiling techniques (S7 Fig.) that requires arbitrary cutoffs and complicated statistical analysis [38,46–48]. Indeed, we clearly show that variations of the growth conditions and the use of quantitative RT-PCR substantially increase the number of the in vitro cleavage targets affected by RNT1 deletion in vivo (Fig. 5D and S6 Table). Therefore, it appears that the in vitro cleavage assays are better indicators of RNA degradation than the expression array. This is supported by the cleavage of native RNA in cell extracts (S11 Table), the detection of many cleavage products in vivo (Fig. 5E), the reduced potential of expression array candidates to carry reactive cleavage signals (Fig. 5C) and the fact that many of the most overexpressed transcripts in vivo could not be cleaved in vitro (Fig. 2D and S20 Table). However, we cannot rule out the possibility that the reactivity of certain RNAs might be artificially modified in vitro due to the absence of specific in vivo conditions like active transcription and cellular compartmentalization.
The NGNN tetraloops (G2-loop) are required for the cleavage of most known Rnt1p substrates [49]. For the selection of G2-loops, the enzyme uses a double-ruler mechanism [28]. In this study, we extended the AAGU tetraloop (A1-loop) into the AHNN category and identified two new categories of Rnt1p substrates that feature triloops and pentaloops (Fig. 4E). The mechanism by which the enzyme recognizes these three categories of substrates remains unclear. Mutational and biochemical analysis of substrates carrying G2- and A1-loop indicate that Rnt1p use a flexible and interchangeable network of nucleotide interactions to identify its substrates with different structures [22,24]. This flexibility may also explain how the enzyme may cleave different structures like triloops and pentaloops. Alternatively, the enzyme may induce fit the new loops for binding and cleavage [50]. In all cases, the discovery of these new substrates indicates that Rnt1p activity is not restricted to a single stem-loop structure but cover a much larger spectrum of structural motifs.
In nature, yeast cells are in constant flux between respiration and fermentation depending on sugar and oxygen levels [51]. These constant changes in the cell’s metabolic state require a highly responsive and dynamic control of gene expression [52,53]. In high concentration of glucose, yeast cells prefer fermentative metabolism to oxidative pathway regardless of oxygen levels [54]. This reversible respiro-fermentative metabolic state is characterized by induction of genes involved in both glucose transport and glycolysis [55] and repression of the TCA cycle and mitochondrial activity [56]. Interestingly, this combined induction of both transport and glycolysis genes were also observed upon deletion of RNT1. Indeed, glucose sensing (e.g. MIG2, MTH1 and RGT1) [14], glucose transport (e.g. HXT9, HXT11, HXT13 and HXT15), glycolysis (e.g. FBP26, GLK1) and electron transport chain genes (QCR7–9 and CYT1) were all upregulated in rnt1∆ cells (S7 Table). Overall, there are now a total of 15 genes in these pathways known to be cleaved by Rnt1p in vitro and their expression is deregulated by its deletion in vivo (Fig. 7 and [13,14]). Changing the expression of any one of these genes, like for example MIG2, may explain the rnt1∆ phenotypes, like the perturbation of mitochondrial functions [57], the induction of galactose controlled genes [57] and impaired aerobic metabolism [58]. Since Rnt1p is not essential for growth in either aerobic or anaerobic condition, we conclude that the role of Rnt1p is most likely to fine-tune the expression of the genes involved in the respiro-fermentative flux.
The wild type haploid strain RNT1 (MATa, lys2∆0, ura3∆0, his3∆200, leu2∆0) and the haploid rnt1∆ strain (MATa, lys2∆0, ura3∆0, his3∆200, leu2∆0, rnt1::HIS3) were generated by the replacement of one RNT1 allele by HIS3 in the LLY36 diploid strain (MATa/α lys2∆0/lys2∆0 ura3∆0/ura3∆0 his3∆200/his3∆200 leu2∆0/ leu2∆0), followed by spore dissection as previously described [26]. The starting LLY36 diploid strain was obtained by mating the strains BY4700 (MATa, ura3∆0) and BY4705 (MATα, ura3∆0, leu2∆0, lys2∆0, ade2∆::hisG, his3∆200, trp1∆63, met15∆0) [59], followed by spore dissection, as previously described [60]. The resulting haploid spores of each mating type and having the desired markers (lys2∆0, ura3∆0, his3∆200, leu2∆0) were selected and crossed together to yield the diploid homozygous LLY36 strain. The rrp6∆ strain (MATa, his3∆1 leu2∆0, met15∆0, ura3∆0, rrp6∆::KMX4) was taken from the Yeast knock out collection obtained from Open Biosystems [61]. Yeast cells were grown and manipulated using standard procedures [62] in YEP media supplemented with 2% dextrose at 26°C (the permissive temperature for rnt1∆ strains). In Fig. 7B, cells were grown in YEP media supplemented with 2% dextrose or 4% galactose. In the case of the nitrogen shift experiments, assays were performed using cells grown in 600 ml of semisynthetic medium (SSD) containing per liter, 3 g of yeast extract, 10 g of dextrose, 0.8 g of NH4SO4, 1 g of KH2PO4, 0.5 g of NaCl, 0.5 g of CaCl2•2H2O, 0.3 g of MgSO4, 1.1 μg of FeCl3•6H2O, supplemented with amino acids, adenine and uracil at 40 μg/ml, 0.1% (V/V) Tween 80, 20 μg/ml of ergosterol and 350 ppm of antifoam B emulsion (Sigma-Aldrich, St. Louis, MO) [63] using Multifors (Infors Canada, Anjou, QC, Canada) bioreactors. Cells were grown to 0.3 OD600 in air-supplemented media than the gas supply was shifted to nitrogen. Forty ml samples were collected at different time points and the cells rapidly harvested by filtration. In Fig. 7C-E, strains were transformed either with an empty plasmid (pRS316) or expressing the wildtype RNT1 allele (pRNT1) and grown in YC-ura media. Growth rates of RNT1 and rnt1∆ cells grown in presence of different carbon sources was performed and calculated as described [60].
The cleavage assays were performed as previously described [36] with few modifications. Briefly, 30 μg of total rnt1∆ RNA was incubated with 6 pmol of purified Rnt1p for 20 min at 30°C in 100 μl of reaction buffer [30mM Tris–HCl (pH 7.5), 5mM spermidine, 0.1mM DTT, 0.1mM EDTA (pH7.5), 10mM MgCl2, 150mM KCl]. The reactions were stopped by phenol-chloroform extraction, and the RNA recuperated using salted ethanol precipitation.
RNA substrates were synthesized using T7 RNA polymerase, radiolabeled and cleaved as described [22]. Three GC base pairs were added at the end of each stem to improve transcription efficiency and structure stability. Briefly, trace amount of radiolabelled substrates (150 cpm/μl) was incubated with 30 nM Rnt1p for 10 min at 30°C in 20 μl reaction buffer [30 mM Tris-HCl (pH 7.5), 5 mM spermidine, 0.1 mM DTT, 0.1 mM EDTA, 10 mM MgCl2 and 10 mM KCl]. Cleavage products were separated on 20% denaturing PAGE and visualized using a Storm 860 imager (GE Healthcare).
Northern blots were performed as described [27] using 15 μg of total RNA and a 1% denaturing agarose gel. The RNA was visualized by autoradiography using randomly labeled probes corresponding to each of the genes examined. 5’-end-labeled oligonucleotide probes were used for detecting snR85 and 5S rRNA. The radiolabeled bands were visualized using a Storm 860 scanner (GE Healthcare) and analyzed using the ImageQuant software (Molecular Dynamics). The primer extension reactions were performed as described [36] using gene specific primers. cDNA synthesis and real-time PCR quantification of relative mRNA expression was performed as described [60] using a Biorad CFX384 Real-Time PCR Detection System. The Ct values of each gene were normalized to the values obtained for the ACT1 mRNA in each samples. The change in gene expression was calculated relative to the values obtained for wild-type RNA. All experiments were performed using at least three independent cultures and the PCR reactions conducted in duplicates. Genes with over 1.2 fold change and p < 0.01 (one-tailed t test) were considered as statistically overexpressed. The list of oligonucleotides used to generate Northern blot and primer extension probes, as well as qPCR reactions, can be provided upon request.
The method used for the prediction of Rnt1p cleavage signals is adapted from an earlier version used for the prediction of snoRNA cleavage signals [25]. The modified algorithm assigned positional weights based on the level of nucleotides conservation in closely related Saccharomyces species (sensu stricto). The outline of the method is shown in S1A Fig. Nucleotide conservation (Fig. 6) was calculated using Fisher test, while base pairing conservation was calculated using chi-squared test p value < 0.05 and both values were adjusted using Bonferroni correction.
The cDNA preparations and biotin end labeling were performed using total RNA by the University of Wisconsin Gene Expression Center (http://www.biotech.wisc.edu/services/gec). Array hybridization was performed at the Centre for Applied Genomics at University of Toronto (http://www.tcag.ca/index.html) according to the protocols supplied with Affymetrix GeneChip WT Double-Stranded Target Assay (without amplification) and Affymetrix GeneChip S. cerevisiae Tiling 1.0R Array (Affymetrix; PN: 900645). Probes were annotated using the S. cerevisiae S288c reference genome (SGD, http://www.yeastgenome.org, August 10th, 2007) as described [16]. The array was performed only once for each condition tested. The raw microarray data was treated as described [16] for reference DNA correction, variance stabilization and normalization using the tiling Array R package and in-house scripts. In addition, variations in probe intensity were corrected based on the predicted hybridization ∆G (S2B Fig.). The signals from the top 5% of the probes with the highest hybridization ∆G were removed and the signal intensities of the remaining probes were adjusted to obtain a null slope and a null average variation. The variation in intensity between RNT1 and rnt1∆ samples was analyzed using Huber segmentation algorithm [64] with consideration of the BIC (Bayesian Information Criterion) optimal number of segment. The level of variation in expression was defined as the median level of variation in all probes within the region. Neighboring segments with more than 2 fold overexpression and less than 48 nucleotides apart were joined together to form a single segment. Segments with less than 12 uniquely matching probes were considered unreliable and removed from further analyses. Overlapping features (e.g. gene name) were identified using SGD reference genome (http://www.yeastgenome.org, August 10th, 2007). Expression values were adjusted so that the deleted auxotrophic genes display the lowest expression level. Comparison between the array and quantitative RT-PCR (S2C Fig.) resulted in Spearman correlation coefficient of 0.798 (p < 2.2E-16, n = 202).
Different methods of data normalization were examined to identify the best strategy for reducing the potential effects of changes in the expression of abundant RNAs like non-coding RNA. The microarray data were normalized using different methods: 1) relative to known unaffected genes (e.g. the constitutively expressed RNAPII transcribed gene, ACT1, and the RNAPIII transcribed genes, U6, RPR1, RNA170 and SCR1), 2) relative to the expression level of intergenic regions, 3) relative to both unaffected genes and intergenic regions or 4) using the most robust parameter (lts.quantile = 0.5) of the classical VSN algorithm [64] (S6 Table). As shown, in new S3 Fig., the different normalization methods produced similar expression graphs and exhibited similar correlation with the data obtained using quantitative RT-PCR (S3E Fig.). However, the array data normalization used in Fig. 2 more accurately represented the values obtained by quantitative RT-PCR. Indeed, the correlation between this array and the quantitative RT-PCR were much better than previously published Rnt1p dependent conventional expression array [25,65]. These earlier arrays displayed a Spearman correlation coefficients with the quantitative RT-PCR data of 0.4 and 0.544 with, respectively. Similarly, the Spearman correlation factors resulting from comparing these earlier arrays to the tiling array performed in this study was 0.44 and 0.41 reflecting the low PCR reproducibility of the earlier array and differences in strains and growth conditions. The genes displaying similar Rnt1p dependent changes in the different array are indicated in S6 Table and S7 Table.
Fifty μg of total RNA cleaved with recombinant Rnt1p was incubated with 8 μl Terminator 5´-Phosphate-Dependent Exoribonuclease (Xrn1p; Epicentre Biotechnologies, Madison, WI) for 90 min in the supplied buffer. The reactions were stopped by phenol-chloroform extraction, and the RNA collected using salted ethanol precipitation. Preparation of the cDNA, biotin labeling and chip hybridization was performed at the Centre for Applied Genomics at University of Toronto (http://www.tcag.ca/index.html) as described above. Microarray data was analyzed as described for expression array using the variation between treated and untreated samples for segmentation. Segments with less than 12 uniquely matching probes were removed. The median and the MAD (median absolute deviation) of the remaining segments were used to choose an appropriate cutoff (median less 1.96 times the MAD = -0.2425). Neighboring segments with a level below the chosen cutoff were grouped if separated by less than 48 nucleotides and regions smaller than 125 nucleotides were removed. The resulting 237 genomic regions were assigned to annotated features in the SGD genome of August 10th, 2007 (http://www.yeastgenome.org).
Hundred μg of RNA cleaved with Rnt1p were purified using the mirVana miRNA Isolation Kit (Ambion, Life Technologies, Burlington, ON) to isolate RNA shorter than ~150 nucleotides. The enrichment of short RNAs was confirmed using the Agilent 2100 Bioanalyzer Small RNA kit (Agilent technologies, Santa Clara, CA). The cDNA libraries were generated from 500 ng of size selected RNA using the Ion Total RNA-Seq Kit v2. The IonTorrent sequencing data were generated using Ion 318 Chip Kit and acquired using Ion PGM System and Torrent Suite 2.2 software. 5' adapter sequences were trimmed using cutadapt 1.2rc2 [66]. Sequences shorter than 16 nucleotides were removed and the remaining reads aligned to S. cerevisiae reference genome sequence R64-1-1 using subread 1.3.5p4 [67]. Sequences with multiple matching positions were removed and reads ranging between 32 and 38 nucleotides were considered for further analysis. Read clusters consisting of 14 or more identical reads found in the cleaved and not the control samples were considered enriched. Enriched clusters of identical reads with over 50% overlap were merged and the resulting clusters were assigned to the transcripts with corresponding sequence. The RNA secondary structure for the longest merged cluster was predicted using Vienna RNA tools version 1.8.5. Wobble base pairs and non-canonical A-C base pairs were permitted in the predicted structures.
Exponentially growing rnt1∆ cells were harvested and washed twice in AGK buffer (10 mM HEPES pH 8.0, 1.5 mM MgCl2, 200 mM KCl, 10% Glycerol) containing protease inhibitors. Cell pellet was resuspended in 1 volume of the same buffer and the slurry was quickly frozen in liquid nitrogen. About 12 grams of the frozen cell suspension was lysed in a 6870 Freezer Mill (SPEX SamplePrep). Grinded powder was then thawed on ice and spun at 20 000g for 30 minutes. The supernatant (S20 fraction) was collected and stored in aliquots at -80°C. Cleavage reactions were performed as described above except that total RNA was replaced by S20 extract (the amount was determined based on the measured RNA content in the extract). Detection of the cleavage products was performed as described for the Cut and Chip method.
The terminal 5’-phosphates of the 3’ cleavage products identified by SALI and those near the stem-loops predicted by Cut and Chip were compared to those detected in the xrn1∆ / dcp2∆ cells using global 5’ RACE (5’ RACE data was obtained from [35]). The RACE detected 5’-phosphates found within the first 5 nucleotides of the 3’ end of the cleavage products were considered a match and used for the generation of Fig. 5E.
Yeast mitochondrial membrane potential was evaluated as previously described with few modifications [68]. Briefly, 4 x 106 cells obtained from an exponentially growing culture were harvested and washed 3 times in PBS solution. Cells were stained with 35 μg / ml Rhodamine 123 for 10 minutes at 26°C or with 500 nM MitoTracker green FM for 50 minutes at the same temperature. Stained cells were washed 2 times with PBS and incubated for 15 minutes at 26°C in PBS. The resulting cells were further stained with 0.1 μg / ml propidium iodide in PBS and analyzed using a Fortessa cytometer (BD Biosciences, Mississauga, ON, Canada) equipped with a 50 mW solid state 488 nm laser. The emitted fluorescence of the Rhodamine123 and the MitoTracker green FM were detected at 530 ± 15 nm, while the propidium iodide detected at 610 ± 10 nm. Forward and side scatter signals were used to exclude debris and cell clumps. Dead cells were identified with propidium iodide staining and excluded from the analysis. For each sample, a minimum of 8 000 positive events by sample were acquired. Fluorescence intensity distribution profiles were traced using Cyflogic software (CyFlo Ltd, Finland) and raw data were exported and an analyzed as previously described [40].
Cells grown to a density of 107 cells / ml in YEPD were stained with 500 nM MitoTracker green FM for 30 minutes at 26°C in growth medium. Stained cells were washed two times in PBS and the mitochondria were visualized using 100 X / 1.46 oil objective with an excitation filter of 470 ± 20 nm and an emission filter of 540 ± 25 nm attached to the Zeiss Axio Observer microscope. Stacks were acquired at 200 nm intervals and deconvoluted using Zeiss Zen iterative algorithm. Maximum intensity projections of the deconvoluted images are shown.
NADH oscillations were measured as previously described [41]. Briefly, 100 ml of YC media buffered at pH 5 with 100 mM potassium phthalate and supplemented with 1% dextrose were inoculated to an OD600 of 0.2 using fresh saturated pre-cultures grown in the same media. Wild type and rnt1∆ cells were grown 16–18 or 36–40 hours, respectively, at 26°C to deplete the sugar in the media. The resulting cells were washed twice with 10 ml of 50 mM potassium phosphate buffer pH 6.8 and finally suspended to 10% wet weight in the same buffer. Suspended cells were incubated 3 hours at 26°C before measuring the NAD / NADH fluorescence in a PTI spectrofluorometer using 3 ml of cell suspension at 30°C in a 4.5 ml PMMA cuvette. Cells were agitated for 5 minutes in the spectrofluorometer before data acquisition. Two readings per seconds were acquired with excitation at 366 nm and emission at 450 nm during 2 minutes for baseline recording before inducing oscillations with 30 mM glucose and 5 mM KCN.
Enriched gene ontologies were detected by standard hypergeometric tests using the GOstats R package (version 2.18.0) [69] and annotation packages version 2.5.0. A Bonferroni corrected p-value of 0.05 was used to select significantly enriched terms. Background gene set contained the top 95% highly expressed mRNAs in rnt1∆ strain.
Raw and processed data presented in this study was deposited in the Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo/), under accession number GSE57450.
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10.1371/journal.pgen.1006172 | Orderly Replication and Segregation of the Four Replicons of Burkholderia cenocepacia J2315 | Bacterial genomes typically consist of a single chromosome and, optionally, one or more plasmids. But whole-genome sequencing reveals about ten per-cent of them to be multipartite, with additional replicons which by size and indispensability are considered secondary chromosomes. This raises the questions of how their replication and partition is managed without compromising genome stability and of how such genomes arose. Vibrio cholerae, with a 1 Mb replicon in addition to its 3 Mb chromosome, is the only species for which maintenance of a multipartite genome has been investigated. In this study we have explored the more complex genome of Burkholderia cenocepacia (strain J2315). It comprises an extra replicon (c2) of 3.21 Mb, comparable in size to the3.87Mb main chromosome (c1), another extra replicon(c3) of 0.87 Mb and a plasmid of 0.09 Mb. The replication origin of c1 is typically chromosomal and those of c2 and c3 are plasmid-like; all are replicated bidirectionally. Fluorescence microscopy of tagged origins indicates that all initiate replication at mid-cell and segregate towards the cell quarter positions sequentially, c1-c2-p1/c3. c2 segregation is as well-phased with the cell cycle as c1, implying that this plasmid-like origin has become subject to regulation not typical of plasmids; in contrast, c3 segregates more randomly through the cycle. Disruption of individual Par systems by deletion of parAB or by addition of parS sites showed each Par system to govern the positioning of its own replicon only. Inactivation of c1, c2 and c3 Par systems not only reduced growth rate, generated anucleate cells and compromised viability but influenced processes beyond replicon partition, notably regulation of replication, chromosome condensation and cell size determination. In particular, the absence of the c1 ParA protein altered replication of all three chromosomes, suggesting that the partition system of the main chromosome is a major participant in the choreography of the cell cycle.
| Unlike higher organisms, bacteria typically carry their genetic information on a single chromosome. But in a few bacterial families the genome includes one to three additional chromosome-like DNA molecules. Because these families are rich in pathogenic and environmentally versatile species, it is important to understand how their split genomes evolved and how their maintenance is managed without confusion. We find that mitotic segregation (partition) of all three chromosomes of the cystic fibrosis type strain, Burkholderia cenocepacia J2315, proceeds from mid-cell to cell quarter positions, but that it occurs in a sequential manner, from largest chromosome to smallest. Positioning of each chromosome is specified solely by its own partition proteins. Nevertheless, the partition system of the largest chromosome appears also to play a global role in the cell cycle, by modulating the timing of initiation of replication. In addition, disrupting the partition systems of all three chromosomes induced specific cell abnormalities. Hence, although such bacteria are governed mainly by the largest, housekeeping chromosome, all the Par systems have insinuated themselves into cell cycle regulation to become indispensable for normal growth. Exploration of the underlying mechanisms should allow us to understand their full importance to bacterial life.
| The long-held view that bacteria carry the essential part of their genomes on a single chromosome blurred about 25 years ago, when the species Rhodobacter sphaeroides was found to carry certain essential genes on a large replicon distinct from the main chromosome [1]. The size and essentiality of this replicon qualified it as a chromosome, albeit a secondary one. Many bacterial genomes have since proven to be multipartite—about 10% of those sequenced and notably those of pathogenic and metabolically versatile species. For example, all Vibrio species carry one secondary chromosome [2,3] and all Burkholderia species have at least one and typically two [4]. They are thought to have arisen by transfer of essential genes to coresident low-copy number plasmids, which thereupon grew through further recombination events. Whether the split-genome arrangements resulting from such events persisted by conferring selective advantage is speculative, but it is reasonable to view expansion of secondary chromosomes as a means of incorporating large numbers of beneficial genes without unduly disturbing the regulation and organization of essential genes on the main chromosome. Our aim here is to determine how the maintenance of one principal and two secondary chromosomes is accommodated within the cell cycle of the beta-proteobacterium Burkholderia cenocepacia J2315, an opportunistic pathogen of sufferers from cystic fibrosis. (We use the term "secondary chromosome" for convenience, and deal with the nomenclature of such replicons in the Discussion.)
The size of secondary chromosomes, which can approach that of the main chromosome, makes them potentially problematic. First, the replication control systems of secondary chromosomes resemble those of low-copy number plasmids. Replication of such plasmids has been seen to lack the close coupling with the cell cycle shown by the main chromosome [5–8]; because it occupies only a brief period it is regulated only to ensure that it precedes cell division [9]. Enlargement of such a replicon requires its replication to occupy a large fraction of the cell cycle and so to be initiated early enough not to delay cell division or to risk DNA cleavage by the closing septum. Thus for large secondary chromosomes to be stable, either the system that regulates initiation of plasmid replication must be augmented in some way, or such chromosomes must develop only from plasmids, yet to be identified, that naturally replicate early in the cycle.
Second, the large size of secondary chromosomes risks confusion or entanglement with the main chromosome during the mitotic segregation (partition) that follows their replication. Low-copy number plasmids and most chromosomes assure efficient partition through assembly of a partition complex based on binding of a specific ParB protein to a cluster of parS sites near the replication origin; poleward movement of the complex on each replicon copy is then mediated by the cognate ParA ATPase. ParABS systems provide the specificity needed to distinguish between coresident chromosomes [10] but whether they alone can assure orderly movement of bulky chromosome copies is unproven. In this they might be aided through regulatory linkage with replication control. Although replication and Par-mediated segregation operate independently in plasmids, recent work has shown the Bacillus subtilis and Vibrio cholerae partition proteins to regulate replication-initiator activity as well as chromosome segregation [11–13].
Of the many split-genome cell cycles that might be studied, only that of V. cholerae has been in depth. The Vc genome comprises chromosomes of 2.9Mb (Chr1) and 1Mb (Chr2). Although the Chr2 origin is plasmid-like, it is replicated in phase with the cycle, but later than Chr1, such that Chr2 and Chr1 replication terminate at about the same time [14]. The question of how the cell manages partition of two bulky replicons has been settled in the case of V.cholerae by adoption of distinct segregation patterns. The Chr1 origin appears to be tethered at one end of the cell so that segregation consists of moving one origin copy to the far pole where it in turn is fixed, as seen also in Caulobacter crescentus, Agrobacterium tumefaciens, and Sinorhizobium meliloti [15,16], while the Chr2 origin is centrally located in new-born cells whence its copies segregate to the quarter positions prior to division [17], as typified by low copy number plasmids and the single chromosomes of bacterial models such as B. subtilis and Pseudomonas aeruginosa [18,19].
Genomes of the Burkholderia group are divided still further. That of the B. cenocepacia J2315 reference strain comprises the principal chromosome (c1, 3.9 Mb), two secondary chromosomes (c2, 3.2 Mb, and c3, 0.9 Mb) and a plasmid (p1, 0.09 Mb) [20]. How are these replicons replicated and segregated without confusion and without perturbing the cell cycle? Each carries a parABS system which displays non-overlapping specificity in stabilization of plasmids in E. coli [10] and in formation of partition complexes in vitro [21]. Nevertheless, the role of the Par systems in their mother organism has not been defined, nor is it known whether they are brought into play independently or in concert via a cell cycle master regulator. We have examined these issues by characterizing the replication mode of each chromosome, by analyzing the number and positioning of each replicon's ori-par region with respect to the cell cycle, and by assessing the consequences for partition and growth of inactivating each ParABS system.
To analyze replication of the genome we first characterized the origin regions and determined base-pair frequency gradients throughout each chromosome. The replication origins had been provisionally located from the similarity of their genetic context to that of known origins and from GC-skew minima [10], and we extended this analysis to substantiate the prior indications. Application of the programme Ori-finder 1.0 (Tubic; [22]), designed to identify origins on the basis of DnaA-box density and sequence disparity, confirmed these general locations, as depicted in Fig 1A. The skewed distributions of KOPS sites (Fig 1B), which bind FtsK to facilitate terminus segregation [23], are largely consistent with these ori positions. Fig 1C shows for each replicon the outline arrangement of elements characteristic of origin regions. In the main chromosome, c1, these features—DnaA boxes, short sequence repeats, IHF site, AT-rich block—are sufficiently dispersed to leave the precise position of the replication origin uncertain; the geography of this region is examined in further detail in the Supplementary Information (S1 Fig). The origin regions of the secondary chromosomes, c2 and c3, each include an ORF with strong similarity to repA genes that specify classic replication regulators of low copy-number plasmids, as well as typical clusters of 19–21 bp iterons to which these regulators bind, leaving no doubt that these replicons originated as plasmids (for detail, see S2 Fig and S1 Table).
Chemical assay of the amount of DNA in exponentially growing Bcen J2315showed it to be 1.6 genome equivalents per average cell (S2A Table). To verify the replicon copy numbers and to examine the basic replication parameters of the Bcen genome, we initially used quantitative Southern blot hybridization to measure the relative abundance of replication origins and termini. The data showed the relative concentrations of the chromosomal ter sequences to be close to unity (S4 Fig), which together with the DNA assay above confirmed that the basic copy number of each chromosome is one per cell; the higher value for the number of the plasmid ter sequences, 1.3–1.4, results from most plasmids completing replication during the first half of the cycle (see below).
The ori/ter ratios obtained from these data should allow us to confirm experimentally the strong indication from GC-disparity analyses (Fig 1; ref. 10) that replication of each chromosome is bi-directional, as well as to determine how long it takes (C period). However, anomalous hybridization behaviour of certain probes led us to use an alternative method, direct determination of base-pair frequency by sequencing. DNAs purified from Nel13 cells growing exponentially in LB (doubling time 72 mins), as well as from cells incubated in stationary phase for 8 hours to allow replication to terminate, were processed and subjected to deep sequencing (see Materials & Methods). Read-number data were binned and plotted as a function of chromosome position (Fig 2A). Scatter arising from variation in amplification and sequencing efficiency was minimized by normalizing the raw data (first row) with respect to the corresponding reads from stationary-phase cells (second row), yielding the relatively tidy curves shown in the third row.
The base-pair frequency gradients show replication of the main chromosome, c1, to proceed from the predicted origin region in both directions and to terminate diametrically opposite. Replication of c2 is essentially the same, although the fully symmetric pattern observed with c1 is not seen; origin-proximal sequences in the left arm are slightly less abundant than those on the right. Possible explanations for this asymmetry are occasional unidirectional, clockwise replication from the c2 origin or delayed initiation of the left arm. The replication mode of c3 is more complex. It appears to be predominantly bidirectional with a difference in frequency of the origin-flanking sequences implying sporadic unidirectional or delayed replication as suggested for c2. But in addition, the steepness and extent of the two gradients differ from each other. The base pair frequency minimum is displaced anticlockwise, such that the left and right replichores occupy, respectively, about 60% and 40% of the c3 replicon. This further asymmetry can be provisionally interpreted as resulting from relatively slow movement of the clockwise fork and from absence of a strong replication terminator opposite the origin, such that the anticlockwise fork terminates within the right chromosome half.
The bp frequency gradients were used to calculate the replication time, C, of each chromosome (strictly, each chromosome arm), from ori / ter = 2C/τ, (C = τ [log (ori/ter)/ log2]; [27]) where τ = culture doubling time. Although the normalized frequency curves had enabled us to discern the overall replication pattern of c1 and c2, they proved unsuitable for quantitative purposes owing to the residual concavity in the corresponding stationary-phase curves used as references; this presumably reflects failure of some cells to terminate c1 and c2 replication even long after cessation of net growth. Accordingly, we estimated ori / ter ratios from the raw data plots (see legend), and the C values derived from them are shown in Fig 2B. For c1 and c2 these are 58 and 51 (average) minutes respectively, representing a replication speed of about 33 kb per minute, comparable to that of E.coli growing at the same rate (~36kb/min at τ = 72 min [28]).
In the case of c3 the essentially flat stationary-phase curve allowed normalization without distortion of ori / ter ratios. The data scatter and the shallow gradients render definition of the replication terminus approximate and prevent a similarly simple estimate of C. If the bp gradients indeed result from fast and slow forks meeting within the left half of c3, C would effectively be set by the slow fork, at about 16 minutes.
These times for the duration of c1 and c2 replication are readily accommodated within the ~75 minute cell cycle but cannot tell us when in the cycle replication is initiated. Direct observation of the positioning and separation of the replicated ori regions, although not a direct indicator of initiation, should enable us to address this question.
To visualize the origin regions of the replicons in Bcen we used the binding of fluorescent derivatives of the native ParB proteins to their cognate parS clusters adjacent to each origin. We saw no indication that the ParB fusion proteins of c1 and c2 interfere with indigenous wt ParB function; indeed they showed partition activity equivalent to that of the native ParB proteins (S5A and S5B Fig), and none of the abnormalities stemming from deletion of par loci or provision of excess parS (see below). The fluorescent ParBc3 derivative was defective in partition function (S5C Fig) as well as aberrantly localized in the presence of wt ParBc3: to visualize the c3 origin region we used the phage P1 ParB-parS system of Li & Austin [29]
Exponential-phase cultures of Nel13 derivatives, each carrying a pMLBAD plasmid from which one of the parB::fps is expressed, were sampled for microscopic observation. In nearly all cells, whether grown in MGCC, LB or SOB medium, the three chromosomes were seen as a single centrally-located focus or as two foci positioned roughly symmetrically about the midpoint (Fig 3A and 3C), in agreement with the Southern hybridization results (S4 Fig). To estimate cell cycle parameters we examined the distribution of ori-proximal foci as a function of the length of cells growing at 30°C in MGCC with doubling times of ~110minutes (equivalent to ~76 mins at 37°C) (Fig 3B). The range of cell length over which replicated origins begin segregation is delimited, observationally, by the first appearance of two-focus cells and the end of the one-focus cell cluster, as arrowed. How these segregation events are placed within the cell cycle cannot be directly estimated from these data, but can be inferred on the assumption that the approximately two-fold range of newborn cell sizes reported for E. coli and B. subtilis [30–32] applies also to Bcen and read from the abscissa as 1.2–2.4 μm. The ori regions of c1 and c2 segregate within the length limits of 1.5- ~2.4 μm and 1.6- ~2.6 μm respectively. This behaviour indicates that segregation of c1 and c2 oris begins early in the cell cycle and that it occurs within a range of cell lengths, ~1 μm, similar to the length range of newborn cells, implying close coupling with the cell cycle. It notable also that segregation of the plasmid-like origin of c2 is as tightly coupled as that of the c1 chromosomal origin.
Segregation of c3 origins is not seen until cells are 0.3–0.4 μm longer than the first to show c1 and c2 segregation, implying cell cycle phasing. However, its longer segregation range, 1.6 μm (1.9–3.5), suggests that any coupling of c3 segregation to the cycle is less strict than for the larger replicons. Plasmid p1segregation is first seen in cells ~0.2 μm shorter than the first to segregate c3; its length-at-segregation range, 1.5 μm (1.8–3.3), is similar to that of c3 and also indicates relatively loose coupling to the cycle.
These data are uninformative as to the time at which replication is initiated, but an indication of whether segregation follows initiation immediately or after a delay can be gleaned from DNA/cell data. Assuming that initiation at the c1 origin occurs at a fixed point in the cycle, as is the case for those bacterial chromosomes studied [14,33,34], and that initiation at the c2 origin is similarly phased with the cycle, as our data suggest (Fig 3, and see Discussion), we can calculate that replication of c1 and c2 is initiated shortly before or at the end of the preceding cycle, as derived in S2B Table, implying that a significant interval probably separates initiation and visible segregation of the c1 and c2 origins seen here.
Replotting of the 2-focus data as a fraction of cell length and as interfocal distances (Fig 3A) confirms two aspects of segregation behaviour: the widths of focus distribution are more restricted for c1 and c2 than for c3 and p1, implying more precise positioning of the former two, and the average distance moved towards the poles follows the order in which partition was initiated, indicating an order of segregation ages, c1 < c2 <p1≤ c3.
The difference in distributions of c1 and c2 two-focus cells over the length range within which segregation occurs (1.5–2.6 μm, see above) was compatible with the order above but for c1 and c2 was at the limit of statistical significance (S6 Fig). Because it appeared possible that experimental variability arising from our use of independent cultures for visualizing each replicon could have reduced the reliability of comparative segregation times, we again measured focus positions, this time using comparison at the single cell level with pairs of replicons marked at their origins. The results shown in Fig 4A confirm that segregation of the c1 origin generally precedes that of c2, the c2 origin that of c3, and the p1 origin also, but more narrowly, that of c3; examples of the cells observed are shown in Fig 4B. These data also confirm the relative average destinations of segregated origins shown in Fig 3A. If the segregation order is correct it should be reflected in the relative frequency of focus combinations. The tabulation of cells in each focus category (Fig 4C) bears this out. Doubling of c1 foci generally precedes that of c2, c2 nearly always precedes c3, and on average p1 also precedes c3. These data can be used to estimate average cell age at segregation for each replicon (S2C Table). The order is not absolute, however. In particular, in a minority of the cells the c2 origins segregated before the c1 origins, behaviour which is concealed in the focus distributions. Possible explanations include a looser regulation of c2 initiation such that it occasionally precedes that of c1 and, more likely, an occasional prolonging of the c1 initiation-segregation interval.
Having defined in outline the main features of the Bcen cell cycle, and knowing that the ParABS systems of each replicon can act independently and specifically to partition plasmids in E.coli [10,21], we next asked whether the Par systems also behave this way in their natural host, where a possible involvement in other processes might influence the coordination of segregation.
Mutation of chromosomal ParABS systems do not only impair segregation but also affect replication, DNA compaction, cell division and viability [19,35–39] through direct, functional interaction of Par proteins with the regulators of these processes, e.g. the initiator, DnaA [11,13], the condensin, SMC [40,41], the division inhibitor, MipZ [42]. We explored the range of roles that the Bcen Par systems play by observing the effects of nullifying each system on growth, morphology, replication and partition. ParAB function was disrupted in two ways: by deletion of parA or parAB from each replicon, and by introduction of excess parS sites to deplete ParB available to the chromosomal parSs (see Materials & Methods). The deletion in the ΔAc1mutant is polar on parB, reducing the ParB protein level to <5%that of wild type (S7 Fig) and rendering this strain phenotypically ParAB-minus. Excess parS sites were introduced either singly or as the natural cluster (for c2, c3 and p1) on the vector pMMBΔ (10–15 copies per cell). To obtain reproducible growth of and focus formation by mutant cells it was necessary to use growth media other than the MGCC used so far, as noted in the figure legends.
Our aim in undertaking this study has been to understand how bacteria with split genomes organize the maintenance of their multiple replicons within the cell cycle—how they time replication and segregation of plasmid-like chromosomes to avoid division delays and how they programme partition to avoid entanglement. For B. cenocepacia J2315, the results obtained point to a basic strategy—successive activation of the replication and segregation of each origin from a single locale, the cell midpoint (Fig 8). This maintenance mode resembles that of the only other multipartite genome for which these issues have been studied, that of V. cholerae, insofar as segregation of replicon copies is staggered through the cycle, but differs from it in that the resting origins of V. cholerae Chr1 and Chr2 are physically distant from each other, at the cell pole and midpoint respectively. The Bcen succession proceeds from segregation of the primary chromosome early in the cycle to that of secondary chromosomec2 shortly afterwards and then of c3 and the plasmid p1 at later, less well-defined cell ages (Figs 3 and 4). Notably, the immediate destinations of the replicated origins follow this order of segregation (Fig 3A), and only at around the time of cell division do all origins assume the midcell location seen in single-focus cells. Time-lapse monitoring of ori and ter movement will be needed to define this repositioning in more detail.
Although we have presented the temporal and spatial aspects of multi-replicon maintenance as separate, for Bcen they may be intimately related. In a newborn daughter cell where all four ori-par regions are close together at midcell, attempts at simultaneous segregation could be self-defeating. The ParA proteins of all four systems are of the Walker-box ATPase type, like those of the F and P1 plasmids and of bacterial chromosomes which appear to use the nucleoid surface to modulate transitions in ParA conformation essential to the partition mechanism [46–49]. It is unclear whether two partition processes of this type can simultaneously use overlapping nucleoid patches and move their origins over them. Staggering partition of the three large replicons should help avoid such scenarios of physical interference, thus improving partition efficiency. Participation of the c1 Par system in this temporal separation is suggested by the apparent delay in c1initiation timing and altered c3 bp frequency gradients in the ΔparAc1strain (Fig 6). The precedents for functional interaction of ParA proteins with DnaA [11,13] and the presence of DnaA-box clusters in the Bcen chromosomal origins lends credence to this proposal.
A subsidiary aim of this study was to verify that the specificity of action which each Par system had manifested previously in E. coli and in vitro [10,21] applied also in the systems' native cells. The importance of this verification was underlined by our discovery of overlapping specificities in other Burkholderia species [21]. The defects in origin positioning that appeared only in cells carrying the cognate parA(B) deletion (Fig 5) confirmed that this was so. It is unlikely that the par systems alone are primarily responsible for timing the segregation of replicated origins. Rather, it is through their regulatory role in initiation, indicated by changes of bp frequency gradients in the ΔAc1mutant (Fig 6) that they could contribute to replication timing. The distinction between global effects on initiation and a specific role in partition is mirrored at the temporal level—our preliminary estimate of initiation age suggests an interval amounting to ~15% of the cycle between initiation and origin segregation (S2B Table). Even if future work proves this accurate, it applies only to c1 and c2, the replicons that contribute significantly to the genome mass on which the calculation is based. We have no results that bear on whether newly replicated c3 and p1 origins remain colocalized or cohered for a period before segregation. We do not know, for example, whether clustering of p1 siblings, a phenomenon held responsible for sub-copy number focus numbers of several E.coli plasmids [50], artificially prolongs the apparent age at segregation of p1 seen in Fig 3.
It is reasonable to question the term "chromosome" as a title for large secondary replicons. In the case of Bcen c2 and c3 the issue is not settled. First, their complement of essential genes is very limited [20], suggesting that acquiring a few of them ensured that the replicon became indispensable and removed any selective pressure to accumulate more. Moreover, none of the acquired core genes is a constant feature of secondary chromosomes, as would be expected if the replicons were chromosomal in the eukaryotic sense. Second, and more importantly in the present context, the replication control systems resemble those of plasmids with a specific initiation regulator and iteron-like binding sites rather than that of primary chromosomes, for which the near-universal DnaA acts as the main regulator. Likewise, the parABS partition systems are variable and specific rather than based on the "universal centromere" [51] of the main chromosome. To reflect these characteristics, as well as necessity for cell viability and a GC content close to that of the chromosome, the term "chromid" has been proposed as a replacement for the often ambiguous labels—secondary chromosome, megaplasmid, etc—in use till now [52]. This sensible proposal appears at first sight applicable to c2 and c3. Nevertheless, certain criteria were not taken into account in the definition of chromids. The asymmetry of KOPS distribution centred on a dif site (Fig 1) is characteristic of chromosomes. Likewise, linkage to the cell cycle can reasonably be considered a chromosomal attribute, and has been in the case of V. cholerae Chr2 [14]. As pointed out above, a replicon size comparable to that of the main chromosome obliges cycle-phased replication, and in this sense is a chromosomal characteristic. Our data (Figs 3 and 4) indicate that in general, segregation, and presumably the prior replication, of c2 are as well phased with the cell cycle as they are for c1. On this basis we propose that the c2 replicon of Bcen J2315 qualifies as a chromosome. The c3 replicon, on the other hand, does not, since its segregation is only loosely timed with respect to the cycle. Moreover, its essentiality is unclear. Agnoli et al [53,54] obtained from many isolates, representing16 species of the 17 in the B. cepacia complex, derivatives cured of c3 whose growth properties were essentially unchanged, demonstrating that Burkholderia c3 replicons are in general neither chromosomes nor chromids but simply plasmids. However, B.cenocepacia J2315 was not among these species. The status of its c3 replicon has still to be determined.
Whether or not synchronization of replication with the cell cycle justifies elevation of c2 to chromosome status, the question of how such coupling came about is important. The simplest answer would be that an inherently synchronized plasmid was the progenitor of the present-day c2; the view that plasmids replicate at random through the cell cycle is based on experiments performed on only a few E. coli plasmids [6–9], which might not be representative of the plasmid universe. Alternatively, a synchronizing host-plasmid interaction might have been selected once a c2 ancestor, already essential, had expanded to a size problematic for the cell cycle [55]. A further possibility is linkage to replication of the c1 chromosome via a common regulator. Inspection of the c1 and c2 ori regions (S1 and S2 Figs) provides no obvious evidence for shared or overlapping regulatory processes (apart from the purely speculative roles of the clustered 7mers). A synchrony element of this type has recently been discovered in the V.cholerae genome [56]. A 70bp sequence situated 0.8kb from the origin on one 1.5kb arm of the primary chromosome (Chr1) was found to modify the replication regulator protein RctB of Chr2 in such a way as to stimulate Chr2 replication. Doubling of the 70bp element by replication was proposed to trigger Chr2 initiation, thus bringing Chr2 replication timing under the ultimate control of DnaA and coordinating it with the cycle. The possibility that an analogous mechanism links c1 and c2 replication in Bcen is worth exploring, although the observation that c2 foci occasionally double before c1 (Fig 4) suggests that such a mechanism is not an absolute requirement.
Although the c2 and c3 replicons appear to have evolved beyond the simple plasmid state, some of our data betray persistence of typical plasmid-like behaviour. Most striking is the asymmetry seen in the left and right arm bp frequency profiles (Fig 2). We favour the idea that this asymmetry reflects frequent failure of bidirectionality, such that c2 and c3 occasionally replicate by reverting to the unidirectional replication mode that presumably characterized their plasmid ancestors [57]. This observation has an important corollary—that a crucial component of the transition of a replicon from low-copy number plasmid to chromosome lifestyle is acquisition of the ability to replicate bidirectionally, thus halving the replication time and allowing the progressively expanding replicon to be replicated within the cell cycle. The alternative of rephasing initiation to allow it on not-yet terminated replicons, as seen in rapidly-growing E. coli, might not be compatible with an essentially iteron-based replication control system.
Apart from the demonstrated necessity of each replicon's ParABS system for its own partition (Fig 5), the appearance of several phenotypes specific to one or other of the disturbed Par systems suggests wider involvement in cell processes. Perhaps the clearest evidence for this is the altered replication of c1 and c3 in the mutant lacking the c1 ParA and ParB proteins (Fig 6). It suggests that in Bcen, as in B.subtilis and V.cholerae, the main chromosomal Par system helps regulate initiation. A further abnormality hinting at an expanded role for this system is displayed by cultures of cells in which c1 ParB is depleted by deletion of ParA or by parSc1 sequestration. About 5% of the cells form a triplet, one of whose terminal cells decondenses its nucleoid, elongates and eventually bursts, while the nucleoids of the two normal-sized partners are mis-positioned and show some lesser degree of compaction anomaly (S8 Fig). The mis-segregation and decompaction are reminiscent of the failure to load the condensin SMC at the B. subtilis and S.pneumoniaer eplication origins upon depletion of their ParB proteins [40,41,58], and suggests that the c1 Par system functions likewise in Bcen, a Gram-negative species.
Deletion of the c2 parAB operon also resulted in a specific phenotype, the reduction in average length and width of cells to about 70% of normal dimensions, and thus to an average cytoplasmic volume about one-third that of wild-type. Such contraction of the space available to the nucleoid might increase segregation deficiency beyond that specifically due to failure of c2 partition and contribute to the high level of anucleate cells generated in ΔparABc2 cultures (Fig 7B). Participation of parABSc2 in regulating c2 initiation, analogous to that reported for the Chr2 chromosome of V.cholerae [39,59], might also contribute. ParSc2 interference does not produce the phenotype, implying that ParAc2 influences the mechanisms governing cell size or division.
Disruption of all chromosomal Par systems retarded growth, but loss of c3 Par function was particularly severe. The parAB deletion abolished growth on LB medium and imposed the longest colony-appearance delay on SOB medium, while the full parSc3 locus provoked a delay twice as long as the next most severe (parSc1; Fig 7B) as well as high cell fragility. These observations suggest a specific effect of parABSc3 on cell physiology. However, our data do not allow us at present to distinguish clearly between direct implication of parABS in host processes and inhibition of these processes by toxin-antitoxin system activation following c3 mis-segregation. All Bcen replicons carry toxin-antidote modules [54], and several of those in c3 appear important for the stability of their own replicons [53]. How these might account for the growth deficiencies seen here remains to be analyzed. Such TA mechanisms could underlie the dramatic loss of cell integrity that follows failure of Chr2 segregation in V.cholerae [59].
Defining the roles of the c2 and c3 Par systems in cell growth and morphology, as well as exploring their involvement in the cell cycle, is one of two major tracks towards elucidation of genome management in Bcen indicated by this study. The other is investigation of the mechanisms that enable the c1 Par system to act specifically in partition of its own chromosome and generally in regulating initiation of replication of all three chromosomes. Probing these aspects should throw light on the reciprocal adaptations that enabled ancestral cells and progenitor plasmids to evolve towards the multipartite genome states we now observe.
E.coli strain DH10B [60] was used as the primary transformation recipient for plasmid construction, and the dam dcm strain SCS110 (Stratagene) for propagating plasmids destined for Bcn. The basic Burkholderia isolate is B.cenocepacia J2315, genomovar III of the ET12 lineage, used as the UK cystic fibrosis reference strain [20]. The antibiotic-sensitive derivative, Nel13,was used for most experiments; instances of J2315 use are noted. Nel13 was obtained by deletion from J2315 c1 of a mexAB-oprM locus (mex1) that encodes an RND efflux pump (see ref. 61, where the strain is called ΔMex1). Deletion of the par loci was carried out by allele replacement: cells transformed with suicide vectors carrying the desired deletion were screened for loss of the integrated-then-excised vector by antibiotic sensitivity and of parA(B) by PCR (see S3 Table for details). The same approach was used to insert the parS site of phage P1near the c3 origin, as described [61], yielding strain Nel35.
Plasmids used to produce fluorescent fusion derivatives of ParB proteins were constructed by first inserting the GFP and mCherry coding sequences (gfp and chfprespectively), tailed at their 5' ends by an NdeI site, into the SmaI site downstream of paraBAD in pMLBAD [62]. The parB genes of the four Bcen replicons were then amplified using primers with EcoRI and NdeI ends, enabling in-frame fusion to gfp and chfp in the pMLBAD vectors; the parBp1 gene had been mutated to remove the internal parSp1 site. For marking the parSP1 site in Nel35, the gfp::parBP1fragment cut from pALA2705 with BsrBI and HindIII was inserted between the SmaI and HindIII sites of pMLBAD, to make pDAG825. Plasmids expressing tandem parB::fp genes were made by inserting NheI-HindIII fragments carrying one between the XbaI and HindIII sites in plasmids carrying the other, giving pDAG845 (parBc1::chfp-parBc2::gfp), pDAG846 (gfp::parBP1-parBc2::chfp), and pDAG847 (gfp::parBP1-parBp1::chfp).
Plasmids for providing excess parS sites were made as described [21], by replacing the EcoRI-MluI and ApaI-HpaI fragments of lacI in pMMB206 [63] with fragments carrying single parS sites and parS clusters respectively. The control plasmid, pMMBΔ, is deleted of the EcoRI-MluI lacI fragment, which inhibits Bcen growth.
Media used were MGCC, composed of M9 salts (0.42M Na2HPO4, 0.22M KH2PO4, 0.009M NaCl, 0.018M NH4Cl, 1mM MgSO4, 0.1mM CaCl2), 3.4mM Na3citrate, 0.1% glucose, 0.2% Casamino acids, 0.04% tryptophan; MglyC, being MGCC with glycerol substituted for glucose; Luria-Bertani (LB) medium (1% NaCl version); and SOB. As an anti-contamination measure, media were routinely supplemented with gentamicin at10μg/ml, a concentration which does not affect Bcen growth. Antibiotics for selecting entry of plasmids into Nel13 and J2315 were used at, respectively, (μg/ml) chloramphenicol 40, 80; trimethoprim 200, 600; tetracycline 200, 400. Cultures were grown with aeration at 37°C or, for fluorescence microscopy, at 30°C.
Growth rate in liquid medium was determined by periodic measurement of the OD600 of samples from cultures grown exponentially at OD < 0.2 for at least three generations. Bcen culture doubling times were observed to be less reproducible than those of E.coli, and showed day-to-day variation in all media, regardless of pre-culture history, number of generations in exponential phase or presence of antibiotics. Doubling times (minutes ± standard deviation) of Nel13-based strains were: SOC—60 ± 5 (37°), LB—108 ± 8 (30°), 75 ± 4 (37°); MGCC—110 ± 13 (30°), 76 ± 5 (37°); MglyC—144 ± 9 (30°), 91 ± 13 (37°). Doubling times of J2315-based strains were 3–4 minutes longer in all media.
The colony-appearance assay consisted of spreading cultured or transformed cells on solid LB or SOB medium, incubating them at 37°C and counting the colonies two or three times per day; the time at which colony number reached its maximum was taken as the colony-appearance time for purposes of comparison. Viability was estimated by applying cells from dilutions of SOB cultures to LB agar in two ways—by spreading using glass beads, and as a 10μl drop—followed by incubation at 37°C; colony counts per OD unit were calculated.
Samples of 10μl taken from exponential SOB cultures at OD600 ~0.2 were applied to polylysine-coated slides and allowed to dry at room temperature. After three rinses in M9 salts and drying in air, the cells were fixed with a drop of methanol and allowed to dry, then covered with 5μl 2μg/ml DAPI and a cover slip and viewed by phase-contrast and fluorescence microscopy using a DAPI broad filter.
In most experiments, MGCC medium was inoculated from freshly-grown colonies at a concentration which ensured cells were still growing exponentially following overnight incubation at 30°C. Cells from the overnight cultures were diluted to OD600 0.05 in 25ml MGCC and incubated to OD600 ~0.10. ParB::FP production was then induced by addition of arabinose. Arabinose concentrations and induction periods appropriate for optimum signal:background ratio were determined empirically, according to whether ParB::FP was used for origin marking, whether one ParB::FP was being produced or two simultaneously, and whether normal or disrupted Par function prevailed in the cells observed. Induction was usually arrested by addition of further glucose or by resuspension in MGCC, but occasional omission of this step proved not to be detrimental. Cells in which the c3 Par system was disrupted (ΔparABc3 and extra parSc3) grew erratically in MGCC but reproducibly in SOB; the latter medium was used in this case.
Microscopy Culture samples were centrifuged and the cells resuspended in ~1/30 volume of medium. 1–2 μl was then applied to a 1% agarose-M9 salts layer on a microscope slide, spread by application of a coverslip and viewed under oil-immersion by phase contrast and epi-fluorescence microscopy. Microscopy was carried out in two laboratories: that of Dr J. Errington (Centre for Bacterial cell Biology, Newcastle-upon-Tyne; Fig 3) and that of the authors' institute (Figs 3–6). At the former, cells were observed with a Zeiss Axiovert M200 microscope equipped with a 300W Xenon lamp and a Zeiss Plan-Neofluar 100x/1.30 objective. The filters used were: Chroma 49002 ET-EGFP (exciter ET470/40X, dochroic T495LP, emitter ET525/50M) and 49008 ET-mCherry (ET560/40X; T585LP; ET630/75M), Schott UV GG385 and Schott IR KG5. Images were captured with a Photometrics Coolsnap HQ monochrome camera and analyzed using MetaMorph V.6.2r6. At the latter, cells were observed using a 100x oil-immersion objective (Plan Achromat, 1.4 NA; Olympus) the equivalent was an Olympus X81 wide-field inverted microscope equipped with an Olympus phase-contrast 100x/1.4 objective. The light source was a monochromator (Polychrome V; Till Photonics GmbH) with a 150W Xenon lamp used with a 15nm bandwidth. For two-colour experiments, multiband dichroic mirrors (Chroma BGR 69002) were used, and specific single-band emission filters (GFP 520/40, mCherry 632/60) were mounted on a motorized wheel. Images were captured with a Roper Coolsnap 2 camera and processed using Metamorph and ImageJ software.
Upon deposition on slides, Bcen cells tend to amass to form large groups in which cell dimensions cannot be accurately determined; accordingly we included only isolated cells or those in small groups in analyses of focus position. Cell poles were located by contrast difference in the grey-scale and the line connecting them was drawn using the ImageJ Straight function. Cells with septal constrictions were considered to be unitary unless septation was estimated to be more than two-thirds complete or the nascent cells were not aligned. Focus positions were determined as local fluorescence maxima at least two-fold higher than background using the plot-profile function of ImageJ. Distances were obtained from pixel size (64.5 nm). The initial pole-focus distance was determined by perpendicular projection to the line (Straight) from the focus maximum closest to a pole; second focus position was measured relative to the same pole.
Cultures of Nel13and its ΔparA1c1 derivative FBP7 were harvested after three generations of balanced exponential growth (72 and 136 minute doubling times respectively) to OD600 ~0.2 and, for Nel13, after 8 hours of incubation in stationary phase. DNA was purified as described and subjected to high-throughput sequencing by the Imagif Platform (Gif-sur-Yvette) using Illumina technology. Base-pair frequencies from the read density profiles were binned (10kb—c1 and c2, 1kb—c3) to generate base-pair gradients for the three chromosomes.
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10.1371/journal.ppat.1006335 | Kaposi Sarcoma Herpesvirus (KSHV) Latency-Associated Nuclear Antigen (LANA) recruits components of the MRN (Mre11-Rad50-NBS1) repair complex to modulate an innate immune signaling pathway and viral latency | Kaposi Sarcoma Herpesvirus (KSHV), a γ2-herpesvirus and class 1 carcinogen, is responsible for at least three human malignancies: Kaposi Sarcoma (KS), Primary Effusion Lymphoma (PEL) and Multicentric Castleman’s Disease (MCD). Its major nuclear latency protein, LANA, is indispensable for the maintenance and replication of latent viral DNA in infected cells. Although LANA is mainly a nuclear protein, cytoplasmic isoforms of LANA exist and can act as antagonists of the cytoplasmic DNA sensor, cGAS. Here, we show that cytosolic LANA also recruits members of the MRN (Mre11-Rad50-NBS1) repair complex in the cytosol and thereby inhibits their recently reported role in the sensing of cytoplasmic DNA and activation of the NF-κB pathway. Inhibition of NF-κB activation by cytoplasmic LANA is accompanied by increased lytic replication in KSHV-infected cells, suggesting that MRN-dependent NF-κB activation contributes to KSHV latency. Cytoplasmic LANA may therefore support the activation of KSHV lytic replication in part by counteracting the activation of NF-κB in response to cytoplasmic DNA. This would complement the recently described role of cytoplasmic LANA in blocking an interferon response triggered by cGAS and thereby promoting lytic reactivation. Our findings highlight a second point at which cytoplasmic LANA interferes with the innate immune response, as well as the importance of the recently discovered role of cytoplasmic MRN complex members as innate sensors of cytoplasmic DNA for the control of KSHV replication.
| KSHV latency-associated nuclear antigen, LANA, is essential for the replication of latent viral episomes, their segregation to daughter cells and overcoming the p53-dependent cell cycle block induced by an activated DNA damage response. In addition, cytoplasmic forms of LANA have been shown to modulate cGAS-dependent innate immune response. The findings presented in this report extend this role of cytoplasmic LANA to an innate immune response that is linked to the repair of double strand DNA breaks, thus reinforcing the importance of LANA as an antagonist of the innate immune response.
| Kaposi Sarcoma Herpesvirus (KSHV or HHV-8, Human Herpesvirus 8), a γ2-herpesvirus or Rhadinovirus categorized as a class 1 carcinogen by the World Health Organization (WHO) [1–3], is responsible for Kaposi’s Sarcoma (KS), the most common cancer among HIV infected individuals (epidemic KS) and among men in Sub-Saharan African countries. KSHV is also the cause of two other rare lymphoproliferative disorders, namely Primary Effusion Lymphoma (PEL) and Multicentric Castleman’s Disease (MCD) [4,5]. Like other herpesviruses, KSHV can establish a lifelong latent infection and exhibits a biphasic life cycle consisting of latency, the default state, in which only few viral proteins are expressed, and lytic replication, which leads to the production of new virions and the death of the host cells [6]. The KSHV major latent protein, LANA (Latency-Associated Nuclear Antigen) is expressed in all infected KS, PEL or MCD cells [6–9]. In the prototypic BC1 strain [10], LANA is a protein of 1162 amino acids and consists of three main domains: the amino-terminal domain, the internal repeat (IR) domain and the carboxy-terminal domain. The N-terminal nuclear localization signal (NLS) is responsible for the nuclear localization of LANA, and is positioned near the chromatin binding domain (CBD) that tethers LANA to histones on cellular chromosomes during mitosis [11–13]. LANA can also bind to the viral latent origin of replication in the terminal repeats (TR) of the KSHV genome by means of a specific DNA-binding domain at its C-terminal end. LANA plays an essential role in latent viral DNA replication and episome maintenance as well as transcription regulation and interaction with crucial cellular factors [6,7,14–24]. More recently, non-canonical LANA isoforms have been identified [25,26]. These include truncated protein isoforms that originate from alternative start codons within the LANA N-terminal domain and therefore lack the NLS and as a consequence are located in the cytoplasm. We could recently show that such cytoplasmic LANA variants promote KSHV lytic reactivation by inhibiting the cGAS-STING-mediated activation of type I interferon response [27], which is triggered by cytosolic viral DNA; they may thus act as antagonists of full-length LANA, which is required for latent replication and partitioning of viral episomes to daughter cells in mitosis. LANA binds to cGAS, as identified by mass spectrometry (MS) and immunoprecipitation [27]. The same MS analysis yielded several other new putative LANA-binding proteins, among them several DNA Damage Repair/Response (DDR) proteins [27]. Here, we focus on the interaction of LANA with the MRN (Mre11-Rad50-NBS1) complex, an important sensor of double-strand DNA breaks (DSBs) that is responsible for the detection of DNA damage and the activation of the repair cascade [28,29]. It is well established that viruses evolved ways to exploit the cellular DDR machinery to their own benefit, such as the replication of viral DNA [30–41]. Moreover, it has also been shown that KSHV de novo infection, as well as lytic reactivation from latency, trigger the DDR response [32,33,38]. Furthermore, LANA has been reported to interact with Chk2 in order to dysregulate the cell cycle during the viral latency [18].
Recent studies have suggested that the ability of the MRN complex to sense damaged DNA also plays a role during the innate immune response to foreign DNA [42–44]. In this context, cytoplasmic Rad50 and Mre11, together with CARD9, sense cytoplasmic DNA and activate the NF-κB pathway [42]. We show here that cytoplasmic LANA isoforms recruit Rad50 and Mre11 in the cytosol and thereby interfere with the activation of the NF-κB cascade induced by transfected DNA, as well as KSHV reactivation from latency. These observations point to yet another antiviral mechanism inhibited by cytosolic LANA isoforms, and highlight the importance of the sensing of cytosolic DNA by the MRN complex in the context of innate immunity against viral infection.
We recently reported the identification of several novel cellular KSHV LANA-interacting proteins by mass spectrometry in the KSHV infected PEL-derived cell line, BCBL-1 [27]. Among them were several cellular DNA damage response (DDR) proteins, including proteins involved in double-strand breaks (DSBs) recognition and repair (Rad50, Mre11, MDC1), mismatch repair (MSH2), and nucleotide excision repair (XPC, HR23B) [27]. Previous reports already point to a link between KSHV infection and DDR activation [15,16,18,23,45–49]. In this study, we focused on the binding of LANA to Rad50 and Mre11. Together with NBS1, Rad50 and Mre11 form the MRN (Mre11-Rad50-NBS1) complex, which is the upstream activator of the DSBs repair pathway, and is also involved in the replication of several DNA viruses [30,32,34–36,40,50]. Therefore, we proceeded to elucidate further the interaction between KSHV LANA and MRN complex components.
We confirmed the interaction of LANA with Rad50, Mre11 and NBS1 in the PEL-derived cell lines BC3 and BCBL-1, as well as in BrK.219 (a BJAB cell line stably infected with a recombinant KSHV virus [51]) by co-immunoprecipitation with anti-LANA-antibody-coupled beads and immunoblotting for Rad50, Mre11 and NBS1 (see Fig 1A and 1B for co-immunoprecipitation from BC3 cells, see S1A and S1B Fig for co-immunoprecipitation from BCBL-1 and BrK.219 cells). Before immunoprecipitation cell lysates were incubated with benzonase to digest nucleic acids and avoid DNA-mediated interactions. The interaction between LANA and Rad50 could also be shown by immuno-precipitating LANA with anti-Rad50-antibody-coupled beads and checking for LANA binding by immunoblotting (Fig 1B and S1 Fig). Both assays show that LANA interacts with the MRN complex in latently KSHV infected B cells. Interestingly, as indicated by an arrowhead in Fig 1B, smaller LANA isoforms were preferentially immunoprecipitated by Rad50 compared to those immunoprecipitated with a LANA antibody (Fig 1B). Subsequently, we investigated which region of LANA (Fig 1C) is responsible for the interaction with the MRN complex by performing GST-pull down assays with the N- (aa1-312) and C- (aa931-1162) terminal domains of LANA fused to GST (Fig 1D). HEK293 cell lysates were incubated with GST-fused LANA domains and the interaction with endogenous MRN complex proteins was analyzed by SDS PAGE and immunoblotting. The results suggest that all three MRN complex components bind to the C-terminal domain of LANA (Fig 1D). We next attempted to map the interaction site in LANA more closely by using GST-fused fragments of the LANA C-terminal domain. The results (S2 Fig) suggested that this interaction may involve multiple contact points in the LANA C-terminal region, in particular within the LANA domain binding to viral DNA (S2 Fig), the structure of which has recently been solved [19,20,22].
According to recent findings, Rad50, together with Mre11, can translocate to the cytoplasm to sense cytoplasmic viral DNA and thereby mediate CARD9-dependent NF-κB activation [42]. The NF-κB cascade is considered to play an essential role in the maintenance of KSHV latency and also in the pathogenesis of KSHV-related diseases [52–56]. Prompted by the observation that Rad50 is associated with smaller isoforms of LANA (Fig 1B), which are known to occur in the cytoplasm [25,27], we investigated whether cytoplasmic forms of LANA interact with Rad50 and Mre11. To that end we performed co-immunoprecipitation assays, in which nuclear and cytosolic fractions from BCBL-1 cells were separated and incubated with anti-LANA-beads or IgG-control beads. We found that LANA recruits Rad50 and Mre11 mainly in the cytoplasm (Fig 2A). As a control, we also probed LANA immuno-precipitates with an antibody to Brd4, a nuclear protein known to be associated with LANA [19,24,57]. As expected, LANA and Brd4 interact in the nucleus, indicating that the buffer conditions in the nuclear extracts used for this experiment did not interfere with the interaction of LANA with its nuclear binding partners (Fig 2A). This experiment also showed that lower molecular weight forms of LANA are found predominantly in the cytoplasm (Fig 2A). To confirm these findings, we performed additional co-immunoprecipitation assays using HEK293 cells transiently transfected with constructs expressing full-length LANA or LANA ΔN mutants (Δ161 and Δ282), which lack the NLS and are therefore mainly located in the cytoplasm [25,27]. Benzonase-treated cell lysates were incubated with anti-LANA-antibody-coupled beads and the interaction with endogenous Rad50 was analyzed by immunoblotting (Fig 2B). Our results confirm that Rad50 can be recruited by both full-length LANA as well as cytosolic LANA ΔN isoforms (Fig 2B).
Our observation that cytoplasmic LANA variants recruit Rad50/Mre11 in the cytoplasm could suggest that cytoplasmic LANA might modulate the recently described CARD9-dependent activation of NF-κB, triggered as a result of cytoplasmic DNA sensed by Rad50 [42]. Since this pathway was shown to operate in myeloid cells [42], we stably infected a human leukemia monocytic cell line (THP-1) with a recombinant KSHV virus (TrK.219, see Materials and methods) and used it to confirm the interaction between LANA and the Rad50-Mre11-CARD9 DNA sensor complex (Fig 2C). Cells were lysed and incubated first with benzonase, then with anti-LANA-coupled (or IgG-coupled) beads. The interactions were analyzed by immunoblotting. Our results (Fig 2C) show that LANA recruits all the cellular proteins (Rad50, Mre11 and CARD9) recently shown to be involved in the sensing of cytosolic viral DNA and the downstream activation of the canonical NF-κB pathway [42].
In order to determine whether cytosolic LANA isoforms can modulate the activation of NF-κB triggered by cytosolic DNA, we transiently transfected HeLa cells (Fig 3 and S3 Fig) with the construct expressing an NH2-terminally truncated cytoplasmic LANA isoform (LANA Δ161) [25,27] and shortly stimulated them with exogenous naked DNA (Interferon stimulatory DNA, ISD). Two different HeLa sublines, HeLa.MZ and HeLa.CNX, were chosen for this experiment (S3 Fig). Subsequently, cells were lysed and the phosphorylation of NF-κB RelA (p65), as well as the regulators of IFN-induction TBK-1 and IRF3, was analyzed by immunoblotting. As previously reported [27], HeLa.MZ cells showed an increased TBK-1 and IRF3 phosphorylation in response to ISD, reflecting an activation of the cGAS-STING cascade; this activation was inhibited by the cytoplasmic LANA Δ161 isoform (S3 Fig), consistent with previously published data [27]. In contrast, HeLa.CNX cells showed no phosphorylation of IRF3 in response to ISD stimulation, suggesting that this pathway is not fully active in this cell line (S3 Fig). However, ISD stimulation induced phosphorylation of p65/RelA in HeLa.CNX cells, and this activation of the NF-κB pathway could be inhibited by transfecting LANA Δ161 (Fig 3 and S3 Fig). This cytoplasmic isoform also inhibited p65/RelA phosphorylation in HeLa.MZ (S3 Fig). As the cGAS-TBK1-IRF3 signaling axis was deficient in HeLa.CNX, we established a HeLa.CNX cell line which was stably infected with KSHV (see Materials and methods) to study the canonical NF-κB modulation by truncated LANA and the MRN complex in the context of KSHV latent infection and in the absence of cGAS-induced IFN activation.
Latently KSHV-infected HeLa.CNX cells (HeLa.CNX.rKSHV) were treated with a recombinant baculovirus expressing the regulator of the lytic replication cycle, RTA [58], and sodium butyrate [27,32,53,59,60] to induce the lytic phase and thereby confirm that KSHV could be reactivated in this cell line (Fig 4Ai and 4Aii). Following the treatment with 20% RTA (vol/vol, see Materials and methods) and 1.5 mM sodium butyrate for 24 hours, the expression of the RFP lytic reporter in the recombinant KSHV.219 virus [51,58] used for these experiments was switched on (Fig 4Ai) and the early KSHV protein K-bZIP and the Orf45-encoded tegument protein were expressed (Fig 4Aii). HeLa.CNX.rKSHV cells have much higher levels of phosphorylated p65/RelA than uninfected HeLa.CNX cells (Figs 4Bi, 4Bii and 5A), in line with the known ability of several latent KSHV proteins such as vFLIP and LANA to activate the NF-κB pathway [52,53,61]. To assess the role of the MRN complex in NF-κB activation and in KSHV lytic reactivation, we inhibited Mre11 expression by siRNA transfection in HeLa.CNX.rKSHV cells (using a pool of three siRNAs, Fig 4Bii, or the same three siRNAs transfected individually, S4 Fig). In these cells Mre11 silencing triggers KSHV lytic reactivation, as indicated by an increase in K-bZIP levels and a reduction in the levels of phosphorylated p65/RelA (Fig 4Bii). We next explored if this contribution of Mre11 to the maintenance of KSHV latency also applied to other KSHV-infected cell lineages. Similar to the results obtained in KSHV-infected HeLa cells, we found that silencing of Mre11 in the PEL cell line BCBL-1 as well as in the KSHV-infected THP-1 cell line TrK.219, resulted in KSHV reactivation from latency, as indicated by increased levels of, respectively, K-bZIP or ORF45, along with a decrease in p65/RelA phosphorylation (Fig 4C and 4D). Together, these results indicate that in these KSHV-infected cells Mre11 contributes to the activation of the NF-κB pathway that promotes KSHV latency [14,56]. We could not achieve an efficient silencing of Rad50 in PEL cells (or any other latently KSHV-infected cell lines), and therefore we were not able to assess if Rad50 contributes to the inhibition of the lytic cycle in a way similar to Mre11.
To explore if cytoplasmic LANA could modulate NF-κB via Mre11 and thereby affect lytic reactivation, infected and uninfected HeLa.CNX cells were transfected with LANA Δ161 or the empty vector and p65/RelA phosphorylation was analyzed by immunoblotting (Fig 5A). Our results show that LANA Δ161 overexpression reduces p65/RelA phosphorylation level in HeLa.CNX.rKSHV cells (Fig 5A). Furthermore, HeLa cells were treated with low amounts of RTA (5% vol/vol, Fig 5B) to induce the lytic reactivation only at a minimal level and were additionally transfected with LANA Δ161 or the empty vector. Our results show that the LANA Δ161 overexpression supports the lytic reactivation in HeLa.CNX.rKSHV cells induced by low levels of RTA, as highlighted by increased levels of K-bZIP expression (Fig 5B). In addition, levels of phosphorylated p65/RelA were reduced following transfection of LANA Δ161 and upon lytic reactivation indicating an antagonistic role of truncated LANA for canonical NF-κB activation (Fig 5A and 5B). In addition, the co-expression of Mre11 together with Δ161 LANA counteracts the Δ161 LANA-mediated downmodulation of p-p65 levels (S5 Fig). To explore the role of NH2-terminally truncated cytoplasmic LANA variants further, we compared the ability of full-length LANA, LANA Δ161 and LANA Δ282 to activate an NF-κB dependent reporter vector in HEK293 cells (Fig 5C). As previously reported [61], full-length LANA was found to activate NF-κB-dependent transcription (Fig 5C). In contrast, LANA Δ161 and LANA Δ282 failed to do so (Fig 5C). However, when we explored the ability of LANA Δ161 to modulate the activation of the NF-κB pathway by the potent NF-κB activator and IKKγ ligand vFLIP [53,62–65], we found that LANA Δ161 could inhibit vFLIP-induced NF-κB activation in a dose-dependent manner, while full-length LANA could not (Fig 5D).
Taken together, our results suggest that cytoplasmic forms of LANA may target Rad50 and Mre11, and thereby antagonize the activation of NF-κB and NF-κB-dependent suppression of the KSHV lytic cycle (Fig 6).
An involvement of some DDR proteins in the innate immune response is increasingly appreciated [42–44]. This highlights the similarities between the recognition of host DNA damage for subsequent repair, and of foreign DNA for the purpose of triggering an innate immune response leading to the activation of type I interferon and NF-κB-dependent pathways. In particular, components of the MRN DSBs repair complex, Rad50 and Mre11, have recently been shown to sense cytoplasmic “foreign” DNA and to activate the NF-κB pathway in a CARD9-dependent manner [42].
In the present study, we found that LANA recruits Rad50 and Mre11 mostly in the cytosol of naturally KSHV-infected B cells (Fig 2A), that a cytoplasmic form of LANA may antagonize the activation of NF-κB induced by transfected DNA (Fig 3) or vFLIP (Fig 5C) and that silencing of Mre11 promotes KSHV lytic replication in parallel to reduced NF-κB p65 phosphorylation (Fig 4Bii). These results are in line with the newly described function of DDR proteins in the context of cytosolic DNA sensing and inflammasome response [42]. In contrast to full-length nuclear LANA, which is found in the characteristic nuclear speckles [8,9], cytoplasmic LANA shows a diffuse distribution [25,27]. This absence of any cytosolic LANA-containing structure prevented us from showing a LANA-Mre11/Rad50/CARD9 co-localization in the cytoplasm of infected cells and we therefore had to rely on co-immunprecipitation experiments from cytosolic fractions as shown in Fig 2A. We observed the interaction of cytoplasmic LANA with Mre11 and Rad50 in cells without a detectable CARD9 expression (e.g. BCBL-1, BJAB.rKSHV) and therefore believe that it is unlikely that CARD9 is responsible for bridging Mre11 and Rad50 to cytoplasmic LANA. However, we cannot formally exclude this possibility.
We have previously reported that cytoplasmic forms of LANA can promote lytic reactivation by antagonizing another cytoplasmic DNA sensor, cGAS [27]. To discriminate between the effect of cGAS-dependent interferon induction and MRN-dependent NF-κB activation on KSHV latency or reactivation, we took advantage of the fact that the HeLa.CNX subline appears to be deficient for cGAS-dependent IRF3 phosphorylation (S3 Fig). In this somewhat artificial experimental setting, we can therefore demonstrate that cytoplasmic LANA isoforms can promote KSHV reactivation by repressing NF-κB activation.
Taken together, our observations therefore suggest that cytoplasmic forms of LANA antagonize not only cGAS-dependent type I interferon responses but also the Rad50-Mre11-CARD9-dependent activation of NF-κB pathway in response to cytoplasmic DNA (Fig 6), which is present during herpesviral lytic replication [27,42,66,67]. The fact that cytoplasmic LANA appears able to neutralize both these pathways testifies to their importance in restricting “lytic”, productive, KSHV replication. The NF-κB pathway has previously been shown to be required for maintaining the latency of γ2-herpesviruses [14,56], and the KSHV vFLIP protein, known to activate both NF-κB and the expression of interferon-dependent cellular genes, also contributes to the maintenance of KSHV latency [53,55,68–71]. This is supported by the observation shown in Figs 4Bi and 5A that KSHV-infected HeLa cells display higher levels of NF-κB p65 phosphorylation than uninfected controls. It is thus conceivable that KSHV needs to counteract both these restrictive pathways to successfully reactivate from latency. This may also be necessary as a cross-talk between these two pathways (cGAS-STING activating NF-κB and vice versa) may be possible [44]. Cytoplasmic isoforms of LANA, which lack the NLS-containing N-terminal region, have been shown to be more strongly expressed during lytic reactivation [27] and may result from the use of alternative in-frame translational start codons [25] or the cleavage of an N-terminal LANA fragment by Caspase 3 [72]. Together with our previous report [27], our recent findings may therefore indicate a role for cytoplasmic LANA isoforms as viral antagonists of the innate immune response. Furthermore, our observations (Fig 5C and 5D) indicate that cytoplasmic LANA isoforms may act as antagonists of full-length, nuclear LANA, at least with regard to antagonizing the activation of the NF-κB pathway, which is thought to contribute to the establishment and/or maintenance of latency [52,54,61]. Cytoplasmic LANA would thus support the action of the lytic switch protein, RTA, encoded by ORF50, which has been shown to counteract vFLIP-dependent NF-κB activation and its contribution to the maintenance of latency by aiding the degradation of vFLIP by the proteasome [53,71]. Taken together, our results suggest a role for cytoplasmic LANA variants in modulating NF-κB activity by recruiting components of the MRN DNA repair complex and thereby regulating KSHV latency.
HEK293 (ATCC CRL-1573), HEK293T (ACC 305 from the German Collection of Microorganisms and Cell Cultures-DMSZ), HeLa.MZ (provided by Marino Zerial, Max Plank Institute of Cell Biology and Genetics, Dresden) and HeLa.CNX (provided by Beate Sodeik, Hannover Medical School, Hannover) cells were cultured in Dulbecco’s modified Eagle medium (DMEM, containing D-glucose, L-glutamine, pyruvate) supplemented with 10% fetal calf serum (FCS). KSHV-infected PEL-derived B cell lines (BC3, BCBL-1), the B cell line BJAB (ACC-757 from the German Collection of Microorganisms and Cell Cultures-DMSZ) stably infected with recombinant KSHV (BrK.219) [51,73] and the human leukemia monocytic cell line THP-1 (ACC-16 from the German Collection of Microorganisms and Cell Cultures-DMSZ) stably infected with rKSHV.219 (TrK.219) were grown in RPMI medium 1640 (containing L-glutamine) supplemented with 20% FCS, and in case of BrK.219 and TrK.219 with 4 μg/mL puromycin (Sigma, P8833). Cells were grown at 37°C in a 5% CO2 incubator. Adherent cells were plated in 6-well plates 24 hours before transfection (5x105 cells per well), or were microporated (1x106 cells per well, in 12-well plates). The suspension cells were split at a ratio 1:2 one day before microporation (1x106 cells per condition) or lysed for binding assays (1x107 cells per condition). HeLa.CNX cells were latently infected with a recombinant KSHV virus containing a puromycin-resistance cassette, which had been produced using BrK.219 cells. Briefly, BrK.219 cells were stimulated with α-IgM (2.5 μL/mL) for 48 hours. After centrifugation, supernatant, containing infectious virions, was collected and filtered using a 0.45μm pore-size filter to remove cell debris and stored at +4°C. HeLa.CNX cells were seeded in a 12-well plate and one day later infected at an MOI of 10 with BrK.219-derived virus. After 48 hours, puromycin (1 μg/mL) was added to the medium for selection of the KSHV-infected (+) HeLa cells. Three weeks later, the stably KSHV infected cell line was tested for viral proteins expression (by immunoblotting). The TrK.219 cell line was established by infecting THP-1 cells with rKSHV.219 at an MOI of 10. Puromycin was added to the medium for selection at final concentration of 4 μg/mL. After four weeks, KSHV stably infected THP-1 cells (TrK.219) were tested by immunoblotting and PCR.
KSHV lytic reactivation was induced as followed: HeLa.rKSHV cells were treated with a combination of RTA, ectopically expressed from a baculoviral vector (calculated as volume of medium containing baculovirus / volume of total medium in one well, vol/vol), and Sodium Butyrate (see figure legends for further details). Cell pictures to check for GFP and RFP expression were taken using a Nikon Intensilight C-HGFI microscope.
Full-length LANA was expressed from a vector with the pcDNA3.1 backbone. Human Mre11 was expressed by transfecting a plasmid purchased from Addgene (plasmid # 82033) and the corresponding empty vector (plasmid # 46960) was used as a control. Adherent cells were transfected using Fugene6 (Promega, E269A) according to the manufacturer’s instructions. Cells were stimulated with naked DNA (ISD Naked, InvivoGen, tlrl-isdn) by transfection with Lipofectamine2000 (Invitrogen by Life Technologies, 11668–027), using the conditions indicated in the figure legends. siRNAs were purchased from Dharmacon: human Mre11 custom siRNA pool (#1: ccugccucgaguuauuaaguu; #2: cugcgaguggacuauaguguu; #3 gaugccauugaggaauuaguu), siGENOME Non-Targeting Pool#2 (D-001206-14-50). siRNAs were prepared according to the manufacturer’s instructions and transfected at the concentrations indicated in the figure legends using the Neon transfection system (Thermo-Fischer Scientific) under the following microporation conditions: 1150V, 30ms, 2 pulses.
Cytosolic/nuclear fractions were prepared from whole cell lysates using NE-PER nuclear and cytoplasmic extraction reagents (ThermoFischer, 78835) according to the manufacturer’s instructions. All extracts were incubated immediately with LANA-beads or stored at -80°C.
Production of GST fusion proteins and GST-pulldown assays were performed as previously described [15,16]. Endogenous co-immunoprecipitation assays were performed using PEL cell lines (1x10^7 cells/condition) harvested with TBS-T buffer (20 mM TRIS-HCl pH 7.4, 150 mM NaCl, 50 mM MgCl2, 1% TritonX-100). Benzonase nuclease (Merck Millipore, 71205–3) was added to whole cell lysates (50U each 2x106 cells) for 30 minutes at RT to digest nucleic acids. Subsequently, the samples were centrifuged at 20800 g for 10 minutes at +4°C and the supernatants used for immunoprecipitation. The input control corresponds to 4% of the lysate used for an individual immunoprecipitation sample. Protein G sepharose beads (GE Healthcare) were washed with TBS-T buffer and incubated for 5 hours at +4°C with α-LANA (rat, from ABI, 18-210-100) or α-Rad50 (mouse, from GeneTex, GTX70228) or negative control (α-IgG rat or α-IgG mouse) antibody. Finally, antibody-coupled-beads were washed with TBS-T buffer, resuspended in PBS and used immediately or stored shortly at +4°C.
Cell lysates, after boiling for 5 minutes at 95°C and centrifugation for 10 minutes at 20800 g, were subjected to SDS-PAGE. Proteins were detected by Ponceau S or immunoblotting, using the following primary antibodies: α-LANA (18-210-100, ABI); α-Rad50 (GTX70228, GeneTex); α-Mre11 (ab33125, Abcam); α-NBS1 (NB100-143, Novus Biologicals); α-GAPDH (14C10, Cell Signalling); α-KbZIP (F33P1, Santa Cruz Biotechnology); α-HHV-8 ORF45 (2D4A5, Santa Cruz Biotechnology); α-HHV-8 ORF57 (LS-C60137, LSBio); α-CARD9 (Cell Signalling, 12416S); α-p65 (sc-109, Santa Cruz); α-p-p65 (S536, Cell Signalling); α-Brd4 (A301-985A100, Bethyl); α-LaminA/C (sc-6215, Santa Cruz); α-IRF3 (sc-9082, Santa Cruz); α-p-IRF3 (4947S, Cell Signalling); α-p-TBK1 (3504S, Cell Signalling). Subsequently, membranes were incubated with the following secondary antibodies: α-mouse (P0260, Dako); α-rabbit (P0488, Dako); α-rat (P0450, Dako). Phospho-p65 levels were digitally quantified using ImageJ and normalized to the corresponding total p65 protein levels and the sample used as negative control.
For luciferase reporter assays, HEK293 cells were transiently co-transfected in duplicates with NF-κB reporter plasmid and expression constructs as reported in the figures legends. At the indicated time points, cells were washed once with PBS and lysed using 125μl per well of Reporter Lysis Buffer (Promega). Luciferase activity was immediately measured at the luminometer (DIGENE DIAGNOSTICS, inc.) using 30μl per condition and 100μl Luciferase Buffer (40mM Tricine, pH 7.8, 10mM MgSO4, 0.5mM ATP, 10mM DTT, 0.5mM Coenzyme A, 0.5mM D-Luciferine). To test for statistical significance a two-tailed T-test was used.
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10.1371/journal.pntd.0006221 | Promising approach to reducing Malaria transmission by ivermectin: Sporontocidal effect against Plasmodium vivax in the South American vectors Anopheles aquasalis and Anopheles darlingi | The mosquito resistance to the insecticides threatens malaria control efforts, potentially becoming a major public health issue. Alternative methods like ivermectin (IVM) administration to humans has been suggested as a possible vector control to reduce Plasmodium transmission. Anopheles aquasalis and Anopheles darlingi are competent vectors for Plasmodium vivax, and they have been responsible for various malaria outbreaks in the coast of Brazil and the Amazon Region of South America.
To determine the IVM susceptibility against P. vivax in An. aquasalis and An. darlingi, ivermectin were mixed in P. vivax infected blood: (1) Powdered IVM at four concentrations (0, 5, 10, 20 or 40 ng/mL). (2) Plasma (0 hours, 4 hours, 1 day, 5, 10 and 14 days) was collected from healthy volunteers after to administer a single oral dose of IVM (200 μg/kg) (3) Mosquitoes infected with P. vivax and after 4 days was provided with IVM plasma collected 4 hours post-treatment (4) P. vivax-infected patients were treated with various combinations of IVM, chloroquine, and primaquine and plasma or whole blood was collected at 4 hours. Seven days after the infective blood meal, mosquitoes were dissected to evaluate oocyst presence. Additionally, the ex vivo effects of IVM against asexual blood-stage P. vivax was evaluated.
IVM significantly reduced the prevalence of An. aquasalis that developed oocysts in 10 to 40 ng/mL pIVM concentrations and plasma 4 hours, 1 day and 5 days. In An. darlingi to 4 hours and 1 day. The An. aquasalis mortality was expressively increased in pIVM (40ng/mL) and plasma 4 hours, 1, 5 10 and 14 days post-intake drug and in An. darlingi only to 4 hours and 1 day. The double fed meal with mIVM by the mosquitoes has a considerable impact on the proportion of infected mosquitoes for 7 days post-feeding. The oocyst infection prevalence and intensity were notably reduced when mosquitoes ingested blood from P. vivax patients that ingested IVM+CQ, PQ+CQ and IVM+PQ+CQ. P. vivax asexual development was considerably inhibited by mIVM at four-fold dilutions.
In conclusion, whole blood spiked with IVM reduced the infection rate of P. vivax in An. aquasalis and An. darlingi, and increased the mortality of mosquitoes. Plasma from healthy volunteers after IVM administration affect asexual P. vivax development. These findings support that ivermectin may be used to decrease P. vivax transmission.
| Malaria is one of the most important infectious diseases in the world with hundreds of millions of new cases every year. The disease is caused by parasites of the genus Plasmodium where Plasmodium vivax represent most of the cases in the Americas. Current strategies to combat malaria transmission are being implemented; however, widespread insecticide resistance in vectors threatens the effectiveness of vector control programs. Ivermectin (IVM) has arisen as a new potential tool to be added to these programs as it has mosquito-lethal and sporontocidal properties making it a promising transmission reduction drug. Plasmodium vivax was drawn from patients, mixed with powdered IVM and metabolized IVM in plasma collected from healthy volunteers receiving IVM, and fed to mosquitoes via membrane feeding. Powdered and metabolized IVM interrupt P. vivax transmission, reducing oocyst infection and intensity rate of two South American malaria vectors An. aquasalis and An. darlingi. We also demonstrate the effect of IVM on asexual stages development of P. vivax, providing evidence that IVM may affect different parasite life cycle stages. Our findings place IVM as a strong candidate for malaria transmission reducing interventions.
| The 2016 World Malaria Report (WHO) estimated 212 million cases of malaria worldwide, leading to 429,000 deaths, which illustrates that malaria remains an important public health problem. In the Americas, 389,390 cases and 87 deaths were reported in 2016 with Brazil reporting 24% and Peru 19% of the malaria cases [1]. The majority (92.5%) of the malaria cases occurred in the Amazon sub-region with 69% being Plasmodium vivax [1–4]. Despite considerable efforts, the majority of South American countries are still far from achieving vivax malaria elimination. Current strategies to combat malaria transmission in South America include diagnosis and treatment with artemisinin-based combination therapy (ACT) [5–7] and long-lasting insecticidal nets [8], supported by indoor-residual spraying of insecticide IRS [9, 10]. However, widespread insecticide resistance in vectors threatens the effectiveness of LLINs and IRS [1–15].
Anopheles aquasalis and Anopheles darlingi are known to be susceptible to P. vivax infection ([16–18]). Anopheles aquasalis is considered the primary vector in coastal areas of Central and South America and has been used as a neotropical anopheline vector for laboratory model to evaluate host-parasite interactions [18–20]. Anopheles darlingi is the primary vector in the Amazonian region of South America [21, 22].
The reemergence of attention on transmission blocking strategies for Plasmodium [23, 24] have raised research efforts in attempt to find vaccines [25–27], drugs [28, 29] or microorganisms [30–32] able to disrupt the life cycle of the parasite in the mosquito vector. In this context, the endectocide ivermectin (IVM) has arisen as a new promising tool to be added to malaria control programs.
Ivermectin is a safe drug with activity against a wide range of internal and external parasites and it is used widely in both veterinary and human medicine [33]. Ivermectin as a single oral dose (150–200 μg/kg) is effective for the treatment or control of Onchocerca volvulus, Wuchereria bancrofti and Strongyloides stercoralis. Ivermectin has been widely distributed to humans via mass drug administration (MDA) campaigns against onchocerciasis and lymphatic filariasis in Africa and Latin America [34–36]. Ivermectin has a secondary effect on ectoparasites that feed on recently treated individuals [37], including activity against Anopheles vectors at concentrations present in human blood after standard doses [38, 39]. Consequently, IVM has emerged as a potential tool for malaria control [40, 41]. Ivermectin MDA provides a unique insecticide dissemination route via mosquito ingestion of the compound through a blood meal rather than physical contact as most insecticides are delivered. In Anopheles vectors, IVM acts as an agonist of glutamate-gated chloride channels, causing flaccid paralysis and eventual death [42]. Both in vitro and in vivo studies have shown that a blood meal containing IVM cause a significant reduction in the adult Anopheles lifespan, with secondary sub-lethal effects delaying time to re-feed [38, 43], a sporontocidal effect [38, 44, 45], reductions in fecundity [39, 46, 47] and egg hatch rate [39], and even reduced locomotor activity [48].
Field studies of the effect of MDA with IVM on malaria transmission showed that a single dose of 150–200 μg/kg reduced the survivorship of wild Anopheles gambiae, with an accompanying reduction in the Plasmodium falciparum sporozoite rate [9, 49, 50]. These studies demonstrated that IVM could be a potential additional tool for malaria control.
Several studies have demonstrated that powdered IVM p(IVM) inhibits Plasmodium development in the vector, including P. falciparum in An. gambiae [44, 45] and more recently P. vivax in Anopheles dirus and Anopheles minimus [38] and An. darlingi [43]. Recent evidence suggests the presence of long-lived IVM metabolites that may have mosquito-lethal activity [38]. Thus, it is critical to determine the sporontocidal effect of IVM and potential metabolites in human plasma/blood after administration of IVM at clinically relevant doses.
Ivermectin has also been shown to inhibit the development of asexual blood stages of P. falciparum in vitro and Plasmodium berghei in vivo [51]. Additionally, it has been reported that IVM has liver stage inhibition in a P. berghei rodent model [52]. It remains to be determined if these blood and liver stage effects of IVM have a prophylactic personal protective effect for a human.
In this study, for the first time, we evaluated the IVM effect and its possible metabolites against P. vivax in An. aquasalis and An. darlingi. Additionally, we investigated the potential effects of IVM against asexual blood stages of P. vivax. A better understanding of the effects of IVM and possible metabolites on parasite maturation and transmission to mosquito vectors would contribute to the development of IVM for malaria control strategies.
The procedures were approved by the Fundação de Medicina Tropical Dr. Heitor Vieira Dourado (FMT-HVD) Ethics Review Board (ERB) (Approval Number: 296.723 CAAE: 14148813.7.0000.0005) and by the U.S. Naval Medical Research Unit No. 6 (NAMRU-6) and Walter Reed Army Institute of Research Institutional Review Boards (NMRCD.2008.0004 and WRAIR#2175). All subjects provided and signed written informed consent.
This study was conducted at FMT-HVD, a tertiary care center for infectious diseases in Manaus, Amazonas State, Brazil and NAMRU-6 in Iquitos (Loreto), Peru. In order to obtain P. vivax samples, adult patients (ages ≥18 years) infected with P. vivax were recruited from the areas surrounding Manaus and Iquitos. P. vivax infection was determined by light microscopy of Giemsa stained blood samples. In Brazil, P. vivax infected patients were identified at FMT-HVD, enrolled and venous blood (15 ml) was collected. In Peru, P. vivax infected patients were identified at Ministry of Health Centers and hospitals in Iquitos, transported to NAMRU-6, enrolled and venous blood (15 ml) was drawn on site for the ivermectin sporogony experiments. The age, gender, history of previous malaria episodes, and place of residence were obtained from the patients and it was verified no signs of severe disease and no previous antimalarial treatment during the preceding 4 weeks. After blood collection, all patients were treated for P. vivax infection following guidelines of the Brazilian Health Ministry or Peruvian Ministry of Health [53].
In order to obtain metabolized (mIVM) plasma samples, healthy volunteers were recruited in Manaus, Brazil. Inclusion criteria were: adults ≥18 years old, history of at least 3 month-negative malaria, and were confirmed as healthy volunteers by health works.
Anopheles aquasalis were reared at Laboratory of Medical Entomology at FMT-HVD in Manaus, Brazil and An. darlingi were reared at NAMRU-6 in Iquitos, Peru. These colonies were kept at a constant temperature (24–26°C) and relative humidity (70–80%) on a 12:12 light-dark cycle. Larvae were hatched in room temperature water and fed ground TetraMin fish food for An. aquasalis and rodent diet for An. darlingi provided daily. The larvae were allowed to pupate and emerge into adults in an enclosed mesh-covered cage with water and 10% sucrose available [18, 54]. Adult mosquitoes used for experiments were between 3–5 days post-emergence.
Powdered pIVM and powdered chloroquine (pCQ) reference material were obtained from Sigma Aldrich (St. Louis, MO, USA). pIVM was dissolved in dimethyl sulfoxide (DMSO) to a concentration of 10 mg/ml and pCQ in Roswell Park Memorial Institute (RPMI) 1640 medium (Sigma Aldrich, St. Louis, MO, USA) to a concentration of 1 mg/ml and aliquots were frozen at -20°C. IVM was serially diluted in PBS to achieve experimental concentrations.
All experimental drug regimens were managed at the FMT-HVD in Manaus, Brazil. IVM tablets (Abbot Laboratórios do Brasil, State, Brazil) were administered at a single dose of 200 μg/kg. Vivax patients received chloroquine (CQ) tablets (Farmaguinhos Laboratórios do Brasil, Rio de Janeiro State, Brazil), administered as a daily dose for three days (i.e., 600 mg in the first day and 450 mg in the second and third day). Primaquine (PQ) tablets (Med Pharma, São Paulo State, Brazil) were administered as a daily dose of 30 mg for 7 days following the Brazilian MoH guideline for vivax malaria treatment [53].
To evaluate the effect of mIVM on P. vivax development, five volunteers, all healthy men from 18 to 50 years, were recruited for the experiments. They received one single dose (200 μg/kg) of IVM. Blood was collected in heparinized tubes at 0 and 4 hours, and 1, 5, 10 and 14 days after drug intake.
For the evaluation of the in vivo mIVM effect on P. vivax, 15 patients with confirmed P. vivax malaria infection were recruited. The patients were divided into four groups and different treatment regimens were provided: (1) IVM plus CQ, (2) CQ alone, (3) PQ plus CQ and (4) IVM plus PQ plus CQ. Before and after 4 hours of drug intake, blood samples were collected. The patients that receive IVM plus CQ or CQ alone have received the first PQ dose after blood was drawn at 4 hours past CQ intake. All patients were treated with PQ and CQ dosage following the Brazilian Ministry of Health Guidelines.
Four experiments were performed to determine the effect of IVM on P. vivax in either An. aquasalis or An. darlingi (Fig 1):
P. vivax infected blood from patients was prepared as described above. The plasma was removed and packed red blood cells (RBCs) were washed with RPMI 1640 medium, repeated twice, and reconstituted to 40% hematocrit with non-immune human AB serum (experiment 1: pIVM) or with plasma from drug-treated volunteers or patients (experiments 2–4: mIVM, double feed mIVM, and in vivo mIVM). Adult female mosquitoes were sugar starved overnight prior to infection via membrane feeding assay (MFA). Blood meals (1ml) prepared as described above were offered to groups of at least 100 mosquitoes for 30 minutes via membrane feeder devices at 37°C as described in detail elsewhere [18, 20]. The fully engorged mosquitoes were separated into different cages and kept until 7 or 14 days post-feeding. Mosquito mortality was monitored on day 7 or 14.
Seven days after P. vivax blood meal ingestion midguts from all experimentally infected mosquito groups were dissected in PBS under a stereo microscope. The midguts were stained with 0.1% commercial Mercurochrome (Merbromin, Sigma- Aldrich, USA), placed under a cover slip and examined for the presence of oocysts with a compound microscope (Optical Microscopy, Olympus, Germany). Infection prevalence was expressed as percentage of mosquitoes with at least one oocyst. Infection intensity was determined as the arithmetic mean of oocysts counted per dissected midgut.
Plasma samples from healthy volunteers and patients were shipped on dry-ice to the Department of Clinical Pharmacology, Mahidol-Oxford Tropical Medicine Research Unit, Bangkok, Thailand for IVM drug concentration measurements. Plasma concentrations of IVM were determined by a newly developed and validated method using solid-phase extraction and liquid chromatography with tandem mass spectrometry (manuscript in preparation). The linear range for quantification was 0.97–384 ng/m. Three replicates of quality control samples at low, middle, and high concentrations were included in the analysis to ensure precision and accuracy. The observed total assay coefficient of variation was <10% in all quality control samples in accordance with US Food and Drug Administration requirements [56].
The P. vivax schizont maturation assay was performed as previously described [57] with five P. vivax samples from Manaus, Brazil. The parasitemia was determined by counting the number of parasites per 200 leukocytes. The plasma and buffy coat were separated from RBCs by centrifugation and packed RBCs were washed with RPMI and this was repeated twice. Leukocytes were removed by passing samples in a cellulose column [58] and afterward, the RBC pellet was suspended in McCoy 5A medium supplemented with 3.2% glucose and 20% human AB serum.
Drug plates were freshly prepared to avoid drug degradation. The pIVM and pCQ stock solutions were subsequently diluted in McCoy’s medium to obtain 9 serial dilutions of the drug (1000–3.9 ng/ml). To determine IC50s, 50 μl of pIVM and pCQ solutions were added into 3 wells of 96 well plates, and 9 serial dilutions of each drug were done in triplicate. Three wells were free of drug and served as a control. Additionally, 50 μl of plasma samples from each IVM-treated healthy volunteer 0 and 4 hours after IVM administration were added at 4 different dilutions (1:2, 1:4, 1:8 and 1:16) in triplicate. Then 50 μl of parasite solution was added to each well to achieve a 4% hematocrit. The plates were incubated in a modular incubator chamber containing 5% CO2, 5% O2, and 90% N2 at 37°C. Incubation was stopped when at least 40% of parasites on the ring stage had matured to schizonts in the drug-free control wells.
After incubation, the plates were allowed to stand for 30 minutes in a semi-vertical position. The supernatant was removed, erythrocytes suspended in the remaining fluid, and a thick blood film was made from each well. Thick blood films were stained with Giemsa stain. The number of schizonts containing more than three nuclei per 200 asexual stage parasites was determined in each blood film. Data for analyses prepared as follows: Schizont maturation in relation to control (%) = 100 x (number of schizonts in treated well/number of schizonts in control wells).
Data were entered in Prism 7.03 (GraphPad, USA) and subsequently analyzed by Stata 11.2 (Data Analysis and Statistical Software, Texas, USA). Mosquito oocyst prevalence and schizont proportions were compared by one-way ANOVA and post-hoc analysis using paired T-test to compare each concentration with respect to the control. Mosquito oocyst intensity was compared by Kruskal-Wallis test and post-hoc analysis using Kolmogorov-Smirnov to compare each concentration with respect to the control. P-values <0.05 were considered statistically significant. The P. vivax mosquito-stage oocyst inhibition in An. Aquasalis and An. darlingi after ingestion of whole blood spiked with IVM or whole blood equivalent from volunteers administered ivermectin were analyzed using GraphPad Prism v7.02. Normalized oocyst inhibition versus human volunteer plasma concentrations of IVM was analyzed using a nonlinear dose-response analysis with a variable slope. The maximum inhibition was fixed to 100%, and the minimum inhibition (zero drug concentration) and the drug concentration producing 50% of maximum effect (IC50) was estimated. The P. vivax asexual blood-stage inhibitory concentrations (i.e. IC50, IC90 and IC99) for pIVM and pCQ were determined using the free software ICE estimator available online at http://www.antimalarial-icestimator.net/.
Median peak plasma concentrations (i.e., plasma samples collected 4 hours post-dose) was 79.8 (50.6–112) ng/mL after a single oral dose of 200 μg/kg of IVM. Only two out of five volunteers had detectable IVM plasma concentrations 10 days post-dose (i.e., 2.64 and 2.32 ng/mL), while only one volunteer had a detectable IVM plasma concentration on day 14 after dose (i.e., 1.87 ng/mL) (S1 Table).
Different concentrations of pIVM were evaluated (5, 10, 20 and 40 ng/mL) on the P. vivax infection prevalence, oocyst intensity, and An. aquasalis mortality (Fig 2A–2C, S1 Dataset). There were significant differences among pIVM treatment groups for infection prevalence [F (3.23) = 11.58, p = 0.001] compared to the control (0ng/ml) group. P. vivax infection prevalence was significantly reduced in mosquitoes that ingested pIVM at 10 ng/ml by 33.2% [32.03% (SD = 5.83%), p = 0.0051, reps = 7, n = 171], 20 ng/ml by 33.7% [31.81% (SD = 5.74%), p = 0.019, reps = 7, n = 68] and 40 ng/ml by 61.3% [18.60% (SD = 5.81%), p<0.0001, reps = 7, n = 178) concentrations but not the 5ng/ml by 12.6% [41.93% (SD = 7.67%), p = 0.141, reps = 7, n = 160) concentration (Fig 2A). Also, the infection intensity (i.e. number of oocysts per mosquito) was reduced in the groups of mosquitoes that fed on infective blood meals containing 10ng/mL by 62.6% [5.89 (SD = 5.87), p = 0.0079, reps = 7, n = 171), 20ng/mL by 72.6% [4.32 (SD = 2.71), p = 0.0298, reps = 7, n = 68] and 40ng/mL by 86.1% [2.20 (SD = 0.97), p<0.0001, reps = 7, n = 178) but not the 5ng/ml by 56.6% [6.84 (SD = 3.20), p = 0.99, reps = 7, n = 160) concentration of pIVM in comparison to the control group (Fig 2B) [15.73 (SD = 12.97), reps = 7, n = 134]. There were significant differences among pIVM treatment groups for mosquito mortality at 7-day [F (3.23) = 2.99, p = 0.052] compared to the control (0ng/ml) group but the mortality rate was higher only in the 40 ng/ml group [78.11% (SD = 7.17%), p = 0.0007] but not 20 ng/ml group [66.34% (SD = 9.72%), p = 0.056], 10 ng/ml group [49.09% (SD = 20.03%), p = 0.646] or 5ng/ml group [63.38% (SD = 13.26%), p = 0.080] compared to the control group [43.93% (SD = 14.46%)] (Fig 2C).
The ingestion of mIVM presented in the infective blood meals affected the infection rate and the infection intensity and the mortality of the vectors An. aquasalis and An. darlingi as seen in the Fig 3A–3F. There were significant differences for among mIVM treatment groups for infection prevalence [F (3.31) = 119.02, p<0.001] in An. aquasalis and [F (2.9) = 9.39, p = 0.0063] in An. darlingi compared to the control (0 hour) group. The oocyst infection rates were reduced in the groups of An. aquasalis fed volunteer mIVM plasma collected at 4 hours (48.0 ng/ml) by 89.2% [7.82% (SD = 7.04%), p<0.0001, reps = 10, n = 232), 1 day (5.55 ng/ml) by 51.5% [35.11% (SD = 8.96%), p = 0.0038, reps = 6, n = 102], and 5 days (1.58 ng/ml) by 24.2% [54.88% (SD = 8.57%), p = 0.0009, reps = 9, n = 300) but not 10 days by 14.3% [62.10% (SD = 9.46%), p = 0.063, reps = 9, n = 258) or 14 days by 2.3% [70.75% (SD = 7.57%), p = 0.073, reps = 9, n = 214) (Fig 3A) compared to the mIVM control [72.38% (SD = 6.05%), reps = 10, n = 448]. The oocyst infection rates were reduced in the groups of An. darlingi fed mIVM plasma collected at 4 hours (43.24 ng/ml) by 91.1% [6.25% (SD = 10.8%), p = 0.019, reps = 4, n = 8] and 1 day (5.69 ng/ml) by 73.9% [18.33% (SD = 18.48%), p = 0.014, reps = 4, n = 11], but increased slightly but not significantly on 5 days (1.35 ng/ml) by 1.95% [71.47% (SD = 22.88%), p = 0.156, reps = 4, n = 67], 10 days (2.48 ng/mL) by 0.48% [70.44% (SD = 28.17%), p = 0.098, reps = 4, n = 97], or 14 days (1.87 ng/mL) by 14.89% [80.54% (SD = 7.5%), p = 0.350, reps = 4, n = 51] compared with the control [70.1% (SD = 25.4%), reps = 4, n = 97] (Fig 3B). The mIVM concentrations imbibed by the mosquitoes were calculated by taking pharmacokinetic values and multiplying by 60% to account for the 40% hematocrit in each blood meal, several volunteers had no detectable ivermectin at days 10 and 14 so no concentrations ingested by mosquitoes could be estimated. We did not find significant differences for infection prevalence among both species in each treatment group: Control (p = 0.9178), 4 horas (p = 0.527), 1 day (0.287), 5 days (0.2883), 10 days (p = 0.734) and 14 days (p = 0.513). However, the mean oocyst intensity for An. darlingi (Fig 3D) is substantially higher than An. aquasalis (Fig 3C).
On the other hand, the oocyst infection intensities were reduced in all the groups: plasma 4 hours (43.24 ng/ml) by 96.1% [0.78 (SD = 2.03), p<0.0001, reps = 6, n = 232], 1 day (5.69 ng/ml) by 56.8% [8.52 (SD = 10.09), p<0.0001, reps = 6, n = 102], 5 days (1.35 ng/ml) by 29.4% [13.91 (SD = 11.39), p<0.0001, reps = 6, n = 300], 10 days by 41.3% [11.57 (SD = 5.50), p = 0.008, reps = 6, n = 258] and 14 days by 58.2% [8.24 (SD = 2.55), p = 0.007, reps = 6, n = 214] after the volunteer pIVM intakes compared with the control [19.70 (SD = 13.09), reps = 6, n = 448] in An. aquasalis (Fig 3C) and in plasma 4 hours (43.24 ng/ml) by 99.9% [0.06 (SD = 0.10), p = 0.003, reps = 4, n = 8], 1 day (5.69 ng/ml) by 97.57% [2.31 (SD = 3.77), p = 0.008, reps = 4, n = 11] and 5 days (1.35 ng/ml) by 56.8% [76.49 (SD = 80.96), p = 0.011, reps = 6, n = 67], but not 10 days by 56.8% [99.77 (SD = 125.9), p = 0.593, reps = 6, n = 97] and 14 days by 56.8% [37.80 (SD = 5.1), p = 0.141, reps = 6, n = 51] after the volunteer pIVM intakes compared with the control [94.8, (SD = 90.18), reps = 4, n = 97] in An. darlingi (Fig 3D). It is important to highlight that An. darlingi showed much less oocyst number on 4 hours and 1 day than An. aquasalis.
At day 7 post blood meal, there were significant differences in the mortality rate among mIVM treatment groups in An. aquasalis [F (5.46) = 16.45, p<0.0001] and mIVM treatment groups in An. darlingi [F (2.9) = 16.92, p<0.0001] compared to the control groups (plasma 0 hours). The mosquito mortality was significant higher 4 hours [78.33% (SD = 17.73%), p = 0.0006], 1 day [60.15% (SD = 6.95%), p = 0.0017], 5 days [36.84% (SD = 11.99%), p = 0.046], 10 days [50.01% (SD = 11.44%), p = 0.014] and 14 days [39.83% (SD = 17.58%), p = 0.043] (Fig 3E) than the control group [21.29% (SD = 15.80%)] in An. aquasalis. However, at day 7 post blood meal only the An. darlingi groups feds on infective blood meals containing plasma collected 4 hours [97.43% (SD = 2.56%), p = 0.027] and 1 day [97.07% (SD = 3.23%), p = 0.025] had significantly increased mortality rates but not when fed plasma from 5 days [71.23% (SD = 14.91%), p = 0.144], 10 days [40.25% (SD = 7.47%), p = 0.855] or 14 days [31.4% (SD = 1.9%), p = 0.873] compared with the control [43% (SD = 22.45%] (Fig 3F).
At 14 days post blood meal, after mIVM plasma from 4 hours, 1 and 5 days intake all the An. darlingi had died, therefore sporozoite prevalence at these time points could not be analyzed. Also, there were no significant differences on the mortality rate with respect to control [36.83% (SD = 20.06%)] in the groups that were blood fed mIVM of 10 days [57.66% (SD = 15.21%), p = 0.265] and 14 days [48.65% (SD = 21.67%), p = 0.200]. An. darlingi sporozoite prevalence at day 14 post blood meal was not reduced with respect to control [90.27% (SD = 13.39%)] in the groups that were blood fed mIVM from 10 days [90.38% (SD = 10.88%), p = 0.992] or 14 days [82.14% (SD = 1.68%), p = 0.824].
Interestingly, the mIVM (IC50 = 5.68 [3.79–7.56] ng/ml [95%CI]) of mosquito-stage P. vivax in An. aquasalis appears to be much lower compared to pIVM (IC50 = 28.15 [16.36–39.94] ng/ml) (Fig 4A and 4B). This demonstrates that mIVM has a more potent sporontocidal effect against P. vivax compared to ivermectin compound.
Anopheles darlingi and An. aquasalis had significantly reduced oocyst infection prevalence and intensity when ingested a second mIVM (4 hours) non-infective bloodmeal 4 days post P. vivax infection compared to the control. An. aquasalis oocyst prevalence was reduced by 84.5% [10.72% (SD = 6.79%), p = 0.047, reps = 4, n = 50] compared to the control [68.83% (SD = 10.69%), reps = 4, n = 94] (Fig 5A) and An. darlingi by 60.3% [33.98% (SD = 17.8%), p<0.0001, reps = 3, n = 31] compared to the control [85.49% (SD = 13.2%), reps = 3, n = 73] (Fig 5B). Oocyst intensity in An. aquasalis was reduced by 93.6% [0.94 (SD = 0.67), p<0.0001, reps = 4, n = 50] compared to the control [14.65 (SD = 7.71), reps = 4, n = 94] (Fig 5C) and in An. darlingi by 97% [0.73 (SD = 0.2), p<0.0001, reps = 3, n = 31] compared to the control [24.0 (SD = 53.3), reps = 3, n = 73] (Fig 5D).
There were significant reductions in oocyst infection prevalence and intensity compared to control when An. aquasalis ingested the drug-treated infected blood meals with unprocessed [F (3.17) = 159.90, p<0.0001] and reconstituted [F (3.17) = 164.82, p<0.0001] blood. For unprocessed blood, IVM+CQ by 63.7% [29.44% (SD = 4.19%), p = 0.029, reps = 3, n = 64]; PQ+CQ by 71.1% [23.44% (SD = 7.95%), p = 0.02, reps = 3, n = 161] and IVM+PQ+CQ by 66.5% [27.15% (SD = 2.20%), p = 0.046, reps = 3, n = 99] but not CQ alone by 26.5% [59.49% (SD = 10.9%), p = 0.072, reps = 3, n = 58] compared with the control [80.91% (SD = 4.12%), reps = 3, n = 615], (Fig 6A). Similar to observed in mosquitos fed with unprocessed blood, the infection rate was significantly reduced on mosquitos fed with P. vivax blood meal reconstituted with plasma from patients that undertook IVM+CQ by 65.6% [27.86% (SD = 4.44%), p = 0.006, reps = 3, n = 205], PQ+CQ 56.55% [35.21% (SD = 4.17%), p = 0.004, reps = 3, n = 180] and IVM+PQ+CQ 62.9% [30.02% (SD = 1.00%), p = 0.042, reps = 3, n = 140] but not CQ alone by 19.2% [65.40% (SD = 7.63%), p = 0.077, reps = 3, n = 91] in comparison to control [80.91% (SD = 4.12%), reps = 3, n = 615] (Fig 6A). There were no significant differences in oocyst prevalence between the unprocessed and reconstituted treatment regimens [IVM+CQ p = 0.246], [CQ p = 0.784], [PQ+CQ p = 0.120] and [IVM+PQ+CQ p = 0.128]. The infection intensity (oocysts number) was significantly reduced in mosquitos fed with unprocessed: IVM+CQ by 90.1% [1.99 (SD = 2.01), p<0.001, reps = 3, n = 64]; PQ+CQ by 90.8% [1.86 (SD = 1.77), p<0.001, reps = 3, n = 161], IVM+PQ+CQ by 89.1% [2.19 (SD = 0.96), p<0.001, reps = 3, n = 99] and CQ alone by 56.5% [8.71 (SD = 8.92), p<0.001, reps = 3, n = 58] compared with the control [20.02 (SD = 4.05), reps = 3, n = 615] (Fig 6B) and reconstituted with plasma from patients that undertook IVM+CQ by 84.1% [3.19 (SD = 1.43), p<0.001, reps = 3, n = 205], PQ+CQ 71.6% [5.69 (SD = 5.14), p<0.001, reps = 3, n = 180], IVM+PQ+CQ by 87.6% [2.50 (SD = 0.64), p<0.001, reps = 3, n = 140] and [20.02 (SD = 4.05), reps = 3, n = 615]. There were no significant differences in oocyst prevalence between the unprocessed and reconstituted treatment regimens [IVM+CQ p = 0.246], [CQ p = 0.784], [PQ+CQ p = 0.120] and [IVM+PQ+CQ p = 0.128].
A total of 5 malaria vivax patients were recruited with high parasitemia (138–245 parasites/200 leucocytes), with at least 80% of parasites in the ring stage. We tested five isolates of P. vivax for pIVM drug sensitivity. The pIVM had some activity against two of five P. vivax isolates tested (Table 1). As expected in our site, chloroquine was fully effective as the blood schizonticidal treatment with pCQ IC50 ranging from 1.96 ng/mL to 7.53ng/mL. Interestingly, when mIVM was added to P. vivax culture in different dilutions, a significant reduction in parasite maturation was observed compared to drug free control 1:2 (34.24 ng/ml) [52.31% (SD = 18.06%), p = 0,011, reps = 5]; 1:4 (17.12 ng/ml) [52.34% (SD = 11.49%), p = 0.0002, reps = 5]; 1:8 (8.56 ng/ml) [54.93% (SD = 11.78%), p = 0.0013, reps = 5] and 1:16 (4.28 ng/ml) [51.77% (SD = 9.92%), p = 0.0001, reps = 5] (Fig 7).
Effective malaria transmission-blocking tools are an integral element in malaria eradication campaigns. MDA of IVM disrupted malaria parasite transmission in West Africa [9, 49] by killing the vector An. gambiae [9, 50] which shifts the population age structure, thereby reducing the sporozoite rate. Additional effects of ivermectin which likely further reduce transmission include inhibiting sporogony in the vector as demonstrated with P. falciparum in An. gambiae [45] and P. vivax in An. dirus, An. minimus [37], and An. darlingi [43]. Previous findings from our group also showed effects of pIVM and mIVM on survivorship, fecundity and even the locomotor activity of An. aquasalis [39, 48]. Therefore, IVM MDA has strong potential to be a novel tool for malaria transmission control. To our knowledge, this is the first study to evaluate mIVM effects on the P. vivax oocyst infection and intensity in Anopheles. This is also the first study to asses IVM effects against P. vivax asexual blood-stage development.
Herein, it was demonstrated that IVM reduces the oocyst infection and intensity of P. vivax. When An. aquasalis were fed with different concentrations of pIVM, a reduction of oocyst infection rate and infection intensity at the 10, 20 and 40 ng/ml concentrations (Fig 2). These ivermectin concentrations were selected based on the human pharmacokinetic curve for IVM which corresponds to approximately the concentration found in human blood between 4 to 60 hours post ingestion of IVM at 200 μg/kg [33, 55]. A recent report demonstrates that An. darlingi had modest sporontocidal pIVM results wherein oocyst prevalence was significantly reduced by 22.6% at the lethal concentration that kills 50% (LC50) (43.2 ng/ml) and 17.1% at the LC25 (27.8 ng/ml) but not significantly by 11.3% at the LC5 (14.8 ng/ml). Furthermore, there were no reductions in oocyst intensity in An. darlingi at any pIVM concentration tested [43]. Other reports also show the effect of pIVM on P. vivax oocyst prevalence and intensity in Asian vectors Anopheles dirus and Anopheles minimus wherein sporontocidal effect was far more impactful [38].
Interestingly, when An. aquasalis was fed P. vivax with mIVM, a reduction on infection rate and intensity was observed with plasma collected at 4 hours, 1 and 5 days after drug intake by the healthy volunteers. A far more potent sporontocidal effect was observed in An. aquasalis ingesting mIVM concentrations from day 1 (5.55 ng/ml) reduced oocyst prevalence by 51.5% compared to pIVM at 5 ng/ml by 12.6%. A similar effect was observed in An. darlingi ingesting mIVM concentrations from day 1 (5.69 ng/ml) reduced oocyst prevalence by 73.9% compared to pIVM at 14.8 ng/ml reduced oocyst prevalence by 11.3% [43]. Here we demonstrate that metabolized ivermectin has a more potent sporontocidal effect compared to ivermectin compound (Fig 4), which suggests that ivermectin metabolites may enhance the sporontocidal effect. Little is known about ivermectin metabolite production in humans, one small trial (n = 4) indicated that mean peak plasma concentration of metabolites was 2.5 fold greater than that of the parent compound and the effective half-lives of the metabolites were approximately 2.9 days while the parent compound half-life was 11.8 hours [59]. Future studies should be designed to elucidate ivermectin metabolite production in orally-treated volunteers and their impact on mosquito survivorship and Plasmodium sporogony. Interestingly, oocyst prevalence and intensity was more intensely impacted with mIVM plasma from 1 day in An. darlingi compared to An. aquasalis (Fig 3) but no significant reduction occurred when plasma from day 5 was fed to An. darlingi which suggests a shorter duration of but stronger sporontocidal effect compared to An. aquasalis.
Mosquito mortality effect was observed at day 7 post-blood feed with mIVM plasma from 4 hours and 1 day in the two-species studied. This IVM effect on mosquito survival is similar to our previous findings in non-infected An. aquasalis which also used metabolized ivermectin showing the higher impact on the survival and reproductive fitness [39] and with other studies in different anopheline species infected and uninfected with Plasmodium [9, 50, 60, 61]. In a single dose of 200 mcg/kg showed an increase in mosquito mortality in An. aquasalis when fed on mIVM at 1 day (5.55 ng/ml) to 5 days (1.58 ng/ml) the drug intake, but there was no longer effect when fed plasma collected from days 10 or 14.
The reduction in An. aquasalis and An. darlingi oocyst infection and intensity when mosquitoes were blood fed mIVM 4 days after infection expands the window that a blood meal containing IVM has an effect on P. vivax mosquito infection. Moreover, IVM was able to impair parasite development even when it was given to the mosquitoes after the midgut epithelium invasion by parasite ookinete suggesting that IVM has direct effects on already established and developing oocysts. Interestingly, these results were different from Kobylinski et al. [45], wherein no sporontocidal effect was observed on early-stage oocyst development when pIVM was fed 3 days after P. falciparum infection in An. gambiae.
This study also evaluates the in vivo exposure of P. vivax to IVM, CQ, and PQ in the human and its subsequent development in An. aquasalis. Mosquito oocyst infection and intensity were significantly reduced when mosquitoes were fed blood from patients treated with IVM+CQ, PQ+CQ or IVM+PQ+CQ but not CQ alone (Fig 6). Importantly, this is the first study to show that primaquine has a sporontocidal effect on P. vivax infection in the mosquito. However, the reduction on infection rate and intensity were not augmented on IVM +PQ+ CQ treated blood in relation to IVM+CQ only, suggesting that PQ does not have an additive or synergistic effect beyond IVM.
The mosquitoes were exposed in parallel to P. vivax with unprocessed and reconstituted blood samples. Unprocessed blood samples allowed for in vivo exposure of P. vivax to the drugs in the human compared to reconstituted blood samples which investigated the impact of metabolized drugs on P. vivax. The reduction in oocyst infection and intensity found in IVM+CQ and IVM+PQ+CQ treatment groups was similar in mosquitoes fed with the unprocessed and reconstituted blood (Fig 6). These data indicate that the ivermectin transmission blocking effect occurs in the mosquito midgut and not in human blood. Similar results were observed in the PQ+CQ and IVM+PQ+CQ groups, suggesting that primaquine effect on mosquito infection also occurs in the midgut. Since these assays were performed at only one-time point after drug intake (4 hours), we could not discard a possible delayed in vivo effect of primaquine or ivermectin on P. vivax asexual stages and gametocytes in the patient.
It is important to note that all patients who received the different regimens treatment were also supplied with CQ at the same time, following the guidelines of the Brazilian Health Ministry, which recommend all the patients ethically have to receive the CQ treatment at the same time that they are diagnosed. As expected, the mosquitoes fed a blood meal containing only CQ did not show a decrease in the oocyst infection prevalence of An. aquasalis with P. vivax, which is in accordance with other studies, where CQ did not affect the oocyst prevalence of Plasmodium berghei in An. gambiae [62]. However, this is the first report to demonstrate that oocyst intensity was reduced in mosquitoes fed a blood meal containing CQ. A reduction in oocyst intensity by CQ could be due to some direct action on P. vivax, or its immunosuppressive potential [63] may interfere with successful parasite midgut invasion leading to fewer oocysts.
Our results showed the highest mortality rate reduction of infection rate and intensity of An. aquasalis and An. darlingi, two important vectors of South American on plasma 4 hours and considering that in the human pharmacokinetic curve of the IVM the mean peak plasma concentrations is (46.6 ± 21.9ng/ml) at approximately 4 hours after dosing, with a IVM half-life from about 12 to 56 hours [33]. Similar peaks have been found in Primaquine and Chloroquine [64]. We can suggest the use of the IVM as a potent way to administration in combination with the other antimalaria drugs and mainly during the first hours after being detected the infection in the patient, which would have a higher impact in the Malaria elimination and eradication programs, specially, in endemics areas like Amazonas Region, which, have high incidence of Malaria cases by P. vivax.
This is the first study to assess the effect of ivermectin against asexual P. vivax. Two previous studies demonstrated an inhibition of pIVM on P. falciparum asexual stage development but with IC50s in the 1–10 μg/ml range [51, 65]. No effect of pIVM (3.9–1000 ng/ml) was observed in the current study against asexual blood-stage P. vivax, but this may have been due to using too low concentration (Fig 7). On the other hand, when asexual P. vivax was incubated with 4 different dilutions (4.28, 8.56, 17.12 and 34.24 ng/ml) of plasma obtained from healthy volunteers 4 hours after IVM administration, there was a significant decrease in P. vivax maturation in relation to the drug free control and incubations with plasma from healthy volunteers collected before IVM administration. It is important to highlight in the present study that when the asexual P. vivax was incubated with the pIVM (3.9 – 1000ng/ml) the development was not affected, however, a considerable reduction in blood-stage development was observed when the asexual stages were incubated with mIVM (4.28–34.24 ng/ml). This discrepancy might be a result of IVM metabolites conferring the parasite maturation inhibition effect. It is important to note that mIVM concentrations that showed blood-stage inhibition were achieved following oral administration with a standard dose of ivermectin (200 μg/kg). Unfortunately, data collected in this study could not be used to elucidate the ivermectin mechanism of action against asexual P. vivax. Further studies are warranted to evaluate the safety and efficacy of ivermectin as an adjunct during P. vivax antimalarial therapy.
We also have assayed the P. vivax sensitivity to pCQ on the same isolates used to examine the asexual maturation inhibition with IVM. These results showed pCQ IC50 values ranging from 1.96 ng/mL to 7.53ng/mL, similar to other reports [66–69], which demonstrate the chloroquine effect on P. vivax. Our findings also confirm the effect of chloroquine in terms of its pharmacodynamics against P. vivax.
In conclusion, our study shows for the first time the effect of mIVM on the oocyst infection and intensity of P. vivax in the South American malaria vectors An. aquasalis and An. darlingi. In both vectors it appears that mIVM has a stronger sporontocidal effect compared to pIVM, this suggests that ivermectin metabolites have sporontocidal effect. We report for the first time, the effect of IVM on ex vivo cultures of P. vivax and demonstrate that mIVM can inhibit P. vivax development. Moreover, it provides evidence that IVM may affect several parameters of Ross-MacDonald model [70], including parasite life cycle stages, placing it as a strong candidate for malaria transmission reduction.
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10.1371/journal.ppat.1003813 | Biphasic Euchromatin-to-Heterochromatin Transition on the KSHV Genome Following De Novo Infection | The establishment of latency is an essential step for the life-long persistent infection and pathogenesis of Kaposi's sarcoma-associated herpesvirus (KSHV). While the KSHV genome is chromatin-free in the virions, the viral DNA in latently infected cells has a chromatin structure with activating and repressive histone modifications that promote latent gene expression but suppress lytic gene expression. Here, we report a comprehensive epigenetic study of the recruitment of chromatin regulatory factors onto the KSHV genome during the pre-latency phase of KSHV infection. This demonstrates that the KSHV genome undergoes a biphasic chromatinization following de novo infection. Initially, a transcriptionally active chromatin (euchromatin), characterized by high levels of the H3K4me3 and acetylated H3K27 (H3K27ac) activating histone marks, was deposited on the viral episome and accompanied by the transient induction of a limited number of lytic genes. Interestingly, temporary expression of the RTA protein facilitated the increase of H3K4me3 and H3K27ac occupancy on the KSHV episome during de novo infection. Between 24–72 hours post-infection, as the levels of these activating histone marks declined on the KSHV genome, the levels of the repressive H3K27me3 and H2AK119ub histone marks increased concomitantly with the decline of lytic gene expression. Importantly, this transition to heterochromatin was dependent on both Polycomb Repressive Complex 1 and 2. In contrast, upon infection of human gingiva-derived epithelial cells, the KSHV genome underwent a transcription-active euchromatinization, resulting in efficient lytic gene expression. Our data demonstrate that the KSHV genome undergoes a temporally-ordered biphasic euchromatin-to-heterochromatin transition in endothelial cells, leading to latent infection, whereas KSHV preferentially adopts a transcriptionally active euchromatin in oral epithelial cells, resulting in lytic gene expression. Our results suggest that the differential epigenetic modification of the KSHV genome in distinct cell types is a potential determining factor for latent infection versus lytic replication of KSHV.
| Although the KSHV genome is linear and chromatin-free in the virions, it circularizes and adopts a repressive chromatin structure in latently infected cells, inhibiting the majority of viral gene expression. In this study, we investigate the epigenetic regulatory mechanism of the pre-latency phase of KSHV infection. We found that upon de novo infection, the KSHV genome undergoes distinct chromatin states in a temporally ordered manner prior to the establishment of latency. Initially, the KSHV genome carried a transcriptionally permissive chromatin structure to allow expression of a subset of viral lytic genes. Subsequently, cellular Polycomb Repressive Complex 1 (PRC1) and PRC2 were recruited to the KSHV genome, resulting in the deposition of repressive histone marks onto the viral chromatin and the accumulation of heterochromatin structures, both of which were critical for the establishment of viral latency. In contrast to the biphasic chromatinization and genome-wide inhibition of lytic genes observed in de novo-infected SLK and TIME cells, KSHV adopts a transcriptionally permissive chromatin form in human gingiva-derived epithelial cells, resulting in prolonged and robust lytic gene expression. Thus, our results suggest that the differential epigenetic modification of the KSHV genome in distinct cell types is a potential determining factor for latent infection versus lytic replication of KSHV.
| Kaposi's sarcoma-associated herpesvirus (KSHV, Human herpesvirus 8 or HHV-8) is one of the seven currently known human tumor viruses and is associated with the pathogenesis of the multifocal, angiogenic and inflammatory cancer called Kaposi's sarcoma (KS) and certain B cell-originated neoplasias, including primary effusion lymphoma (PEL) and multicentric Castleman's disease (MCD) [1], [2]. KSHV results in persistent infection in immunocompetent humans by establishing latency in CD19+ B cells [3]. The establishment of latency is the most fundamental immune evasion strategy of KSHV, as the severely limited viral gene expression characteristic of latently infected cells allows the virus to escape detection by the host immune system. However, immune suppression along with other environmental and physiological factors can trigger the reactivation of KSHV from latency, leading to the temporally ordered expression of viral genes and release of infectious virus [4], [5].
In KSHV-associated tumors, the majority of tumor cells harbor KSHV in the latent phase and virus production is restricted to a small population, indicating that it is the latently infected cells that play a critical role in the development of KSHV-associated cancers [6]. Indeed, the latent proteins of KSHV have several important roles, such as the promotion of malignant transformation by facilitating the proliferation and survival of infected cells, as well as the maintenance of the KSHV genome in dividing cells [7]. During latency, the KSHV genome exists as a circular episome in the nucleus and adopts a nucleosome structure similar to the bulk chromatinized cellular genome [8], [9]. In this latent phase, the latent genes of KSHV are continuously expressed, while the lytic genes are repressed. Since chromatinization limits the access of transcription factors to the promoter regions of viral genes, modification of the viral chromatin plays an essential role in the control of viral gene expression. Based on the different combinations of activating (acetylated H3K9/K14 or acH3 and H3K4me3) and repressive (H3K9me3 and H3K27me3) histone modifications that can be found on the chromatin of the KSHV genome during latency, we have previously shown that the chromatin of the viral episome is organized into distinct domains [10], [11], [12].
One of the major cellular transcription repressors is the Polycomb Repressive Complex 2 (PRC2), which is composed of three core subunits (EZH2, EED and SUZ12) and can interact with other transcription repressors, such as histone deacetylases (HDACs), H3K4me3 demethylases and DNA methyltransferases [13]. EZH2 catalyzes the trimethylation of histone H3K27, which is one of the hallmarks of PRC2 function. We have previously shown that both EZH2 and H3K27me3 are highly enriched on lytic gene-coding regions of the KSHV genome during latency and their association with the viral DNA decreases upon reactivation, indicating that PRC2 plays a critical role in the repression of lytic gene expression [11]. PRC2 often co-operates with another polycomb group protein (PcG) complex called PRC1 in the inhibition of cellular genes involved in cell proliferation, differentiation and development. The enzymatic subunits of PRC1 are the RING1A/B E3 ubiquitin ligases, which mono-ubiquitinate lysine 119 of H2A (H2AK119ub). This modification is thought to play a role in chromatin condensation and transcription repression [14]. It has been shown that the PRC1 can be recruited to its target promoters via the CBX protein, a subunit of PRC1 that binds to H3K27me3, suggesting that PRC1 is recruited to its target loci in a PRC2-dependent manner. However, there are at least six different PRC1 complexes, and the RING1 and YY1 binding protein (RYBP) DNA-binding factor-containing PRC1 can also be recruited to several PcG target sites and repress cellular genes independently of PRC2 [15], [16]. Whether PRC1 is also recruited to the KSHV genome during latency and contributes to the repression of lytic genes has not yet been addressed.
Establishment of latency is an essential step for herpesvirus persistent infection. KSHV can infect a variety of cell types where it establishes latency in the majority of cases, suggesting that lytic gene expression is constantly inhibited and only latent gene expression is permitted [6]. Although the KSHV genome exists in a linear and histone-free form in the viral capsid, upon infection, it becomes a closed circular episome that subsequently associates with cellular histones and persists as a non-integrated minichromosome in the nucleus of infected cells [9], [10]. Interestingly, it has been shown that some lytic genes possessing immunomodulatory or anti-apoptotic functions are temporarily expressed after infection [17]. These observations prompted us to hypothesize that the KSHV genome undergoes a dynamic transition from an active to a repressive chromatin state following de novo infection, which allows transient lytic gene expression prior to the establishment of latency.
To investigate the molecular details of the “pre-latency” phase of KSHV infection, we analyzed the recruitment of chromatin regulatory factors onto the KSHV genome following de novo infection. Based on our results, we propose that the KSHV genome undergoes a biphasic chromatinization after de novo infection. Initially, a transcriptionally active euchromatin, characterized by high levels of H3K4me3 and acetylated H3K27 (H3K27ac), is deposited on the viral episome and is later switched to the PcG protein-regulated heterochromatin. We show that both PRC2 and PRC1 are involved in the inhibition of lytic gene expression following de novo infection. Furthermore, while the KSHV genome undergoes a temporally ordered euchromatin-to-heterochromatin transition in infected endothelial cells, KSHV adopts a transcriptionally active euchromatin form in oral epithelial cells, resulting in lytic gene expression. Thus, we hypothesize that the deposition of differential epigenetic modifications on the KSHV genome in distinct cell types potentially determines whether KSHV infection results in latent or lytic replication.
We investigated how the chromatin of the latent KSHV genome formed on the initially histone-free KSHV genome following de novo infection. We primarily used SLK cells as a model system for the de novo KSHV infection experiments for the following reasons: (i) SLK cells are highly susceptible to KSHV infection leading to the establishment of latency, which is thought to be the default pathway of natural KSHV infection, (ii) SLK cells support lytic replication upon treatment with chemical inducers or the overexpression of the replication and transcription activator protein (RTA) of KSHV, and (iii) the histone modification pattern of KSHV chromatin in latently infected SLK cells significantly resembles that in PEL cells [12], [18]. Furthermore, we used the recombinant KSHV BAC16 throughout the study that constitutively expresses GFP [19]. FACS and immunofluorescence analysis indicated that nearly 100% of SLK cells were GPF-positive at 16–24 hours post-infection (hpi), showing the efficiency of infecting SLK cells with KSHV (data not shown and Figure S1).
In order to investigate the chromatin assembly on the viral genome following de novo infection, we performed FAIRE (Formaldehyde-assisted isolation of regulatory elements) analysis, which technique had been used to identify nucleosome depleted regions in the KSHV genome [20] and histone occupancy measurements on several KSHV promoters in SLK cells at 1, 8 and 24 hpi (Figure 1). Latently infected SLK cells that were maintained for more than 6 months after initial KSHV infection were used as a reference point to represent fully chromatinized viral episomes. The selected viral promoters represent the gene regulatory regions of the four kinetic classes of KSHV genes: latency (LANA), immediate early (IE, RTA), early (E, K2) and late (L, ORF25). The promoters of actin (ACT) and myelin transcription factor 1 (MYT1) served as cellular controls. The FAIRE assay is used for the separation of chromatin-free DNA fragments from the chromatin-associated ones based on their differential retention in the aqueous phase during phenol-chloroform extraction. Chromatin-free DNA fragments in the aqueous phase are subsequently purified and measured by real-time quantitative PCR analysis. Figure 1A shows the relative amounts of chromatin-free viral and cellular promoter DNA fragments purified from infected cells at different time-points. These results revealed that a significant proportion of the viral promoters were initially chromatin-free, but they rapidly underwent chromatinization and the degree of their chromatin association ultimately became similar to that of the cellular genes (Figure 1). Accordingly, the H3 and H2A histone occupancy of the KSHV genome gradually increased shortly after de novo infection, indicating the assembly of the KSHV genome into a nucleosome structure, which ultimately resulted in comparable levels of histone H3 and even higher enrichment of H2A on the viral genome relative to the cellular genome (Figures 1B and C). Furthermore, in accordance with previous findings [17], we detected transient expression of lytic genes upon de novo infection and found higher levels of lytic gene expression at 24 hpi compared to latently infected cells (Figures 1D and S1). These data indicate that the KSHV genome undergoes rapid chromatinization following infection, suggesting that the initial burst of lytic gene transcription likely originates from transcriptionally permissive chromatin rather than naked DNA.
In order to investigate whether the deposition of activating and repressive histone marks on the KSHV genome occurs in a spatially and temporally regulated manner following de novo infection, we performed the ChIP assay on the KSHV genome at 1, 4, 8, 16, 24 and 72 hpi in SLK cells (Figure 2). The chromatin of the latent KSHV genome was used as a reference, as previously described [12]. In addition, the histone modification ChIPs were normalized for the total amount of relevant histone at a given genomic region. The transcriptionally active promoter of cellular actin (ACT) gene and the transcriptionally silenced promoter of cellular Polycomb-targeted MYT1 gene were used as controls to show the comparable efficacy of histone mark ChIPs at each time point throughout the experiments. This time course ChIP assay revealed a temporally ordered deposition of activating and repressive histone modifications on the KSHV genome following de novo infection. Specifically, the activating H3K4me3 histone mark gradually increased on the latent (LANA), IE (RTA) and E (K2) promoters, peaking at 24 hpi and then declining by 72 hpi, while the level of H3K4me3 on the L (ORF25) promoter was considerably lower compared to those of other promoters at all of the time points analyzed (Figure 2A). The H3K27 can be either acetylated (H3K27ac), characteristic for transcriptionally active genes, or mono- (H3K27me1), di- or trimethylated, characteristic for transcriptionally inactive genes. H3K27ac was detected on the latent, IE and E promoters as early as 1 hpi, declined by 72 hpi and remained low on the IE and E promoters during latency (Figure 2B). H3K27me1 significantly increased on the lytic promoters, peaked at 8 hpi, and declined thereafter, while H3K27me3 started to increase on lytic promoters at 24 hpi, and reached a level comparable to that seen on the latent genome by 72 hpi (Figure 2C and D). As shown with repressed cellular genes where the PRC2-mediated H3K27me3 often coexists with the PRC1-mediated H2AK119ub histone modification, the H2AK119ub was also enriched on the lytic promoters in conjunction with H3K27me3 (Figure 2E). The levels of these histone modifications on the cellular promoters (ACT and MYT1) remained similar in the course of de novo infection, suggesting that the histone modification changes specifically occur on the viral genome (Figure 2A–E). In addition, we showed that the temporally ordered deposition of activating and repressive histone marks on the KSHV genome was not restricted to the infected SLK cells, as it was also detected in de novo-infected TIME cells (Figure S2).
To determine whether the temporally ordered deposition of histone marks occurs on the same KSHV episome, sequential ChIP assays were applied. In the first set of experiments, H3K27ac ChIP was performed initially at 1 hpi and 8 hpi, followed by the elution of the immunoprecipitated chromatin for use in a second ChIP with anti-H3K27me1 antibody (Figure 2F). ChIP DNAs were quantified by qPCR using specific primers for the promoters of RTA and LANA genes. This showed that H3K27me1 coexisted with H3K27ac on the same viral genome at 8 hpi. In the second set of sequential ChIPs, H3K4me3 ChIP was performed at 24 and 72 hpi, followed by second ChIPs with anti-H3K27me3 antibody (Figure 2G). These results showed that the RTA promoter carried a high level of H3K4me3 and a low level of H3K27me3 at 24 hpi, whereas a large amount of H3K27me3 was deposited onto the RTA promoter associated with a low level of H3K4me3 at 72 hpi (Figure 2G). In contrast, the H3K4me3-enriched LANA promoter remained relatively H3K27me3-free (Figure 2G). This finding is in accordance with the previous findings that the RTA promoter possesses a bivalent chromatin, evidenced by the presence of both activating and repressive histone modifications on this promoter during latency [11], [12]. Taken together, our results show that KSHV undergoes different chromatin states upon de novo infection before it adopts the H3K27me3/H2AK119ub-enriched heterochromatin characteristic of latency. Specifically, the viral genome has a transcriptionally permissive chromatin immediately after infection, which is then switched to transcriptionally repressive chromatin. In addition, the switch from active to repressive chromatin is concurrent with the inhibition of lytic gene expression.
In order to obtain a genome-wide and comprehensive view of the changes in viral chromatin that occur following de novo infection, a series of ChIP-on-chip experiments was performed. We mapped the genome-wide deposition of the activating histone marks, H3K27ac and H3K4m3, and the repressive histone mark, H3K27me3, on the KSHV genome in SLK cells at 4, 24 and 72 hpi (Figure 3A). For this, we used our KSHV-specific 15-bp tiling microarray, which contains 60 nucleotide-long oligos covering the entire KSHV genome and enables high resolution mapping of histone marks on the viral genome [11]. Based on the genome-wide ChIP data, we generated a heat map that offered a close-up view of the chromatin structure of the regulatory regions of KSHV genes and quantified the changes that occurred in these regions following de novo infection (Figure 3B). For this, we plotted the signal intensities of probes derived from the ChIP-on-chip analysis at 1 kb upstream and 1 kb downstream of the translational start site (TSS), as previously described [11]. The rationale of this strategy is based on the considerations that (i) due to the compact structure of the KSHV genome, the promoters are generally closely localized upstream of the TSS and (ii) the distinct modification of histones in the 5′ region of the gene bodies usually plays a role in the regulation of gene expression.
ChIP-on-chip experiments revealed highly dynamic and global changes in the posttranslational modifications of the viral chromatin following de novo infection (Figure 3). In agreement with our initial gene-specific ChIP experiments, we found that while activating histone marks were detected on the viral chromatin as early as 4 hpi, the repressive H3K27me3 histone mark was completely absent at an initial stage. Furthermore, our analyses revealed that the genome-wide enrichment of the activating histone marks H3K27ac and H3K4me3 highly correlated with each other on the KSHV genome throughout infection (Table S2 and Figure 3). The high Pearson correlation of the enrichment of H3K27me3 on the KSHV genome between 24 hpi and 72 hpi indicated that EZH2 was targeted to those genomic regions as early as 24 hpi, where the H3K27me3-rich chromatin domain would ultimately be established at 72 hpi (Table S2). In addition, our analysis indicated that the histone modification changes on the viral genome were highly gene specific: (i) The latency locus, which encodes constitutively expressing genes, such as ORF73/LANA, adopted activating histone modifications H3K27ac and H3K4m3 during de novo infection. (ii) While the activating histone marks were initially enriched at a few IE (K4.2, ORF48 and ORF50/RTA and E lytic genes (e.g. K2, K3, K4, K5, K6 and vIRF1) at 4 hpi, this enrichment expanded to nearly all lytic genes at 24 hpi, and subsequently declined concomitantly with the increased enrichment of the repressive histone mark H3K27me3 occurring between 24 and 72 hpi. (iii) Interestingly, the gene regulatory regions of IE and E genes (e.g. K5, K6, K7, ORF74 and vIRF1) were largely devoid of H3K27me3 at 24 hpi, where both H3K4me3 and H3K27ac were highly enriched. (iv) While the enrichment of H3K27ac was dramatically reduced and restricted to a few genes (e.g. ORF73, K4.2, K4, K5, K6 and vIRF1) by 72 hpi, a high level of H3K4me3 remains at several genomic regions (e.g. 15–30 kb, 70–90 kb) mainly encoding IE and E lytic genes. (v) Genomic regions encoding a large number of late genes (e.g. 30–60 kb and 95–115 kb) showed low levels of activating histone modifications at 72 hpi and were enriched with the repressive histone mark, H3K27me3. These results indicate that the majority of the KSHV genome undergoes a biphasic euchromatin-to-heterochromatin transition after de novo infection.
Previous studies have shown that a subset of lytic genes, including RTA, is temporarily expressed following de novo infection [17]. To address whether the transient expression of RTA plays a role in the regulation of chromatinization of the KSHV genome during de novo infection, we infected SLK cells with either wild type (wt) or RTA knockout (RTAstop) KSHV [21] and performed ChIP assays for H3K4me3, H3K27ac and H3K27me3 at 8, 24 and 72 hpi. Interestingly, the levels of both H3K4me3 and H3K27ac were significantly lower on several lytic promoters at 8 hpi in RTAstop virus-infected cells relative to WT virus-infected cells, while the deposition of H3K27me3 was similar in both cells (Figure 4A). In contrast, the levels of these histone modifications were comparable on the LANA promoter and the cellular ACT and MYT1 promoters in WT virus- vs. RTAstop virus-infected cells (Figure 4A). Consequently, the transient induction of lytic genes (K2, K6, K7, ORF46 and vIRF2) was lower in RTAstop virus-infected cells compared to WT virus-infected cells (Figure 4B). Since RTA has been shown to bind to its responsive viral promoters (e.g. RTA and K2) and recruits CBP, the histone acetyltransferase of H3K27ac [22], we also performed ChIP assays for RTA and CBP and found that both RTA and CBP bound to the RTA and K2 promoters following de novo infection, but not during latency, whereas their binding was not observed on the ORF25 promoter, which lacks RTA responsive elements (Figure 4C). Furthermore, while CBP-binding to the RTA and K2 promoters was abolished in RTAstop KSHV-infected cells, it was still detected on the LANA promoter possessing the H3K27ac mark (Figure 4D). These data indicate that CBP can be recruited to the KSHV genome by two distinct mechanisms. The KSHV RTA recruits CBP to its responsive lytic promoters following de novo infection and thereby facilitates the deposition of activating histone marks on the KSHV genome. Alternatively, CBP can be recruited to the KSHV genome independently of RTA.
While KSHV RTA is sufficient to induce robust expression of lytic genes and completion of a full cycle of lytic replication [11], [23], its temporal expression during de novo infection leads only to the transient expressions of a few lytic genes prior to the establishment of latency [17]. Therefore, we asked whether the continuous expression of RTA during de novo infection affected the chromatinization of the viral genome. Doxycycline (Dox)-inducible RTA-expressing iSLK cells [18] were pre-treated with Dox for 8 hours, followed by KSHV infection for 24 and 72 hours. Indeed, continuous RTA expression during de novo infection not only led to the induction of lytic gene expressions and full-scale viral replication (Figure S3A, B and C), but also reduced chromatinization of the replicating KSHV genome, evidenced by the reduction of histones H3 and H2A occupancy on the KSHV promoters (Figure S3D). These data indicate that, unlike overexpression of RTA, the transient expression of RTA during de novo infection may not be sufficient to induce the full lytic gene expression program.
The deposition of both repressive histone modifications, H3K27me3 and H2AK119ub, on the KSHV genome during de novo infection indicates that both PRC2 and PRC1 are recruited onto the viral episome. To test the binding of the PRC complexes on the viral genome, we performed ChIPs for the PRC2 subunit, EZH2, and the PRC1 subunits, RING1B and RYBP, in infected SLK cells at 4, 24, 72 hpi and again used latently-infected SLK cells as a reference. The results showed that each PcG subunit was readily detected on the promoters of the lytic genes (RTA, K2 and ORF25) at 72 hpi and during latency, while they were not recruited to the LANA promoter at any of the time points (Figure 5A). In order to demonstrate the genome-wide binding of the PRC2 and PRC1 complexes, we performed ChIP-on-chip for the EZH2 and RING1B PRC subunits in SLK cells at 4 and 72 hpi (Figures 5B and S4). This showed that EZH2 and RING1B were barely detected on the KSHV genome at 4 hpi, while they were highly enriched on the viral genome with an extensive co-occupancy at 72 hpi (Pearson correlation 0.6). To further address whether they contributed to the inhibition of lytic replication following de novo infection, we measured lytic gene expressions upon the shRNA-mediated depletion of either EZH2 or RING1B in SLK cells at 72 hpi. Immunoblot analysis indicated that the gene-specific shRNA treatments robustly reduced endogenous EZH2 and RING1B levels (Figure 6A). RT-PCR analysis showed that shRNA-mediated depletion of EZH2 or RING1B resulted in the induction of viral gene expression similarly to that of the PRC cellular target gene, MYT1 (Figure 6B).
Because a functional PRC2 complex has been shown to be required for the recruitment of PRC1 complex to the majority of PcG target genes, we tested whether the recruitment of PRC1 to the KSHV genome also depended on the PRC2 function. shEZH2-treated SLK cells were infected with KSHV and subjected to ChIP assays at 72 hpi (Figure 6C). shRNA-mediated depletion of the EZH2 led to the significant decline of H3K27me3 on lytic promoters, which resulted in the reduction of RING1B recruitment and H2AK119ub deposition (Figure 6C). In contrast, the levels of activating histone marks, H3K27ac and H3K4me3, were increased upon these conditions (Figure 6C). Furthermore, treatment by GSK343, a novel and specific EZH2 inhibitor [24], efficiently reduced the H3K27me3 levels without affecting the expression of EZH2 and also increased the H3K27ac levels (Figure 6D). ChIP assays also showed that GSK343 treatment resulted in the reduction of EZH2 recruitment and H3K27me3 levels on KSHV lytic promoter regions, which was accompanied by the decrease of RING1B recruitment and the increase of the activating histone mark H3K27ac (Figure 6E). Consequently, the GSK343-mediated inhibition of EZH2 increased the expression of KSHV lytic genes following de novo infection of various cell types (Figures 6F and S5A). Finally, we examined the effect of GSK343 treatment on de novo infection of 293T cells with rKSHV.219, a recombinant virus that expresses the green fluorescent protein (GFP) from the cellular EF-1α promoter and the red fluorescent protein (RFP) during lytic replication from the viral early PAN promoter [25]. This showed that GSK343 treatment resulted in significant increase of RFP expression compared to control cells (Figure S5B and C). These data collectively show that both PRC2 and PRC1 complexes bind to the KSHV genome and mediate the inhibition of lytic gene expression following de novo infection.
While KSHV results in latent infection in most cell types, oral epithelial cells have been reported to support lytic replication following de novo infection [26]. To test whether the KSHV genome undergoes a distinct chromatinization in oral epithelial cells compared to other cell types, we used three different oral epithelial cells for KSHV infection: OEPI E6/E7-immortalized human gingiva-derived epithelial cells, SCC15 human tongue squamous carcinoma cells and primary normal oral keratinocytes (NOK) cells. The susceptibility of these oral epithelial cell lines to KSHV infection was comparable with that of SLK cells based on GFP-positivity, analyzed using immunofluorescence and FACS analyses (Figure S6A and data not shown). When SLK, OEPI, SCC15 and NOK cells were infected with KSHV for 4, 24, 48 and 72 hours and measured for the viral DNA loads at each time point, KSHV replication was only detected in OEPI-infected cells and this increase in viral DNA load was sensitive to inhibition by the viral DNA polymerase inhibitor phosphonoacetic acid (PAA) (Figure 7A). Accordingly, the FAIRE assay showed that while KSHV was initially chromatinized in infected OEPI cells at 8 and 24 hpi, the viral DNA became chromatin-depleted during replication (Figure 7B). The induction of KSHV gene expression in infected OEPI cells was confirmed by immunoblotting for the lytic KSHV proteins, RTA and K3 (Figure 7C), by quantitative RT-PCR for several other lytic genes (Figure 7D), and by immunostaining for K3 expression (Figure S6). ChIP analysis of infected OEPI cells revealed increasing euchromatinization (H3K4me3 and H3K27ac) on the representative latent (LANA), IE (RTA), E (K2) and L (ORF25) promoter regions, but a lack of efficient deposition of heterochromatin histone marks (H3K27me3 and H2AK119ub) on these regions (Figures 7E and F). While OEPI, SCC15 and NOK cells were efficiently infected by KSHV (Figure S6A), the expression of Polycomb proteins was lower in OEPI cells than in NOK and SCC15 cells (Figure 7G), and the higher level of KSHV replication was detected only in OEPI cells. This indicates that the weak expression of PcG proteins correlates with the high level of KSHV replication in OEPI cells. This suggests that the weak expression of the EZH2, SUZ12 and RING1B PRC subunits may result in inadequate deposition of H3K27me3 and H2AK119ub on the lytic promoters of infected OEPI cells, resulting in the activation of lytic gene expression. It should be noted that despite the viral DNA replication in OEPI cells following de novo infection, we did not detect infectious virus particles, suggesting that additional factors downstream of viral DNA replication may prevent the production of infectious virions.
Taken together, these data suggest that the differential epigenetic modification of the KSHV genome in distinct cell types may determine whether KSHV establishes latent or lytic gene expression program following de novo infection.
While the KSHV genome is histone-free in the virions, the viral DNA adopts a highly organized chromatin structure in latently infected cells, which is an essential step in establishing the latency-associated viral gene expression program necessary for persistent infection of the host [10]. A previous study by Gunther and Grundhoff has shown that at 5 days after infection of SLK cells, KSHV adopts a highly structured chromatinized episome that resembles the viral chromatin structure found in latently infected B cell lymphoma cells [12]. Here, we demonstrate that the latent chromatin structure of KSHV gradually develops following de novo infection and is fully established as early as 3 days post-infection. Based on our results, we propose that the KSHV genome undergoes a spatially and temporally ordered chromatinization following de novo infection prior to the establishment of latency (Figure 8). We demonstrated that the viral DNA rapidly associates with histones after infection and that initially, there is a transient enrichment of H3K4me3 and H3K27ac activating histone marks on the viral chromatin and concomitant expression of lytic genes. This is followed by the decline of activating histone marks and the transition from a transcriptionally active viral chromatin to a H3K27me3/H2AK119ub-enriched heterochromatin, a transition that is regulated by the PRC2 and PRC1 cellular transcription repressor complexes and ultimately results in the inhibition of lytic gene expression and the establishment of latency (Figure 8). Thus, our results indicate that the KSHV genome undergoes a biphasic chromatinization after de novo infection prior to the establishment of latency.
Our previous study has shown that the distinct chromatin domains of the latent KSHV genome are characterized by different combinations of histone modifications [10], [11], [12]. The results of this study show that the KSHV genome undergoes a specific biphasic euchromatin-heterochromatin transition upon de novo infection. Both studies indicate that activating and repressive histone marks are enriched only on specific viral genomic regions in the early stage of infection and during latency. For example, the deposition of H3K27me3 is always excluded from the latency-associated locus but enriched on lytic genes-encoding regions throughout both de novo infection and latency (Figures 2 and 3). Also, the deposition of H3K4me3 occurs mainly on latent genes and some IE/E genes at 4 hpi and during latency. These findings indicate targeted recruitment of specific histone-modifying enzyme complexes to different sites of the KSHV genome from the beginning of infection, a process that is likely orchestrated by sequence-specific DNA-binding factors. These factors might be, for example, the CTCF/cohesin complex, which is involved in the regulation of nuclear organization of the KSHV chromosome or RBP-Jκ, which controls viral transcription by binding to many KSHV promoters [27], [28]. Additional studies will be required to demonstrate how the positions of these chromatin domains are specifically determined on the KSHV genome and what types of DNA-binding factors are involved in the recruitment of chromatin regulatory complexes onto the KSHV genome.
Our data also show that the deposition of activating and repressive histone marks on the KSHV genome occurs in a temporally ordered manner following de novo infection. One of the striking examples of this is the differential modification of H3K27 (Figure 2). In mammalian cells, H3K27 can either be acetylated by the histone acetyltransferases CBP/p300 or mono-, di-, or trimethylated by EZH2 [29]. By using sequential ChIP assays, we found that the acetylation of H3K27 is followed by its gradual switch to methylation on the same KSHV genome, indicating the sequential action of different histone modifying enzymes on the same viral genome following de novo infection. We also observed that the decrease of H3K27ac on lytic promoters resulted in increasing levels of H3K27me3 in a coordinated manner, further supporting that the histone modifying enzymes are recruited to the KSHV genome in a temporally ordered manner following de novo infection.
Because the KSHV genome is initially chromatin-free following infection, the viral promoters are easily accessible by the RNAPII transcription machinery to induce viral transcription, which might explain the transient expression of lytic genes during de novo infection (Figure 1) [17]. RNAPII has been shown to interact with H3K4me3 histone methyltransferases and the H3K27me3 demethylase JMJD3 [30], [31], [32], [33]. Therefore, these histone modifying enzymes might be recruited onto lytic promoters by RNAPII and thereby contribute to the deposition of histone modifications on the viral chromatin during de novo infection. In addition, viral proteins expressed during de novo infection can also be involved in the modulation of the evolving viral chromatin structure. Indeed, we found that the transient expression of the lytic protein RTA had a significant effect on the deposition of activating histone modifications on lytic promoters. In the absence of RTA, the level of both H3K4me3 and H3K27ac was significantly lower on the KSHV genome after infection and this was accompanied by the reduced expression of several lytic genes (Figure 4). RTA binding to lytic promoters also recruited CBP, the histone acetyltransferase of H3K27ac to lytic promoters during de novo infection. These data suggest that the RNAPII transcription machinery and the viral transcription factor RTA may promote the deposition of activating histone marks on the KSHV genome during de novo infection, contributing to the temporal induction of lytic genes.
RTA is a potent viral transcription factor, which is sufficient for the induction of the lytic gene expression program in infected cells [34]. However, upon de novo infection, KSHV usually does not undergo lytic replication, even though RTA is expressed. This might be due to the weak endogenous promoter of RTA, which seems to be prone to repression and thus, it is unable to sustain its continuous expression after infection. Indeed, heterochromatin-associated cellular factors such as the PRC2 complex, HDACs and transcription repressor KAP-1 have been shown to be recruited onto the RTA promoter, leading to the silencing of the promoter [8], [11], [35]. On the other hand, KSHV infection of RTA-expressing cells (Figure S3) or infection of cells with a recombinant KSHV constitutively expressing RTA [36] resulted in efficient lytic replication. Thus, these data indicate that the temporal expression of RTA during de novo infection may not be sufficient to induce the full cycle of KSHV lytic replication, thereby leading to the establishment of latency.
We have previously shown that PRC2 is involved in the repression of lytic genes during latency [11], [12]. Here, we have found that the PRC1 complex is also recruited onto the KSHV genome and both PRCs are involved in the repression of lytic genes following de novo infection (Figures 5 and 6). Furthermore, while PRC1 was preferentially recruited onto the lytic gene promoters via H3K27me3 pre-deposited by PRC2, PRC1 may also bind to specific lytic gene regions independently of PRC2 via its RYBP DNA-binding factor [15]. We found that, during de novo infection, the binding of RYBP on the viral genome precedes that of EZH2, indicating a sequential recruitment of PcG proteins, where RYBP may recruit PRC1 independently of PRC2 at specific lytic promoters (Figure 5). These PRCs have been shown to be recruited to their cellular target loci by various non-coding RNAs and distinct transcription factors [13]. Thus, it would be intriguing to determine what host or viral factors are responsible for the recruitment of PRCs on the KSHV episome during de novo infection and latency.
Surprisingly, while KSHV infection results in latency in most cell types, we found that in infected human OEPI cells, the KSHV genome acquires H3K4me3/H3K27ac-enriched chromatin, which is accompanied by lytic replication (Figure 7). Interestingly, the expression of both EZH2 and RING1B was lower in OEPI cells compared to SLK and NOK cells, likely contributing to the reduced level of H3K27me3 and H2AK119ub on the KSHV genome in OEPI cells. This suggests that the levels of chromatin regulatory factors that KSHV encounters during infection of host cells may influence the outcome of viral infection. However, overexpression of EZH2 alone in OEPI cells was not sufficient to force KSHV to establish latency (unpublished results). It is possible that all or most of the components of PRC complexes must be overexpressed in a specific ratio or additional repressors, other than PRC complexes, are required for the establishment of KSHV latency.
Establishment of latency is a common feature of all herpesviruses and is characterized by the silencing of lytic genes and the inhibition of viral replication [37]. Emerging evidence shows that Polycomb proteins are involved in the repression of some lytic genes of Herpes simplex virus type 1 (HSV-1) [38], [39], Human cytomegalovirus (HCMV) [40] and Epstein-Barr virus [41], [42]. These observations indicate that PRCs may function as part of a common intrinsic immunity defense system against all herpesviruses, wherein they act as inhibitors of viral replication and gene expression. The incoming viral genomes of HSV-1 and HCMV have also been shown to rapidly associate with histones following de novo infection [43], [44]. As with KSHV infection of oral epithelial cells, the viral chromatin of HSV-1 and HCMV is enriched in activating histone marks, facilitating lytic replication [45]. However, the regulation of chromatinization of the HSV-1 and HCMV genomes during the establishment of latent infection has yet to be studied.
In summary, our results are the first comprehensive study showing that, following de novo infection of different cell types, the KSHV genome undergoes a well-orchestrated transition between distinct chromatin states and that this process is regulated by site-specific recruitment of chromatin regulatory factors onto the KSHV genome. Because of the compact size of its genome and the presence of several unique viral DNA sequences, KSHV may use a specific set of transcription and chromatin regulatory factors to regulate its viral chromatin structure. Therefore, the identification of the relevant cellular and viral proteins involved in the regulation of chromatin structure of KSHV can lead to the discovery of new therapeutic targets for controlling KSHV infection and pathogenesis.
293T and SLK cells were maintained in DMEM medium supplemented with 10% FBS, 100 U/ml penicillin and 100 µg/ml streptomycin (P/S). TIME cells were cultured in VasculoLife VEGF medium according to the manufacturer's specifications (Lifeline Cell Technology). The generation of the iSLK cell line is described elsewhere [18]. iSLK cells were cultured in DMEM medium supplemented with 10% FBS, P/S, 1 µg/ml puromycin and 250 µg/ml G418. The iSLK cell lines carrying BAC16 or BAC16RTAstop were maintained in the presence of 1 mg/ml hygromycin. The construction of BAC16RTAstop virus was described previously [21]. NOK (gift from Yang Chai, USC), SCC15 (ATCC) and gingival OEPI epithelial cell lines were cultured in Keratinocyte-SFM medium (Gibco) according to the manufacturer's instructions. The primary gingival epithelial cell line was a generous gift from Jennifer Webster-Cyriaque (UNC, Chapel Hill), which was immortalized by the papillomavirus E6/E7 to make OEPI. KSHV was prepared from iSLKBAC16 and iSLKBAC16RTAstop cell cultures by treatment with 1 µg/ml doxycycline and 3 mM sodium butyrate and the viral titer was calculated as described previously [21]. De novo infection was performed by spin-infection using a MOI of 1 (2000 rpm, 45 min at 30°C). After infection the media was changed and the infected cells were harvested at the indicated time points.
The following antibodies were used in ChIPs and/or immunoblots: rabbit anti-histone H3 (Abcam ab1791), rabbit anti-H3K27me1 (Millipore 07-448), rabbit anti-H3K27me3 (Millipore 07-448, Active Motif 39155 and Cell Signaling 9756), rabbit anti-H3K4me3 (Millipore 04-745 and Active Motif 39159), rabbit anti-H2A (Millipore 07-146), rabbit anti-H2AK119ub (Cell Signaling 8240), rabbit anti-H3K27ac (Abcam ab4729), mouse anti-BMI1 (Millipore 05-1322), rabbit anti-CBP (sc-369), rabbit anti-RING1B (Abcam ab101273), mouse anti-EZH2 (BD Biosciences 612666), rabbit anti-SUZ12 (Abcam ab12073), rabbit anti-RYBP (Abcam ab5976), rabbit anti-Spt5 (sc-28678) and mouse anti-actin (Abcam). The rabbit anti-RTA antibody was a generous gift from Drs. Yoshihiro Izumiya and Hsing-Jien Kung (UC Davis, Sacramento). LANA and K3 KSHV protein-specific antibodies have been described previously [11]. GSK343 was obtained from Structural Genomics Consortium (Toronto, Canada). GSK343 was used at 50 µg/ml in cell culture.
ChIPs and ChIP-on-chips were performed as have been published previously, with a few modifications [11]. The primer sequences used in ChIP-qPCR are listed in Table S1. ChIP graphs show the average of at least two independent experiments. For ChIP-on-chip, 10 µg of chromatin and 2 µg of antibodies were used for each reaction. The ChIP-on-chips were performed with a custom-designed Agilent tiling microarray, as described previously [11]. Briefly, amplified ChIP DNA and input DNA samples (2 ug) were submitted to the UC Davis Comprehensive Cancer Center Genomics Shared Resource for target labeling (Cy3, Cy5), array processing, and microarray scanning. Raw image files were processed with Agilent Feature Extraction software (version 10.5.1.1) to quantify feature signal intensities and to perform normalization, dye bias correction, and background subtraction. The raw data was pre-processed by blank subtraction (one-step Tukey biweight subtraction) and intra-array (dye-bias) median normalization in order to equalize the ChIP (Cy5) and input (Cy3) DNA channels. Binding events, or enrichments, were represented as increases in the ratios of the ChIP to input DNA signal intensities. The ChIP-on-chip data is freely available at the GEO repository (GSE51660). Data analysis of the 1 kb genomic region surrounding the TSS of each viral gene was performed as described in details previously [11]. The ChIP-on-chip data was imported into Java TreeView (version 1.1.6r.4) for visualization.
Formaldehyde-assisted isolation of regulatory elements (FAIRE) analysis was performed as described by Giresi et al with some modifications [46]. DNA was purified from an equal amount of formaldehyde-crosslinked and de-crosslinked chromatins, followed by the quantification of DNAs via qPCR using viral or cellular promoter specific primers. DNA purified from the formaldehyde-crosslinked chromatin represents chromatin-free DNA, while DNA derived from de-crosslinked chromatins is for the calculation of total DNA. The ratio of chromatin-free and total DNAs shows the degree of chromatinization of a given genomic DNA fragment in cells.
Total RNA was extracted from cells using Tri reagent (Sigma) according to the manufacturer's instructions. 1 µg of total RNA was treated with DNase I (Sigma), reverse transcribed by iScript cDNA Synthesis kit (Bio-Rad) and the cDNA was quantified by qPCR using gene specific primers. The primer sequences are listed in Table S1. The relative level of gene expression was calculated by the 2−dCt or the ddCt method, where either actin or 18S was used for normalization. The RT-qPCR graphs represent the average of at least two independent experiments.
The pLKO.1 lentiviral vector was used to express the EZH2 and RING1B specific shRNAs. Supernatants from 293T cells transfected by the shRNA and packaging vectors were collected 60 hours post-transfection, followed by concentration of the virus (24000 rpm, 1.5 hr, 4°C) and used for infection of cells in the presence of 10 µg/ml polybrene. 2 days after lenti-shRNA infection, the cells were split, and then infected with KSHV the following day.
Cells were fixed with 4% paraformaldehyde and then permeabilized by 0.5% Triton X100. 10% FBS was used for blocking nonspecific antibody binding, followed by incubation of cells with antibodies against the KSHV protein K3. After extensive washing with PBS, TRITC-conjugated secondary antibody was applied, followed by Hoechst staining.
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10.1371/journal.ppat.1003412 | Efficient Sensing of Infected Cells in Absence of Virus Particles by Blasmacytoid Dendritic Cells Is Blocked by the Viral Ribonuclease Erns | Plasmacytoid dendritic cells (pDC) have been shown to efficiently sense HCV- or HIV-infected cells, using a virion-free pathway. Here, we demonstrate for classical swine fever virus, a member of the Flaviviridae, that this process is much more efficient in terms of interferon-alpha induction when compared to direct stimulation by virus particles. By employment of virus replicon particles or infectious RNA which can replicate but not form de novo virions, we exclude a transfer of virus from the donor cell to the pDC. pDC activation by infected cells was mediated by a contact-dependent RNA transfer to pDC, which was sensitive to a TLR7 inhibitor. This was inhibited by drugs affecting the cytoskeleton and membrane cholesterol. We further demonstrate that a unique viral protein with ribonuclease activity, the viral Erns protein of pestiviruses, efficiently prevented this process. This required intact ribonuclease function in intracellular compartments. We propose that this pathway of activation could be of particular importance for viruses which tend to be mostly cell-associated, cause persistent infection, and are non-cytopathogenic.
| Plasmacytoid dendritic cells (pDC) represent the most potent producers of interferon type I and are therefore of major importance in antiviral defences. A TLR7-dependent induction of interferon-α in pDC by infected cells in the absence of virions has been demonstrated for hepatitis C virus. Here, we show that this pathway is also very efficient for classical swine fever virus, a pestivirus that is also a member of the Flaviviridae. Our data indicate a transfer of RNA from the donor cell to pDC in a cell-contact-dependent manner requiring intact lipid rafts and cytoskeleton of the donor cell. Importantly, we demonstrate that the enigmatic viral Erns protein unique to pestiviruses efficiently prevents this pathway of pDC activation. This novel function of Erns is dependent on its RNase activity within intracellular compartments. The present study underlines the importance of pDC activation by infected cells and identifies a novel pathway of virus escaping the interferon system. Considering that Erns is required for pestiviruses to establish persistent infection of foetuses after transplacental virus transmission resulting in the development of immunotolerant animals, this report also points on a possible role of pDC in preventing immunotolerance after viral infection of foetuses.
| Although representing a rare cell type of the immune system, plasmacytoid dendritic cells (pDC) are the most important source of systemic interferon (IFN) type I in the early phase of many virus infections, and as such a critical early alarm system against viruses [1], [2]. This is based on the ability to produce around 1000 times more IFN type I than any other cell type [1]. Accordingly, pDC possess the necessary cell biological features such as Toll-like receptor (TLR)7 and TLR9 and constitutive high levels of IFN regulator factor (IRF)-7 to sense viruses with high efficiency [2]. While it is evident from the literature that TLR7 is the most important sensor of RNA viruses and TLR9 for DNA viruses, it is less clear how the viral nucleic acids have access to these compartments and how the encapsulated viral nucleic acids get in contact with the TLR's. Recently, a novel process of pDC stimulation by infected cells independent of viral particles and their uptake by pDC has been described for hepatitis C virus (HCV) in which pDC sense infected cells in a cell contact- and TLR7-dependent manner [3], [4]. This process has been described to be more effective than stimulation of pDC with cell-free virions. Stimulation of pDC by infected cells has also been reported for HIV [5]. However, this process was blocked by neutralizing anti-envelope antibodies [5] implying a different mechanism of RNA transfer to the TLR7 compartment as observed with HCV where stimulation is not blocked by virus neutralization [3]. For the latter virus, lipid rafts and tetraspanin-enriched membrane microdomains have been described to be involved in infected cell sensing by pDC [4].
Considering the potential importance of this mechanism of pDC activation, we initiated this study with a focus on another member of the Flaviviridae, classical swine fever virus (CSFV) belonging to the pestivirus genus. CSFV is the causative agent of a viral hemorrhagic fever in pigs with disease characteristics resembling dengue hemorrhagic fever if pigs are infected with highly virulent CSFV strains. However, with low virulent strains chronic disease and persistent infections are observed [6]. CSFV, in contrast to HCV, has a particular tropism for cells of the macrophage and DC lineage and most efficiently infects pDC [7]–[9].
Our results demonstrate that the basic characteristics of pDC stimulation by infected cells resemble those of HCV. In addition, using such cultures the present study identified a striking function of the enigmatic viral Erns, an essential structural protein with ribonuclease (RNase) activity [10]. The pestivirus genus is composed of major veterinary pathogens, the most important being CSFV and bovine viral diarrhea virus (BVDV). Interestingly, Erns is unique to pestiviruses. Despite its function as structural protein, Erns exists as soluble form secreted from infected cells and has been proposed to be involved in immune evasion of pestiviruses (for review, see [11]). Removal of the RNase activity was demonstrated to result in virus attenuation [12], [13] and abrogation of the capacity of pestiviruses to establish immune-tolerance and persistent infections after infection of fetuses [14], [15]. However, it has been difficult to understand the mechanism of immune evasion using in vitro studies. Recombinant Erns degrades synthetic single-stranded and double-stranded RNA added to the cultures [16]–[18] but pestiviruses with or without RNase activity do not induce IFN type I in cell culture and replicate to the same titers as their wild type counterpart. In this study we have identified how Erns potently counteracts IFN-α induction in pDC. It represents the first example of a viral protein that prevents the stimulation of pDC by infected cells, and thus represents a novel pathway of viral evasion of the type I IFN system. Furthermore, it underlines the importance of stimulation of pDC by infected cells, rather than virions.
In accordance to previous studies [7], [8], CSFV as well as virus replicon particles (VRP) lacking the Erns gene (VRPΔErns) were poor stimulators of pDC , inducing between 0 and 550 IFN-α units per ml, dependent on the experiment. Interestingly, stimulation of pDC by co-culture with CSFV-infected or VRPΔErns -infected SK-6 cells induced up to 100-fold more IFN-α compared with direct infection of the pDC, with an optimum at 40'000 to 80'000 infected SK-6 cells per 2×105 CD172a+ enriched pDC (Figure 1A and B). While no significant difference between direct CSFV and VRPΔErns stimulation was observed, CSFV-infected SK-6 cells stimulated an average of 5.1 more IFN-α when compared to direct stimulation by CSFV (Figure 1C and D). This difference was even more evident when direct simulation with VRPΔErns was compared to stimulation by VRPΔErns-infected cells (Figure 1E). Interestingly, VRPΔErns-infected SK6 cells were in average around 8 times more stimulatory than CSFV-infected SK6 cells (Figure 1F).
In accordance to previous studies demonstrating that pDC were the only cell type able to respond to CSFV by IFN-α production [19]–[21], pDC were the only source of IFN-α following stimulation with infected cells, as demonstrated by intracellular IFN-α staining which was only found in the CD4highCD172a+ pDC population. Furthermore, purified monocytes did not produce IFN-α in response to any of the stimuli tested (Supplementary Figure 1).
The above results suggested that infected SK-6 cells would transfer viral RNA to pDC resulting in pDC activation. Considering the fact that VRP deliver self-replicating RNA which replicates for many days in SK-6 cells [22], we tested if functional replicon RNA was transferred between SK-6 cells and pDC by determining the expression of the viral NS3 protein in pDC. NS3 is generated by post-translational processing of the CSFV precursor polyprotein. Detectable amounts of NS3 in cells can only be obtained with replication competent pestivirus genomes, i.e. full-length genomes and replicons. As shown in Figure 2, after co-culture of pDC with VRPΔErns-infected SK-6 cells for 22 h, approximately 12–14% pDC expressed NS3, indicating either a transfer of intact full-length replicon RNA or viral NS3 protein between the cells. Interestingly, co-culture of CSFV-infected cells with pDC resulted in a higher degree of infection (94%) compared to direct infection by the virus (65%). Another observation was that, when infectious CSFV or VRP were present, the percentage of NS3-expressing pDC was higher when compared to the monocytes that were co-purified using the CD172a selection. It is also noteworthy that NS3+ monocytes were found after co-culture with VRP-infected SK-6 cells. The results presented in Figure 2 also highlight that there is no correlation between infectious titers, percentage of viral protein expressing pDC and IFN-α responses. An over 20 times higher IFN-α response was found when pDC were stimulated with VRP-infected SK-6 cells in which infectious virus was barely detectable. We confirmed these results by employing RT-PCR to quantify the number of viral genome copies in these cultures. pDC were stimulated either directly with VRPΔErns or with VRPΔErns-infected SK-6 cells, and then re-sorted from these cultures by CD172a MACS sorting. This demonstrated that around 0.1% of the total viral genome present in infected SK-6 cells was transferred to pDC/monocytes. Direct infection of enriched pDC was ∼6 times more efficient in delivering RNA but induced much lower IFN-α levels (Table 1).
In order to rule out a role for free virions in the induction of IFN-α, we measured the infectivity in VRPΔErns-infected SK-6 at the time of co-culture with pDC and found titers below 102 TCID50/ml. This corresponded to MOI of less than 10−4 TCID50/enriched pDC, suggesting that stimulation of pDC does not occur by infection of pDC with VRP. The lack of involvement of free virions in pDC stimulation by infected cells was further confirmed by addition of a neutralizing monoclonal antibody against the main glycoprotein E2 of CSFV or by addition of a porcine polyclonal hyper immune serum obtained from an infected pig which contained antibodies against both glycoproteins of the virus, E2 and Erns. The antibody preparations completely blocked direct activation of pDC by cell-free virions (Figure 3A and B), but were unable to inhibit activation by infected cells, independent whether the stimulation used CSFV- or VRPΔErns-infected SK-6 cells (Figure 3C and D). With CSFV-infected SK-6 cells, the antibodies even enhanced pDC stimulation. Antibodies from naïve animals or an irrelevant monoclonal antibody had no effect (data not shown). Similarly, the neutralizing antibodies blocked infection of pDC after direct stimulation but were unable to inhibit the expression of NS3 in pDC after stimulation by infected cells (supplementary Figure S2).
We further confirmed the absence of any virus particle in pDC stimulation by employing RNA-transfected SK-6 cells to stimulate pDC. To this end, SK-6 cells were transfected with in vitro transcribed RNA synthesized with plasmids encoding the Erns-deleted genome of CSFV (pA187-ΔErns, ΔErns RNA) or a genome of CSFV devoid of all structural proteins (pA187Δ-Apa, ΔApa RNA). The results shown in Figure 3E demonstrate that CSFV RNA-transfected SK-6 cells can activate pDC, although the levels of IFN-α were low, compared to VRP-infected SK-6 cells. This was explainable by the relatively low transfection efficiency of 15–20% in terms of NS3+ expression (data not shown). We also generated VRP with a deletion of E2 instead of Erns. SK-6 cells infected with such VRPΔE2 were more potent in inducing IFN-α in pDC than VRPΔE2 virions. Interestingly, SK-6 cells infected with VRPΔErns were more stimulatory than SK-6 cells infected with VRPΔE2 (Figure 3F).
In order to determine the role played by TLR7 in sensing CSFV-infected cells, we used the immunoregulatory sequence 661 (IRS661) representing an oligodeoxynucleotide inhibitor of TLR7 which had been previously established for the human, murine and porcine immune systems [23], [24]. IRS661 at a concentration of 0.7 µM efficiently inhibited CSFV- and VRP-induced pDC activation (Figure 4A). With infected cells inducing much higher levels of IFN-α, the reduction caused by IRS661 was still over 80% with the highest inhibitor concentration (Figure 4B).
A striking observation was that in all experiments VRPΔErns-infected SK-6 cells were clearly more efficient at inducing IFN-α compared to CSFV-infected SK-6 cells (Figure 1F). Considering that VRPΔErns lacks the Erns gene, we postulated a role for this viral protein in inhibition of this novel type of IFN-α induction and tested this by comparing SK-6 cells stably expressing Erns to the parent wild type SK-6 cells. Indeed, the SK-6(Erns) cells infected with VRP or CSFV were very inefficient at inducing IFN-α in contact with pDC (Figure 5A and B). This was not a result of different susceptibility to the viruses as the infection rate of the SK-6(Erns) was 99%, similar to CSFV (Figure 6F). As expected, Erns expressed by the SK-6(Erns) trans-complemented VRPΔErns to generate infectious particles resulting in a higher degree of NS3+ pDC (supplementary Figure S3). Considering that this must be associated with a higher viral RNA load in pDC, these results indicate a potent function of Erns in preventing the stimulation of pDC by infected cells.
Erns has been shown to be mainly associated with intracellular membranes in particular of the ER, with almost no cell surface expression [25]. However, considering that approximately 16% is secreted to the extracellular space [26], [27], we tested if Erns secreted by SK-6(Erns) cells was responsible for the observed inhibition. To this end, supernatants of SK-6(Erns) or SK-6 were added to co-cultures of VRPΔErns-infected SK-6 cells and pDC. As shown in Figure 5C, there was no evidence for any suppressive effect of soluble Erns in the supernatants.
We next determined the role of Npro, a well established type I IFN antagonist of CSFV in non-pDC and pDC targeting IRF3 and IRF7 transcription factors [8], [19], [28]. To this end, we compared the ability of SK-6 cells infected with VRPΔErns and VRPΔErns D136N expressing a non-functional mutant of Npro [22], to activate pDC. Our results demonstrated that only Erns, but not Npro prevents the activation of pDC by infected cells (Figure 5D).
In order to confirm this function of Erns, we compared the effect of SK-6 and SK-6(Erns) cells infected with two other RNA viruses, the picornavirus foot-and-mouth disease virus (FMDV) and the coronavirus transmissible gastroenteritis virus (TGEV), on pDC. When the stimulation used FMDV- or TGEV-infected SK-6 and SK-6(Erns) cells for comparison, the responses with infected SK-6(Erns) cells were 3 to 6-fold lower compared to SK-6 cells (Figure 5 E and F). This was not caused by an inhibitory effect of Erns on virus replication (Supplementary Figure S4). In order to exclude a potential “toxicity” derived from Erns expressing cells we also tested the responses of pDC to CpG when stimulated in co-cultures with SK-6 and SK-6(Erns) cells, and found similar levels of IFN-α (Supplementary Figure S5).
In order to test the requirement of RNase activity for the above Erns function, we constructed an RNase-negative mutant of CSFV by deleting the histidin codon at position 346 [13]. The mutation abolished the RNase activity (Figure 6A), confirming previously published data [13]. Interestingly, RNase activity of Erns was detectable in cell extracts only (Figure 6A). While SK-6 cells infected with wild type CSFV induced under 500 U/ml of IFN-α, cells infected with the CSFV-ErnsΔ346 mutant induced approximately 10-fold higher responses (Figure 6B). Furthermore, direct stimulation of pDC by virus also dramatically increased when the CSFV-ErnsΔ346 mutant was employed. Nevertheless, the levels of IFN-α remained clearly under those induced by infected cells. For further confirmation that the inhibitory effect of Erns in pDC depends on the RNase-activity of Erns, we constructed lentivirus-transduced SK-6 cell lines expressing the parent Erns [SK-6LV(Erns)] or an RNase-inactive mutant Erns [SK-6LV(ErnsΔ346)]. As expected, the deletion of the histidine at position 346 completely abolished the RNase activity of Erns (Figure 6C). Again, RNase activity of the parent Erns was detectable only in the cell extracts. As expected, only SK-6 expressing RNase active Erns prevented IFN-α induction by VRP infection of the cell lines (Figure 6D). All SK-6 cell lines expressing both wild-type and mutant Erns had comparable levels of Erns, demonstrating that the striking differences observed in RNase and inhibitory activity for activation of pDC were not caused by lack of Erns expression. In addition, the levels of Erns in these cells were below those found after natural infection by wild-type CSFV (Figure 6E). Furthermore, all Erns-expressing cell lines were found to be fully susceptible to VRPΔErns infection as determined by NS3 expression (Figure 6F). The observed higher levels of NS3 expression in Erns expressing cells was a consequence of trans-complementation of VRPΔErns and generation of infectious virions in these cultures.
Considering that macrophages (MΦ) and endothelial cells (EDC) represent important target cells for CSFV, we also tested if infected MΦ and EDC, similar to SK-6 cells, were able to activate pDC. Indeed, VRPΔErns-infected and CSFV-infected MΦ were able to activate pDC while no responses were detectable with CSFV alone (Figure 7A). VRPΔErns were relatively inefficient at infecting MΦ (26% NS3+ versus 97% NS3+ with CSFV), which explains the lower IFN-α responses when compared to CSFV. The highest levels of IFN-α were induced by MΦ infected with the CSFV-ErnsΔ346 mutant (83% NS3+ MΦ) confirming the functioning of Erns also in MΦ (Figure 7A). Neither the VRPΔErns, nor WT CSFV or the CSFV-ErnsΔ346 mutant were able to induce IFN-α in MΦ (data not shown). We also further confirmed our findings using the immortalized porcine EDC line PEDSV.15. VRPΔErns-infected and CSFV-ErnsΔ346-infected EDC were more potent at activating pDC when compared to CSFV-infected EDC (Figure 7B). In contrast to MΦ, the rate of endothelial cells infection was comparable with the viruses and VRPΔErns (VRPΔErns: 80% NS3+, CSFV: 92% NS3+ and CSFV-ErnsΔ346 mutant 92% NS3+).
Based on the above results we postulated that viral RNA is transported from the infected cells to the TLR7 compartment of pDC in a manner avoiding contact of the RNA with the extracellular space. Consequently, we were interested to investigate if membrane vesicles transporting viral RNA from the infected cell to the pDC could be involved in pDC activation by infected cells. To this end, we co-cultured pDC with VRP-infected SK-6 cells in transwell culture dishes using 0.4 or 1 µm pore sizes. Only pDC with direct contact to infected SK-6 cells produced large quantities of IFN-α (Figure 8A and B). Notably, pDC responses to the CpG control were in the same level of magnitude if the pDC were cultured in the insert or in the well of the plate (data not shown). When the same experiments were performed with CSFV-infected SK-6 cells, low IFN-α responses were also observed in all transwell conditions (Figure 8B). Considering that CSFV virions are only 60 nm, this response was probably mediated by direct stimulation of pDC with virions passing the membranes. Similar to VRP-infected or CSFV-infected SK-6 cells, infected MΦ and EDC were unable to induce IFN-α when separated from pDC using a transwell culture system (data not shown).
During a virus infection, pDC will not only encounter virions but also virus-infected cells. The ability to sense the latter has the advantage of being able to sense infection before or without release of virions and also to better sense viruses which are particularly cell-associated and tend to cause persistent virus infections, such as HIV, HCV and pestiviruses. The present study underlines the importance of this by demonstrating that pDC stimulation by infected cells can be much more efficient than stimulation by virions. This is also emphasized by the identification of a viral protein that appears to have evolved to efficiently inhibit this pathway. After HIV [5], HCV and Venezuelan equine encephalitis virus [3], CSFV is now the fourth virus for which this mechanism of pDC stimulation has been found to be more potent than direct stimulation of pDC by virions. Although free CSFV was also able to stimulate pDC, the IFN-α responses were inferior to those induced by cell-free virions. Interestingly, also with FMDV, a virus which does not or very inefficiently stimulate pDC [31], infected cells were able to stimulate pDC.
The basic characteristics of pDC stimulation by CSFV-infected cells are similar to those observed with HCV. It represents a TLR7-dependent process which cannot be blocked by neutralizing antibodies and does not require expression of viral glycoproteins [3]. This is in contrast to the situation for HIV [5]. Our data indicate that this pathway of pDC activation is dependent on cell contact, intact actin filaments, microtubules as well as cell membrane cholesterol indicating a role for lipid rafts, although future studies are required to directly demonstrate the functional interaction of these cellular components with viral proteins. We have also identified that the pestiviral Erns potently prevents pDC activation by infected cells. Erns possesses several remarkable features, of which the RNase activity is of particular interest, considering that it is expressed by an RNA virus. Erns has structural similarities with plant T2 RNases which have their optimal catalytic activity at an acidic pH [32] with a preference for cleaving single-stranded RNA [33], [34]. This would point on an activity within the endosomal compartment, which is also supported by our data indicating that genomic RNA does not appear to be degraded. The protein also has an unusual membrane anchor composed of an amphipathic helix without a typical membrane anchor [27], [35], but with a retention signal ensuring its association with intracellular membrane compartments [25]. Whether this enables accumulation in the endosomal system with appropriate orientation needs to be investigated. We postulate that viral RNA may have access to the endosomal system via the autophagy process. These endosomes could be transferred to pDC by a pathway to be defined and then fuse with the TLR7 compartment. Alternatively, both cytoplasmic viral RNA and Erns could be autophagocytosed after transfer to pDC followed by fusion of autophagosomes with TLR7-containing endosomes [36]. Only at this acidic location the RNAse function would be highly active and rapidly degrade the viral RNA to reduce TLR7 triggering. Consequently, one of the first questions to be addressed in future studies is whether Erns is transported from SK-6 cells to pDC to degrade RNA in the TLR7 compartment of pDC. Certainly, Erns can act in pDC as our data is showing that CSFV with Erns lacking RNase activity induce much higher IFN-α responses compared to wild type CSFV when pDC are directly stimulated by virions. Strikingly, Erns can exert its inhibitory function when expressed independently of the viral context in the infected cells that stimulate the pDC. In addition, our results suggest that Erns functions by using intracellular rather than extracellular pathways, since SK-6(Erns) supernatants did not suppress pDC activation by VRPΔErns-infected SK-6 cells. This is contrary to a role proposed for secreted Erns by several authors. Based on the observation that a minor part of the protein was found to be secreted from infected cells or cells expressing Erns [25]–[27], recombinant Erns was tested and found to degrade synthetic single-stranded and double-stranded RNA added to the cultures [16]–[18].
Considering that pDC are by far the most potent producers of IFN type I and of crucial importance in linking innate to adaptive immunity, our data shed light into one of the most fascinating aspects of pestivirus biology. These viruses cause persistent infections, in both cattle and pigs. When bovine fetuses are infected transplacentally by BVDV between the 2nd and 4th month of pregnancy, a time point at which the fetuses are not yet immunocompetent [37], persistently infected calves are born which are fully immunotolerant to the virus and cannot mount any adaptive immune responses against the virus. These persistently infected calves play a major role in the epidemiology of the disease by shedding virus. Similar observations have been reported for pigs after infection of pregnant sows with low virulent strains of CSFV before eradication of CSFV from most European countries and the U.S.A. [38]–[44]. This contrasts with the pathogenesis of highly virulent strains of CSFV which induce acute disease with high mortality which is associated with high systemic levels of IFN-α [45]. This appears contradictory to the present report but compared to other viruses the ability of CSFV to activate pDC is weak [9]. Even if pDC are stimulated by cells infected with wild type CSFV, IFN type I responses remain relatively weak compared to influenza virus. Our concept to explain this apparent contradiction to the in vivo situation with virulent strains of CSFV is based on the prominent tropism of CSFV for MΦ and DC, and its localization predominantly in lymphoid tissue. Highly virulent isolates of CSFV cause a very rapid and strong viremia, in which the virus reaches simultaneously all primary and secondary lymphoid tissues where relatively high numbers of pDC are localized. This situation could explain why such viruses are able to induce a potent IFN-α response despite the activities of Npro and Erns [9].
Interestingly, it has been demonstrated that the virus not only needs a functional Npro, but also an Erns with active RNase to establish persistent infections in cattle [14]. Npro induces the degradation of IRF3 and thereby efficiently prevents IFN type I induction in all host cells including conventional DC, which have been induced to express IRF7 by IFN type I pre-treatment [19], [28]. However, Npro can only partially prevent IFN-α responses in pDC [8] and is unable to stop the much more potent activation of pDC by infected cells (this study). We thus propose that Erns has evolved to prevent this pathway of innate immune system activation, which is much more potent and therefore likely to be essential for the virus to be able to establish persistent infections, representing a main survival strategy of pestiviruses [46].
Bleeding and care of donor pigs was carried out in accordance with EU standards and National laws (Tierschutzgesetz SR455). Specifically, approval of the protocol employed was obtained by the Animal Welfare Committee of the Canton of Bern, Switzerland (animal license BE26/11).
The porcine kidney cell lines SK-6 [47], PK-15 (LGC Standards-ATCC, Molsheim, France) and the porcine immortalized endothelial cells PEDSV.15 [48](obtained from Dr. Jörg Seebach, University of Geneva, Switzerland) were propagated in Earle's minimal essential medium (MEM) substituted with 7% horse serum and in Dulbecco's minimal essential medium (DMEM) supplemented with 5% horse serum, nonessential amino acids and 1 mM Na-pyruvate, respectively. SK-6 cells stably expressing Erns of CSFV strains Alfort/187, termed SK-6(Erns) were generated as described previously [49]. Baby Hamster Kidney (BHK) 21 cells were grown in Glasgow's minimum essential medium (Life Technologies) supplemented with 5% v/v fetal bovine serum (FBS, Biowest, Nuaillé, France). Peripheral blood mononuclear cells (PBMC) were obtained from blood of specific-pathogen-free pigs using ficoll-paque density centrifugation (1.077 g/L, Amersham Pharmacia Biotech). pDC were enriched as described previously [31] by cell sorting of CD172a+ PBMCs using the magnetic cell sorting system (MACS) with LD columns (Miltenyi Biotec GmbH, Germany). This permits a 10-fold enrichment of functional pDC to 2–5%. In some experiments, pDC were enriched by a first depletion of CD14+ monocytes followed by CD172a enrichment [24], permitting a pDC enrichment of around 5–10%. Enriched pDC were cultured in DMEM with Glutamax, 20 µm β-mercaptoethanol (Life Technologies). 10% FBS was only added to when indicated. Porcine monocytes were isolated by CD14+ selection using MACS with LS columns. Porcine MΦ were generated from CD14+ PBMCs as previously described using a 3-day culture in DMEM supplemented with 10% autologous porcine serum [50], [51].
CSFV strain vA187-1 was derived from the full-length cDNA clone pA187-1 [52]. Plasmid pA187-1 carries a full-length cDNA copy of the CSFV strain Alfort/187 and served as basis for all viruses and replicon cDNA constructs. The vA187-ErnsΔ346 virus (referred to as CSFV-ErnsΔ346 mutant), with a histidine deletion in Erns at position 346 of the viral polyprotein resulting in loss of RNase activity [13] was rescued by standard procedure [52], [53] from plasmid pA187-ErnsΔ346. This latter construct was generated from pA187-1 using PCR-based site-directed mutagenesis with oligonucleotide primers encompassing the deletion and PfuUltra DNA polymerase (Agilent), employing standard cloning techniques as previously described [54]. The plasmids pA187-ΔErns carrying an in frame deletion of the Erns gene in the pA187-1 backbone, and pA187-D136N-ΔErns carrying the same deletion and expressing a non-functional D136N mutant of Npro were described elsewhere [22], [49]. Plasmid pA187-E2del373 carrying an in frame deletion of the complete E2 gene in the pA187-1 backbone was also described previously [22], [49], [53]. Plasmid pA187-ΔApa encoding a CSFV replicon with a deletion of the structural protein genes C, E1, Erns and most of the E2 gene (i.e. the codons encoding amino acids 96 to 962 of the polyprotein) was described earlier [53]. In vitro transcribed replicon RNA was produced using SrfI-linearized pA187-ΔErns, pA187-D136N-ΔErns, pA187-E2del373 or pA187-ΔApa as described [22], [49], [53]. VRPΔErns carrying a genome with a complete deletion of the Erns gene were described previously, and produced by transfection of SK-6(Erns) cells with A187-ΔErns or A187-D136N-ΔErns replicon RNA. The SK-6(Erns) cells express the Erns protein required for the generation of VRPΔErns by trans-complementation [22], [49]. Similarly, VRPΔE2 carrying the pA187-E2del373-derived replicon with a complete E2 deletion were rescued by transfection of SK-6(E2p7) cell line expressing the CSFV E2 and p7 proteins as described previously [22], [49], [53]. TGEV (TGEV; strain Perdue 115) was propagated in PK15 cells [55]. The FMDV type O UK/2001 isolate was grown in (BHK21) cells as described previously [56]. All virus titers were determined on SK-6 cells, PK-15 cells or BHK21 cells (for CSFV, TGEV and FMDV, respectively) by standard endpoint dilution and were expressed as 50% tissue culture infectious doses (TCID50) per ml.
CSFV or VRP RNA was quantified using a published real-time RT-PCR [57]. Briefly, RNA was extracted using the Trizol method and RT-PCR performed with the SuperScript III Platinum One-Step qRT-PCR System (Life Technologies) using 7500 Real-time PCR System, Applied Biosystems. To determine the absolute number of RNA copies, vA187-1 RNA transcripts generated in vitro were employed.
A lentivirus (LV) expression system using plasmids obtained from the laboratory of Dr. Didier Trono (http://tronolab.epfl.ch/ Ecole Polytechnique Federale de Lausanne, Switzerland) or through Addgene (Cambridge MA, USA) [58], [59] was employed. For cloning of Erns and its RNase-knock-out mutant variant into the lentiviral transfer plasmid pWPT-GFP (Addgene) the pA187-1 and pA187-ErnsΔ346 plasmids were used as template for PCR amplification using primers to insert the MIuI and Sall restriction sites excising the GFP in the pWPT-GFP vector. The PCR products were first inserted into the pJET vector (Fermentas). The cloned Erns genes of pJET-Erns and pJET-ErnsΔ346 were verified by nucleotide sequencing and then excised with MIuI and Sall and ligated into the pWPT-GFP vector employing standard techniques and Stbl2 bacteria, resulting in pWPT-Erns and pWPT-ErnsΔ346 respectively. All primer sequences and construction details can be obtained on request. In order to generate lentiviruses, HEK293T cells were transfected with the envelope plasmid (pMD2.G), the packaging plasmid (pCMV-R8.74) and the pWPT-Erns or pWPT-ErnsΔ346 plasmid using standard calcium phosphate precipitation. Medium was changed after overnight incubation at 37°C and the supernatant harvested after 48 h, centrifuged (350× g, 10 min) and filtered. The virus was purified and enriched by centrifugation on a 20% sucrose cushion at 100'000× g for 90 min at 4°C. The cells were transduced twice with 1∶100 dilutions of the purified lentiviruses in 1 ml serum free medium of a T25 cell culture flask followed by culture overnight at 37°C and medium change between the transductions. Transduction efficiency was found to be over 90% in terms of detectable anti-Erns expression by flow cytometry and found to remain stable over at least three passages.
For pDC phenotyping and isolation, monoclonal antibodies (mAb) against CD172a (mAb 74-22-15A), CD14 (CAM36A) and CD4 (mAb 74-12-4 and PT90A) were used. Hybridomas for mAb 74-22-15A and 74-12-4 were kindly donated by Armin Saalmüller (University of Veterinary Medicine, Vienna, Austria). The mAb PT90A and CAM36A were purchased from VMRD (Pullman, WA). The hybridomas for mAb HC/TC26 [60] and C16 [61] binding the CSFV glycoprotein E2 and nonstructural protein NS3 respectively were kindly provided by Irene Greiser-Wilke, Hannover Veterinary School, Hannover, Germany. Erns expression was demonstrated with mAb 140.1 (used at a 1∶200 dilution, Prionics, Switzerland) as described previously [62]. For NS3 and Erns detection the cells were fixed and permeabilized with FIX&PERM solution (An der Grub Bio Research GmbH). As fluochromes, isotype-specific fluorescein isothiocyanate (FITC), R-phycoerythrin (RPE) (Southern Biotechnology Associates), RPE-Cy5 (Dako) and APC (Becton Dickinson, Basel , Switzerland) conjugates were used as described previously [63].
For stimulation of enriched pDC by cells infected with CSFV and VRP, SK-6 cells were infected at an multiplicity of infection (MOI) of 5 TCID50/cell, cultured for 24 h and then washed four times to remove the inoculums. Infectivity was verified and found to be above 90% NS3+ cells following infection with CSFV or VRP at these conditions. The SK-6 cells were then added at 40'000 cells/well for 96 well plate (if not indicated otherwise) or at 200'000 cells/well for 24-well plates. Freshly isolated CD172a+ enriched pDC were then added to the cultures at 200'000 cells/well of 96-well plates or 1×106 per well of 24-well plates. After another 22 h, supernatants were isolated for IFN-α ELISA and the cells for NS3 expression in some experiments. For stimulation of pDC by cells infected with FMDV or TGEV virus the cells were infected at the indicated MOIs, incubated for 90 min at 37°C and washed 4 times before addition of enriched pDC. For FMDV BHK21 cells were employed, for TGEV PK15 cells. In some experiments we employed 24-well plate transwell inserts with 0.4 µm or 1.0 µm pore sizes (Corning, Sigma Chemicals, Buchs Switzerland and Becton Dickinson, Basel Switzerland). All cultures with enriched pDC were done at 39°C, 6% CO2. As a positive control for pDC stimulation, direct CpG D32 at 10 µg/ml [63] was used.
A 50-mer RNA oligonucleotide probe complementary to nucleotides 12242-12193 of the vA187-1 genome sequence (GenBank accession number X87939.1) and carrying a Dyomics 781 modification at the 5′ end (Dy-781-O1-RNA) was synthesized by Dr. Fabian Axthelm (Microsynth AG, Balgach, Switzerland). The Dy-781-O1-RNA probe was mixed at 40 nM final concentration with MEM containing 3×10−3 U RNase A/ml as digestion control, with 50 mM TrisHCl pH 7.4 as negative control, and with the samples to be tested for RNase activity, and incubated for 1 h at 37°C. The treated probes were mixed with 2 volumes of 97% Formamide (Sigma) and separated on a 10% polyacrylamide and 35% urea gel in 133 mM TrisHCl, 45.5 mM boric acid and 3.2 mM EDTA. Image acquisition was performed with the Odyssey Infrared Imaging System (LI-COR).
As TLR7 inhibitor IRS661 (5′-TGCTTGCAAGCTTGCAAGCA-3′) and a control oligonucleotide (Ctrl-ODN; 5′-TCCTGCAGGTTAAGT-3′) was used [23]. IRS661 and Ctrl-ODN were purchased from Eurofins MWG Operon (Ebersberg, Germany). The impact of various metabolic inhibitors on SK-6 cells was tested by addition of the inhibitor for the last 2 h of infection, in order to avoid interference with infection. Before addition of pDC, the inhibitors were removed and the cells washed three times. The following final concentrations were used: 1 µM latranculin B, 5 µM nocodazole or 20 mM methyl-β-cyclodextrin (MβCD). All chemicals were purchased from Sigma Chemicals.
IFN-α was quantified by enzyme-linked immunosorbent assay (ELISA) using the mAbs K9 and F17 (kindly provided by Dr. B. Charley, INRA, Jouy-en-Josas, France) as described previously [31]. For detection of IFN-α by intracellular staining mAb F17 with a previously published protocol was employed [63].
P values were calculated by an unpaired t-test using the GraphPad Prism Software.
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10.1371/journal.pgen.1004506 | Playing RNase P Evolution: Swapping the RNA Catalyst for a Protein Reveals Functional Uniformity of Highly Divergent Enzyme Forms | The RNase P family is a diverse group of endonucleases responsible for the removal of 5′ extensions from tRNA precursors. The diversity of enzyme forms finds its extremes in the eukaryal nucleus where RNA-based catalysis by complex ribonucleoproteins in some organisms contrasts with single-polypeptide enzymes in others. Such structural contrast suggests associated functional differences, and the complexity of the ribonucleoprotein was indeed proposed to broaden the enzyme's functionality beyond tRNA processing. To explore functional overlap and differences between most divergent forms of RNase P, we replaced the nuclear RNase P of Saccharomyces cerevisiae, a 10-subunit ribonucleoprotein, with Arabidopsis thaliana PRORP3, a single monomeric protein. Surprisingly, the RNase P-swapped yeast strains were viable, displayed essentially unimpaired growth under a wide variety of conditions, and, in a certain genetic background, their fitness even slightly exceeded that of the wild type. The molecular analysis of the RNase P-swapped strains showed a minor disturbance in tRNA metabolism, but did not point to any RNase P substrates or functions beyond that. Altogether, these results indicate the full functional exchangeability of the highly dissimilar enzymes. Our study thereby establishes the RNase P family, with its combination of structural diversity and functional uniformity, as an extreme case of convergent evolution. It moreover suggests that the apparently gratuitous complexity of some RNase P forms is the result of constructive neutral evolution rather than reflecting increased functional versatility.
| Many biocatalysts apparently evolved independently more than once, leading to structurally unrelated macromolecules catalyzing the same biochemical reaction. The RNase P enzyme family is an exceptional case of this phenomenon called convergent evolution. RNase P enzymes use not only unrelated, but chemically distinct macromolecules, either RNA or protein, to catalyze a specific step in the biogenesis of transfer RNAs, the ubiquitous adaptor molecules in protein synthesis. However, this fundamental difference in the identity of the actual catalyst, together with a broad variation in structural complexity of the diverse forms of RNase P, cast doubts on their functional equivalence. Here we compared two of the structurally most extreme variants of RNase P by replacing the yeast nuclear enzyme, a 10-subunit RNA-protein complex, with a single-protein from plants representing the apparently simplest form of RNase P. Surprisingly, the viability and fitness of these RNase P-swapped yeasts and their molecular analyses demonstrated the full functional exchangeability of the highly dissimilar enzymes. The RNase P family, with its combination of structural diversity and functional uniformity, thus not only truly represents an extraordinary case of convergent evolution, but also demonstrates that increased structural complexity does not necessarily entail broadened functionality, but may rather be the result of “neutral” evolutionary mechanisms.
| RNase P is the endonuclease that generates the 5′ end of tRNAs by removing transcriptional extensions [1]–[3]. It is an indispensable enzyme found in essentially all forms of life. Despite the apparently simple function and the highly conserved structure of the tRNA substrates, a bewildering diversity of enzyme forms arose during evolution. Intriguingly, in many cases RNase P is a ribonucleoprotein (RNP), whose catalytic subunit is a structurally conserved RNA rather than a protein. In the most simple form of this RNP enzyme, found in Bacteria, the RNA is associated with a single small protein [4], [5], yet in Archaea, and even more so in the nucleus of Eukarya, the protein moiety of the RNP is complex, comprising up to 10 proteins (ranging in size from 15 to more than 100 kDa) [6]–[8]. While RNase P RNA is generally considered a relic of a primordial “RNA world” and thereby a kind of “living fossil” among modern-life's protein enzymes, it remains obscure why an RNA enzyme has been widely preserved for an endonucleolytic task that seems easily accomplishable by a protein. Likewise, the significance of the increasing complexity of the RNP's protein moiety during evolution is not understood, as the enzyme's tRNA substrates have remained essentially unchanged.
In contrast to the different RNPs, another major form of RNase P, only found in Eukarya, comprises a single monomeric 60-kDa protein (without any RNA subunit) that is called proteinaceous or protein-only RNase P (PRORP). This most simple, single-polypeptide-enzyme form acts in the nucleus and/or organelles of plants and some protists [9]–[11], as well as in animal mitochondria, where, however, two further protein subunits are involved in tRNA 5′-end maturation [12]. The identification of these RNase P forms exclusively composed of protein has revived the question as to why RNA catalysis has been retained by so many organisms just in the case of tRNA 5′-end maturation. It has also raised the issue whether RNase P forms structurally that different are at all functionally equivalent. In case of the nuclear RNase P forms found in different Eukarya the contrast in enzyme makeup is most striking: whereas a single protein apparently suffices in plants or trypanosomatids [10], [11], an RNP of considerable complexity evolved in the nucleus of animals and fungi [6]. This complexity has been suggested to increase the RNP enzyme's versatility, broadening its functionality, although the presumed substrates and functions beyond tRNA precursor processing remain poorly characterized [6],[7],[13]–[15]. Substrates apart from tRNA precursors have not been identified or studied so far for any of the nuclear protein-only forms of RNase P and it seems plausible that the full functional spectrum of the distinct RNase P forms may turn out to be quite diverse.
We recently addressed possible differences in the functional spectrum of RNA- and protein-based forms of RNase P by genetic complementation. In Escherichia coli we were able to rescue the otherwise lethal knockdown of the endogenous RNase P RNA by the expression of PRORP1 from Arabidopsis thaliana [9]; the deletion of Saccharomyces cerevisiae RPR1, the gene encoding nuclear RNase P RNA, could be rescued by Trypanosoma brucei PRORP1 [11]. While these experiments demonstrated a high degree of functional similarity/overlap between RNP and protein forms of RNase P, the consequences of the replacements were not further explored. Here we report the generation and characterization of yeast strains whose nuclear RNase P RNP was permanently replaced with the PRORP3 protein from A. thaliana by genome engineering. In addition to the molecular consequences of this swap at the level of RNA processing, we studied the strains under a variety of growth conditions to also address possible differences owing to hitherto unidentified functions of the RNP enzyme.
The RNA component and catalytic core of the yeast nuclear RNase P RNP is encoded by RPR1 [6]. We recently showed that an otherwise lethal deletion of RPR1 can be rescued by the expression of T. brucei PRORP1, the gene encoding the nuclear RNase P of this protist [11], from a plasmid. To test further PRORPs for their ability to substitute for yeast nuclear RNase P we set up a plasmid shuffle procedure (Figure S1). We were particularly curious to see whether PRORP variants naturally found in mitochondria or chloroplasts rather than the nucleus would be able to replace the nuclear RNP enzyme; accordingly, the organellar variants were expressed without their N-terminal targeting sequence.
The expression of any of the three A. thaliana or the two of T. brucei PRORPs allowed the tester strain to lose the RPR1 expression plasmid harboring the URA3 gene and thereby survive selection on 5-fluoroorotic acid (Table S1); only the human mitochondrial PRORP variant was not able to rescue the loss of RPR1. The ability to complement the deletion of RPR1 depended on the enzymatic activity of the different PRORPs, as strains expressing active-site variants of the proteins were not able to survive. Loss of the RPR1 expression plasmid and deletion of the chromosomal copy were verified by growth tests on selective media and PCR genotyping of the retrieved colonies (Figure S2). Although all PRORPs except for the human mitochondrial one supported the survival of the RPR1 deletion strain after loss of the plasmid-borne RPR1 copy, growth of the derived strains (as judged by colony size) differed markedly. Growth of strains based on A. thaliana PRORP2 and PRORP3, or T. brucei PRORP2 resembled that of the wild type; yeast cells expressing T. brucei PRORP1 grew conspicuously slower and A. thaliana PRORP1 supported growth most poorly. Consistent with a previously observed temperature sensitivity of A. thaliana PRORP2 [16], growth of the respective strain was impaired at 37°C. All the strains, even the slow-growing ones, could be maintained and re-grown in liquid or on solid media. Taken together, PRORPs of nuclear or organellar, protist or plant provenance provided sufficient (nuclear) RNase P activity for yeast cell survival.
For an in-depth characterization of the consequences of the RNP-to-protein enzyme swap, we constructed strains in which RPR1, the gene encoding the catalytic RNA, was directly replaced by the gene expressing the protein catalyst. We opted for A. thaliana PRORP3, a nuclear RNase P in plants [10], which supported wild type-like growth in the plasmid-based complementation system. The genetic swap was achieved by homologous recombination of a PRORP3 expression cassette (linked to a selectable marker) into the RPR1 locus (Figure 1A). Haploid and diploid strains with a PRORP3-for-RPR1 exchange (rpr1Δ::PRORP3) were viable, displayed wild type-like growth, and were able to mate and sporulate. Plant PRORP3 localized to the nucleus in yeast cells, showing a diffuse distribution without any apparent subnuclear (e.g., nucleolar) enrichment (Figure S3).
For the expression of PRORP3 we used a truncated form of the S. cerevisiae ADH1 promoter that was reported to be suitable for an efficient constitutive expression of heterologous proteins throughout the yeast growth cycle [17]. We also succeeded in the construction of haploid strains that expressed PRORP3 from presumably weaker promoters, including the promoter of POP1, a gene encoding a subunit of the endogenous RNase P RNP. The growth of these strains (as judged by colony size), however, was substantially slower than that of strains with ADH1 promoter-driven PRORP3 expression, indicating that these promoters yielded only insufficient amounts of the alien enzyme. All further studies were thus confined to strains based on the ADH1 promoter-PRORP3 combination.
We aimed to also remove all further components of the former RNase P RNP presumably not needed anymore in an RNase P-swapped yeast. In addition to the catalytic RNA encoded by RPR1, the RNP comprises nine proteins, eight of which are, however, also integral components of another related and essential RNP, RNase MRP [6]. Rpr2p is the only protein specific to RNase P. Like RPR1 and the other eight protein-coding genes, RPR2 is normally essential for cell viability. We were nonetheless able to replace RPR2 in rpr1Δ::PRORP3 cells with a floxed marker (Figure 1B). Again, haploid and diploid rpr1Δ::PRORP3 rpr2Δ0 strains were viable and their growth and physiology comparable to rpr1Δ::PRORP3 or wild type cells.
To avoid subtle differences between the RNase P-swapped strains and the parental wild type strain, possibly resulting from unpredictable epistatic effects of auxotrophic markers or antibiotic resistance genes [18], we removed the markers used for selection during gene replacement (Figure 1). At all stages of strain construction, genotypes were verified by selective growth and PCR analyses (see Figure S4 for the analyses of the final diploid strains). At the final stage we isolated three clones each of the diploid rpr1Δ::PRORP3/rpr1Δ::PRORP3 and rpr1Δ::PRORP3rpr2Δ0/rpr1Δ::PRORP3 rpr2Δ0 strains and used them as replicates in all subsequent analyses.
5′-end processing of tRNAs by RNase P is an early and essential step in tRNA biogenesis. We analyzed six nucleus-encoded tRNAs in the different RNase P-swapped yeast strains by Northern blotting. In all cases, hybridization signals corresponding to mature tRNA appeared indistinguishable between the parental wild type strain and both its RNase P-swapped derivatives (Figures 2A and 2B), although RPR1 RNA (and RPR2 mRNA) were, as expected, undetectable in the latter (Figure 2C). A quantitative analysis of the hybridization signals confirmed that the relative quantity of four of the six tRNAs was unaffected by the RNase P swap, and even slightly increased in the case of the remaining two (Figure 2D). In addition to the mature tRNA band, hybridization signals consistent with the size of tRNA precursors (and/or processing intermediates) were noticeable in the case of some of the analyzed tRNAs (Figures 2A and 2B). An increase of these precursor species in the PRORP3 strains was evident for tRNALeu, while the putative tRNASer precursor(s) remained unaffected by the RNase P swap. Diffuse hybridization signals in the precursor range were observed for tRNAArg, tRNAAsn, and tRNAVal in the PRORP3 strains, but not or only very weakly in the wild type cells. Essentially no precursors or processing intermediates could be detected for tRNAGly, irrespective of the strains' RNase P status. Regardless of the precursor species identified, we like to emphasize that the mature tRNAs were qualitatively and quantitatively unimpaired by the RNase P swap in all analyzed cases.
Beyond tRNA precursors, yeast nuclear RNase P was proposed to be additionally involved in the processing of a broad range of non-tRNA substrates [6], [7], [13]–[15]. The RNP enzyme was reported to efficiently cleave RNA in vitro in an apparently structure- and sequence-independent way [19], and numerous unspliced mRNAs, snoRNA precursors, and other noncoding RNAs were found to either copurify with the enzyme or to accumulate in a temperature-sensitive model of RNase P deficiency [20], [21]. We selected eight of those most strongly accumulating RNAs and set up specific RT-PCR assays for their quantitative analysis. Using the temperature-sensitive rpr1-ts strain to validate our assay we could reproduce previous findings of a 10- to almost 30-fold accumulation of these mRNA and snoRNA precursors at the restrictive temperature. However, none of these RNAs was increased in either of the PRORP3-based strains (Figure 3).
The capability of the nuclear RNP enzyme to recognize and cleave such a diverse range of substrates was suggested to be related to the increased complexity of its protein moiety [6], [13]–[15]. Finding that the simplest form of RNase P, composed of a single polypeptide is apparently able to process the same wide range of substrates was surprising, and prompted us to evaluate the accumulation of the putative non-tRNA RNase P substrates in another RNase P-deficiency model. We made use of strains with titratable promoter alleles [22] to deplete yeast cells of RNase P proteins. The pertinent strain supposed to contain a repressible RPR2 allele turned out to be flawed and we therefore used strains with repressible alleles of POP4 and POP8, respectively. Depletion of either of the two proteins resulted in a marked decrease of RPR1 RNA (Figure S5A), accumulation of tRNA precursors and reduced levels of mature tRNA (Figure S5B). The effect of the Pop4p depletion was more pronounced and comparable to that of the temperature-sensitive model after six hours, whereas 12 hours of depletion were required in the case of Pop8p to achieve similar effects. Remarkably, of the mRNA and snoRNA precursors strongly accumulating in the rpr1-ts mutant, none increased by more than a factor of 1.5, and several had even slightly reduced levels relative to the parental wild type strain (Figure S5C). In conclusion both, Pop4p and Pop8p depletion, proved to be appropriate RNase P-deficiency models, yet they did not recapitulate the accumulation of the supposed non-tRNA RNase P substrates, suggesting that the accumulation of those RNAs in the rpr1-ts mutant is not related to a deficiency in RNase P activity itself.
During strain construction and routine cultivation on solid or in liquid media the RNase P-swapped strains appeared largely indistinguishable from the wild type. Discerning more subtle differences in growth, however, requires quantitative analyses, and some functional deficiencies might only be revealed under specific conditions. Exploring a wide variety of growth conditions and different forms of stress moreover addresses cellular enzyme function in the broad range of its facets, including such of enzyme regulation and metabolic integration.
We used a high-throughput micro-scale cultivation approach with automated density reading to follow yeast growth throughout the complete cycle of adaption to the environmental change (lag phase), exponential growth, and stationary phase. From the growth curves we derived the maximal growth rate, the duration of the lag phase, and the final density reached at the endpoint (stationary phase). In pilot experiments, we determined the respective stressor concentrations that impaired, but did not prevent growth. Initial experiments involving both, rpr1Δ::PRORP3 and rpr1Δ::PRORP3 rpr2Δ0 strains, did not reveal any significant growth differences between the two RNase P-swapped strains. As the two genotypes had not differed in the RNA analyses too (see before), we restricted the comprehensive phenotypic analyses to a comparison of the rpr1Δ::PRORP3 rpr2Δ0 genotype with the wild type.
Both types of RNase P, the native RNP as well as the transgenic protein, supported the growth of their respective strains under all the conditions tested (Figure 4). Surprisingly, a slight yet significant growth advantage was associated with the replacement of the RNP enzyme by PRORP3 for many of the conditions (reflected primarily in a higher maximal growth rate; Figure 4A). To assess the biological significance of this unexpected result, we decided to further examine whether genetic background affects the quantitative phenotype associated with the RNase P-swap. For comparison with the S288C background of the BY4743 parental strain [23] we chose CEN.PK, a distantly related, well-characterized laboratory strain with genomic relations to wild industrial strains [24], [25]. We reproduced the entire genetic exchange of RNase P in CEN.PK, again generating diploid rpr1Δ::PRORP3 and rpr1Δ::PRORP3 rpr2Δ0 strains. Like their BY4743-based cousins, the CEN.PK-based strains had normal levels of mature tRNA and did not accumulate any of the putative non-tRNA substrates of yeast nuclear RNase P (Figure S6).
The comparative phenotypic profile of the rpr1Δ::PRORP3 rpr2Δ0 versus the wild type RPR1 RPR2 genotype was qualitatively similar for the BY4743 and CEN.PK background, i.e., no condition exclusively allowed or strongly favored the growth of either one RNase P genotype (Figures 4, S7, and S8). However, in the BY4743 context, PRORP3 under many growth conditions slightly raised the maximal growth rate of the cells (Figure 4A) and/or shortened their lag phase (Figure S7A), whereas the opposite was the case in the genetic context of CEN.PK (Figures 4B and S7B). The endpoint density reached was generally negatively correlated with these differences, i.e., faster growth resulted in a lower final density of the cultures (Figure S8). These basic effects of RNase P genotype and strain background were observed regardless of variations in media composition, type of carbon source, or nutrient availability, and no matter what kind of stress was applied. Only in the case of high salt (NaCl) stress the RNase P-swap resulted in a general growth advantage of PRORP3 strains (growth rate and endpoint) irrespective of the genetic background (Figures 4 and S8). However, like in essentially all other cases the difference was small; the only major change was a shortened lag phase for the CEN.PK RNase P-swapped strain in the presence of the fungicide amphotericin (Figure S7B). In summary, we did not find any serious growth deficiency when replacing the natural yeast RNase P RNP with A. thaliana PRORP3.
As batch growth analyses generally involve only a limited number of generations (6–9 in our experiments), we devised a more long-term, competitive growth experiment to address possible fitness consequences of the RNase P swap. We labeled the RPR1 RPR2 wild type and the rpr1Δ::PRORP3 rpr2Δ0 strains by inserting an appropriate green fluorescent protein (yeGFP) expression cassette into one allele of their respective leu2 locus. Pairs of wild type and RNase P-swapped strains, mutually labeled and unlabeled to exclude a possible GFP bias, were mixed in equal proportion and co-cultured. Rather than monitoring continuous (exponential) growth, we subjected the strain mixtures to repetitive cycles of batch culture, during each of which the cells traversed the phases of adaption, outgrowth, exponential growth, nutrient depletion, and stationary phase. Cells were sampled after each cycle and the fraction of GFP-positive cells was analyzed by flow cytometry. Consistent with the genetic background-dependent differences in growth rates and lag phases (Figures 4 and S7), the RNase P-swapped yeasts either outcompeted their parental wild type (BY4743 background; Figure 5A) or were themselves displaced (CEN.PK background; Figure 5B); after 78 generations the latest, all populations were homogenous to more than 90%. Hence, even without any evolutionary or experimental adaption, the artificial substitution of the endogenous RNP enzyme by a proteinaceous form of RNase P was, at least in a certain genetic background, able to produce strains that could outcompete the wild type.
Highly divergent forms of RNase P are found in the nucleus and organelles of different Eukarya. Besides their dissimilar composition and structure, these enzyme forms are also of distinct evolutionary origin. The RNA-based forms trace back to the origin of life, and, via a common ancestor with Archaea, developed to the complex RNPs found in the eukaryal nucleus; a bacterial-type RNase P was introduced with endosymbiotic organelles and either remained bacterial-like or evolved to the highly degenerate RNPs as found in extant mitochondria and plastids. Proteinaceous RNase P, in contrast, seems to have its origin (by fusion of functional domains) at the root of eukaryal evolution, possibly before the last eukaryal common ancestor. While its early evolution must have taken place in the presence of the RNP enzyme, the protein displaced the RNP in the nucleus and/or organelles of some eukaryal branches, and was lost in others. In contrast to the RNP forms, the protein-only enzyme apparently underwent little structural change and generally remained a “simple” 60-kDa monomer. The apparent discrepancy of convergent evolution towards tRNA processing function and divergent principles and trends in enzyme design has suggested that the complete functional spectrum of the disparate RNase P forms might actually differ noticeably. To put two of the structurally most divergent forms of RNase P to the litmus test for exchangeability and functional identity, we swapped one for the other in a suitable genetic model organism. We replaced the 10-subunit RNase P RNP of the yeast S. cerevisiae with A. thaliana PRORP3, a single plant protein. Finding that the artificial, RNase P-swapped strains were not only viable, but had essentially unchanged growth properties under a wide variety of conditions and, at least in a certain genetic background, a fitness that even slightly exceeded that of the wild type, demonstrated an unexpected extent of functional overlap, necessitating a revision of some of the current conceptions about RNase P function and evolution.
tRNA 5′-end maturation is the enzymatic function defining the RNase P family. The quantitative phenotypic and Northern blot analyses, indirectly and directly showed that there was no shortage of mature tRNAs in the RNase P-swapped yeasts; the alien plant enzyme was thus sufficiently expressed and active for a qualitatively and quantitatively proper maturation of tRNAs transcribed in the yeast nucleus. The major alteration done to the tRNA maturation machinery was nevertheless reflected in the appearance of precursors for some tRNAs. However, rather than indicating an intrinsic insufficiency of the plant enzyme, this more likely results from the change in the spatial organization of the natural pathway of yeast tRNA biogenesis in the RNase P-swapped strains. In line with this notion, the plant enzyme was distributed throughout the nucleoplasm rather than being confined to the nucleolus. This might actually reflect an inherent difference between early tRNA processing events in yeast and plants, nucleolar in the former [26] and nucleoplasmic in the latter [9]. Inappropriate localization, but also inadequate integration into the tRNA maturation pathway might moreover explain that a rather strong promoter was required to provide sufficient PRORP3, as it can reasonably be assumed that the plant protein lacks proper interactions with other components of the enzymatic machinery in yeast. Despite the unavoidable alterations of the natural pathway, the genetic RNase P swap demonstrates that PRORPs are fully proficient RNase P enzymes capable of faithfully processing an entire set of cellular tRNA precursors.
Apart from tRNA precursors, E. coli RNase P processes several other RNAs, the best characterized of which are 4.5S RNA and tmRNA [4]. Most of them mimic tRNAs with respect to the features critical for recognition by the bacterial enzyme. Similarly, tRNA-like structures in plant mitochondrial transcripts (t-elements) and in two human long noncoding RNAs were shown to be substrates for A. thaliana PRORP1 and human nuclear RNase P, respectively [9], [10], [27], [28]. In fact, any given RNase P's ability to cleave a certain RNA solely depends on the appropriate configuration of the structural determinants for interaction and cleavage site positioning, and not necessarily on a genuine tRNA structure. Thus, all types of RNase P are potentially involved in the processing of tRNA-related RNA structures, even though no such tRNA mimics have been identified in, e.g., yeast so far. The nuclear RNase P RNP, however, was proposed to have a substrate range that extends far beyond tRNA-resembling RNAs, and this versatility was related to the RNP's protein complexity [6], [7], [13]–[15]. The concept was based on the apparent propensity of the nuclear RNP enzyme to bind and cleave RNA in an apparently structure- and sequence-independent way [19], on the copurification of various RNAs with the enzyme [20], and, in particular, on the accumulation of a variety of different RNA (precursor) species in different models of conditional RNase P deficiency [20], [21], [29]. A selection of the RNAs most highly enriched in the best-characterized, temperature-sensitive rpr1-ts model failed to even slightly accumulate in our RNase P-swapped strains, strains whose phenotype and fitness did not point to any functional deficit in RNA processing either. The same RNA precursors also failed to accumulate in a novel model of RNase P deficiency when we tried to reproduce and verify the published findings, suggesting that their accumulation in the rpr1-ts mutant is an indirect, strain-specific effect and not directly caused by insufficient RNase P cleavage activity. It is worth noting that the rpr1-ts enzyme showed wild type-like activity at the restrictive temperature in vitro [30] and the molecular basis of the temperature-sensitive phenotype of the rpr1-ts strain thereby appears unclear. Furthermore, consistent in vitro cleavage by RNase P could not be demonstrated for any of the accumulating RNAs, and the RNA sets identified in the three previously published models (rpr1-ts and pop1-ts mutants; depletion of Rpp1p) were vastly different and showed an only negligible overlap [20], [21], [29]. This was attributed to either differences in the time course of the respective models, or the fact that all RNase P deficiency models except for rpr1-ts also affected the related RNP RNase MRP [20]. It is unclear, however, why presumptive RNase P substrates would not accumulate if RNase MRP were co-affected. In our RNase P-swapped strains RNase MRP was unaffected anyway and the largely unchanged phenotypic properties and fitness of the strains suggest that either the plant protein is able to process the same wide range of structurally diverse RNAs as the complex yeast RNP, or, more plausibly, the spectrum of RNA substrates of nuclear RNase P in yeast is much narrower than currently assumed, conceivably restricted to tRNA and tRNA-like structures.
Beyond RNA processing, still further functionality has been ascribed to the nuclear RNase P RNP. The human enzyme was reported to be required for transcription by RNA polymerase I and III through either direct transcriptional activation or chromatin remodeling; intriguingly, components of the RNP were reported to be recruited to chromatin stepwise, although the entire RNP holoenzyme was required for transcriptional activation [31], [32]. Based on a genetic interaction of RPR1 with the RNA polymerase III transcription factor TFIIIB [33], a similar role in transcription was proposed for yeast nuclear RNase P [7], [34]. However, the straightforward replaceability of the yeast RNP by an unrelated plant protein appears not compatible with a crucial role of RNase P in transcription. Unless protein subunits of the RNP other than Rpr2p are responsible for transcriptional activation on their own, this would require A. thaliana PRORP3 to be productively engaged in the same interactions with chromatin and/or the transcriptional machinery as the yeast RNP. The latter possibility appears unlikely, and the lack of a phenotype that could have pointed to a specific functional deficit of the RNase P-swapped strains largely rules out any specialized function of the yeast RNP that cannot be accomplished by the protein enzyme. Surprisingly, the only phenotypic difference between wild type and RNase P-swapped strains consistently observed in both genetic backgrounds was a growth advantage of the latter under sodium chloride stress, which might hint that a monomeric protein is better able to cope with the associated changes of the intracellular milieu than the multi-subunit complex. The specific reasons for the otherwise mostly opposing growth and fitness differences of RNase P-swapped strains in the two genetic backgrounds remain unclear, either. The genomes of the S. cerevisiae strains BY4743 and CEN.PK are distinguished by a huge number of single nucleotide variations and small insertions/deletions, and by a few strain-specific genes [25], but none of those currently offers any obvious explanation for the genetic context-dependent fitness differences of RNase P-swapped strains.
Altogether our findings indicate that structural diversity and trends towards higher complexity within the RNase P family are not accompanied by functional diversification or increasing versatility. It seems that rather than broadening the enzymes functional spectrum as a result of positive selection, the increasing number of proteins in the nuclear RNP may have co-evolved with the RNA's loss of structural integrity through a process called constructive neutral evolution [35], [36]. According to this concept, the initially catalytically proficient RNase P RNA gathered RNA-binding proteins during evolution that fortuitously stabilized its structure. This stabilization allowed the RNA to accumulate destabilizing mutations, whereby its capacity to fold into the catalytically active conformation became dependent on the initially facultative binding partner(s). The dependency increased by a ratchet-like process, as further destabilization of the RNA structure was increasingly more likely than reversion to autonomy. Constructive neutral evolution was suggested to (at least in part) explain the evolution and gratuitous complexity found in some macromolecular machines, like, e.g., the spliceosome [35], [36]. The numerous proteins of the nuclear RNase P RNP would thus primarily serve to support and stabilize the RNA catalyst's structure. Consistently, even in the complex eukaryal RNP, the substrate specificity is primarily held by the RNA subunit, as the RNA alone is able to cleave tRNA precursors and model substrates at the correct cleavage site, although with very low efficiency [37]. The possibly only major functional diversification within the RNase P family, the RNase P/MRP split, contributes another relevant aspect. The two RNPs that obviously originate from a gene duplication of the ancestral RNase P RNA early in eukaryal evolution, (still) use essentially the same set of proteins to support their individual functions [3], [6]. If the protein subunits had a major role in conferring substrate specificity, the functions of the two RNP enzymes would be expected to either largely overlap, or the protein moieties would be expected to have diverged more markedly during eukaryal evolution. As the opposite of the two scenarios appears to be the case, the functions of RNase P and MRP must be essentially defined by their RNA moieties. Functionally distinct RNPs having a similar protein composition are actually not without precedent, and exemplified, e.g., by spliceosomal snRNPs and their common set of Sm proteins [38]. Intriguingly, the wide overlap in protein composition with RNase MRP is what might have rescued the nuclear RNase P RNP from extinction. Savings for a cell when substituting the functionally proficient, multi-subunit RNP with a single protein enzyme would be negligible, as (most) protein subunits would anyway have to be synthesized to maintain the essential functions of the sibling RNase MRP.
While it remains unclear why the RNA-based form of RNase P has been so widely preserved and a protein-only form exclusively “invented” in Eukarya, there are some further aspects of PRORP evolution that require mention. PRORP and RNP must have coexisted in the same genetic system, either nucleus or organelle, for quite some time in early Eukarya, yet there is currently no evidence for such redundancy in any living organism. This is surprising given that we did not observe a negative synthetic interaction of PRORP and the RNP enzyme during strain construction or plasmid shuffle. The ease with which the yeast nuclear system could be experimentally swapped from an RNP to a protein enzyme suggests that the evolutionary re-routing of PRORP to a different cellular compartment or its de novo acquisition from an endosymbiont, in contrast to that of a multi-component RNP, is a rather straightforward process. Whereas this might explain the apparent predominance of PRORPs in organellar systems [39], there is currently no solid evidence for the subcellular re-routing of an entire RNase P RNP. Our data also indicate that the common assertion that organellar RNase P enzymes are simpler than their nuclear counterparts, is probably incorrect. At least for PRORPs there seems to be no fundamental functional difference between nuclear and organellar forms; both were able to replace the yeast nuclear RNP enzyme. Taken together, the RNase P family seems not only characterized by a bewildering structural diversity, but also by a remarkably high degree of functional uniformity, and exemplifies convergent evolution at the enzyme level in an unprecedented extreme. While non-homologous, isofunctional (protein) enzymes are not an uncommon phenomenon as such [40], [41], and among the tRNA maturation enzymes exemplified by tRNA:m1G37 methyltransferases [42] or tRNA ligases [43], here an RNA and a protein independently evolved the same enzymatic activity and identical cellular function, a convergence of chemically unlike molecules apparently triggered by their substrates, tRNAs, a class of macromolecules that themselves have remained structurally unchanged throughout evolution.
The plasmids pFA6-kanMX4 [44], pUG6 [45], and pUG27 [46] were used as templates to prepare gene disruption/replacement cassettes by PCR. The S. cerevisiae RPR1 gene, including ∼200 nucleotides of up- and downstream sequence, was PCR-cloned (for cloning primers see Table S3) into the EcoRI/PstI sites of the 2μ plasmids YEplac195 (URA3 marker) and YEplac181 (LEU2) [47]. The complete, uninterrupted coding sequences (without the organellar targeting sequence, where applicable) of the different PRORP genes were PCR-cloned into the XbaI/PstI or XbaI/ScaI sites of a derivative of YEplac181 containing the truncated S. cerevisiae ADH1 promoter that was generated as described previously [48]. Active-site mutants of PRORP genes were generated by site-directed mutagenesis using the QuikChange protocol (Agilent Technologies).
A. thaliana PRORP3 together with the ADH1 promoter was PCR-amplified from the PRORP3-YEplac181 construct and ligated together with a S. cerevisiae ADH1-terminator PCR fragment into the BsiWI/SalI sites of pUG6 [45]. pYM44 [49] was the template to prepare the PRORP3-yeGFP-tagging cassette. The S. cerevisiae TEF1 promoter, the yeGFP coding sequence (from pYM44), and the S. cerevisiae ADH1 terminator were PCR-amplified and ligated together into the BsiWI/SalI sites of pUG6.
CRE recombinase was expressed from pSH47 [45]. For all plasmids generated in the course of this study the inserted sequences were verified.
Standard, complex rich (YPD), or glucose-containing synthetic complete, drop-out or minimal media were used, and standard techniques of cultivation and yeast genetics (selection on drop-out or antibiotic-containing plates, sporulation, tetrad dissection, mating, replica plating, etc.) employed [50], [51]; cells were grown at 30°C unless otherwise specified. For negative URA3 selection (plasmid shuffle and removal of pSH47) appropriate dropout media were supplemented with 1 mg/ml 5-fluoroorotic acid and 50 µg/ml uracil. Plasmid DNA and PCR products were introduced into yeast cells by lithium acetate/polyethylene glycol-mediated transformation [52].
All S. cerevisiae strains generated in this study were derived from either BY4743 [23] or CEN.PK [24]. Gene disruption/replacement cassettes were prepared by PCR using primers with ∼40 nucleotides of homology to the target site at their 5′ end [53]. The primers with their targets and amplified genes/markers are listed in Table S4. The genotype of all new strains was confirmed by PCR using primers listed in Table S5.
One RPR1 allele of BY4743 and CEN.PK, respectively, was replaced by the A. thaliana PRORP3 expression cassette and kanMX4 (Figure 1A), sporulation was induced and G418-resistant spores selected. Spores of opposing mating type were selected and mated after removing the floxed kanMX4 [45] in one of them. In the case of the BY4743 background, spores of opposing genotype with respect to the heterozygous LYS2 and MET15 loci were selected, to recreate the LYS2/lys2Δ0 MET15/met15Δ0 genotype of the parent strain. The remaining floxed kanMX4, and pSH47 were subsequently removed to create strains isogenic with their parents except for the RPR1 locus. Three clones were selected from the final strain construction step. In the case of CEN.PK, rpr1Δ::PRORP3/rpr1Δ::PRORP3 clones were obtained from different spores/mates and one of the wild type clones from a mate of two wild type spores (including transformation of plasmid based markers for selection of diploids and their subsequent removal). To delete RPR2, the gene was replaced with a floxed HIS3MX cassette [46] in two rpr1Δ::PRORP3 spores of opposing mating type (Figure 1B). After the removal of HIS3MX in one of them, they were mated, and the remaining floxed HIS3MX and pSH47 removed from the diploids. Again, three clones, isogenic with their parent strains except for RPR1 and RPR2, were selected from the final step.
For the plasmid shuffle experiments one allele of RPR1 was disrupted in BY4743 using a kanMX4 cassette (Figure S1). PRORP3 was GFP-tagged at its C-terminus in a BY4743 rpr1Δ::PRORP3 strain using a yeGFP-HIS3MX cassette. Wild type BY4743 and CEN.PK, and their RNase P-swapped (rpr1Δ::PRORP3 rpr2Δ0) diploid derivatives were “GFP-labeled” by integration of a yeGFP expression cassette and kanMX4 into one allele of the leu2Δ0 and the leu2-3_112 locus, respectively. The floxed kanMX4 and pSH47 were subsequently removed.
The strain JLY1 (rpr1::HIS3 [RPR1]) and its temperature-sensitive derivative (rpr1::HIS3 [rpr1-ts]) were kindly provided by David R. Engelke [20], [21], [30]. Strain R1158 and the derivatives TH 5545 (PPOP4Δ::tetO-PCYC1) and TH 3877 (PPOP8Δ::tetO-PCYC1) were obtained from the yeast Tet-promoters Hughes collection (yTHC, Open Biosystems) [22]. POP4 and POP8 gene expression were shut down by addition of doxycycline to 50 µg/ml to the culture medium.
Total cellular RNA was prepared by cell breakage with acid-washed glass-beads in a Precellys homogenizer and acidic guanidinium-phenol extraction using a commercially available reagent (RNAtidy G, AppliChem). 750 ng RNA were resolved by denaturing urea-polyacrylamide gel electrophoresis, electroblotted to nylon membrane, and cross-linked by UV. After prehybridization (6× SSC, 10× Denhardt's solution, 0.5% SDS, 100 µg/ml sheared, denatured salmon sperm DNA) the membranes were probed with 32P-labeled oligonucleotides in 6× SSC, 0.1% SDS over night at 42°C; oligonucleotide probes are listed in Table S6. Blots were sequentially washed with 6× SSC, 4× SSC, and 2× SSC, all with 0.1% SDS, subjected storage phosphor autoradiography, and quantitatively analyzed with ImageQuant TL 7 (GE Healthcare). Blots were stripped in 40 mM Tris·Cl (pH 7.6), 1% SDS, 0.1× SSC for 1 hour at 80°C before re-probing.
cDNA synthesis with gene specific primers and quantitative SYBR green real-time PCR was carried out as previously detailed [12]. The primers are listed in Table S7. Primary data analysis and normalization were carried out with the MxPro qPCR software (Agilent Technologies).
The strains were grown to stationary phase in synthetic complete medium on 2% glycerol and 2% ethanol. Cells were washed twice with H2O, and suspended in sterile H2O at 5×106/ml. Suspensions of these respiration adapted, stationary, starved cells were kept shaking at room temperature and used to start micro-cultures for phenotypic profiling. Starter-cultures prepared in this way gave reproducible results in growth-kinetic experiments over a period of at least 4 weeks.
Growth under different conditions was monitored in 96-well plates in 150 µl per well inoculated with 106 cells. The plates were incubated at 30°C (unless otherwise indicated) in an EnSpire plate reader (PerkinElmer) with discontinuous “linear” shaking (60 sec at 1000 rpm, 0.5 mm amplitude; 36 sec pause) and automated turbidity readings at 600 nm taken every 10 min until all micro-cultures had apparently reached stationary phase. Growth data were processed in Excel (Microsoft) essentially as previously described [54], [55]. Optical densities were blank subtracted, corrected for non-linearity and path length, and finally loge-transformed. The maximal growth rate corresponds to the steepest slope that was stable (r2>0.995) for at least 4 hours and the lag phase to the time-intercept of the steepest slope [54]. Endpoints (final optical densities) were arbitrarily defined at the time when the growth rate had dropped below 0.025. Cultures in rich, complex medium did not reach a stationary phase during the experimental window; optical densities at the diauxic shift (sudden drop of the growth rate below 0.07) were derived instead.
Starter cultures were grown in glucose-containing synthetic complete medium to early logarithmic phase and pairs of unlabeled and GFP-labeled strains mixed in equal proportion. A sample was withdrawn for analysis of the initial ratio and the co-cultures were grown to stationary phase. Samples were taken for analysis, and fresh medium inoculated at 1∶1000 to reinitiate growth. Subsequently, seven full cycles of growth and dilution were carried out with samples always taken at stationary phase. The samples were fixed with 4% paraformaldehyde and the fraction of GFP-positive cells in the samples was measured with an Accuri C6 flow cytometer (BD Biosciences).
The means of the three strains were compared by one-way ANOVA and, pairwise, by Tukey's multiple comparison test using Prism (GraphPad). The means of two strains were compared by unpaired Student's t test.
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10.1371/journal.pbio.0060327 | BEAF Regulates Cell-Cycle Genes through the Controlled Deposition of H3K9 Methylation Marks into Its Conserved Dual-Core Binding Sites | Chromatin insulators/boundary elements share the ability to insulate a transgene from its chromosomal context by blocking promiscuous enhancer–promoter interactions and heterochromatin spreading. Several insulating factors target different DNA consensus sequences, defining distinct subfamilies of insulators. Whether each of these families and factors might possess unique cellular functions is of particular interest. Here, we combined chromatin immunoprecipitations and computational approaches to break down the binding signature of the Drosophila boundary element–associated factor (BEAF) subfamily. We identify a dual-core BEAF binding signature at 1,720 sites genome-wide, defined by five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers. Dual-cores are tightly linked to hundreds of genes highly enriched in cell-cycle and chromosome organization/segregation annotations. siRNA depletion of BEAF from cells leads to cell-cycle and chromosome segregation defects. Quantitative RT-PCR analyses in BEAF-depleted cells show that BEAF controls the expression of dual core–associated genes, including key cell-cycle and chromosome segregation regulators. beaf mutants that impair its insulating function by preventing proper interactions of BEAF complexes with the dual-cores produce similar effects in embryos. Chromatin immunoprecipitations show that BEAF regulates transcriptional activity by restricting the deposition of methylated histone H3K9 marks in dual-cores. Our results reveal a novel role for BEAF chromatin dual-cores in regulating a distinct set of genes involved in chromosome organization/segregation and the cell cycle.
| The genome of eukaryotes is packaged in chromatin, which consists of DNA, histones, and accessory proteins. This leads to a general repression of genes, particularly for those exposed to mostly condensed, heterochromatin regions. DNA sequences called chromatin insulators/boundary elements are able to insulate a gene from its chromosomal context by blocking promiscuous heterochromatin spreading. No common feature has been identified among the insulators/boundary elements known so far. Rather, distinct subfamilies of insulators harbor different DNA consensus sequences targeted by different DNA-binding factors, which confer their insulating activity. Determining whether distinct subfamilies possess distinct cellular functions is important for understanding genome regulation. Here, using Drosophila, we have combined computational and experimental approaches to address the function of the boundary element-associated factor (BEAF) subfamily of insulators. We identify hundreds of BEAF dual-cores that are defined by a particular arrangement of DNA sequence motifs bracketing nucleosome binding sequences, and that mark the genomic BEAF binding sites. BEAF dual-cores are close to hundreds of genes that regulate chromosome organization/segregation and the cell cycle. Since BEAF acts by restricting the deposition of repressing epigenetic histone marks, which affects the accessibility of chromatin, its depletion affects the expression of cell-cycle genes. Our data reveal a new role for BEAF in regulating the cell cycle through its binding to highly conserved chromatin dual-cores.
| Chromatin insulators/boundary elements (BEs) [1,2] are defined as sequences able to insulate a transgene from its chromosomal context and to block promiscuous enhancer–promoter interactions or heterochromatin spreading [1,3–5]. These elements are thought to subdivide the genome into functional chromosome domains, through their ability to cluster DNA loops [1,2] and to control the deposition of histone epigenetic marks [6–8] to regulate chromatin accessibility for gene expression [9–13].
No common signature and/or mechanism of action has been identified among characterized insulators/boundary elements [2]. Rather, several factors confer insulating activity by targeting different DNA consensus sequences in the known insulators. In Drosophila, insulating factors include dCTCF [14,15], Zw5 [16], boundary element–associated factor (BEAF) [17], and the well-characterized suppressor of Hairy-wing (Su(Hw)) [1,18,19], which targets hundreds of distinct, largely uncharacterized genomic sites [20–22]. Whether each of these factors and subfamily of insulators might possess distinct cellular functions is of particular interest.
BEAF blocks both enhancer–promoter communication [17,23–25] and repression by heterochromatin, as shown using reporter transgenes [5,25]. This insulating activity of BEAF was also evidenced by a genetic screen in yeast [4], confirming that, unlike de-silencing activity, BEAF binding sites must bracket a transgene for insulation. The hundreds of BEAF binding sites have not been characterized in situ, however, and the cellular function of BEAF remains to be elucidated in vivo.
Here we have combined computational and experimental approaches to address the function of BEAF binding sites in vivo. We have identified ≈1,720 BEAF dual-core elements genome-wide that share an unusual organization conserved over 600 bp. The dual-core signature consists of five to six BEAF binding motifs bracketing 200 bp AT-rich nuclease-resistant spacers. BEAF dual-cores juxtapose to hundreds of genes highly enriched in gene annotations regulating chromosome organization/segregation and the cell cycle. Accordingly, BEAF depletion leads to cell-cycle and chromosome segregation defects. Quantitative RT-PCR analyses further show that dual-cores regulate the expression of key cell-cycle genes including cdk7 and mei-S332. These results are also reproduced in embryos expressing truncated beaf mutants, which abolish the proper targeting of BEAF to dual-cores and its insulating activity. Chromatin immunoprecipitation analyses show that BEAF acts by restricting the deposition of methylated H3K9 marks in dual-cores. Our data reveal a new role for BEAF in regulating chromosome organization/segregation and the cell cycle through its binding to highly conserved chromatin dual-cores.
The DNA-binding activity of BEAF has been well-characterized in vitro [17,20,23,24]. Each subunit of the BEAF complex targets one CGATA motif. Point mutations within this consensus abolish both its binding and insulating activities. Clusters of three to four CGATA motifs can create high-affinity (Kd ∼ 10–25 pM) BEAF in vitro binding sites, which we call single elements. A computational scan of the Drosophila genome revealed thousands of single elements, yet immunostaining analysis demonstrated that they were not good predictors for BEAF binding in vivo. For example, Chromosome 4 was found to contain hundreds of single elements, yet immunodetection analysis showed only three major BEAF signals on this chromosome (Figure 1A). Interestingly, statistical analysis showed that single elements were often organized in a pair-wise configuration. Genome-wide, 988 single elements form 494 so-called “dual-cores,” which harbor two separate clusters of three CGATAs, a statistically significant result (p-value ∼ 1e-9). Moreover, 1,226 additional “dual-core–like” elements have a second cluster of two (instead of three) CGATAs. These elements include all characterized BEAF insulators whose activity involves a second, lower-affinity CGATA cluster (Kd ∼ 400–600 pM) where BEAF binding is abolished when the first high-affinity cluster is mutated [20,23].
Detailed analysis by alignment of all 1,720 dual-core and dual-core–like elements showed a highly organized distribution of their 12,058 CGATAs, which preferentially segregate into two clusters separated by spacers of approximately 200 bp (Figure 1B). For scs' and other characterized BEAF insulators, these spacers were found to be relatively AT-rich [20,24,26]. Scanning the 1,720 dual-cores for A+T content showed that they all harbor significant AT-rich (>70%) sequences in their spacers (Figure 1C, Figure S1). The remarkably conserved organization of dual-cores indicates that they likely correspond to a highly specific BEAF-binding signature.
We tested this possibility by assaying BEAF binding to dual-cores by chromatin immunoprecipitation (ChIP) and ChIP-on-chip (Figures 1D and 2). Based on the signals obtained with anti-BEAF antibodies, dual-cores are expected to be precipitated much more efficiently than single elements (Figure 1A). Indeed, ChIP analysis confirmed that single elements were not bound by BEAF (Figure 1D). In contrast, dual-cores from the 7C locus of the X chromosome were efficiently bound by BEAF (Figure 1D, probes 4 and 5), while nearby control sequences or single elements were not (probes 1, 2, 3, and 6). Altogether, 25 out of 25 dual-cores and dual-core–like elements assayed by ChIP were found to be efficiently bound by BEAF in vivo (Figures 1D and 2; unpublished data). The actin promoter region, which contains six unclustered CGATA motifs, was not bound by BEAF (Figure 1D; last row), indicating that the distribution of CGATAs in dual-cores, rather than the number of CGATAs per se, is important for BEAF binding. Furthermore, ChIP-on-chip analysis over 350 Kbp of the X chromosome strengthens our conclusions, as all major peaks corresponding to regions where BEAF binds in vivo fit into a dual-core or a dual-core–like element (hereafter called “dual-cores”, see black rectangles in Figure 2; see our database at http://www.sfu.ca/~eemberly/insulator/ for a complete listing). We note that computer analysis occasionally retrieved minor peaks present in the shoulder of the major BEAF peaks (enrichment <2; red bars in Figure 2) that may be attributed to the cooperative binding of BEAF to additional CGATA motifs present in single elements juxtaposed to dual-cores (Figure 2, see black bars for “single”). However, no peaks were present in regions corresponding to dispersed single elements (Figure 2; see our database), confirming that they are not sufficient for BEAF binding. These results establish that BEAF elements organized into dual-cores indeed define a characteristic in vivo BEAF-binding signature (Figure 1E).
Analysis of the positioning of dual-cores relative to genes showed that they are preferentially associated with gene-dense regions. 545 dual-cores reside within 500 bp of promoter/transcriptional start sites (TSSs) (p-value = 6.7e-119) (Figure 3A), and more than 850 are within 2,000 bp. As dual-cores are preferentially distributed in pairs separated by approximately 5–15 kbp (p-value = 1.01e-33; Figure 3B), the remaining elements might be found at the 3′ borders of genes. However, we could not find any specific enrichment for dual-cores in the 3′ UTR of genes (unpublished data), indicating that the clustering of dual-cores can be attributed to the clustering of genes/TSS rather than the bracketing of genes by dual-cores per se. These features (see our genome-wide database) raise the possibility that dual-cores might exert a function distinct from that of Su(Hw) binding sites, which rarely juxtapose the TSS of genes [21,22,27].
Strikingly, genes containing a dual-core near their promoter were statistically enriched in gene-class ontology (GO) groups that include the cell cycle, chromosome organization/segregation, apoptosis, and sexual reproduction (p-value < 1e-6; Figure 3C). These essential cellular processes require constitutive regulation, whereas genes associated with non-constitutive processes such as sensing and behavior were not enriched for BEAF dual-cores (Table S1). Inspection of Table S1 also shows that other cell functions enriched in BEAF dual-cores include GOs that can be linked to phenotypes observed in beaf mutants [25,28,29], such as chromosome architecture, germ-cell and imaginal-disc development, and eye morphogenesis defects. We asked whether BEAF might be involved in regulating the cell-cycle and/or chromosome organization by siRNA-mediated depletion of BEAF from cells. Reduction of BEAF levels to background occurred from day 3–4 (Figure 3D), when defects in cell growth are first observed (Figure 3E). In addition, FACS and microscope analyses showed that BEAF depletion led to a significant and reproducible increase (>3×) in the proportion of cells with 4N DNA content and with phenotypes typical of chromosome segregation defects (Figure 3F and 3G). These observations support our conclusion that the selective association of the corresponding GOs with closely linked dual-cores likely reflects a biologically significant localization.
We next asked whether the phenotypes observed upon BEAF-depletion can be attributed to the loss of activity of BEAF dual-cores associated with 160 genes that control cell-cycle chromosome dynamics. These include mei-S332 and cdk7, two major chromosome-segregation and cell-cycle regulators [30–32] whose promoter regions are bound by BEAF in vivo (Dual-cores 38/56, Figure 1D). Remarkably, further DNA-motif searches showed that the dual-cores associated with cdk7 and mei-S332, and more generally with all genes belonging to the cell-cycle and/or chromosome dynamic GOs, also contain the TATCGATA consensus sequence recognized by DREF (p-value ∼ 2.4e-6; Figure 4A). DREF activates hundreds of cell-cycle regulatory genes [33] and, importantly, might compete with BEAF for binding to the overlapping consensus [34]. Hence, DREF-regulated dual-cores may define a distinct regulatory subclass (Figure 4A, right).
To test how BEAF might affect the expression of genes associated with dual-cores that do or do not contain a DREF consensus site, we performed quantitative RT-PCR expression analysis from BEAF-depleted or control cells (Figure 4). BEAF depletion did not affect the expression of control genes (see Figure 4A, left), including those located near a single element (Figure 4B; actin, CG9745) where BEAF does not bind (Figure 1). The expression of all genes associated with a dual core lacking a DREF element was consistently found to be positively regulated by BEAF by approximately 4-fold to 5-fold (CG1430, CG10946, CG1444, snf, ras, janus; Figure 4B). These data are in complete agreement with previous work showing that BEAF has a positive effect on gene expression by de-repressing a transgene from surrounding chromatin [17,20,23,24]. In stark contrast, the expression of all genes associated with a dual-core harboring a DREF consensus, including cdk7 and mei-S332, specifically increased by approximately 4- to 6-fold upon depletion of BEAF (Figure 4B; CG32676, mei-S332, cdk7, CG10944, ser). Accordingly, Western blot analysis showed that Cdk7 and Mei-S332 protein levels increased under these conditions (Figure S2). Therefore, two categories of dual-cores may be found. In those lacking a DREF consensus, BEAF positively regulates gene expression; in those that contain a DREF consensus, BEAF may prevent binding of DREF to its overlapping consensus, thereby controlling the activation of the associated cell-cycle and chromosome organization/segregation GOs.
Quantitative RT-PCR analysis showed that DREF depletion resulted in a more than 10-fold down-regulation of cdk7 (Figure 5), confirming the role of DREF as a transcriptional activator of this gene. To further characterize the respective roles of BEAF and DREF in regulating cell-cycle regulatory genes by binding to dual-cores, we eliminated the DREF consensus from the dual-core associated with cdk7 (dre mutant, Figure 5A) and transfected this construct or its wild-type version into cells depleted of BEAF or of DREF by siRNA (Figure 5B). Because the dre mutant does not modify the CGATA BEAF consensus and still harbors the dual-core signature (2× 3CGATAs separated by the spacer; Figure S7B), this construct may be used to reveal the effect of the BEAF dual-core on the expression of cdk7 independently of DREF. Importantly, mutating the DREF consensus site led to a down-regulation of cdk7 (Figure 5B, cdk7-mut, blue bar), similar to what is found by depleting DREF. Strikingly, BEAF depletion further impaired the expression of cdk7 by approximately 5-fold (Figure 5B, cdk7-mut, red bar) compared to the expression of the identical construct in control cells (Figure 5B, cdk7-mut, blue bar). We conclude that, although BEAF regulates DREF-mediated activation, it additionally positively regulates the expression of cdk7, as found for other genes associated with a dual-core lacking a DREF consensus. In support of this conclusion, we obtained a similar result for snf, which is transcribed in opposite orientation relative to cdk7 (Figure 5A). Snf is under the influence of the same dual-core as cdk7, yet its expression is not regulated by DREF (Figure 5B). However, BEAF depletion reproducibly impaired snf expression by approximately 6-fold, similar to what we obtained for cdk7 in the absence of DREF. These results show that BEAF has a positive role on the expression of genes associated with dual-cores, in addition to its role in controlling activation by DREF.
BEAF insulating activity can protect a transgene from repression by chromatin [5,25]. The expression of genes positively regulated by dual-cores might implicate mechanisms similar to those required for insulation, and we asked whether BEAF might control the deposition of epigenetic marks, as shown for other types of insulators [7,35,36]. We tested this possibility by measuring the levels of histone H3 methylated on lysine 9 (H3K9me3), a characteristic mark of heterochromatin, as a function of BEAF depletion. The deposition of H3K9me3 was strongly increased upon BEAF depletion (Figure 6A). Double immunostaining analysis showed that this increase was specific, as RNA polymerase II, actin, or unmodified histone H3 levels were unchanged (Figure 6A and 6B, Figure S3A and S3B). Numerous and broader H3K9me3 foci not restricted to heterochromatin regions appeared in BEAF-depleted cells (Figure 6B, 3× panels; [37]), strengthening the view that H3K9me3 also acts to influence gene expression in euchromatin [8,38,39]. ChIP-on-chip analysis confirmed that discrete H3K9me3 peaks are found in many promoter regions, including those associated with a dual-core (Figure S3C). However, these H3K9me3 peaks appear to be distinct from the broader and larger H3K9me3 peaks found in regions where genes are known to be repressed (e.g., eye, Figure S3C) and where the methylK27 mark is also present (not shown; B. Schuettengruber unpublished data).
We further tested if BEAF affects the deposition of H3K9me3 marks into dual-cores by performing ChIP analysis using anti-H3K9me3 antibodies on BEAF-depleted, DREF-depleted, or control cells (Figure 6C). BEAF-depletion led to a significant and reproducible increase of approximately 8-fold in H3K9me3 levels for the dual-cores linked to snf-cdk7, similar to that obtained for mei-S332 and CG1430, and in stark contrast to the stable levels found for the actin control (Figure 6C; unpublished data). In contrast, no variation in H3K9me3 levels could be found upon DREF depletion (Figure 6C), showing that this increase is specific to BEAF depletion. This result also rules out that the changes we observe overall could be due to off-target effects. Moreover, CDK7 depletion, which severely impaired cell-cycle progression (unpublished data), did not affect the levels of H3K9me3 (Figure 6C), indicating that their increase is not due to an indirect perturbation of the cell cycle upon BEAF depletion. Finally, H3K9me3 levels did not vary in control regions located a few kbp away from the dual-core, suggesting that BEAF controls the deposition of this mark locally (unpublished data). These results show that BEAF dual-cores are involved in blocking the deposition of H3K9me3 marks, fully consistent with their ability to positively regulate the expression of dual core-associated genes.
To confirm that the observed increase in H3K9me3 levels is directly linked to the activity of BEAF, we introduced mutations in two of the CGATA motifs of the dual-core associated with cdk7 (“beaf-mut”, Figure 7A) and transfected this construct or constructs harboring a wild-type dual-core or a dual-core mutated in the DREF site (dre mutant, Figure 7A) into cells. Quantitative PCR analysis of chromatin immunoprecipitated with anti-H3K9me3 antibodies showed that mutation of the BEAF site led to an increase in H3K9me3 levels of approximately 3.8-fold compared to wild-type or dre mutant constructs (Figure 7B), establishing that BEAF directly controls the deposition of H3K9me3. This did not affect the levels of H3K9me3 in the endogenous cdk7 dual-core, as measured from the same batch of transfected cells, showing that the observed increase is indeed specific for the mutated dual-core. We conclude that BEAF serves to restrict the deposition of H3K9me3 marks into dual-cores.
The deposition of epigenetic marks is critical for regulating gene activity at the level of chromatin accessibility [9,12,13], which may account for the positive effect of BEAF on gene expression. We sought to determine whether BEAF-regulated deposition of H3K9me3 marks affects the expression of cell-cycle genes. BEAF-depleted or control cells were treated with anacardic acid (AA), a histone acetyltransferase (HAT) inhibitor that globally affects gene expression by altering the accessibility of chromatin [40]. AA treatment did not affect the expression of either control genes or dual core-associated genes (compare grey and black bars in Figure 8). In contrast, AA severely compromised the activation of snf, cdk7, or mei-S332 upon BEAF depletion compared to untreated BEAF-depleted cells (Figure 8; unpublished data). This result strongly supports a model whereby BEAF restricts the deposition of methylated H3K9 marks, thereby protecting the expression of dual core-associated genes from repression by chromatin (see Discussion).
Are these variations in gene expression related to the cooperative binding of BEAF to the two clusters of CGATAs present in dual-cores? We sought to answer this question by using transgenic fly lines expressing the C-terminal BEAF self-interaction domain (BID in Figure 9A) under the control of a GAL4 activator. BID lacks the BEAF DNA-binding domain, impairing the insulating activity of BEAF [25] by preventing its cooperative binding to two nearby CGATA clusters (Figure 9B). Importantly, defects in expression of cdk7, snf, and/or mei-S332 were highly similar in embryos expressing BID to that observed in BEAF-depleted cells (compare Figures 9C and 4B). This result supports our conclusion that BEAF binding is required to regulate these genes in vivo. It also suggests that the cooperative binding of BEAF to conserved dual-cores, which is abolished by BID, may be important for the regulation of gene expression by BEAF. Accordingly, cell functions enriched in BEAF dual-cores include GOs (Table S1) that correspond to phenotypes observed following expression of beaf mutants, which are lethal to flies [25], or to GOs found to genetically interact with these mutants [28].
Results of our in silico analysis reveal ∼1,720 BEAF dual-cores in the Drosophila melanogaster genome that share a striking organization (Figure 1E). Genome-wide ChIP-on-chip analysis detects approximately 1,800 significant BEAF binding sites (C. M. Hart, unpublished observations), suggesting that our dual-core database encompasses most in vivo BEAF binding sites. The few (<100) additional peaks not included in our database but detected by ChIP-on-chip analysis may correspond to elements initially scored as single elements but whose organization is close to that of dual-cores. These rare exceptions are in part due to the computer stringency of the dual-core signature. For example, BEAF-1255 can be bound by BEAF in vivo (Figure S4), yet this element could not be scored as a dual-core because one out of five of its clustered CGATA motifs lies 3 bp outside the defined 100-bp window (‘out' in Figure S4). Furthermore, approximately 10% of the minor BEAF sites are found in regions lacking any CGATA motifs, including the scs insulator (unpublished data) [16]. Since this region is not directly bound by BEAF, it is thus possible that some of the minor BEAF peaks are due to indirect interactions between BEAF and other insulator proteins, as previously suggested for the scs′–scs pair of insulators [16]. Other protein–protein interactions that regulate BEAF binding could also involve the splicing variant of the beaf gene itself, called BEAF-32A [23], which does not harbor the BEAF DNA-binding domain that recognizes clustered CGATA motifs. ChIP-on-chip analysis using antibodies that also recognize this isoform showed no additional major peaks (Figure S5, compare ‘−32A' with ‘+32A'), indicating that dual-cores constitute the main binding sites for both BEAF isoforms. Finally, we note that the BEAF-32A isoform is unlikely to play a major role in the activities described here, as its binding is dispensable for the insulating function of BEAF [20], and its expression is not essential for the development of embryos into adult flies [29]. Taken together, our results show that the BEAF dual-core signature is a bona fide mark that identifies a cis-regulatory element that regulates the expression of nearby genes.
Results of our experiments using both BEAF depletion in tissue culture cells and BID expression in vivo provide clear evidence for specific functions of the BEAF dual-cores, reflected by a selective association with genes that control cell-cycle and/or chromosome organization/segregation. The competition between DREF and BEAF for binding to nested consensus sequences is also supported by ChIP analyses showing that DREF targets' identical sites [34] clearly enriched nearby genes associated with the cell cycle and chromosome dynamic GOs (Figure S6; unpublished data). Thus, while DREF levels increase at the G1/S transition to activate mei-S332 and cdk7 within the appropriate window for cell-cycle progression [30–32], BEAF may further facilitate this activation by restricting the deposition of H3K9me marks. Indeed, over-expressing BEAF was shown to reduce the phenotypes related to cell-cycle progression in flies that over-express DREF [33], supporting a role for BEAF in controlling the cell cycle. Such a model is also supported by our observation that AA treatment strongly represses these genes in BEAF-depleted cells and that mutation of the BEAF-binding site in a dual-core results in a local increase in H3K9m3 levels. In addition, computer analysis of micro-array expression data for Drosophila embryos during early development shows that the 545 genes associated with dual-cores are positively correlated with beaf expression (Figure S7A), in contrast to genes unlinked to these elements (p-value ∼ 3e-17 according to the Kolmogorov-Smirnov test). This strict correlation further indicates that BEAF has a global positive role on gene expression genome-wide, and similar analyses did not reveal any significant correlation change between genes whose TSS is closely juxtaposed (<100 bp) to dual-cores, including snf or cdk7 (Figure S7B), compared to genes whose TSS is more distant (500 bp). Accordingly, the cell-cycle and chromosome dynamics GOs that include cdk7 and mei-S332 are enriched for positively correlated genes (see our database for a detailed list). Taken together, our results show that BEAF could play an important role in chromosome organization during the cell cycle through a regulated switch involving the BEAF–DREF competition: According to such a mechanism, BEAF would restrict the deposition of H3K9me3, allowing dual-core–associated genes to remain in a potentially active state, while controlling the time of activation of cell-cycle GOs by DREF. Accordingly, BEAF depletion leads to down-regulation of genes associated with a dual-core lacking a DREF element (CG10946, ras, CG1430, Janus, CG1444), but to increased expression of CG32676, mei-S332, cdk7, CG10944, and ser, which are under the control of DREF-associated dual-cores (Figure 4). In the latter case, the apparent contradiction between the positive—restriction of H3K9me3 deposition—and negative effects of BEAF can be reconciled by our results showing that BEAF controls the activation of these genes by DREF. BEAF depletion relieves the competition for binding by DREF, leading to the increased expression of cdk7 or mei-S3332 in spite of an increased deposition of H3K9me3 marks under these conditions. Mutating the DREF or BEAF binding sites of DREF-associated dual-cores (Figures 5 and 7) allows for distinguishing between these different effects on the expression of linked genes.
It is intriguing that the spacers of dual-cores are well-conserved. One possibility is that they may be preferentially bound by a nucleosome, as recently shown for CTCF insulators [41]. Supporting this idea, the known dual core-spacers correspond to nuclease-resistant “cores”, between two nuclease-hypersensitive sites (BE76, scs′) [20,24,26] (Figure S8), where a nucleosome may be present (C. M. Hart, unpublished observations). Indeed, we found that dual core-spacers fall within predicted nucleosome-positioning sequence (NPS) databases [42–44], as indicated by NPS/dual-core sequence alignments (Figure S8; not shown), possibly accounting for the conserved organization of dual-cores. Our results further suggest that the cooperative binding of BEAF across these AT-rich spacers may be important for BEAF function. Indeed, expression of BID, which prevents its cooperative binding across the spacers, mimics the effect of BEAF depletion on the expression of dual-core–associated genes, as also found by mutagenesis of two CGATA motifs from one dual-core cluster. However, BEAF still efficiently binds in vivo to the few dual-cores that harbor a shorter spacer (<150 bp; e.g., see Dual-core 1,254, Figure 1; unpublished data), indicating that the conserved dual-core–spacer is dispensable for BEAF binding. Recent reports have shown that gene expression is differentially regulated through nucleosome positioning in several species [12,13,42,43]. Positioned nucleosomes may restrict promoter accessibility in yeast, and pausing of RNA polymerase II facing the +1 nucleosome may be regulated through nucleosome positioning in Drosophila [44]. Similarly, dual-cores are also closely associated with TSSs, and a potential link to nucleosome positioning strengthens the view that BEAF may regulate chromatin accessibility for gene expression through a restriction of the deposition of methylated H3K9 marks into dual-cores.
Our model whereby dual-cores regulate the deposition of specific epigenetic marks is in agreement with the activity of other known insulators [6,7,9–11]. Variations in H3K9me3 levels might affect the interplay between the deposition of H3K9me3 and acetylated histone H4 (H4Ac) marks [45]. However, no variation in the deposition of H4Ac could be found in dual-cores compared to control regions after BEAF depletion (unpublished data). This is not surprising, as BEAF has no de-silencing activity on its own [5,25]. Computer analysis failed to reveal any enrichment of dual-cores near the 3′UTR of genes, and the activity of dual-cores may thus essentially play a role in regulating chromatin accessibility near promoter regions, but not within the 3′ border of genes. Furthermore, the insulating activity of BEAF was demonstrated in the context of two dual-cores bracketing a transgene [5,25], and most likely also involved higher-level chromatin organization [2]. Although not enriched near the 3′UTR of genes, dual-cores still bracket/separate groups of genes clustered within 5–15 Kbp, a genomic context that may further require insulating activity to block promiscuous enhancer–promoter interactions and involve DNA looping between distant insulators [2]. It has recently been shown for a Su(Hw) insulator that the regulation of gene expression may further depend on its genomic environment [46]. Also, other dual-cores are often found in the vicinity of genes exposed to repression by heterochromatin (see our genome-wide database), and the function of BEAF may be particularly important in this context [17,20,23,24]. We propose that the BEAF dual-cores closely linked to a restricted array of several hundred genes define a family of insulators that provide a link between chromatin organization and the cell cycle.
All genome-wide predictions and analyses are available on our Web site: http://www.sfu.ca/~eemberly/insulator/. Additional information, including DNA sequences of single elements, dual-cores or dual-core–like elements, and their position relative to genes or other genomic features (GOs) can be directly retrieved from our Web site.
Each single BEAF element that was not a part of a dual-core element was analyzed for the presence of a “dual-core–like” signature. We define single elements as consisting of three CGATAs within 200 bp, and a dual-core–like element as a single BEAF element (three CGATAs) associated with a second nearby (<800 bp) cluster of two CGATA sites within a 100-bp window. 1,226 BEAF elements fit into this classification, including all previously identified dual-cores (BE76, BE28, BE51, Jan/Ser(BE83)). The position of each CGATA site within a dual-core sequence was analyzed relative to the position of the rightmost site of the first BEAF single element. In Figure S1, the position of each CGATA motif was measured from the average position (taken as position 0 on the x-axis) of all the CGATA locations in the first BEAF single element of the dual-core. This removes any ambiguity in defining the starting position of the sequence, allowing more precise mapping of dual-cores with respect to gene promoters.
We predicted dual-cores by pairing together the genome-wide set of 7,045 single BEAF elements that were separated by a spacer <L bp. The statistical significance of the number of predicted dual-cores as a function of spacer length L was assessed by comparing it to the expected number for randomly spaced elements. The p-value was found to reach a flat minima for 600 bp < L < 3,000 bp. For larger L values, the predictions decreased in significance, eventually becoming no more significant than chance. There are 1,720 dual-cores, L = 800 bp with a p-value of 1e-9, in the sequenced Drosophila melanogaster genome.
The statistical significance of the number of dual-cores within +/− d bp of a promoter was assessed by comparing it to the number expected for randomly placed elements. Out of 1,720 dual-core elements, 545 fall within +/− 500 bp of a promoter. Beyond this distance, the p-value was found to decrease in statistical significance, yet 850 dual-cores reside within 2,000 bp of a promoter. Additional dual-cores are found close to genes or groups of genes (see our database).
In order to analyze the distribution of dual-cores, we calculated the statistical significance for a minimum number of dual-cores, 2, 3,…x dual-cores (DC) to be found along 5, 10, …100 kbp of DNA (W). For a given W and DC, we predicted N(W,DC), the frequency of dual-cores for a certain DNA length. To assess the significance of N(W,DC), we compared it to the number of randomly distributed elements for the same DNA length. If the probability of a random dual-core element to occur within a window of size W is p, then the probability that there are ≥DC elements in W is P(W,DC) = B(x > DC,W,p), where B is the binomial distribution. The expected number of domain predictions for these random elements is then E(W,DC) = Nwin(W)*P(W,DC), where Nwin(W) is the number of non-overlapping windows of size W in the entire genome. The p-value for N(W,DC) can then be evaluated using the expected number E(W,DC) as a function of W and DC. We find W = 10 kb and DC = 2 to yield the statistically most significant BEAF dual-core distribution in pairs (p-value ∼ 1.01e-33).
The statistical significance of a GO class was assessed using the binomial distribution, p-value = B(x, N, p), where x is the number of genes within the given GO class in a set of N predicted genes, and p is the probability of that GO class in the entire annotation. See our database for a complete listing of all GO analyses of positively correlated genes with or without BEAF dual-cores or DREF elements in their promoters.
Genome-wide Drosophila gene expression data (Figure S7) covering the first 12 hours of embryonic development are available from the Berkeley Drosophila Genome Project. Twelve time points were collected, each with three replicates. Each gene g in the genome has an expression profile containing 12 data points (gi = (x1, x2, …, x12)). For a given pair of genes, we calculated the Pearson correlation coefficient between their respective expression profiles. We then calculated the correlation coefficient between a given set of genes and a given reference gene. To test whether two sets of genes had statistically different correlation coefficient profiles, we used the Kolmogorov-Smirnov test, which assigns a p-value to the likelihood that two samples of a continuous random variable come from the same parent distribution.
Chromatin immunoprecipitation (ChIP) was done according to the Upstate protocol using control or beaf siRNA-treated cells. Equivalent amounts of chromatin samples were sonicated using a Diagenode Bioruptor and immunoprecipitated with 4 μl of anti-H3K9me3 (Abcam). Precipitated DNA was analyzed by real-time PCR in parallel with genomic DNA using a Roche Light Cycler and a Light Cycler FastStart DNA Master SYBR green kit. The amplified DNA fragments (<250 bp) cover regions corresponding to the indicated elements (Figures 6 and 7). ChIP with anti-BEAF was performed as previously described [34] with 10 μl affinity-purified anti-BEAF antibodies that recognize (Figure S5) or not (Figure 2) the BEAF-32A isoform or IgG. The immunoprecipitated DNA was analyzed in parallel with input genomic DNA as a standard. For ChIP-on-chip assays using H3K9 antibodies, precipitated DNA was amplified by ligation-mediated PCR (LM-PCR). 4μg of each amplified sample was used to hybridize on 3 × 385 K tiling microarrays representing the euchromatic, non-repetitive regions of the Drosophila melanogaster genome sequence (Flybase release 4.3) from Nimblegen Systems (GEO accessions: GPL3352, GPL3353, GPL3354). To calculate whether the levels of enrichment are statistically significant for each array, a normal distribution was calculated, with the assumption that the mode and median absolute deviation of the normalized log2 ratios are the average and the standard deviation of the normal distribution, respectively. Assuming that the normal distribution covers the entire background noise (non-significant signals), a p-value was calculated for each oligonucleotide signal. For the two replicate samples of each profile, each pair of probe p-values were then combined using a Chi Square law with 4 degrees of freedom. Finally, correction for multiple testing [47] was applied to the combined p-values. Only oligonucleotides with final p-values (for combined replicates) < 1E-04 were considered to be significantly enriched for the signal.
For siRNA treatments, exponentially growing Drosophila Schneider SL2 cells were maintained between 1 and 4 × 106 cells/ml in Schneider's Drosophila medium (SDM, GIBCO, Invitrogen) supplemented with 10% Fetal Bovine Serum (FBS, Sigma) and 1% penicillin/streptomycin (GIBCO, Invitrogen). Cells were diluted to a final concentration of 1 × 106 cells/ml in SDM without FBS, and 400 μl of 2 μM beaf32, dref or cdk7 double-stranded RNAs (dsRNA) were added directly to 10 ml of cells which were then plated on 75-cm2 T-flasks (Sarstedt), immediately followed by vigorous agitation. dsRNAs were synthesized using full-length cDNAs of the above genes as templates. Primers consisted of a complementary template portion, a floating end with a T7 promoter and an EcoR1 site located at the other end. 5 μg of DNA template were transcribed for 2 hours at 37 °C in the presence of 0.5 mM rNTPs, 10 mM DTT, 120 units RNAse inhibitor, 60 units T7 polymerase in its 1× buffer in a 100 μl final volume. cDNA degradation was performed for 30 to 40 minutes at 37 °C in the presence of 4 units RQDNase in a 400 μl final volume of the recommended buffer. dsDNAs were then extracted with phenol/chloroform, ethanol-precipitated, and solubilized in 20 μl TE, pH 7.5. The resulting sequences were checked for potential off-target effects by performing searches with dsCheck [48] (http://dsCheck.RNAi.jp/). Treated cells were incubated for 2 hours at 25°C, followed by addition of 20 ml of SDM containing FBS, and cells were incubated for an additional 5 days. Depletion of beaf32 mRNA was assayed by RT-PCR at 1, 3, or 5 days after treatment. Cells were grown for 5–6 days, and samples were recovered for total RNA, immunostaining, or immunoblotting analysis. FACS analyses were performed after resuspending control or BEAF-depleted cells and staining their DNA with propidium iodide. Analysis of gene expression was performed by quantitative RT-PCR on cDNAs prepared by RT-PCR from BEAF-depleted or control cells (+5–6 days), untreated or treated with AA (5 μM) for 24 hours. Each measurement was reproduced three times and in two independent RNA extraction experiments. For gene expression analysis, cDNAs prepared from control or BEAF-depleted cells were quantified in parallel with genomic DNA by RT-PCR using a Qiagen Light Cycler. Transfections of plasmids were performed using Lipofectamine (Invitrogen) for 2 hours according to the manufacturer's instructions, 48 hours before RNA purification. Measurements of gene expression for the transfected (wild-type or mutant) constructs were performed using primers that specifically amplify cDNAs from the tags introduced at the 5′ and 3′ borders (see Figure 5) and that were unable to amplify cDNAs from untransfected cells (unpublished data). Expression was normalized to the copy number of transfected constructs estimated by quantitative PCR of input genomic DNA. For endogenous genes, the primer sequences were selected from the coding regions (≈1,000 bp 3′ from promoter start) of each gene. For endogenous cdk7/snf, the selected primers lie outside (15 bp 5′ or 3′) of the tags. For other analyses, two primer pairs were used alone or in combination to confirm the specific increase/decrease in gene expression, using actin as a control. For quantitative RT-PCR analysis in embryos, males with the BID transgene on Chromosome 2 (CyO/Sp1; BID2B) were crossed with virgin females harboring an embryonic da-GAL4 driver (daughterless) on Chromosome 3. The corresponding measurements were compared to those from embryos expressing da-GAL4 alone or from BID2B embryos without a da-GAL4 driver.
For mutagenesis of Dcore38_D, a genomic DNA fragment harboring the first exons of cdk7 and snf was cloned, and PCR-mediated mutagenesis was performed using primers that contain mismatches as followed: the dre (DREF site) mutant sequence is TAgCGATA and disrupts DREF binding but preserves the CGATA consensus of BEAF. The BEAF site mutant was produced by mutagenesis of two of the CGATA consensus in one cluster of the dual-core, using the ttATA mismatches critical for BEAF binding [17,23–25].
Immunostaining analyses were performed using affinity-purified mouse or rabbit anti-BEAF-32B (1:100) as previously described [34,49], using the indicated affinity-purified antibodies or commercially available anti-acetyl-Histone H4, anti-H3K9me3, anti-H3, anti-RNA polymerase II (Upstate), or anti-actin antibodies (Sigma). Double immunostaining of siRNA-treated cells was performed in duplicates and in parallel for control or BEAF-depleted cells treated for 1, 3, or 5 days. Each experiment was repeated three times. DNA was stained with 500 ng/ml DAPI or 1 μg/ml Hoechst, and coverslips were mounted with 4 μl of antifading mix and sealed with nail polish. Slides with siRNA control or BEAF-depleted cells were analyzed using the same acquisition parameters using a Leica DMRA2 microscope. Mapping of BEAF dual-cores and immunolocalization of anti-BEAF signals was performed over >10 Mbp for Chromosome 2 and X chromosome, showing striking correspondence (analysis available upon request). For mapping of nucleases-sensitive sites (Figure S8), freshly isolated nuclei from approximately 108 cells were digested with very low concentrations of either microccocal nucleases or DNAase I essentially as previously described [17,20,23,24], and the purified DNA was further digested with PvuII and run onto a 1.2% agarose gel for Southern blotting. Naked DNA controls were similarly digested. A PvuII-EcoRI end-labeled DNA fragment was used to probe specifically the region containing the dual-core region. Western blotting was performed using anti-actin or anti-BEAF antibodies. As a control, genomic DNA was first purified and then digested with MNase and Pvu II (+/− EcoRI to mark the 5′ border of the dual-core) before analysis by Southern blotting. Western blotting was performed as previously described [17,24] using anti-actin, anti-H3K9me3, anti-mei-S332, or anti-BEAF antibodies.
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10.1371/journal.pcbi.1006206 | Stochastic shielding and edge importance for Markov chains with timescale separation | Nerve cells produce electrical impulses (“spikes”) through the coordinated opening and closing of ion channels. Markov processes with voltage-dependent transition rates capture the stochasticity of spike generation at the cost of complex, time-consuming simulations. Schmandt and Galán introduced a novel method, based on the stochastic shielding approximation, as a fast, accurate method for generating approximate sample paths with excellent first and second moment agreement to exact stochastic simulations. We previously analyzed the mathematical basis for the method’s remarkable accuracy, and showed that for models with a Gaussian noise approximation, the stationary variance of the occupancy at each vertex in the ion channel state graph could be written as a sum of distinct contributions from each edge in the graph. We extend this analysis to arbitrary discrete population models with first-order kinetics. The resulting decomposition allows us to rank the “importance” of each edge’s contribution to the variance of the current under stationary conditions. In most cases, transitions between open (conducting) and closed (non-conducting) states make the greatest contributions to the variance, but there are exceptions. In a 5-state model of the nicotinic acetylcholine receptor, at low agonist concentration, a pair of “hidden” transitions (between two closed states) makes a greater contribution to the variance than any of the open-closed transitions. We exhaustively investigate this “edge importance reversal” phenomenon in simplified 3-state models, and obtain an exact formula for the contribution of each edge to the variance of the open state. Two conditions contribute to reversals: the opening rate should be faster than all other rates in the system, and the closed state leading to the opening rate should be sparsely occupied. When edge importance reversal occurs, current fluctuations are dominated by a slow noise component arising from the hidden transitions.
| Discrete state, continuous time Markov processes occur throughout cell biology, neuroscience, and ecology, representing the random dynamics of processes transitioning among multiple locations or states. Complexity reduction for such models aims to capture the essential dynamics and stochastic properties via a simpler representation, with minimal loss of accuracy. Classical approaches, such as aggregation of nodes and elimination of fast variables, lead to reduced models that are no longer Markovian. Stochastic shielding provides an alternative approach by simplifying the description of the noise driving the process, while preserving the Markov property, by removing from the model those fluctuations that are not directly observable. We previously applied the stochastic shielding approximation to several Markov processes arising in neuroscience and processes on random graphs. Here we explore the range of validity of stochastic shielding for processes with nonuniform stationary probabilities and multiple timescales, including ion channels with “bursty” dynamics. We show that stochastic shielding is robust to the introduction of timescale separation, for a class of simple networks, but it can break down for more complex systems with three distinct timescales. We also show that our related edge importance measure remains valid for arbitrary networks regardless of multiple timescales.
| Variability in dynamical biological systems is ubiquitous. Discrete state, continuous time Markov process models are used throughout cell biology, neuroscience, and ecology to represent the random dynamics of processes transitioning among multiple locations or states [1–3]. Examples include transitions between states defined by degree of phosphorylation and subcellular compartment location in a signaling network [4], transitions among several conducting and non-conducting states in populations of ion channels [5], random genetic drift across a fitness landscape [6], random dispersal of mobile populations [7], and many other processes [8]. Often fluctuations arise at the molecular level, whether from discrete population effects, thermal (Brownian) effects, or deterministic high dimensional nonlinear dynamics (chaos) at microscopic scales.
In general, nonlinear stochastic systems cannot be solved mathematically in closed form. Even if we limit ourselves to Markov processes, i.e. models for which the probability distribution of future states is independent of the past history, given the current state (meaning that the current state is as complete a description of the process as possible, and no additional “hidden” variables exist), the effects of noise on biological dynamics must usually be studied via computer simulation. However, exhaustively simulating all noise sources within a given molecular level Markov process is often computationally prohibitive. Hence there is a need for complexity reduction methods.
In this paper we investigate a complexity reduction method for discrete state, continuous time Markov process models known as stochastic shielding which we summarize in the next paragraph [9, 10]. Complexity reduction for such models aims to capture the essential dynamics and stochastic properties of a system via a simpler representation, with minimal loss of accuracy. There is substantial literature on the approximation of complex random walk models with simpler models by mapping states of the full model to the nodes of a smaller set of states [11–23]. This includes coarse-graining of complex networks [11–13], elimination of fast variables via quasi-steady state approximation [24], marginalization of a partially observed Markov process through the solution of a filtering problem [25], the k-core decomposition (first proposed in [14] and shown to be effective for visualization in [15]), and various clustering algorithms that have been developed recently [16–20] (reviewed by [21]). Aggregation of tightly interconnected nodes and adiabatic elimination of fast variables lead to reduced models that are no longer Markovian [20, 22]. As another approach, one may eliminate rarely visited nodes, again leading to a reduction in the number of states [23]. Stochastic shielding provides an alternative approach by simplifying the description of the noise driving the process, while preserving the Markov property, by removing from the model those fluctuations that are not directly observable [9]. As illustrated in Fig 1, rather than reduce the number of nodes in the graph, the stochastic shielding approximation reduces the number of independent noise sources used to drive the stochastic process on the graph, while preserving the dynamical behavior of a particular projection of the random process.
As discussed in more detail in Methods §Summary of Stochastic Shielding in the Langevin Case, in the Langevin approximation for a time homogeneous first-order transition network, the population fraction occupying states 1, …, n is a vector X ( t ) ∈ R n satisfying
d X d t = L X + ∑ k ∈ E B k ξ k (1)
where L, the graph Laplacian, captures the mean flux along each directed edge k ∈ E (edge set). The matrix Bk gives the effects of fluctuations ξk around the mean flux along the kth edge. The noise terms, ξk, are independent, white and Gaussian, one for each directed edge. Given an observable of interest, represented by a vector M ∈ R n, the stochastic shielding approximation consists in finding a partition of the edge set into edges of primary importance (E 1) and secondary importance (E 2) that gives an approximate process Y ( t ) ∈ R n satisfying
d Y d t = L Y + ∑ k ∈ E 1 B k ξ k , Y ( 0 ) = X ( 0 ) (2)
by neglecting the noise forcing along the edges of secondary importance. Such an approximation typically creates a (small) pathwise discrepency relative to X that can be quantified by our edge importance measure, also defined in Methods and discussed in more detail below [10].
The stochastic shielding approximation exploits filtering properties intrinsic to any network. Given an observable defined on the network (for example the indicator function for a subset of states representing nodes of interest), the fluctuations in population flux along some edges will have a greater impact on fluctuations in the observable, while other edges’ fluctuations will have a lesser impact. Hence the network “shields” the observable from some fluctuations, which may therefore be ignored with little loss of accuracy. To put it another way, the effects of a fluctuation in the movements of populations far removed from a location of interest do not directly affect the fluctuations in the population of interest; their effect reaches the observed nodes only via the indirect effect of influencing the population immediately surrounding the node or nodes of interest. One may view the source of fluctuations (relative to the average flux along a given edge) as independent noise forcing associated with each edge in the graph [10]. Edges that connect nodes that are indistinguishable, with respect to the measurement vector M, are themselves not directly observable. The fluctuations in rates of transition along these hidden edges are “averaged over” and their effect on the observed value (M⊺ X(t)) is reduced.
This filtering effect leads to the possibility of a novel approximation scheme. Rather than approximating a random process on a graph by aggregating together subsets of nodes, we may replace the fluxes along a subset of edges with the mean flux along the respective edge. If a graph has K directed edges, there are 2K − 1 such “approximations”, as the independent noise along each edge can be either included or excluded from the approximation. Including all noise terms gives the original model, whereas excluding all noise terms gives a model with no fluctuations.
Which of these 2K − 1 different approximations is the “best approximation”? The stochastic shielding method provides the following rule: suppress the noise along those edges connecting indistinguishable nodes. We extended this method by introducing an edge importance measure that quantifies the effect of suppressing noise along each edge separately. For a linearized Langevin equation (multidimensional Ornstein-Uhlenbeck (OU) process) approximating the full population process, we showed that when the process satisfies detailed balance, the variance of the observable states can be decomposed into a sum of fluctuations attributable to each pair of directed edges in the graph. Thus, the edge importance measure allows one to rank the edges such that the most important edge contributes the most to the stationary variance of the observable states.
We previously applied the stochastic shielding method to Markov processes arising in neuroscience (Hodgkin and Huxley’s sodium and potassium ion channel models) and processes on Erdos-Renyi random graphs [10]. However, these processes do not include significant timescale separation. In the present paper we study processes with nonuniform stationary probabilities and multiple timescales, including ion channel models with “bursty” dynamics.
Separation of timescales is an important property of many neural systems [26]. For instance, many ion channels exhibit bouts of repeated channel opening and closing, interspersed by long periods of channel closure—often referred to as bursty conductances. The nicotinic acetylcholine receptor (nAChR) is a well studied ligand-gated ion channel that can exhibit bursty behavior [27–29]. Acetylcholine (ACh) is a neurotransmitter that plays a key role in motor function via this ion channel, and the opening of the nAChR channel pore requires the binding of ACh. For low acetylcholine concentration ([ACh]), the nAChR is a classic example of a bursty ion channel.
In the next section, we explore the robustness of the stochastic shielding phenomenon and the accuracy of the approximation under conditions of timescale separation and sparsity in the stationary distribution, by way of the edge importance measure described in [10]. We show that typical edge importance hierarchy is robust to the introduction of timescale separation for a class of simple networks, but that it can break down for more complex systems with three or more distinct timescales, such as the nAChR described above. Nevertheless we also establish that the edge importance measure remains a valid tool for analysis for arbitrary networks regardless of multiple timescales.
The shielding phenomenon leads the fluctuations associated with directly observable transitions to dominate the variance of the observable states in many networks, but this rule does not hold universally. The edge importance measure (see Eq 42 in Methods) provides an exact means to evaluate the applicability of stochastic shielding to any model (Markovian, with first-order transitions) by quantifying the effect of suppressing noise along each edge separately. This measure considers the pathwise mean square error between two trajectories: the full stochastic process with all fluctuations included, and an approximate process with a subset of fluctuations excluded. We use this measure to rank the edges in order of importance with respect to the stationary variance of the observable states. Moreover, we show that the stationary variance decomposes into a sum of contributions from each edge. This decomposition is unique and follows from a straightforward calculation that we describe and prove in Theorem 1 in the last subsection of Results. We apply the stochastic shielding method and compute the edge importance measure for the acetylcholine receptor model introduced above and for a set of simple networks (3-state chains) with timescale separation.
The nicotinic acetylcholine receptor is a ligand-gated ion channel and the opening of the channel pore requires the binding of acetylcholine. For low acetylcholine concentration, the nAChR is a classic example of a bursty ion channel. This channel has been described many times in the literature, and we will follow the formulation from Colquhoun and Hawkes [30]. Following Figure 4.1 in their paper, the channel has five states with ten possible transitions between states. The states form a graph with vertices i ∈ V = { 1 , 2 , 3 , 4 , 5 } and edges k ∈ E = { 1 , ⋯ , 10 } (see Tables 1 and 2). Fig 2A shows the transition state diagram. The channel can be bound to zero, one, or two ACh molecules. When singly or doubly bound the channel may be open or closed, whereas the unbound state is always closed. Table 1 gives the definition of the states and labels each state as open (observable) or closed (unobservable). State 5 (T) is the unbound state (closed), state 4 (AT) is singly bound (with 1 molecule of ACh) and closed, state 3 (A2T) is doubly bound and closed, state 2 (A2R) is doubly bound and open, and state 1 (AR) is singly bound and open. The measurement vector M specifies which states are open and which are closed by labeling each state with a 1 or 0, respectively. In this case, M is given by
M = ( 1 1 0 0 0 ) ⊺ (3)
meaning that states 1 and 2 are open/conducting states and states 3, 4, and 5 are closed/non-conducting states. Table 2 gives the definition of the edges and the transition rates. Note that the ten transitions are numbered starting with the pair of transitions connecting states 1 (AR) and 2 (A2R) and moving clockwise back to state 1; these are reactions 1-8. The last pair of transitions (9 and 10) connect states 4 (AT) and 5 (T). We will write the per capita transition rate for the kth reaction, with source node i and destination node j, either with a single index denoting the reaction (αk) or with a double index denoting the source followed by the destination (αij). Thus, α1 and α21 are synonymous.
Burstiness is defined by the observation of isolated single channels opening and closing in bouts [29–31]. Fig 2D shows a sample trace of our model simulation exhibiting burstiness of the channel for low agonist concentration ([ACh] = 0.5μ Mol). (For details on the model simulation, see Numerical Methods, in Methods.) Fig 2E zooms in on the burst in panel D labeled by the red arrow. The distribution of closed intervals shows a mixture of slow and fast timescales, requiring combinations of two or more exponentials with widely separated time constants. These time constants are related to the eigenvalues of the graph Laplacians (see Eq 4, and see Methods for details). The ratio of eigenvalues will be used as a measure of timescale separation. Fig 2B shows the presence of timescale separation at low [ACh] concentrations by plotting the ratios of the eigenvalues {λ2/λj}j = 3,4,5. Significant timescale separation occurs when λ2/λj << 1, or in words, when the two eigenvalues differ by at least one order of magnitude. The graph Laplacian has leading eigenvalue λ1 = 0. For the acetylcholine receptor, and for the systems we study here, the remaining eigenvalues are real and negative, and are ordered so that 0 > λ 2 ≥ λ 3 ≥ … ≥ λ | V |, where | V | is the number of states.
We apply the stochastic shielding method to the nAChR model and show that it works well for high acetylcholine concentration, but not in the bursty regime characterized by low ACh concentration. In fact, we see a reversal of edge importance at low agonist levels (see Fig 2C and discussion below). In light of the network filtering effect underlying stochastic shielding, we might naïvely expect that the edges connecting states 2 and 3, and states 1 and 4, should contribute the most to the stationary variance of the observable states (1 and 2), but this is not the case. There is even a regime where the observable edge pair (edges 3 and 4) is only the third most important edge, as defined by our edge importance measure.
Computing the edge importance measure (Eq 42 in Methods), the fraction of the stationary variance contributed by edge k, requires the graph Laplacian L (and its corresponding eigenvalues and eigenvectors), the noise coefficient matrix B (defined below), the stationary mean flux Jk, and the measurement vector M. The graph Laplacian L as a function of ACh concentration c is
L = ( - ( a 1 + k + 2 * c ) 2 k - 2 * 0 b 1 0 k + 2 * c - ( a 2 + 2 k - 2 * ) b 2 0 0 0 a 2 - ( b 2 + 2 k - 2 ) k + 2 c 0 a 1 0 2 k - 2 - ( b 1 + k + 2 c + k - 1 ) 2 k + 1 c 0 0 0 k - 1 - 2 k + 1 c ) (4)
and matrix B is
B = ( J 1 ζ 1 J 2 ζ 2 … J 10 ζ 10 ) , (5)
where Jk = Ntot αij πi(k) is the stationary flux across edge k for a total population of Ntot ion channels, αij is the appropriate transition rate of reaction k (Table 2) and ζk is the stoichoimetry vector for reaction k. The kth stoichoimetry vector describes how an individual moves from node i to node j in reaction k. For instance, the first two stoichoimetry vectors are
ζ 1 = ( 1 − 1 0 0 0 ) ⊺ (6)
ζ 2 = ( − 1 1 0 0 0 ) ⊺ , (7)
which correspond to transition 1 (an individual moves from state 2 to state 1) and transition 2 (an individual moves from state 1 to state 2), respectively, in Fig 2A. Note that ζ1 = −ζ2, and this relationship holds for each edge pair in the ACh transition graph.
The matrix B depends on the equilibrium population distribution π → = ( π 1 , … , π 5 ) ⊺. Since π → is the leading eigenvector of the graph Laplacian L, the equilibrium fraction πi of the population in state i will change as a function of c (ACh concentration). Lastly, recall that the measurement vector M = (1 1 0 0 0)⊺ as described in Table 1.
Fig 2C plots the relative edge importance Rk (fraction of the stationary variance contributed by edge k) for each edge k ∈ {1, …, 10} as a function of acetylcholine concentration over the range [ACh] ∈ [10−1, 102] μMol. At high concentrations, the most important edges are those connecting the doubly bound closed state to the doubly bound open state (edges 3 and 4), that is, the edges along which transitions are directly observable. This situation is consistent with results for Hodgkin-Huxley ion channels and generic Erdos-Renyi random graphs with randomly assigned binary measurement vector [9, 10]. In contrast, the most important edges at low concentrations are those connecting the singly bound state to the doubly bound closed state (edges 5 and 6 in Fig 2A). Although transitions along this edge are not directly observable, they make a greater contribution to the stationary variance of the open state than the opening/closing transitions.
Moreover, we find that edges 5 and 6 have the highest relative importance for low and intermediate concentrations, followed by edges 3,4 and 9,10. Just below a concentration of 10 μMol, the relative importance switches so that edges 3 and 4 become the most important for higher concentrations (≥ 10 μMol). To begin to understand why the edge importance ranking changes for low [ACh], we note that the relative importance depends heavily on state occupancy probability.
As has been previously observed, one of the nodes in the 5-state nAChR model has very low occupancy probability across all agonist concentrations [32]. In particular, states 2 (A2R) and 5 (T) are the most likely states to be occupied over the range of [ACh] considered. However, state 1 (AR, one of the open states) has very low occupancy probability and hence is rarely visited by the process. As a result, the most likely path between the unbound/closed state 5 (T) and the doubly bound/open state 2 (A2R) is 5 → 4 → 3 → 2. This means that transitions 7,8 and 1,2 do not happen very often. The stochastic shielding method predicts that these reactions should be important, but if they rarely happen, they contribute little to the stationary variance. Thus, their relative importance as computed by our edge importance measure is very small. Indeed, for all values of [ACh], the equilibrium occupancy probability of state 1, π1 is ≪ 1. The variance of the open states for a population of Ntot channels at equilibrium is
V [ Open ] = N tot ( π 1 ( 1 - π 1 ) + π 2 ( 1 - π 2 ) - 2 π 1 π 2 ) ≈ N tot π 1 ( 1 - 2 π 2 ) + N tot π 2 ( 1 - π 2 ) + O ( π 1 2 ) , as π 1 → 0 + .
Although the goal of the stochastic shielding approximation is not to change the network topology by eliminating nodes as other authors have suggested [23, 32, 33], when edges are “unimportant” it is natural to consider eliminating them. If all the edges to a node are unimportant, eliminating them would eliminate the node, and in this case the change in stationary variance of the open states would be approximately Ntotπ1(1 − π1) − 2Ntotπ1π2, if π1 is small. (Compare to [32], “Scheme 1”.)
The edge importance measure Rk (for each edge k) provides an intrinsic idea of how many edges could be suppressed in an approximation (whether by suppressing the fluctuations generated by that edge, which is the focus here, or by removing the edge entirely). For the typical operating range of the nAChR, roughly 1-10 μM [ACh], there are three transition pairs with similar edge importance (edge pairs 3,4, 5,6, and 9,10), suggesting that accurate simulations of stochastic effects would require keeping the fluctuations generated by all three of these edge pairs.
The acetylcholine receptor example suggests that the inversion of edge importance is related to timescale separation. In the next subsection, we investigate the edge importance measure in the presence of timescale separation, as well as a combination of sparsely and abundantly populated vertices. We show that edge importance ranking is preserved despite the introduction of arbitrary timescale separation in simple graphs (3-state chains) with per capita transition rates at two distinct timescales. As we will see, a system needs at least three distinct timescales in order to see the method break down. Nevertheless, the edge importance measure remains exact, and informative, for arbitrary networks, and can be used to extend the original stochastic shielding method to systems with timescale separation and bursty behavior.
Motivated by the example of the acetylcholine receptor, we systematically study the effects of introducing timescale separation into the simplest nontrivial model to which stochastic shielding applies: the 3-state chain with one observable state (or one pair of observable transitions into and out of the observable state). Specifically, we consider a discrete state, continuous time Markov jump process N ( t ) ∈ N 3 with Ntot random walkers moving independently on a graph with three nodes. See Fig 3 for an illustration of the graph, and see Methods for general notation and see S1 Supporting Information for a detailed description of the 3-state model. Here we assume that state 3 (black disk) is the observable state, which yields the following measurement vector: M = (0 0 1)⊺.
If we think of this model as a simplified ion channel with three states, then the observable state is the open or conducting state of the system, and all other states are closed or non-conducting. There are four directed edges in the graph, and edge k represents a transition from source node i(k) to destination node j(k) which happens at rate αk (or αij, see Methods for details on notation). We focus on the observed process M⊺N(t) which describes the evolution of the open state, and approximate processes that suppress noise along a subset of the four edges. In particular, we use the following two approximate processes to illustrate how stochastic shielding “usually” works: (i) suppress noise along edge pair 1,2 (and preserve noise along edge pair 3,4) and (ii) suppress noise along edge pair 3,4 (and preserve noise along edge pair 1,2). In most cases (i) is the best approximation; we investigate here whether or not this heuristic holds universally.
The mechanism of stochastic shielding can be readily understood by considering the power spectrum of the observed process M⊺N(t). The relationship between the power spectrum and the covariance matrix of a stochastic process is well known; the power spectrum is the Fourier transform of its covariance [34]. The stationary covariance C of a discrete state Markov process (such as N described above) is given by Gadgil, et al. [8], and satisfies the Lyapunov Eq 46 (see Methods).
The stationary variance R of the full and approximate observed processes has the following connection to the power spectrum: integrating over the power spectral density (PSD) S(ω) gives the stationary variance. Moreover, since the stationary variance decomposes into a sum of contributions from each edge in the graph (R = ∑kRk where Rk is the edge importance measure of edge k given in Eq 42), the power spectrum decomposes as well (S(ω) = ∑k Sk(ω), see Eq 66). We provide more details on how the power spectrum is obtained in Methods §Numerical Methods.
Fig 4B shows sample trajectories for the full process (denoted by X, black trace) and the two approximations (i) and (ii) described above (denoted by X3,4 (red trace) and X1,2 (blue trace), respectively) in the Gaussian (OUP) version of the model. Fig 4A shows the corresponding power spectral contributions for the three processes: S(ω) is the total PSD (shown in black), S3,4(ω) is the PSD for approximation X3,4 (red), and S1,2(ω) is the PSD for approximation X1,2 (blue). See Methods §Numerical Methods for details on model simulation and calculation of the power spectra.
At all frequencies, the power from the observable edge pair 3,4 predominates, as shown by the red dashed line (S3,4(ω)) closely following the black line (total PSD). This spectral decomposition agrees with our edge ranking based on edge importance (i.e. edge pair 3,4 contributes the most to the stationary variance), and illustrates why the stochastic shielding method says that the best approximation of the full process is to preserve the noise along edge pair 3,4 and to suppress the noise along edge pair 1,2. Fig 4B illustrates the consequence in the time domain: the red trajectory closely follows the black trajectory, but the blue trajectory only captures a rough approximation of the full process.
However, this situation breaks down and leads to edge importance reversal for certain bursty systems, which we aim to understand in the rest of the paper. In the remainder of this section, we show that edge importance inversion cannot be obtained by taking a 3-state chain and accelerating or decelerating any single edge, pair, or trio of edges with a single parameter (i.e. by introducing two distinct timescales). As we shall see, in order to invert the edge importance as we did in the nAChR example for low agonist concentration, we need to introduce a third timescale. This will be addressed in §Generalized 3-State Model with Timescale Separation.
For completeness, we may consider the same 3-state chain as in Fig 3, except that we set the middle state (state 2) to be the open/conducting state instead of state 3. The measurement vector in this case is M = (0 1 0)⊺. See the left column of Fig 6 for an illustration, and note that there are five possible cases to consider. In this version of the 3-state chain, all transitions are observable since each edge connects the conducting state to a closed state, and hence, all edges should be important in terms of stochastic shielding. State 2 no longer acts as a “shield” as it did when state 3 was the conducting state. We expect that the most important edges will either depend on the parameter α or all edges will be equally important in terms of the edge importance measure. We repeat the same analysis as in the previous section and the results are shown in Fig 6.
Fig 6 has the same three column format as Fig 5. The left column shows the 3-state diagram with accelerated/decelerated transition rates (1 or α as outlined in Table 3) where again α ∈ [10−4,104]. The middle column shows timescale separation as defined by the ratio of the two non-zero eigenvalues (λ2/λ3) versus α. The right column shows the relative edge importance Rk versus α. In contrast to the previous cases with state 3 conducting, now we see edge importance reversal or convergence in every case. This is what we expect, given that the stochastic shielding method says that all edges are important in this version of the model. However, we find edge importance reversal in Case 10 without corresponding timescale separation since λ2 and λ3 differ by less than one order of magnitude in that case.
We showed above that the presence of two distinct timescales was not sufficient to see an inversion of the edge importance in a 3-state network. However, as we show next, a network exhibiting three separate timescales can lead to edge importance reversal. In order to find examples of inversion, we study an ensemble of 3-state chains with observable state 3 (see Fig 2) with arbitrary transition rates {α12, α21, α23, α32}. We randomly draw the transition rates αij independently from a lognormal distribution with a given width w, that is, log(αij) is Gaussian distributed with mean zero and standard deviation w. Then we calculate the edge importance for each realization of transition rates for this general 3-state model and look at the instances for which R12 = R21 > R23 = R32. Note that Rij refers to the importance measure for the edges connecting node i to node j.
For an ensemble of 105 samples with log ( α i j ) ∼ N ( 0 , 10 ) (i.e. w = 10), we find that inversion of the edge importance occurs approximately 9.8% of the time. This observation raises a number of questions. Which factors contribute to inversion of the usual edge importance relation (e.g. timescale separation)? Given an arbitrary set of transition rates, is there a canonical transformation leading to edge importance reversal? Can we obtain an exact expression for the relative contribution of the hidden edges to the stationary variance? The balance of this section addresses these questions.
Fig 7 illustrates the distribution, for this ensemble, of several factors that might be expected to play a role in inverting edge importance. Each panel plots the relative importance of the hidden edges
η = R 12 R 12 + R 23 (9) versus factors representing node occupancy, timescale separation, flux distribution, and local timescale difference. Inversion of edge importance occurs when R12 > R23, that is, when η > 1/2.
Node Occupancy: The left column of Fig 7 plots η versus the stationary occupancy probability of each state: π3 for state 3 (panel A), π2 for state 2 (panel C), and the ratio π2/π3 (panel E). Panel A suggests that edge importance can be inverted for any values of π3 (mutatis mutandis π1), but panel C suggests that inversion requires π2 ≲ 1/6. Moreover, panel E indicates that inversion requires π2 < π3 (equivalently, α23 > α32 since π2/π3 ≡ α32/α23). Together, these conditions suggest that sparse occupancy of the hidden state directly connected to the observable state (relative to the occupancy of the observable state) contributes to inversion of edge importance. However, this condition alone is not sufficient, as shown by Example A below (see Examples subsection), for which the relative importance due to the hidden edges is η = 0.4132 < 0.5.
We can extract several strict inequalities relating η to properties of the 3-state process. Maximizing η with π2 fixed, we find
η ≤ ( 1 − π 2 1 + π 2 ) 2 . (10)
Fig 7C shows this inequality is tight (dashed red curve superimposed on the dots matches the upper boundary). In panel E, maximizing η with π2/π3 fixed, we observe that
η ≤ 1 - ( π 2 / π 3 1 + π 2 / π 3 ) = π 3 π 3 + π 2 = α 23 α 23 + α 32 (11)
(dashed red curve matching boundary), which shows that inversion (η > 0.5) is only possible if π2 < π3, or equivalently, α23 > α32. More extreme edge importance inversion requires a more extreme likelihood difference between the observable state and its neighbor or between the transition rates connecting these states.
Timescale Separation: We introduce two different notions of timescale separation. First, we define
ν = λ 3 / λ 2 (12)
which is the ratio of the two non-zero eigenvalues of the graph Laplacian L. This quantity is shown in Fig 7B where η is plotted versus ν. (Note ν is the reciprocal of the ratio used to define timescale separation in the previous 3-state model sections with two distinct timescales and discussed in Figs 5 and 6). Large timescale separation, defined via the eigenvalues of the graph Laplacian, occurs when ν ≫ 1. Specifically, Fig 7B shows that edge importance reversal requires timescale separation such that |λ3| ≳ 15|λ2| or (ν ≳ 15).
Second, we consider the relaxation time
τ i j = ( α i j + α j i ) - 1 (13)
for an isolated 2-state Markov processes with rates αij, αji between the nodes i, j. The ratio of two such local relaxation times gives an alternative measure of timescale separation within the network. Specifically, consider the two possible 2-state processes in our 3-state model (nodes 1-2 and nodes 2-3). In the first system (between nodes 1 and 2), the eigenvalues of the graph Laplacian are 0 and α12 + α21 = 1/τ12. Likewise, looking at states 2 and 3 as a 2-state Markov process yields eigenvalues 0 and α23 + α32 = 1/τ23. Fig 7F shows the dependence of η on the ratio of the non-zero eigenvalues for these two 2-state systems. Empirically, we see that
η ≤ τ 12 / τ 23 1 + τ 12 / τ 23 = τ 12 τ 12 + τ 23 (14)
(dashed red curve in Fig 7F where η is plotted versus τ12/τ23). That is, inversion of the edge importance (η > 0.5) occurs only when equilibration along the hidden edges is slower than along the observable edges (τ12 > τ23).
Stationary Flux Distribution: Recall that the stationary flux along edge k is given by Jk = Ntotαkπi(k). We can also represent this term as Jij, the stationary flux from node i(k) to node j(k) (see §Notation in Methods). Here we define
Δ J = J 12 - J 23 J 12 + J 23 (15)
which is the relative fraction of the stationary flux generated by the hidden edges. In Fig 7D, we observe that the upper boundary is given by
η ≤ 1 2 - Δ J 2 (16)
which says that edge importance reversal (η > 0.5) requires larger mean flux along the observable edges than along the hidden edges. In other words, the system needs to satisfy ΔJ < 0 or J12 < J23.
Reproducing edge importance reversal in 3-state chain models is advantageous because such simple Markov models can be analyzed more completely than models with greater numbers of states [23]. Fortunately, explicit expressions may be derived for the eigenvalues and eigenvectors of the 3-state chain model which allows direct calculation of η, the fraction of the stationary variance generated by the hidden edges (see S1 Supporting Information §Explicit calculation of η for detailed derivation):
η ≡ R 12 R 12 + R 23 = ( α 21 α 12 + α 21 ) ( α 23 α 12 + α 21 + α 23 + α 32 ) = ( π 1 π 1 + π 2 ) ( α 23 Tr [ - L ] ) (22)
where Tr[L] ≡ ∑i Lii is the trace of L.
The fraction in Eq 22 is a product of two factors (denoted by F1 and F2 and shown in Fig 7G for the ensemble). The first factor F1 is the ratio of the speed of transition from hidden state 2 to hidden state 1 (α21) to the sum of the transition rates between states 1 and 2. Equivalently, this is the proportion of time spent in hidden state 1 relative to hidden state 2. F1 approaches 1 as α12 decreases, which only occurs if condition 2 holds (α12 ≪ α21). The second factor F2 is the ratio of the opening transition rate (α23) to the sum of the four rates. This factor is large if and only if the opening rate is much faster than the other rates, and this is exactly condition 1 (α23 ≫ max{α12, α21, α32}). Together these two conditions bring about a reversal of edge importance (η > 0.5) in this simple scenario.
While the exact formula for the relative edge importance (22) applies only for the 3-state chain model considered here, we anticipate that analogous results may hold for more general Markov processes. We consider this question further in §Discussion.
Additional insight into the error arising from different noise-suppressing approximations can be obtained by examining the power spectral density distributions of the true and approximate processes. Recalling Fig 4A in the case αij ≡ 1, the power spectra for the full process with all noise sources included (S, black curve) and the approximate process with hidden edge flux noise suppressed (S3,4, red curve) are very similar, with an order of magnitude less power arising from the hidden edges at all frequencies. In contrast, Fig 8A shows the power spectra for the 3-timescale model. In particular, it shows that at low frequencies, the power spectrum for the approximate process with visible edge flux noise suppressed (S1,2, blue curve) is very similar to the PSD for the full process, but that the blue and red curves cross at an intermediate frequency (ω ≈ 3) so the red curve dominates at high frequencies.
The change in power spectral contributions is also reflected in model simulations (see Numerical Methods for details on simulations). Fig 8B illustrates sample trajectories for the three processes described above: full process X (black), approximation X3,4 (red), and approximation X1,2 (blue) where α = 10. Comparing this edge importance reversal case to the base case shown in Fig 4B, we see that the blue trajectory (instead of the red one) closely follows the black trajectory. Hence, X1,2 is the better approximation to the full process in this case.
Thus, the edge importance reversal observed under the combined conditions α12 ≪ α21 and α23 ≫ max(α12, α21, α32) may be understood as resulting from enhancement of the noise power contribution from the hidden edges at low frequencies, as well as the small amplitude of the full process’ power spectrum at high frequencies.
We see a similar mechanism at work in the 5-state acetylcholine receptor model in the low-[ACh] regime (where a hidden edge becomes more important than a visible edge) as opposed to the high-[ACh] regime, in which the usual edge importance ordering is observed. Figs 9 and 10 show the power spectrum and Gaussian model trajectories in the high-[ACh] and low-[ACh] regimes, respectively. Here we have similar notation to the 3-state cases: X (black) is the full observed process (Gaussian version) with all sources of noise included and Xi,j is the approximate process that preserves noise on edge pair i, j but suppresses noise on all other edges. In particular, we focus on the red trace (X3,4, noise preserved on visible edges 3,4) and the blue trace (X5,6 noise preserved on hidden edges 5,6).
The usual edge ordering via the edge importance measure for high [ACh] ranks edge pair 3,4 the most important, followed by edges 5,6, then 9,10 (the last two edge pairs have relative importance close to 0 and make the two lowest spectral contributions); See Fig 2. Fig 9A shows that for [ACh] = 100 μM, most of the power is attributable to the observable edge pair 3,4, and this agrees with the edge importance ranking. Model trajectories in panel B illustrate that X3,4 is the best approximation of the full process X and that the other approximations at best only capture the mean behavior of the system.
In the low-[ACh] case shown in Fig 10 ([ACh] = 0.5 μM), however, we see the crossing of the top blue and red power spectral density curves at an intermediate frequency (ω ≈ 2). As in the 3-state case, this indicates a reversal of edge importance whereby now the hidden edge pair 5,6 contributes the most to the stationary variance of the observable process. Again, this change in spectral contributions is reflected in model trajectories shown in panel B. We see that the blue curve X5,6 closely follows the full process X, and is the best approximation in this case, but the blue curve misses some of the fluctuations captured by the red curve X3,4 even though the red curve clearly deviates from the other two processes.
Gadgil et al. showed rigorously that the time evolution of the second moments of a discrete population evolving as a first-order reaction network system can be represented explicitly in terms of the eigenvalues and eigenvectors of the matrix that governs the evolution of the mean population dynamics [8]. We apply their general results to the specific example of a first-order transition network in two ways. First, we use the spectral decomposition of the stationary variance to establish our main stochastic shielding result. Second, their result on time varying systems allows us to obtain the decomposition of the power spectrum in terms of the eigenvalue spectral decomposition, shown in Eqs 64–66.
Consider an arbitrary first-order reaction network with graph Laplacian L and matrix B satisfying Eqs 32–36 (see Methods). The fact that the stationary covariance matrix decomposes into a sum of contributions from each edge in the graph follows from a straightforward calculation that we describe in Lemma 1 and Theorem 1. We defer the proof of Lemma 1 to §Methods, below.
Definition 1 Let X denote the set of n × n real matrices C such that for all j = 1, …, n, ∑ i = 1 n C i j = 0. Let Y = {C ∈ X | C⊺ = C}.
Lemma 1 Let L be an n × n real valued matrix with Lij ≥ 0 for i ≠ j, and Lii = −∑i,i ≠ j Lij (so that ∑ i = 1 n L i j = 0) for j = 1, …, n, and satisfying dim(ker(L)) = 1, and with a null eigenvector Lv = 0 satisfying vi ≥ 0 for i = 1, …, n. Then for any F ∈ Y, the equation L C + C L ⊺ = F (27) has a unique solution C ∈ Y.
Theorem 1 For an arbitrary first-order reaction network with graph Laplacian L and matrix B satisfying Eqs 32–36, there is a unique linear decomposition of the stationary covariance matrix C as a sum of contributions from each edge:
C = ∑ k ∈ E C k w h e r e (28)
C k = ∫ 0 ∞ ( e t L ) B k B k ⊺ ( e t L ) ⊺ d t (29)
Proof 1 Proof of Theorem 1. Consider a first-order reaction network defined by graph Laplacian L and matrix B, satisfying Eqs 32–36. We want to solve the Lyapunov equation L C + C L ⊺ = - B B ⊺ (30) for matrix C. Note that L satisfies the conditions in Lemma 1, and BB⊺ ∈ Y since BB⊺ is an n × n real symmetric matrix with columns that sum to zero. Then by Lemma 1, Eq 30 has a unique solution C ∈ Y. By replacing F with BB⊺ in the proof of Lemma 1, we see that the unique solution is C = ∫ 0 ∞ e t L B B ⊺ ( e t L ) ⊺ d t (31) since all eigenvalues of L have negative real part (except for the Perron-Frobenius eigenvalue λ1 ≡ 0), u 1 ⊺ B = 0, and B⊺u1 = 0.
Since BB⊺ can be written as a sum of
B
k
B
k
⊺
, we can repeat the calculation above to get Eq 29 for each k separately. The integral in Eq 29 holds for all k since the kth stoichiometry vector ζk appearing in the kth column of B is orthogonal to the steady state eigenvector. Therefore, C decomposes into a sum over the Ck terms, and Eq 28 holds.
The decomposition in Theorem 1 allows us to rank each edge in the network in terms of its contribution to the stationary variance of any given node, which we call its “importance” relative to that node. In the case of a single open or conducting node, we refer simply to the edge importance. Moreover, the decomposition allows us to quantify the accuracy of the stochastic shielding approximation with respect to the population process projected onto individual nodes. The decomposition given by Theorem 1 is exact regardless of timescale separation or node sparsity.
Markov chains provide a general framework for mathematically modeling and simulating stochastic processes in natural and artificial systems. However, Markov chains are computationally expensive as their simulations require random numbers at each time step for every transition (edge). The stochastic shielding approximation relies on the fact that, when hidden states are present, the edges are not equally important, so that random fluctuations in some (typically most) edges can be neglected. Here, we provide a thorough study addressing how to identify the relevant and irrelevant edges when the stochastic fluctuations span slow and fast timescales. Our analysis shows that the stochastic shielding approach not only provides a practical increase in computational efficiency, but also facilitates a systematic understanding of the propagation of fluctuations in a general Markovian network, and hence, is applicable to many areas of mathematical biology and related disciplines.
The stochastic shielding method is being used increasingly to incorporate fast, accurate simulation of stochastic ion channels into larger neuronal network models. A recent paper [35] comparing different methods for simulating ion channels, based on diffusion approximations, recommended using the stochastic shielding approximation in conjuction with a direct Langevin approach advanced by Orio and Soudry [36]. Two examples in which stochastic shielding makes large-scale simulations tractable include [37] and [38]. In the first paper, the use of stochastic shielding allowed for a significant reduction in computation time of multiple simulations of a conductance-based model with synaptic and ion channel noise that are necessary to reliably estimate the entropy and information rate of neuronal firing. In the second paper, stochastic shielding is applied to a heterogeneous neural circuit for the first time, allowing the authors to investigate the role of channel noise in the generation of breathing variability in the isolated central pattern generator of respiration. In both cases, these studies would have not been possible in practice without the stochastic shielding approximation.
The analysis conducted here and in [10] is restricted to the case of a stationary Markov process, i.e. with time-invariant per capita transition rates. In many applications, for example under current-clamp (rather than voltage-clamp) in electrophysiology, the transition rates vary over time. In [9], which introduced the stochastic shielding method, stochastic shielding was shown to produce accurate approximations through comparison of voltage traces and spike trains generated via both stochastic shielding and full Monte Carlo simulations.
In the present paper, we have shown that in the presence of multiple timescales, for instance as seen in the dynamics of the nicotinic acetylcholine receptor (nAChR) under low agonist concentrations, one or more unobserved edges can become more important than the observable edges, in terms of making a greater contribution to the stationary variance of the occupancy of the open channel state (and hence the variance of the ionic current through the population of channels). In such a case the stochastic shielding phenomenon is still present, but is significantly reduced, to the point that the approximation given by suppressing the noise on the hidden edges does not provide the best approximation. Indeed, as seen in Fig 10, one may conclude that in this situation there is no suitable approximation of the type we consider, since the traces generated by reduced models with noise suppressed either on the observed or unobserved edges do not bear much similarity to the trace generated by the full model (with identical noise forcing where the noise is included). On the one hand, the edge importance measure remains exact under all conditions, as long as the network is irreducible (meaning here that α12, α21, α23 and α32 are all nonzero). On the other hand, the stationary variance does not capture the full shape of the trajectories. The decomposition of the fluctuations at one node as a sum of contributions from distinct edges extends to the correlation function and the power spectrum and the cross-spectrum, as well as to the variance.
Motivated by the example of the nicotinic acetylcholine receptor, we systematically studied the effects of introducing separation of timescales into the simplest nontrivial model to which stochastic shielding applies: the 3-state chain with one observable state. We found that, in the case of two distinct timescales, accelerating or decelerating a subset of edges relative to a baseline case (αij = 1 for all adjacent nodes (i, j)) could in some cases enhance, and in other cases reduce the gap in edge importance between the observed and unobserved edges, but in no case could induce a reversal of the edge importance (as observed in nAChR).
Finally, by sampling an ensemble of different transition rates, we found that inversion of edge importance can be seen in a 3-state chain when the channel opening rate is large (that is, α23 ≫ max(α12, α21, α32)), and also the rate of return from the first hidden state to the middle hidden state is small (that is, α12 ≪ α21). These complementary conditions are captured by the exact expression for the relative edge importance (Eq 22). Together, these conditions lead to sparse occupation of the middle node, introducing a bottleneck, while also introducing timescale separation in such a way that equilibration between the observable node and its immediate neighbor occurs much faster than between the two unobservable nodes.
Although our exact formula applies only to the 3-state chain model from which it was derived, we are optimistic that it may be extended to broader classes of Markov processes. The forms of such extensions are not a priori obvious, for several reasons. Consider the case of an ion channel with n states of which a single open conducting state (On) is connected to the closed, non-conducting states (C1, …, Cn−1) through a single bottleneck state (Cn−1); the closed states may interconnect arbitrarily with rates αij, 1 ≤ i, j ≤ (n − 1). In this case the analog of the first factor in Eq 22 would be the conditional occupancy probability of the bottleneck node Cn−1, given the channel is in any of the states C1, …, Cn−1. However, the analog of the second factor, the ratio of the Cn−1 → On transition to some combination of all the rates in the system, is far from clear. For ion channel models with multiple transitions into and out of a single open state (see Fig 1), the parallel to our exact 3-state chain analysis is scarcely obvious, and remains for future investigation.
The stochastic shielding approximation and method provide an approach distinct from aggregation based on community structure [20] or similarity of spectral components [13, 39], and pruning of sparsely populated nodes [23, 33], although there are some relations between these methods. Both spectral coarse graining [13] and our edge importance measure [10] rely on spectral decomposition of the graph Laplacian. As Ullah et al. point out, finding eigenvalues and eigenvectors of the Laplacian for a large complicated graph can be challenging [23]. An advantage of the stochastic shielding method is that it can be applied in the vast majority of cases without calculating the edge importance explicitly. Exceptions can occur when there is significant timescale separation with fast relaxation of the observable node with its immediate neighbors and slow relaxation among unobservable states, with a hidden bottleneck state separating the observable from a well populated pool of unobservable nodes. Except in this particular case, the stochastic shielding method can be applied without necessarily having to calculate the edge importance in detail. The effect of fluctuations in rates along the hidden edges is filtered by the network, and their impact on fluctuations at the observable nodes is diminished.
In this section we fill in the details behind the results. We introduce notation, define the edge importance measure relative to an arbitrary measurement vector, justify our use of the Lyapunov equation, prove Lemma 1, and describe our numerical methods.
In S1 Supporting Information, we establish the decomposition of the stationary variance. We provide explicit calculations for the 3-state process, and calculate η, the fraction of variance of the observable state arising from the hidden edges. We review the connection between the population process and Gaussian approximations thereof, and give a detailed derivation of the Lyapunov equation for the 3-state case.
We begin with a directed graph G = ( V , E ) with edge weights αij ≥ 0 representing a population of Ntot individuals moving randomly and independently among n states (i , j ∈ V) along m edges {i(k)→j(k)}1≤k≤m, with per capita transition rates {αk}1≤k≤m. We emphasize that edge k is the unique directed edge connecting source node i(k) to destination node j(k). The n × 1 stoichiometry vector ζk corresponding to edge k is defined such that ζk(i) = −1 and ζk(j) = +1, otherwise ζk(l) = 0; these vectors represent the effect of a transition along edge k. We use this notation to be consistent with the edge importance formula in the next subsection which is a sum of contributions to the variance of the observable state coming from each edge. Also, note that we will write the per capita transition rates either with double indexing denoting the source and destination nodes (αij) or with a single index denoting the reaction (αk).
We represent the population state at time t with an integer-valued vector N(t) = (N1(t), …, Nn(t))⊺, where Ni(t) ≥ 0 and ∑ i = 1 n N i ( t ) = N tot for all t. In other words, N(t) is a discrete state continuous time Markov process. Such processes are ubiquitous in biology [1].
We denote by M a measurement vector indicating a direction in the state space along which there is an observable of interest. For instance, Mi ∈ {0, 1} could denote the conducting state ({closed, open}) in a multi-state ion channel model. We denote the observed process by Y(t) = M⊺N(t). The remainder of our set up follows standard nomenclature for representing a population process on a graph [8, 40–42].
Let L be the Laplacian of graph G which is the n × n matrix defined by L = (A − D)⊺ where A is the weighted adjacency matrix and D is the diagonal matrix of node out-degrees. Specifically, the entries in A are Aij = αij(k) = αk ≥ 0 and the diagonal entries in D are D i i = ∑ j = 1 n A i j for i ∈ {1, …, n}. Note that L = Q⊺ where Q is the standard generator matrix of the Markov process. It follows that, for any vector x ∈ R n, L satisfies the following equation
L x ≡ ∑ k = 1 m ζ k α k x i ( k ) . (32)
The stoichiometry vector ζk is a difference of two standard unit vectors, ζk = ej(k) − ei(k). Although we do not assume that the graph Laplacian L must be a symmetric matrix, we do assume that the stationary system satisfies detailed balance, and that L has only real eigenvalues. Moreover, we assume that L has an expansion into real-valued biorthogonal eigentriples (wλ, λ, vλ) such that
L v λ = λ v λ (33)
L ⊺ w λ = λ w λ (34)
w λ ⊺ v λ ′ = δ λ λ ′ . (35)
We further assume that G is connected and the process is irreducible. The Perron-Frobenius theory guarantees the existence of a unique null eigenvector with nonnegative components summing to unity, corresponding to the stationary distribution on the graph. We denote the stationary probability vector π → = ( π 1 , … , π n ) ⊺ and the stationary mean flux along edge k by Jk = Ntotαkπi(k).
Let B be the n × m matrix defined such that
B = ( J 1 ζ 1 J 2 ζ 2 ⋯ J m ζ m ) . (36)
In other words, the kth column of B is given by the square root of the stationary flux Jk multiplied by the stoichoimetry vector ζk. We can express B as a sum of matrices
B = ∑ k = 1 m B k (37)
where all the entries of Bk are zero except for the kth column. Moreover, we will exploit the fact that the product BB⊺ can be represented with a similar sum
B B ⊺ = ∑ k = 1 m B k B k ⊺ . (38)
This product appears on the right hand side of the Lyapunov equation (see Eq 46 below) and its decomposition into the above sum is a key factor in establishing the decomposition of the stationary variance into a sum over the edges.
Computations were done either by hand, or using Matlab or Mathematica.
In the Langevin approximation for a time homogeneous first-order transition network, the population fraction occupying states 1, …, n is a vector X ∈ R n satisfying
d X d t = L X + ∑ k ∈ E B k ξ k (39)
where L, the graph Laplacian, captures the mean flux along each directed edge k ∈ E. The matrix Bk gives the effects of fluctuations ξk around the mean flux along the kth edge. The noise terms are independent, white and Gaussian, with 〈ξk(t)ξk′(t′)〉 = δkk′ δ(t − t′), one for each directed edge. Given an observable of interest, represented by a vector M ∈ R n, the stochastic shielding approximation consists in finding a partition of the edge set, E = E 1 ∐ E 2, into edges of primary importance (E 1) and secondary importance (E 2) such that | E 2 | ≫ | E 1 | and, at the same time lim t → ∞ E | | M ⊺ ( Y ( t ) - X ( t ) ) | | 2 ⪡ lim t → ∞ E | | M ⊺ X ( t ) | | 2 (stationary variances), where Y is the approximate population vector satisfying
d Y d t = L Y + ∑ k ∈ E 1 B k ξ k , Y ( 0 ) = X ( 0 ) . (40)
The noise samples ξk for k ∈ E 1 are identical in the full and approximate models. Neglecting the noise forcing along the edges of secondary importance causes a pathwise discrepancy U(t) = Y(t) − X(t) that satisfies
d U d t = L U - ∑ k ∈ E 2 B k ξ k , U ( 0 ) = 0 . (41)
The stochastic shielding effect consists in suppression of the resulting fluctuations in the observable process M⊺U(t) due to the filtering effects of the network—hence “stochastic shielding”.
The (stationary) mean squared pathwise approximation error can be written exactly as a sum of contributions Rk from each directed edge neglected in the approximation, lim t → ∞ E | | M ⊺ U ( t ) ) | | 2 = ∑ k ∈ E 2 R k. This error is small compared to lim t → ∞ E | | M ⊺ X ( t ) ) | | 2 = ∑ k ∈ E R k = ∑ k ∈ E 1 R k + ∑ k ∈ E 2 R k. We call Rk the importance of the kth directed edge (defined in the next section). As we show below, the decomposition holds exactly not only for the Langevin process but for the discrete population process as well.
The general formula for the edge importance measure is as follows. For an arbitrary stationary population process N(t) satisfying detailed balance on a graph with n nodes, m edges, and measurement vector M (defining the observable states), R = ∑ k = 1 m R k is the stationary variance of the observable states where
R k = J k ∑ i = 2 n ∑ j = 2 n ( - 1 λ i + λ j ) ( M ⊺ v i ) ( w i ⊺ ζ k ) ( ζ k ⊺ w j ) ( v j ⊺ M ) . (42)
In this formula, λn ≤ λn−1 ≤ ⋯ ≤ λ2 < 0 are the nontrivial eigenvalues of the graph Laplacian L (which always has λ1 ≡ 0); vi and wi are the right and left eigenvectors of L, respectively. Here and henceforth, Rk is normalized to the variance due to a single random walker by dividing out Ntot.
The stationary variance R is related to the power spectral density (PSD) S(ω) of the observed process M⊺N. From the Wiener-Khinchin theorem, integrating the PSD gives the stationary variance: R = ∫ - ∞ ∞ S ( ω ) d ω. Moreover, since the stationary variance decomposes into a sum of contributions from each edge in the graph, the power spectral density decomposes as well. By introducing
R k = ∫ - ∞ ∞ S k ( ω ) d ω (43)
we define a power-spectral edge importance such that the integral of Sk(ω), the power spectral density for the observed process with noise suppressed everywhere except edge k, gives the edge importance corresponding to edge k.
To see this, note that the power spectral density of the observed process is
S ( ω ) = ∑ k ∈ E S k ( ω ) where (44)
S k ( ω ) = 1 2 π J k ∑ l = 2 n ∑ j = 2 n ( 1 λ l + i ω ) ( 1 λ j - i ω ) ( M ⊺ v l ) ( u l ⊺ ζ k ) ( ζ k ⊺ u j ) ( v j ⊺ M ) (45)
provided ω > 0. For more details, see §Numerical Methods: Calculation of power spectra, below. We can use this power spectral decomposition to explore how the spectral contributions differ between the typical cases (where edge importance ranking agrees with the stochastic shielding method) and in the edge importance reversal cases.
The Perron-Frobenius null eigenvector, suitably normalized, gives the stationary probability vector π → = ( π 1 , … , π n ) ⊺ of Markov process N(t). Snapshots of the process N(t), taken under stationary conditions, are multinomial with parameters N tot , π →, so the covariance matrix C is known. In particular, each diagonal entry in C is the variance of state i, Cii = Ntotπi(1 − πi), and each off-diagonal entry in C is the covariance of states i and j, Cij = −Ntotπiπj for i ≠ j.
The stationary covariance matrix C satisfies Lyapunov’s equation (a special case of Sylvester’s equation) [43]
L C + C L ⊺ = - B B ⊺ . (46)
The fact that C satisfies Eq 46 above is widely known for linear Gaussian processes such as multivariate Ornstein-Uhlenbeck processes [34], but it also holds for discrete state population processes in which the transition rates are linear functions of the population at each node, i.e. first-order transition networks, such as those we consider here (see [8, 44]).
Our system is an important special case of the general first-order reaction network presented in [8]; we only consider conversion type reactions (denoted by kcon in [8]). For our system Pi represents v λ u λ ⊺, summed over all identical λ if they occur with multiplicity (we both assume semisimple eigenvalue spectra). The following parameters in [8] are zero for our system: C(i, k, l), kcat, ks, and kd. This simplifies Equation 50 in [8] (representing the variance of the lth reactant in the network) and is equivalent to our edge importance measure (Eq 42). However, to our knowledge, we are the first to describe the unique decomposition of the stationary variance into a sum of contributions from each edge in the network, and [9, 10] were the first to propose the stochastic shielding approximation and justify it based on this decomposition.
The Lyapunov equation has also been used in the context of stochastic gene networks under the name of “linear noise approximation” [45, 46]; in particular [45] (pg. 1, ¶5) further cites Eqs 3.46 and 6.115 in Risken [47] for additional details. See also [48] Supporting Information §4. For the linear networks we consider here, the equation is exact.
We restate the lemma for the reader’s convenience. Recall from Definition 1 that Y is the space of n × n symmetric matrices with columns (and rows) summing to zero.
Lemma 1 (restated) Let L be an n × n real valued matrix with Lij ≥ 0 for i ≠ j, and Lii = −∑i,i ≠ j Lij (so that ∑ i = 1 n L i j = 0) for j = 1, …, n, and satisfying dim(ker(L)) = 1, and with a null eigenvector Lv = 0 satisfying vi ≥ 0 for i = 1, …, n. Then for any F ∈ Y, the equation L C + C L ⊺ = F has a unique solution C ∈ Y.
Proof 2 Proof of Lemma 1. Given L ∈ X, define the linear operator A by A: C → LC + CL⊺. First, we show that A: Y → Y. If C ∈ Y then for all j = 1, …, n,
∑i=1n(LC+CL⊺)ij=∑i,k=1n(LikCkj+CikLjk)=∑k=1nCkj∑i=1nLik+∑k=1nLjk∑i=1nCik=0, (47) because each sum over i is zero, by assumption. Moreover, (LC + CL⊺)⊺ = LC + CL⊺. Therefore LC + CL⊺ ∈ Y whenever C ∈ Y, so A maps Y into itself.
By the Fredholm alternative(cf. [49], Theorem 2.27), A(C) = F has a unique inverse for F ∈ Y provided F is in the range of A and the homogeneous equation A(C) = 0 has only the trivial solution C = 0.
Let C0 ∈ Y be a solution of the homogeneous equation, LC0 + C0L⊺ = 0. Because C0 ∈ Y is symmetric and the nullspace of L is one dimensional, C0 must have the form C0 = (c1v|⋯|cnv) for constants c1, …, cn. However, the columns of C0 must sum to zero, and ∑ i = 1 n v i > 0, therefore c1 = … = cn = 0, hence C0 = 0.
To see that F is in the range of A, we construct an explicit solution as follows:
C = ∫ 0 ∞ e t L F ( e t L ) ⊺ d t , (48) and we show that this integral is well defined whenever F ∈ Y. To see this, first note that if all eigenvalues of L have negative real part, then L C + C L ⊺ = ∫ 0 ∞ S d t (49) where S = L e t L F e t L ⊺ + e t L F e t L ⊺ L ⊺ (50)
= d d t ( e t L F e t L ⊺ ) (51) and the solution in Eq 48 follows from the fundamental theorem of calculus.
It remains to show that the integral in Eq 48 is well defined whenever F ∈ Y. Assuming detailed balance, a unique null space, and that L is diagonalizable, we have that all eigenvalues of L are negative (and real) except λ1 ≡ 0, and we can write L = ∑ λ v λ u λ ⊺ (52)
⇒ e t L = v 0 u 0 ⊺ + ∑ λ < 0 e t λ v λ u λ ⊺ . (53) Then C = ∫ 0 ∞ e t L F ( e t L ) ⊺ d t (54)
= ∫ 0 ∞ { ( v 1 u 1 ⊺ ) F ( v 1 u 1 ⊺ ) ⊺ + ( v 1 u 1 ⊺ ) F ( ∑ λ < 0 e t λ v λ u λ ⊺ ) ⊺ (55)
+ ( ∑ λ < 0 e t λ v λ u λ ⊺ ) F ( v 1 u 1 ⊺ ) ⊺ + ∑ λ < 0 , λ ′ < 0 e t ( λ + λ ′ ) v λ u λ ⊺ F u λ ′ v λ ′ ⊺ } d t (56)
= ∫ 0 ∞ { v 1 ( u 1 ⊺ F _ ) ( u 1 v 1 ⊺ ) + v 1 ( u 1 ⊺ F _ ) ∑ λ < 0 e t λ u λ v λ ⊺ (57)
+ ∑ λ < 0 e t λ v λ u λ ⊺ ( F u 1 _ ) v 1 ⊺ + ∑ λ < 0 , λ ′ < 0 e t ( λ + λ ′ ) v λ u λ ⊺ F u λ ′ v λ ′ ⊺ } d t (58)
= ∑ λ < 0 , λ ′ < 0 v λ u λ ⊺ F u λ ′ v λ ′ ⊺ ( ∫ 0 ∞ e t ( λ + λ ′ ) d t ) (59)
= ∑ λ < 0 , λ ′ < 0 - 1 λ + λ ′ v λ u λ ⊺ F u λ ′ v λ ′ ⊺ . (60) The underlined expressions in parentheses are all zero because the columns (and rows since F is a symmetric matrix) of F sum to zero by assumption; u 1 ⊺ ≡ ( 1 , … , 1 ) is orthogonal to every column of F and u1 is orthogonal to every row of F and so u 1 ⊺ F = 0 and Fu1 = 0. Thus, the integral in Eq 54 is finite and Eq 60 gives an explicit expression for it.
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